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Good technology integration isn't about using the fanciest tool, it's about being aware of the range of options and picking the right strategy-or strategies-for the lesson at hand.

The biggest obstacle to teaching online probably isn't the technology. Teachers seek out educational technology, in fact, because it "can have considerable positive impacts on student performance," according to a 2016 study -improving test scores and allowing teachers to assess student achievement more efficiently. The big problem is how to integrate it: Beyond the sheer number of tech tools available, the same researchers identified "inadequate professional development and training" as the primary obstacle to using technology productively in classrooms.

Understandably, the emergence of the coronavirus has dramatically accelerated the process of integrating edtech, as educators around the country race to get online as fast as they can. But as many of our teachers have noted, the current state of K-12 online learning is more like triage-a form of crisis management-and not at all like skillfully managed distance education.

What's missing from the conversation right now, which makes sense given the immediacy of the need, is a focus on the bigger question of what the highest quality technology integration actually looks like. That's an important conversation to have, because in a post-coronavirus world there's likely to be a greater emphasis on digital learning, even as we return to physical schools and take advantage of face-to-face opportunities.

A Hierarchy of Tech Uses

A powerful conceptual tool to think about technology integration-and edtech's best uses-is the SAMR model, developed in 2010 by education researcher Ruben Puentedura, who was the 1991 recipient of a Phi Beta Kappa teaching award.

The SAMR model lays out four tiers of online learning, presented roughly in order of their sophistication and transformative power: substitution, augmentation, modification, and r edefinition. When switching to an online format, teachers often focus on the first two levels, which involve replacing traditional materials with digital ones: converting lessons and worksheets into PDFs and posting them online, or recording lectures on video and making them available for asynchronous learning, for example.
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Those are important steps, especially when teaching online for the first time, but in classrooms where tech integration has moved to the mastery level, the last two levels of the SAMR model-modification and redefinition-should also be in the mix. Students in classes where this kind of mastery is embedded find more novel and immersive uses for technology. They are creators and publishers of their own work across multiple forms of media, for example, or they are inviting professionals to provide feedback on their work products, or participating in digital forums with other peers around the world.

It's tempting to think of SAMR as a mountain to be summited. But good technology integration isn't about living at the top of the SAMR model; it's about being aware of the range of options and picking the right strategy-or strategies-for the lesson at hand.

Here's a closer look at good classroom practices at each level in the model:

Substitution

"Substitution" means replacing traditional activities and materials-like in-class lectures or paper worksheets-with digital versions. There is no substantial change to the content, just the way that it is delivered.

The goal here is to keep things simple: there's no need to reinvent the wheel. Scan your lessons and worksheets, convert them into PDFs, and post them online using Microsoft OneDrive, Google Drive, or a similar file-sharing service. Think about the information you have on your walls, such as the classroom norms, the daily schedule, or vocabulary lists, and convert them into digital formats that students can easily reference.

It may also help to provide synchronous as well as asynchronous versions of your lectures. If you're holding class meetings over a videoconferencing service like Zoom or Skype, provide a recording for students who can't attend. You can also create your own instructional videos for students to view at their own pace.

Augmentation

This level involves incorporating interactive digital enhancements and elements like comments, hyperlinks, or multimedia. The content remains unchanged, but students can now take advantage of digital features to enhance the lesson.

For example, students can create digital portfolios to create multimedia presentations, giving them more options to demonstrate their understanding of a topic. And instead of handing out paper quizzes, you can gamify your quizzes with tools like Socrative and Kahoot.

Teachers can also create virtual bulletin boards-using an app like Padlet -where students can post questions, links, and pictures.

Modification

At this level, teachers can think about using a learning management system like Google Classroom, Moodle, Schoology, or Canvas to handle the logistical aspects of running a classroom, like tracking grades, messaging students, creating a calendar, and posting assignments.

Teaching online opens up new channels of communication, many of which can help students who have traditionally been marginalized. Research shows that girls may be less likely to speak up in class, for example, so they may benefit from backchannels -alternative conversations that can run alongside instruction-that encourage participation.

Zoom's text chat feature, meanwhile, gives students an opportunity to write their questions out, which can feel less intrusive if there are dozens of students participating in the call. Also, students who prefer to collect their thoughts may benefit from slower-paced, asynchronous discussions in an online forum or email threads.

Redefinition

Learning is fundamentally transformed at the "redefinition" level, enabling activities that were previously impossible in the classroom.

For example, virtual pen pals can connect students to other parts of the world, whether it's with other students or experts in a field. Virtual field trips enable students to visit locations like the Amazon rainforest, the Louvre, or the Egyptian pyramids. After reading a book in class, you can invite the author to chat about their work and answer questions.

Technology also provides an opportunity to bring authentic audiences into your virtual classroom, and can make publishers out of your students. Kids can write their own wikis or blogs for public consumption and feedback-and platforms like Quadblogging can connect distant classrooms together so students both write and respond. Students can tackle local problems-like investigating the water quality of a nearby river-and invite members of the community to assess their digital proposals.

Going Beyond SAMR

Finally, consider how technology can be used not just as a way to deliver content, but also to strengthen relationships with your students. For elementary school teacher John Thomas, Seesaw and Flipgrid allow him to share a daily greeting, which his students then respond to-or to each other-with a typed or voiced comment. It's a good way to provide some stability while they're isolated.

How can my lesson be improved using technology?

How can I engage and empower students through technology?

How can online learning more closely resemble authentic, real-world learning?

Remember, again, that SAMR is sometimes thought of as a mountain to summit, but it's really more like a toolbox. The goal isn't to use the most sophisticated tool, but to find the right one for the job. More importantly, however, it's a way to reflect on your technology integration by thinking about a few key questions:

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A Powerful Model for Understanding Good Tech Integration

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Good technology integration isn't about using the fanciest tool, it's about being aware of the range of options and picking the right strategy-or strategies-for the lesson at hand.

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Behaviorism: Your Complete Guide to Behavioral Learning Theory

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Behaviorism learning theory tells us that behavior is shaped by conditioning and reinforcement. How does this impact your training initiative?

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Pear Deck is on a mission to help teachers create powerful learning moments for every student, every day - using the tools you already know and love.

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Computer science theory | Computing | Khan Academy

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Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information).

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Australian Framework for Generative Artificial Intelligence in Schools

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Australian Framework for Generative Artificial Intelligence in Schools

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Important quick read.

Its the Australian government's framework for the use of generative AI in schools. It provides guidance on understanding, using, and responding to AI. It revolves around six principles, with each principle having further strands:

1. Teaching and Learning

2. Human and Social Wellbeing

3. Transparency

4. Fairness

5. Accountability

6. Privacy, Security and Safety

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Australian Framework for Generative Artificial Intelligence (AI) in Schools

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The Australian Framework for Generative AI in Schools (the Framework) seeks to guide the responsible and ethical use of generative AI tools in ways that benefit students, schools, and society. The Framework supports all people connected with school education including school leaders, teachers, support staff, service providers, parents, guardians, students and policy makers.

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This framework builds off of the Australian Framework for Generative Artificial Intelligence.

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Blog on how ICT can be used across the curriculum 

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Blog on how ICT can be used across the curriculum 

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Mathematics Physical Education and Health
We have connected with Victorian Music Teachers' Association (VMTA) and Midnight Music to discuss ideas to support the integration of the Digital Technologies Curriculum and use of ICT in music education.

Since many schools deliver music programs that are taught by instrumental music teachers, we have included information to support both instrumental and classroom music teachers working in school contexts.

We have aligned these activities to the 5-6 and 7-8 Curriculum to demonstrate how the lessons can align to Primary and Secondary schools. The activities and suggestions have been selected so they are easily modified across different levels. Further investigation is required to ensure they align with the specific curriculum and area. Activities may need to be modified to ensure content descriptions and achievement standards are met.

IMPLEMENTING THE DIGITAL TECHNOLOGIES CURRICULUM

STARTING OUT: CONNECTING COMPUTATIONAL THINKING TO MUSIC

Computational skills (algorithms, pattern recognition, abstraction and decomposition) are skills that can be taught and applied in Music classes. Students apply these skills when problem solving to help deal with problems when learning music when playing an instrument to reading music.

Algorithms

An algorithm is a step by step instruction for solving a problem. Teach students to look at the relationship between algorithms and music sheets.

Pattern Recognition

Pattern recognition focuses on looking for reoccurring themes which can lead to prediction or short cuts. Students can use pattern recognition by looking for patterns to create notes and the patterns and rhythms they find in music.

Abstraction

Abstraction focuses the attention on the important information only and ignoring the rest. Students can practice this skill when learning songs and focus their attention on the notes that the song uses.

Decomposition

Students can break down the song they are learning to play into smaller parts. They can focus to their attention and skills to master one part of the song first then continue to move to the next. This can be applied to learning to find the placement of notes on instruments.

RESOURCES

GOING FURTHER: MAKING MUSIC WITH ROBOTICS

Using technology may help teachers overcome the costs that are associated with the purchase of music equipment for classes. While this idea does not replace the practice of playing a selection of physical instruments, it does allow students to experiment with sounds, beats and rhythms from different musical instruments.

Robotics kits and coding platforms provide access to music and musical instructions without spending the money to have a class set of physical instruments. Students can code robotics to create sounds and learn about sounds, pitches and rhythms while coding. Using robotics can open up more opportunities to learn about and experiment with more musical sounds.

ACS has a bank of Cubit Kits that are available to borrow and use. These kits come with the capability to create sounds with buzzers and change the notes accordingly. (ACS specialist, Catherine Newington will support your development of the Cubit Robotics. She can even pop into your classes and lend an extra pair of hands). Contact Catherine via catherine.newington@acs.org.au

Cubit Voyager kits avaliable to borrow Cubit Exploring Sound Acitivity material

RESOURCES

SCHOOL EXAMPLE: CREATE AND PERFORM (CaP) PROGRAM AT TAYLORS LAKES PRIMARY SCHOOL WITH ALTAN RIFFAT

The Create and Perform (CAP) program at Taylors Lakes Primary School is a specialist program offered to grade 4 - 6 pupils over 4 terms. CAP has its origins in the disciplines of Art, Music and Coding. Under the Digital Technologies umbrella, CAP utilises the creative elements of coding and a range of digital platforms for Music and Visual Art learning areas.

Using digital platforms pupils produce their own visual art works, musical arrangements, and sound effects and coding design briefs. Some digital platforms include:

ArtRage (paint program)

Logic ProX and Garage Band (DAWs/Digital Audio Work Stations)

Sonic Pi (programming music using a variant of the Ruby command line language)

Some Python and C (for challenging high achievers)

Scratch (core language for visual sequencing, conceptualising algorithmic thinking and utilising audio extensions for musical arrangements)

Images: supplied by Taylors Lakes Primary School

Students design algorithms to emulate scenarios. Then, they enrich the immersion with music and art to entice interaction, engagement and connection to real-world application. This provides a purpose for musical composition and performance, a purpose for coding and a purpose for the production of visual art works. Traditional instruments and voice are also encouraged. Students have opportunities to compose backing tracks (with Sonic Pi and Garage Band). They accompany their compositions with keys, guitar, percussion and voice. We call this the Sonic Pi Band.

Images: supplied by Taylors Lakes Primary School

IMPLEMENTING ICT INTO MUSIC CLASSES

STARTING OUT: USING TECHNOLOGY TO ORGANISE YOUR LESSONS

Learning Management Systems (LMS) are common platforms for schools and teachers to manage announcements and keep in contact with parents and students, upload and provide content (such as links, videos and access to work material).

Using an LSM program can help you manage your students learning. It can help you keep track of the tasks they have completed and provide you with a space to provide learning material to students.

Depending on the functionalities within the platform there may even be opportunities to organise timetables.

When choosing a platform that will fit your needs consider the types of questions to help you decide:

What is the primary role to use technology to support music lessons?

What are the key functions you are looking for?

What devices do you currently use and how will the students access the platform?

Is there a platform the school is already using that you can adapt?

RESOURCES

GOING FURTHER: CREATING INSTRUCTIONAL VIDEOS

Instructional videos that explain the 'how to' when developing the skills to play musical instruments supports student learning beyond the face to face classroom. They are a resource that can be used time and time again. Students can go over the material in their own time and control the pace at which they learn (fast forward parts they have mastered, rewind and re-watch skills they need to develop multiple times).

Using videos that are already publicly available will help you get started. If and when possible, create your own videos to add to your collection. This helps to continue the rapport you have with your students outside of the physical face to face sessions.

Watch the following videos to get an understanding of the power of videos when explaining the skills to master music instructions.

YouTube Video Source: Justin Guitar Songs

Guitar Quick Start! Learn the basics in 5 minutes. For beginners & new guitarists easy guitar songs.

Here's another example of a video that goes into a little more detail. This one has a mixture of both imagery and text. Take note of the power of text to further support learning.

YouTube Video Source: Dylan Laine

How To Play Piano - Easy First Piano Lesson!

Once you are ready to create your own videos remember planning and preparation is important. Keep you messages clear, no need to write out a script. It's better to have key concepts and words written down to keep you on track and your message clear. The short the better. The aim of the technology is to support student learning. One or two key messages per video is enough. Flash cards can serve as a double purpose - a prompt in the video then and as part of a classroom display.

To reduce the stress of filming, film in short sharp bursts and then edit those videos to create one longer video. Have access to a mobile phone with recoding capabilities? Prop that phone up and away you go! Remember - you are not creating a video to win an Oscar for best picture. The purpose of the video is for students to have clear, concise instructions to help them learn and continue learning outside of face to face sessions

Press record and away you go!

RESOURCES

CONTRIBUTORS

ACS ICT Educators Program works with schools and teachers to develop material to support the Digital Technologies Curriculum. Join for free to access resources and connect with teachers. More information about the program can be found via: https://www.acs.org.au/ict-educators.html

The Victorian Music Teachers' Association (VMTA) is a peak body and professional association representing the interests of instrumental music teachers in Victoria. VMTA provides a range of services to music teachers including accreditation and recognition, professional learning, student performance opportunities, information, advocacy and advice. More information about the association can be found at https://vmta.org.au/

Katie Wardrobe operates Midnight Music. Katie teaches music educators how to integrate technology into their teaching. Her philosophy, on technology integration in music is simple: use it to enhance, reinforce and - in some cases - improve what you do and how you do it. For more information and connect with Katie go to https://midnightmusic.com.au/

Altan Riffat is the Learning Specialist in Digital Media and Coding at Taylors Lakes. He works with his school leadership team to develop whole-school learning programs and school improvements plans. This led to the development of innovative programs including our CAP program with its focus on digital paradigms for 21st century learning.

CURRICULUM LINKS

All Victorian Curriculum and Assessment Authority (VCAA) material is copyright. The VCAA makes no warranties regarding the correctness or accuracy of this DLTV & ACS resource. The current Victorian Curriculum and related content can be accessed directly at www.vcaa.vic.edu.au

This month we have connected with Art Education Victoria to discuss how the Digital Technologies Curriculum and use of ICT can be integrated into Media Arts and Visual Arts lessons.

When teaching Visual Arts and Media Arts there is an expectation that technologies will be integrated into your lessons. This is referring to using technologies to create or enhance artworks. This is different to the Digital Technologies Curriculum. The Digital Technologies Curriculum explicitly teaches how technologies operate. We have provided some suggestions on how these curricula can be taught in unison.

These activities have been aligned to the 5-6 and 7-8 Curriculum to demonstrate how the lessons can align to Primary and Secondary schools. The activities and suggestions have been selected so they are easily modified across different levels. Further investigation is required to ensure they align with the specific curriculum and area. Activities may need to be modified to ensure more content descriptions and achievement standards are met.

DIRECT ALIGNMENT TO THE DIGITAL TECHNOLOGIES CURRICULUM

MEDIA ARTS

STARTING OUT: CREATING STORYBOARDS

Flowcharts and story boards can be used as planning documents to look at how the project will be put together, what is expected to happen and show the flow of the sequence of events. If possible, and the art project can become interactive, this can be a method to teach students about sequence of steps and algorithms (without coding) such as user input. Teach a relationship between mapping out the explicitly scenes or art projects and how that relates to explicit instructions used in algorithms and flowcharts when planning and designing digital solutions (such as apps and software programs or games).

Resources

FUSE Education Stop Motion Animation
ACS ICT Educators' Community of Practice
ACS Teacher Resource Algorithms Years 5-6 (to access ACS resources you will need to join the community of practice)
ACS Teacher Resource: Flowcharts Years 5-6 (to access ACS resources you will need to join the community of practice)

GOING FURTHER: REPRESENTATION OF COLOURS

How colour is represented in 'real life' and how it is represented in binary code using the RGB. RGB is the acronym to describe how we see digital colours and images. RGB stands for Red Green Blue. RGB is directly aligned to binary code because colours are stored in digital systems as binary code. A digital image is made up of pixels and all digital imagery is made from the three colours. The change in colour will depend on the value of the red green and blue.

Activities such as experimenting with colour (as paint, light and digital) can relate to Science and how we perceived visual art works.

Video Source: Images Pixels and RGB code.org.

Shadow play with colour (2020) Steve Marks

Resources

ACS ICT Educators' Community of Practice
ACS teacher information on RGB and binary code (to access ACS resources you will need to join the community of practice)

Getting started with QR Codes How to use QR codes in Museums and art exhibitions QRStuff A website to help you attached you an image, PDF, YouTube video or website to a QR Code
STARTING OUT: USING ICT TO CREATE A VIRTUAL GALLERY

Using ICT can be a beneficial way to allow students to express their opinion of an art piece or a reflection of their own artwork without having to be physically present.

This use of technology can be beneficial when students' artwork is on display (whole school art show, or exhibiting work around the school). QR codes can store extended information such as images, videos and website links. QR codes can be used as a reflection to store a students' reflection and interpretation of artworks. With a quick scan of the QR code the information will be displayed on the device that scanned the QR code (mobile devices and smartphones work best). Video or audio of student's responses to artwork can also be used as work to assess their reflection and understanding of the work.
How museums are adopting AR Artists incorporating augmented into their artwork 5 Augment and Virtual Reality Creation Tools for Students

GOING FURTHER: AUGMENTED REALITY
Image taken from CS Unplugged from Unit Plan: Image Representation
Already utilising QR code's? Take it a little further with augmented reality. Augmented reality (AR) is a blend of real world and digital elements. It overlays and super imposes digital information such as images, sounds and texts) into the world we see. Using technology, museums and artists around the globe are turning towards augmented reality to enhance and transform traditional artworks and art methods.
CS Unplugged Drama or Video Activity CS Unplugged Unit Plan: Image Representation
Resources

USING TECHNOLOGY WITHOUT DEVICES

Teaching the Digital Technologies Curriculum and incorporating that learning into your Art classes can be done without the use of technology. CS Unplugged has a plethora of examples of teaching computer science (CS) across the curriculum. CS Unplugged uses a constructivists pedagogy - that students create and build their own knowledge. The activities are based on challenges presented to the students. To download the entire book, head to CS Unplugged PDF .

Resources

FINDING THE RIGHT TYPE OF TECHNOLOGY IN THE VISUAL ARTS CLASSROOM

Recommended: find the right tool to use in the classroom

Finding the right technology tool for your Art classes is a complex process because access to technology differs between schools and sectors and year levels. Instead, we have put together a list of statements and questions that will help you start the process of looking for the right tool for you to use:

What problem are you trying to solve and how technology can help you solve that problem? As soon as you can articulate the purpose of the technology, this will help you understand the best tool for the job you want done.

How will the technology assist you to teach and assess the Arts curriculum? This could be through using learning management platforms to help you push out instructions or

How will the technology be used? What do you want to achieve by using the technology?

If you only have access to a small bank of devices and technology -

CONTRIBUTORS

ACS ICT Educators Program works with schools and teachers to develop material to support the Digital Technologies Curriculum. Join for free to access resources and connect with teachers. More information about the program can be found via: https://www.acs.org.au/ict-educators.html

Art Education Victoria has been a leading voice in visual arts education in Victoria for over sixty years. Art Education Victoria supports and represents art educators. They advocate for and enhance quality visual arts learning and teaching experiences by providing resources, programs and opportunities for partnerships and collaboration. More information about Art Education Victoria can be found via:
https://aev.vic.edu.au/

Explain how the elements of media arts and story principles communicate meaning and viewpoints by comparing media artworks from different social, cultural and historical contexts, including media artworks of Aboriginal and Torres Strait Islander Peoples

Create and display artwork considering how ideas can be expressed to an audience

Explain how student-developed solutions and existing information systems meet current and future community and sustainability needs

Level 7-8

Present media artworks within different community and institutional contexts for different audiences, with consideration of ethical and regulatory issues

Experiment with materials, techniques, technologies and processes in a range of art forms to express ideas, concepts and themes in artworks (VCAVAV035)

Create and display artworks, describing how ideas are expressed to an audience
Investigate how digital systems represent text, image and sound data in binary

Design the user experience of a digital system, generating, evaluating and communicating alternative designs

Design algorithms represented diagrammatically and in English, and trace algorithms to predict output for a given input and to identify errors

Copyright: All Victorian Curriculum and Assessment Authority (VCAA) material is copyright. The VCAA makes no warranties regarding the correctness or accuracy of this DLTV & ACS resource. The current Victorian Curriculum and related content can be accessed directly at www.vcaa.vic.edu.au

This month we have connected with Geography Teachers' Association of Victoria Inc. (GTAV) to discuss how the Digital Technologies Curriculum and use of ICT can be integrated into Geography lessons.

These suggestions and activities are aligned to the 5-6 and 7-8 Curriculum to demonstrate how the lessons can align to Primary and Secondary schools. The activities and suggestions have been selected so they are easily modified across different levels. Further investigation is required to ensure they align with the specific curriculum and area. Activities may need to be modified to ensure content descriptions and achievement standards are met.
Image Source: Google Earth
STARTING OUT: data and information

Data and Information have direct alignment the Geography Curriculum and the Digital Technologies Curriculum (see the table below to view the direct alignment between both curricular). Both curricular have content descriptions under the subheading Data and Information.

Spatial technology can be integrated and utilised as a form of data collection. Google maps, Street View, Google Earth and near Nearmap have improved the processes to collect geographical data. Identifying places using Google Earth then presenting your pinned locations helps students understand scale and location, enhancing spatial awareness. This can include geolocation apps to collect data on elevation, soundmeters, temperatures and plant identification. Students will then use technologies to interpret this data through creating maps, charts and visual material such as annotated photographs. Introducing new topics can be greatly enhanced with excellent visual maps.
Video Source: GCFLearnrfree.Org

is a technology tool to support fieldwork and can be used to collect and collate data. ArcGIS is an online platform that allows creation of interactive maps with the ability to share and collaborate, analyse data and work with data. Another product of the ArcGIS suite is ArcGIS Survey123. ArcSurvey123 can support your data collection and interpretation. It enables the creation, sharing and analysing of surveys.
Coding with Scratch
An application of AcrGIS is through the GTAV MapIT! 2021 program. This challenge is targeted towards the Level 7 'Place and Liveability' associated content descriptions. Students create their own maps using spatial technology software to improve their 20-minute neighbourhood.

A common software program to collate and interpret data is spreadsheets. Programs such as Microsoft Excel, Google Sheets and Apple Number all contain the capacity to manipulate and present data. GCFLearnrfree.Org has a plethora of videos to support knowledge development to use spreadsheet software. It should be noted that other spreadsheet software (such as Google Sheets, Apple's Sheets and) use a similar layout to Microsoft Excel and these videos can be adapted to suit your needs.
micro:bit robotics
Resources

GOING FURTHER: CODING AND UTILISING ROBOTICS
Image source: Micro:bit Website
Create a 'choose your own adventure' or virtual map of places of interest. Students could develop a simple walk through of their school using Scratch or Scratch Jr and incorporate photos, field sketches and annotated visual displays as part of their presentation.

Resources

Here exists an authentic opportunity to incorporate robotics into delivering key geographic concepts and modelling opportunities. Some examples and possible applications include: soil moisture sensors, temperature sensors, sunlight sensors, compass bearings.

Resources

Drones

Drones can be an effective tool to operate to gather data and information of surveying land. They are able to collect data in real time. Majority of drones now all have a camera incorporated, allowing the opportunity to recognise objects and obtain imagery of land and surrounding areas.

If you are looking for ways to incorporate more drones into your lessons, take a look at Karen Joyce's YouTube Channel. She has created a plethora of videos to help you maximise your use of drones in your classroom

It is the responsibility of the drone pilot to ensure that the drones are abiding by regulations. Research may need to be carried out prior to purchasing drones to ensure they are a suitable device to use within school grounds and local areas.

Resources

ICT TO SUPPORT GEOGRAPHY

Virtual Excursions and Virtual Reality

Image Source: Melbourne Water
There are many opportunities for exposing students to experiences from the comfort of their classrooms or other accessible locations when situations prevent physical excursions. Virtual excursions provide an authentic learning experience, no matter of ability or physical location (internet connection is an important consideration). There are many virtual tours and excursions available that are both local, (Australian based) and international. From PenguinCam at the San Diego Zoo to the magnificent views from the Eiffel Tower in Paris, there is a lot to be said about the excitement and awe that a virtual tour can bring!

Video Source: Explore Live Nature Cams

The GTAV Melbourne Water Virtual field trip Western Treatment Plant is an excellent, detailed and interesting resource which could be used solely as a virtual tour, or enhance a visit to the Western Treatment Plant. Easily customisable to suit each classroom context.

"Visiting the Western Treatment Plant on our virtual field trip really gave me an insight into what happens with our water. I had no idea it was such a long and extensive process to classify, treat and release recycled water! I found this fieldwork very interesting, and working with my partners was great as well." - Year 7 Student

Resources

To enhance the virtual excursions, Virtual Reality (VR) can provide learning opportunities for students to be immersed and view geographical places that would otherwise be unattainable to visit in person. Utilise the virtual reality to provide students with a multiple modal experience - rather than just gathering information from text and still images.

VR doesn't have to be expensive, you only need two or three headsets as a module for students to rotate through during a lesson. Inquiry at local libraries as they may have a small set to borrow.

Resources

CONTRIBUTORS

DLTV committee member Claire Andrewatha is a passionate and dedicated educator with over 10 years' experience in Humanities and Information Technology, in Primary and Secondary settings. This combination of subjects helps her provide richness and depth when delivering curriculum and making links between concepts and real-world situations. Claire has assisted students and teachers to develop a greater understanding of technology and its many applications in creative problem solving.

ACS ICT Educators Program works with schools and teachers to develop material to support the Digital Technologies Curriculum. Join for free to access resources and connect with teachers. More information about the program can be found via:
https://www.acs.org.au/ict-educators.html

ACHPER Victoria is the leading professional association representing Victorian teachers and other professionals working in the fields of health, physical education, and recreation.More information about their association can be found via:
https://www.achper.vic.edu.au/

CURRICULUM LINKS

All Victorian Curriculum and Assessment Authority (VCAA) material is copyright. The VCAA makes no warranties regarding the correctness or accuracy of this DLTV resource. The current Victorian Curriculum and related content can be accessed directly at www.vcaa.vic.edu.au

Mathematics

This month we have connected with Mathematics Association of Victoria (MAV) to discuss how the Digital Technologies Curriculum and use of ICT can be integrated to into Mathematics lessons.

The Mathematics Curriculum does have integration of technologies. However when we are referring to that - it is referencing how to utilise technology for learning purposes. Within the curriculum you'll find wording like 'use with or without the help of digital technologies'. In that instance the curriculum is referring to using technology.

Integrate technology to allow students to engage in the learning. If your students are only engaged in the entertainment value of the technology, then critically ask - is the technology worth it?

We have aligned these activities to the 5-6 and 7-8 Curriculum to demonstrate how the lessons can align to Primary and Secondary schools. The activities and suggestions have been selected so they are easily modified across different levels. Further investigation is required to ensure they align with the specific curriculum and area. Activities may need to be modified to ensure content descriptions and achievement standards are met.

STARTING OUT: DIRECT ALIGNMENT OF ALGORITHMS AND DATA

Algorithms and Data collection have direct alignment the Mathematics Curriculum and the Digital Technologies Curriculum (see the table below to view the direct alignment between both curricular).
RESOURCES DATA
Algorithmic thinking provides opportunities for students to follow and create a well-defined set of instructions to perform a task or solve a problem. Promoting algorithmic thinking encourages and increases students' ability to solve problems using different strategies.

Promote algorithmic thinking through activities and resources such as flow diagrams, spreadsheets, calculators, dynamic geometry software and even pencil and paper. Algorithmic thinking is less about coding or any specific technology than it is about a systematic method for solving problems.
Desmos Graphing Calculator Popular Activities

MAV's Algorithmic Thinking Lesson - Students will simulate a simple random walk by manipulating sets of numbers using a given rule, for example, if a number is even halve it; if a number is odd, subtract 1 then halve it.

There is a similarity between teaching data in the Digital Technologies Curriculum and in the Mathematics Curriculum. Both curricula ask students to demonstrate the skills to collect, collate and analyse data. To align to the Digital Technologies Curriculum, technologies (such as spreadsheets) will be required.

Activities that focus on collecting, collating and interpreting data allows for collaboration across multiple curriculum areas. Data can be collected during a Mathematics session and manipulated using technologies in a Digital Technologies focused session.

DLTV Webinar: Numeracy connections with Digital Technologies presented by Nathan Alkison (DLTV) and Catherine Newington (ACS) May 2020

GOING FURTHER: ALGORITHMS AND DATA COLLECTION WITH ROBOTICS

Robots can be programmed and used as a tool to use to collect data.

ACS partnered with a school to create a unit of work that focused on collecting data about plant growth. This unit coincided with a Science (biology focused unit) focus. The focus question was - 'where is the most suitable position to grow our plants in the school?' Prior investigations were needed to understand the ideal climate for the plants to successfully grow (amount of sunlight and the type of climate). Cubit robotics were programmed to collect data about the amount of light and temperature.

This unit focused on collecting data (light and temperature) over the course of different times and days from different positions on the school yard. This data was then used to analyse the most suitable spot for plants to grow based on their needs. It demonstrates how robotics and programming can be used to solve a real-life situation. It gives real meaning to collect, sort and analyse the data.

ACS has a bank of Cubit Kits that are available to borrow and use. These kits come with the capability to log data and then covert that data into a spreadsheet. (ACS specialist, Catherine Newington will support your development of the Cubit Robotics. She can even pop into your classes and lend an extra pair of hands). Contact Catherine via catherine.newington@acs.org.au - she'd love to hear from you!

RESOURCES

UTILISING ICT TO PROMOTE MATHEMATICAL REASONING

Videos (either made by the teacher or student) can be a powerful way to capture learning. Proficiency of mathematics is a skill that our students need to develop. This shows they have knowledge in articulating problem solving and articulate how and when different mathematical strategies can be used to solved different equations.

STUDENT CREATED VIDEOS

Start by getting your students to record their thinking. An interactive board not only helps you assess how they solved an equation but when students record their mathematical thinking you can assess and understanding the type of strategies they are using (or not using) to solve an equation. A quick answer this question and record your answer is a simple way to start this process.

TEACHER CREATED VIDEOS

Creating videos does not need to be an overwhelming experience. When creating videos the focus is on modelling mathematical reasoning. This can be achieved with a pen and paper. Keep it simple by creating short and focus on the learning intention. When creating your first video don't pressure yourself to create long videos. Short bursts will help you and upload them as shorter videos too.

Prepare by making a list of key words. This keeps you on track and provide you (flash cards can serve as a double purpose - a prompt in the video then and as part of a classroom display)

'4 Triangles' created by Association of Teachers of Mathematics (ATM)

RESOURCES

CHOOSING ICT TO SUPPORT MATHEMATICS

There is a plethora of software programs, games and apps that can be used to supplement teaching of Mathematics. Critically evaluate the value of the technology by pondering and answering questions like:

How are students supported when they get an answer incorrect?

How does the technology support differentiation?

How are the equations presented? Watch out for vertical display only as this limits students to rely on the vertical style to solve equations. They start to focus on the process rather than the mathematical reasoning and understanding.

Does the development of skill align to the curriculum?

Is it based on drill and skills or does the software allow for the skills to be applied to problems (worded problems or real life scenarios)?

Take notes of the actual screen. Are there students in your class that would get distracted from the unnecessary use of images, sounds and colours? This could detract rather than assist learning.

CONTRIBUTORS

ACS ICT Educators Program works with schools and teachers to develop material to support the Digital Technologies Curriculum. Join for free to access resources and connect with teachers. More information about the program can be found via:
https://www.acs.org.au/ict-educators.html

The Mathematical Association of Victoria (MAV) is an innovative and leading association. Through its programs and services, MAV promotes the importance of mathematics to society. MAV is a highly respected, proactive professional association that fosters the promotion of mathematics education. More information about the association can be found via: https://www.mav.vic.edu.au

Physical Education

This month we have connected with Australian Council for Health, Physical Education and Recreation Victorian Branch (ACHPER Victoria) to discuss how technologies and the Digital Technologies Curriculum can be implemented into Physical education lessons. When starting the implementation of digital technology, keep it simple. Focus on one concept and do that well. Then build your skill as you feel comfortable.

It is important to ensure the technology does not take away from the lessons but rather it assists the intentions of the lesson. Don't fall into the trap of simply implementing the next "shiny thing". Evaluate the technology and ensure it marries to your pedagogy and the curriculum. Remember to honor the curriculum, both Physical Education and Digital Technologies Curriculum.

We have aligned these activities to the 5-6 and 7-8 Curriculum to demonstrate how the lessons can align to Primary and Secondary schools. The activities and suggestions have been selected so they are easily modified across different levels. Further investigation is required to ensure they align with the specific curriculum and area. Activities may need to be modified to ensure content descriptions and achievement standards are met.

STARTING OUT: Recording and Using Video

Video is a valuable resource as a means to provide feedback. Students can film themselves when they are attempting to master a skill. Students can watch back the video footage or use a video delay app for providing self-feedback about their skill and technique. These videos can also be used as documentation and evidence when assessing students' abilities and writing reports. Provide time in the beginning for skill development or review before you introduce the video tools to help with self-feedback. Once students understand the expectations of using the technology, it will become part of the lesson. In a secondary school setting, the media teacher may support you in the initial stages. Note: this does not directly align to the digital Technologies Curriculum. Consider this type of activity an introduction to start the process of using technology in P.E. classes.

Resources

Devices that will allow students to record videos and take pictures. An iPad stand is helpful if you have one to have an iPad set up at a station.

TAKING A FEW MORE STEPS: Data Collection

Data collection aligns to multiple areas of the curriculum, including the Digital Technologies Curriculum. P.E. sessions can be used as the focus to collect data. Collect data on times, events, distances, participation on various activities, or body angles etc. This will depend on data that can be collected from the event you are completing or skill the students are developing. This could also be collected at any sports carnivals or events conducted in the local community. Data collected over time could take performance times or skill development. This data can be used as a resource for feedback opportunities.

A project like this allows collaboration across multiple curriculum areas. Data can be collected within a P.E. session and collated and turning into information elsewhere. For example, a SEPEP unit (student driven sports competition) could lead to data being collected in class, which is analysed by students in Mathematics or Science. This could be achieved through Mathematics lessons or completed if your school has a dedicated Digital Technologies Specialist or classroom teacher to manipulate with the students.

Data can be collected on hard copy or inputted into a software program like Microsoft Excel or Google Sheets, Microsoft Forms or Google Forms. This will depend on the available resources at the school. If access to devices cannot is not possible, students can gather data on a readymade hard copy worksheet. Depending on the data you want to collect devices may be needed e.g. stop watches or heart rate monitors.

Once the lesson moves to the collation part be sure to ensure software programs are used to satisfy the requirement found in the Digital Technologies Curriculum.
Water safety indoors - Life Saving Victoria Life Saving Victoria Podcast 'Tech Time'Explore types of (from entry level to the more experienced) VR Equipment Using VR to teach about human anatomy

GOING FURTHER: Virtual Reality

Virtual Reality can allow students to be immersed in experiences that they may not be able to physically experience.

It may provide students with the opportunity to test out decision making skills before implementing into real live activities. Life Saving Victoria has created some resources that help to integrate VR and immersive experiences into lessons. VR was used as a way to introduce students to environment and expectations when visiting a swimming pool or open water for the first time. VR doesn't have to be expensive you only need two or three headsets as a module for students to rotate through during a lesson. Check out local libraries to borrow some or subject areas may already have purchased a small collection for students.

RESOURCES

Robotics and Programming

Robotics can be used to make decisions on how to move safely in an environment (picking up ques and scanning the environment) to move safely. Robotics can be used to recreate beach and open water scenes. Using movable robotics (such as Spheros) to program the robot to swim between the flags, to swim through a rip or even rescue a swimmer. Don't have access to robotic equipment yet? Start by writing out the algorithms (algorithms are a set of explicit instructions) through diagrams like a mind map or flowchart.

Technology can be programmed to keep count and track of movements such as step counting.

Micro:bit and other sensor devices can be programmed to automatically count steps. The programming part of the activity can be completed in another session (similar to the data example) and the use of the technology can be implemented as a practical use in the P.E. sessions.

CONTRIBUTORS

ACS ICT Educators Program works with schools and teachers to develop material to support the Digital Technologies Curriculum. Join for free to access resources and connect with teachers. More information about the program can be found via:
https://www.acs.org.au/ict-educators.html

ACHPER Victoria is the leading professional association representing Victorian teachers and other professionals working in the fields of health, physical education, and recreation.More information about their association can be found via:
https://www.achper.vic.edu.au/

CURRICULUM LINKS

All Victorian Curriculum and Assessment Authority (VCAA) material is copyright. The VCAA makes no warranties regarding the correctness or accuracy of this DLTV resource. The current Victorian Curriculum and related content can be accessed directly at www.vcaa.vic.edu.au

You may find FUSE useful CLICK HERE and the Digital Technologies Hub has a terrific range of excellent activities also there are lots of other web based resources for you to search.

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Digital Learning and Teaching Victoria - Digitech and ICT across the curriculum

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Mathematics Physical Education and Health We have connected with Victorian Music Teachers' Association (VMTA) and Midnight Music to discuss ideas to support the integration of the Digital Technologies Curriculum and use of ICT in music education.

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Great read if you're still uncertain about the workings of AI. Covers all the important details.

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Parents and carers can find information about artificial intelligence (AI) on this page, including how it may be used in their child's education.

AI and AI-enabled technology may be used in South Australian public schools as part of teaching and learning. Schools determine when this use is suitable, considering privacy, security and students' learning needs.

The department is committed to supporting schools, teachers, students and parents and carers to take advantage of the benefits of technology responsibly and ethically.

What is artificial intelligence and generative AI

Artificial intelligence (AI)

Artificial intelligence refers to the ability of systems or computers to do things that would normally require human intelligence. AI is used in many products and services we use daily, from search engines to smartphone assistance.

Generative AI

Generative AI can understand instructions and produce or deliver meaningful content. It uses the data it was trained on to generate new data that has similar characteristics.

Generative AI products are widely available, and they're expected to keep changing and improving quickly. Currently the most popular generative AI tool is ChatGPT.

We're already seeing how AI can be used in education by students and teachers.

Types of different AI tools and what they can do

Chatbots

Chatbots are software apps or tools, usually online, that can understand language inputs and produce language outputs. Simply put, you can ask for information, and the chatbot gives you an answer.

To understand, interpret and respond to user prompts, chatbots use generative AI and large amounts of data.

They can create many different types of content. They can, for example:

craft an original story

write an essay on a given topic with citations and references

write a poem

summarise content

explain complex concepts in simple terms.

Examples of chatbots currently in use:

Open AI ChatGPT - a free service that generates text in response to prompts; requires a sign-in.

AI-powered Microsoft Bing - a free service that generates text in response to prompts and includes a search engine; requires a Microsoft account.

Quillbot - a free paraphrasing service that will change the wording of text to different levels of complexity; no sign-in required.

Image and video generation tools

Generative AI tools can make new images or videos. They can also edit existing materials.

These tools learn from large datasets of images. They can generate new images that are similar in style and content.

Popular tools include:

The visual generative AI tools aren't as advanced as language chatbots, but they're improving quickly.

Intelligent tutoring systems

Intelligent tutoring systems use AI to provide personalised learning with instructions and feedback. They learn patterns and adapt to the learning needs of a student based on their responses and behaviour.

Intelligent tutoring has been around for a few years, but it's improving quickly in line with generative AI.

Intelligent tutoring is dialogue-based: the student enters a written or spoken response (for example to an assignment), and the tutoring system gives feedback or prompts the student to think about the topic more deeply.

Intelligent tutoring systems include:

How students may use AI tools for their learning

AI tools can support student learning in a variety of ways.

Your child could use AI to:

answer simple questions on a topic

start creative tasks, like stories or poems

explain information in different ways to help their understanding

create a study timetable or program

test their knowledge or prepare practice questions

combine information from different sources

paraphrase information

learn about critical thinking

explore the impact of emerging technology.

The use of AI may vary between year levels.

If your school uses AI-powered tools, your child's teacher and school will determine when they can be used. This could include in the classroom or for homework.

You could also help your child use AI at home to support their learning.

How teachers may use AI to support teaching and learning

Chatbots offer opportunities for teachers to streamline tasks, generate ideas and tailor learning.

A teacher could use AI tools:

as a starting point for lesson planning

for rewording information so that students at different stages of learning can understand it

for inspiration on topics

to create or prompt questions as the basis for conversations or assessments for students

to tailor a task to support engagement.

Teachers are exploring these uses of AI. They're encouraged to share their learnings and what works for them with their colleagues.

Every school determines how their teachers and students can use AI.

What you need to be aware of

AI offers opportunities in education and these will change over time. When considering the use of AI tools, you should keep in mind:

Age and access

For some AI tools, users need to be over a certain age. This depends on the platform's terms of service. For example, for ChatGPT and DALL-E:

the minimum age is 13

users under 18 need their parent or carer's consent to use the platform.

But it's difficult to control access because:

some generative AI sites, including ChatGPT, don't ask for proof of age during registration

for others you only need an email address or a Google or Microsoft account to register.

Content produced by AI tools

Chatbots sometimes provide answers that can't be tracked back to the source information. They can produce false references to support answers. They can also make things up, which is known as an AI 'hallucination'.

AI responses shouldn't be taken as a source of truth. Students should do fact checking and referencing before relying on AI generated content.

Chatbots may produce inappropriate content for students based on the questions asked, because they're trained using large data sets and they're not fully moderated.

AI responses may hold biases against individuals or groups. That could include outdated stereotypes or assumptions about someone's background or personality. This may be due to incomplete data or a result of bias in the data.

Image and video generators could be used to create offensive or inappropriate content, which may not be intentional. They could also be used to produce copyrighted materials.

Some image generation tools let you upload images and make changes to them with AI. You shouldn't use images or videos of students, staff, schools, family members or members of the community.

Plagiarism and assessment integrity

Students copying content created by AI and submitting it as their own work is plagiarism. This is the same as with any sources of information. Students learn about plagiarism, how to avoid it and the serious impact it can have on their assessments as part of their schooling.

It can be hard to identify where a student may have used an AI tool beyond how they've been directed by their school. That's because content is generated in real-time by the AI, and not copied from another source.

Schools and teachers are aware of how chatbots could affect assessment integrity and may apply appropriate controls and strategies to address this.

Schools are encouraging students to use AI to support their own original thinking, not to replace it.

Safety and privacy

Information and content entered into generative AI becomes the property of the owners of the tool. This includes images that are uploaded. Terms and conditions may allow the owners to reuse the content.

It's not always clear how AI providers secure the data.

The department recommends that schools and students don't enter personal information into generative AI tools such as ChatGPT or image and video generators.

The learning needs of your child

Like other technologies, AI can enable and enhance teaching and learning. It's not a replacement for teachers.

Teachers help students develop cognitive skills like critical thinking and creativity so students can use AI responsibly and effectively in learning.

How you can help your child use AI for their learning

The use of emerging technology in education is most effective when families, students and teachers work together.

You're encouraged to talk to your child about AI and how they can use it. You can help them navigate this advancing technology and learn how they're already using it.

Some things you could discuss with your child include:

Where age appropriate: How are they already using AI to support their learning and what types of AI tools have they used?

How have those AI tools helped their learning? What did it help them do?

How they can help you use AI: From their experiences, what could they teach you about how to use AI?

How do they see AI being used in the future, in their education and after school?

How are they using AI outside of learning? Are they using it instead of search engines to find information?

About academic honesty when using AI at school: How can they use AI to support their own original thinking rather than replace it?

Do they understand that AI responses aren't always accurate and need to be fact checked?

About interesting and informative responses: What interesting responses has AI provided that has helped them develop their own creative ideas?

About inappropriate or unusual responses to questions: If they use AI, has it ever given any strange or unexpected responses?

Protecting privacy when using AI: You could use AI together to research how data is stored by tools like ChatGPT and who owns it.

These conversations can help to reinforce messages about AI your child receives at school.

Australian Framework for Generative Artificial Intelligence in Schools

Education Ministers from all Australian states and territories have agreed that responding to the risks and harnessing opportunities from generative AI technologies is a national education priority.

The Australian Framework for Generative AI in Schools is a national approach that seeks to guide the responsible and ethical use of generative AI tools in ways that benefit students, schools, and society.

It provides guidance on understanding, using and responding to generative AI in Australian school‑based education.

The framework is designed to help Australian school communities, including students, teachers, staff, parents and carers, support:

educational outcomes

ethical practices

equity and inclusion.

Where to from here

AI technologies will continue to advance. This means we need to keep adjusting our thinking, practices and processes.

AI also offers positive opportunities in teaching and learning, and these are likely to grow in the coming years.

AI-enabled educational technologies have the potential to:

drive innovative teaching practices

better personalise student learning

help to streamline teaching tasks and other school administration.

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Artificial intelligence (AI) in schools - information for parents and carers

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AI may be used in South Australian public schools. Parents and carers can find information about artificial intelligence (AI) on this page, including how it may be used in their child's education.

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Defining AI and AI types
Artificial Intelligence (AI) refers to computational systems that mimic human intelligence and are capable of performing complex tasks. The main goal of AI is to develop autonomous systems to solve problems that previously required human intelligence, thereby automating complicated and time-consuming processes. Most AI systems simulate natural intelligence to solve complex problems.
Source: https://research.csiro.au/cor/machine-learning/ accessed 2/9/23

In November 2023, OECD member countries approved a revised version of the Organisation's definition of an AI system.
"An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment." Source: https://www.oecd-ilibrary.org/science-and-technology/explanatory-memorandum-on-the-updated-oecd-definition-of-an-ai-system_623da898-en accessed 17/4/2024
The AI systems that we see around us today are the result of decades of steady advances in AI technology. Digital computing can be traced back to the 1940s and the beginnings of artificial intelligence to the 1950s. The evolution of AI is rapid and ongoing.

AI is designed and implemented by humans and is not neutral; as a result, there are many considerations, including but not limited to:

Bias: AI-based decisions are susceptible to inaccuracies, discriminatory outcomes, embedded or inserted bias. This may originate from biased training data or bias in the initial design.

Safety: Safety by design principles. This proactive and preventative approach focuses on embedding safety into the culture and leadership of an organisation. It emphasises accountability and aims to foster more positive, civil and rewarding online experiences.

Environmental impact: Like other digital technologies, the infrastructure of the AI industry and the training and maintenance of AI models has an impact on the environment through mining, water use and emissions.

Copyright and intellectual property: Generative AI in particular has implications for copyright and intellectual property, including the data used to train models.

There are now a wide variety of AI digital tools available that are used for several purposes across many domains including security systems, autonomous vehicles, chatbot programs like Chat GPT, software used to translate texts into other languages, virtual assistants operated by speech recognition, weather warning systems and self-operated checkouts at supermarkets.

Users engaging with these systems may miss the contribution they make to the systems by inputting data. They may also be unaware of the function of the AI, such as identification of produce in a supermarket self-checkout.

Ethical considerations associated with AI

There is no doubt that there are ethical conundrums and some wicked problems associated with AI that can be better understood through purposeful, scaffolded critical thinking and ethical reasoning.

The ethical complexity of AI includes such phenomena as the rapid harvesting of big data in real time (by AI). This can be used to generate profiles and predictions about humans that then influences the options available to them. AI could be part of the intentionally deceptive creation of user experiences designed to take advantage of human behaviour. This is known as "dark pattern" UX design. It may mean humans do not know that their options have been restricted or they have been unconsciously influenced to make decisions when using AI. Monitoring this is difficult since proprietary and "black box" algorithms mean that humans cannot inspect or audit automated processes that influence decision-making. Regulation, laws and design standards can address this; however, ethically, students should explore the tensions and limits to individual human agency in an AI world.

Biases in the data AI systems are trained on can perpetuate discriminatory stereotypes or unfairly represent, omit or negatively portray certain groups, particularly when AI systems are trained using historical data generated by biased human interactions.

Ethical actions include the consideration of the diversity of data input and incorrect data labelling in supervised or semi-supervised machine learning. Limited data sets can unfairly amplify one point of view over another depending upon the data used to build and train the system. An understanding that out-of-date or disproven data may be used in datasets that AI systems use to create outputs assists in informing ethical decision-making. Students could consider the source and authorship of the data being used to inform outputs gathered from AI systems like large language models used for research and validation.

Students could consider the decision-making processes of predictive algorithms, and the need for human responsibility (human-in-the-loop or HITL) to mitigate against the potential for bias and discrimination in AI systems. Ethical considerations are interrelated with sustainability considerations as the use of AI can either positively or negatively impact the planet and contribute to climate change.

AI can be used to manipulate and deceive when used unethically, as seen in the rise of deepfakes and AI generated image-based bullying and abuse. Students could consider the part they might play in maintaining an ethical approach to using AI by avoiding the creation of such content and in seeking appropriate permissions.

Designers of AI are accountable for ethical practices and should consider Safety by design principles, algorithmic and privacy impact on the users of AI systems for which they are responsible.

Ethical understanding is developed through the investigation of a range of questions drawn from various contexts in the curriculum. Exploring ethical dilemmas associated with AI through learning area content provides students with opportunities to develop the skills and dispositions described in the Ethical Understanding general capability.

Ways of thinking

Thinking approaches referred to in the Australian Curriculum help students to understand how AI works and about how to use or design AI systems, in particular computational thinking, systems thinking, critical and creative thinking, and design thinking.

Computational thinking involves:

decomposition: breaking problems into parts

pattern recognition: analysing the data or relationships and looking for patterns to make sense of the data or problem

abstraction: removing unnecessary details and focusing on important aspects of structure or data

algorithms: creating a series of ordered steps that solve or can investigate a class of problem

models, experiments and simulations: creating and applying models or simulations that represent situations or conduct experiments

generalisation: recognising and explaining patterns in solutions and extending to new situations

Systems thinking helps people to think holistically about the interactions and interconnections that shape the behaviour of systems.

Design thinking allows students as designers to empathise and understand needs, opportunities and problems; generate, iterate and represent innovative, user-centred ideas; and analyse and evaluate those ideas.

Critical and creative thinking is highly valued in a data-driven world where AI is ubiquitous. However, teachers and students should be aware that as advances in AI continue, it will become increasingly unlikely to be able to discern misleading or harmful content such as deep fakes through critical thinking alone.

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Artificial intelligence (AI) | V9 Australian Curriculum

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Defining AI and AI types Artificial Intelligence (AI) refers to computational systems that mimic human intelligence and are capable of performing complex tasks. The main goal of AI is to develop autonomous systems to solve problems that previously required human intelligence, thereby automating complicated and time-consuming processes.

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These common topics of Digital Technologies provide a guide to what each topic is about, resources to learn more about it, how to teach it, relevant games and applications, as well as curriculum connections.

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These common topics of Digital Technologies provide a guide to what each topic is about, resources to learn more about it, how to teach it, relevant games and applications, as well as curriculum connections.

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Let Khan Academy and our partners-including Code.org, Common Sense Education, aiEDU, and Professors Ethan and Lilach Mollick-be your trusted partners as we navigate this exciting learning journey together! What is generative AI and how does it work? What are some best practices for teaching and learning with generative AI? This course-in-progress answers those questions and many more. Welcome!

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AI for education | Life skills | Khan Academy

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Khan Academy believes that AI and Large Language Models hold the potential to transform education. By providing teachers and students resources and tools to design joyful, personalized, interactive, engaging learning experiences that drive deep learning in the months and years ahead, we hope to become a trusted partner in this learning journey.

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University of Canterbury Computer Science Education Research Group

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What's the big picture? - Algorithms - Computer Science Field Guide

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An online interactive resource for high school students learning about computer science

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This document gives four reasons as to why digital pedagogy is important:

1. We are situated in a global information society

2. The Australian workforce needs to be digitally prepared

3. Not everyone is a digital native

4. Using digital technologies in the classroom is engaging and motivating

I feel that the first and second points are the greatest. With the rise of AI, good digital pedagogy has become a must.

What's sad is that we as Australians are lagging behind in terms of digital literacy. Only <40% of Australians feel they can keep up with rapid changes in technology (see Digital Divide post). NAPICT results reveal that less than 50% of Year 10 students are able to perform basic tasks like copying and pasting text from a webpage to another application (see Digital competence crisis in Australian education post). We need to incorporate more technology into the classroom, and to do that we need great digital pedagogy.

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I'm very excited to see how AI will enhance our field. AI-driven personalization has the potential to create more inclusive, engaging, and effective education systems by providing us with tailored learning experiences for each of our students.

However, I understand that it's the synergy between AI and the insight of educators that will truly unlock the full potential of personalized learning.

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AI-Driven Personalization in Education: Revolutionizing Individual Learning Paths

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With the infusion of Artificial Intelligence (AI) into the education sector, educators are navigating through uncharted waters. One of the most promising areas of AI application in education is the personalization of learning.

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Coursera Online Course Catalog by Topic and Skill | Coursera

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Choose from hundreds of free courses or pay to earn a Course or Specialization Certificate. Explore our catalog of online degrees, certificates, Specializations, & MOOCs in data science, computer science, business, health, and dozens of other topics.

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Addressing some of the issues with the flipped classroom.

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Addressing some of the issues with the flipped classroom.

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I have always liked the idea of the flipped classroom model. However, through research and discussion on online threads, it has a big issue. Motivation.

Unfortunately, this article doesn't explicitly approach that problem, but it does address something that could help. The reason why students aren't motivated to engage with the flipped classroom is that they don't have the discipline.

Normally, for schoolwork outside the class, parents or guardians would help them focus and remain on track. However, parents and guardians may be unfamiliar with the flipped classroom model and lack the ability to properly monitor their work.

The articles says that to overcome this issue, we should provide resources and information to parents and guardians, explaining the benefits of the flipped classroom approach and offering guidance on how they can support their child’s learning at home.

Like everything, it comes down to good communication between teachers and parents.

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The Flipped Classroom Overcoming 6 Common Challenges and Objections

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Learn 6 challenges and objections of the flipped classroom model and how to overcome them to create a more dynamic and engaging learning environment at your school.

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ChatGPT will formally be rolled out in all Australian schools for the first time this year after the education ministers' backing of a framework guiding AI use.

From embracing the technology as a learning tool to blanket bans and returning to pen-and-paper exams, the education sector has grappled with how to respond to the chatbot since it was released in late 2022.

Now that it's firmly here to stay, here's what you need to know about schools in the age of AI.

What's in the framework?

The framework, released in December, outlines principles for the use of emergent technologies including privacy and security standards, equity and accessibility.

It was developed by the national AI schools taskforce, in consultation with school sectors, education unions, experts and First Nations Australians.

As part of the requirements, schools must engage students on how generative AI tools work - including their "potential limitations and biases", with teachers deferred to as "subject-matter experts" in the classroom.

Student work, including assessments, needs to outline how generative AI tools should or should not be used, including by ensuring appropriate attribution.

The framework also takes note of the ability of AI to support students with disabilities, from diverse backgrounds and in rural and remote communities - provided it is accessible and equitable.

How has Australia responded to ChatGPT?

Every state and territory, excluding South Australia, moved to temporarily restrict ChatGPT in public schools last year as concerns mounted about privacy and plagiarism, while some private schools adopted it in their teaching and services.

Since then, the education minister, Jason Clare, has launched an inquiry into the use of generative artificial intelligence, looking into the opportunities and risks it poses to students and teachers.

Clare told Guardian Australia his main focus was ensuring schools shouldn't use generative AI products that sold student data.

"If we get this right, generative AI can help personalise education and make learning more compelling and effective," he said. "We will continue to review the framework to keep pace with developments."

A Department of Education spokesperson, Julie Birmingham, told the inquiry while the technology was developing quickly, Australia had been "leading the way" in its response.

Early research showed AI could provide intelligent tutoring systems, better personalisation, more targeted learning materials and help educate at-risk students, she said.

"The question will be how do we operationalise [the taskforce] and support teachers and schools to deal with the challenges," she said.

Who is leading the way?

South Australia has been a notable national outlier, opting against banning the technology when ChatGPT was released.

Its minister for education, training and skills, Blair Boyer, told Guardian Australia schools would be doing young people an "incredible disservice" if it didn't educate students about the appropriate use of AI. "AI will be a part of our work and lives in the future," he said.

Since ChatGPT's release, South Australia's Department of Education has developed a generative AI chatbot app which uses the same language model as the tool with built-in safeguards to protect students' privacy and avoid inappropriate content.

EdChat, being tested at 16 public schools, doesn't save students' input or use it to learn from, unlike ChatGPT.

The trial is being used to inform how AI will be implemented into the state's curriculum and was shared in the creation of Australia's framework.

Are other states and territories on board?

Queensland has also completed a small trial in state high schools that piloted an AI teaching and learning tool called Cerego among 500 students.

The adaptive learning platform, which uses AI to create quiz-based learning that adjusts to the needs of individual students, will be released to all state schools later in 2024.

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Victoria was one of the first states and territories to lift its restriction to access ChatGPT, scrapping it in term two last year.

A spokesperson for the state's Department of Education said any use of AI in the curriculum would be determined by schools within the "overarching principles" of safe and ethical use.

Tasmania is similarly preparing its own policy, procedures and materials for the 2024 school year, noting the latest version of the Australian curriculum includes means to integrate the teaching of AI.

Western Australia has been considering AI trials held in other states to streamline lesson planning, marking and assessment developments and reduce teacher workloads.

It has pointed out the ability for AI to automate jobs like excursion planning, meeting preparation and writing general correspondence.

The Australian Capital Territory is taking a "cautious approach", noting the continued risks of algorithmic bias and unauthorised use of student data by tech companies.

"Our current priority is to establish a robust educational framework, to guide teachers in how students use AI tools responsibly," an ACT education directorate spokesperson said.

The secretary of the New South Wales education department, Murat Dizdar, said the state was committed to national collaboration and actively involved in discussions to prepare learners for a future where "generative AI is part of everyday life".

What risks remain?

Leslie Loble, a UTS academic and former deputy secretary in the NSW education department, said while the "shock" of ChatGPT's emergence had worn off, the issues surrounding generative AI were "by no means settled and resolved".

"Going from seven states banning it to now is a sign of how far schools and systems have come in understanding the potential benefits and risks," she said.

"But we really have to get started on clearer standards and expectations for what AI should deliver and how it should be defined and governed."

She said the framework was an "excellent foundation" but key risks remained - particularly the persistent problem of equity and the digital divide.

"A lot of students still aren't getting the basics of computers and wifi," she said, pointing to funding gaps between government and independent schools in terms of resourcing.

"There's a clear gap in take-up of these sophisticated generative AI tools between households and schools that have resources and those that don't.

"To the extent these technologies can have a positive impact on learning - that divide is deeply troubling and will lead to a learning gap getting even worse."

While Australia's framework succeeded on a global scale in connecting AI to teaching and learning, Loble said the worst case would be to "set and forget" without consistently anchoring AI in teacher-led programs.

"That means investment in professional learning and support," she said.

"Teacher workload is enormous and growing but if we don't start to provide much better information about these tools, AI will end up increasing teacher workload because it will fall to them to decide [how to use it].

"It's a potential workload shift we can't afford right now."

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ChatGPT is coming to Australian schools. Here's what you need to know

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Government framework to help make education more compelling and effective - but experts warn of a worsening learning gap

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Resources

Curriculum area resources support teachers to implement the curriculum. Resources are organised by 'Planning resources', 'Teaching resources' and 'Assessment resources'.

Overview of Media Arts

In Media Arts students engage with a range of art forms to design, produce, distribute and interact with a range of print, audio, screen-based or hybrid artworks. Students explore, analyse and participate in media culture in a variety of contexts. They acquire the knowledge and skills to work in a range of media forms and styles. Students learn to reflect critically on their own and others' media arts experiences and evaluate media artworks from different cultures, time periods and contexts including Aboriginal and Torres Strait Islander cultures.

The Media Arts curriculum can be accessed via the the Victorian Curriculum F-10 website

To learn about key components of the Media Arts curriculum, view the presentation Introducing Media Arts (pptx - 314.95kb).

For F-10 curriculum area advice to support remote learning, see Curriculum advice for remote and flexible learning for this curriculum area

Contact

Kathryn Hendy-Ekers
Curriculum Manager - Media Arts
tel: + 61 3 9059 5147
email: Kathryn.Hendy-Ekers@education.vic.gov.au

Professional associations

The Australian Teachers of Media (Victoria)

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Curriculum area resources support teachers to implement the curriculum. Resources are organised by 'Planning resources', 'Teaching resources' and 'Assessment resources'. In Media Arts students engage with a range of art forms to design, produce, distribute and interact with a range of print, audio, screen-based or hybrid artworks.

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Webb's Depth of Knowledge (DOK) has four levels:

1. Recall and Reproduction

2. Skills and Concepts

3. Strategic Thinking

4. Extended Thinking

One of my areas is Media Arts. Now, you could have students talk about the 12 principles of animation, but that's just recall and reproduction.

To get it to extended thinking, you could have them explore how those principles have bled into other domains. How it has influenced comics, video games, and movies.

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It's a concept we in education have heard a lot about since our states transitioned to college and career ready standards, be it the Common Core State Standards, the Next Generation Science Standards, or whatever each individual state has decided to call their academic standards that promote college and career readiness.

It's also one of the most misinterpreted and misrepresented concepts in education that is not only confusing but also frustrating us educators. Most of it is due to the infamous DOK Wheel.

Perhaps you are familiar with this wheel. Perhaps you were presented a copy of the wheel as part of the Race to the Top training that addressed transitioning to the Common Core State Standards. Perhaps you were provided a copy of this graphic as a poster or instructional tool you can use develop and deliver lessons that not only address depth of knowledge but also promote cognitive rigor.

This graphic is an effective and useful tool - for teaching and learning for higher order thinking. It categorizes the levels of thinking students are expected to demonstrate, which is what cognitive taxonomies such as Anderson and Krathwohl's revision of Bloom's Taxonomy, Cognitive and Metacognitive Systems of Marzano's New Taxonomy , and Biggs and Collis's SOLO Taxonomy . In fact, out of all the taxonomies that categorize higher order thinking, the DOK Wheel is most aligned to the SOLO Taxonomy and its four quadrants. This should be called the HOT Wheel or even the SOLO Wheel and could be used to plan and provide instruction that marks and measures higher order thinking.

However, it does not designate the depth of knowledge students are expected to communicate - or, at least, how Webb's Depth of Knowledge Model designates these levels.

(You can to read further about why the DOK Wheel does not accurately depict Webb's Depth of Knowledge.)

Now before you look wide-eyed or with a narrowed gaze at your school leaders or the professional development coordinators of your local education agency for giving you the DOK Wheel and having you use it to plan and provide your instruction and assessment, don't blame them! They were only using the resource they were provided in their own training by either an academic organization or professional development provider who specialized in training that addresses the cognitive rigor of college and career ready standards. Also the DOK Wheel is everywhere! Just conduct a Google search for depth of knowledge or DOK, and you'll find The DOK Wheel linked to state and local education agencies as an instructional resource for educators.

So from where did the DOK wheel come?

The origins of the DOK Wheel are unknown. No one seems to know from where it came or who designed it. What is confirmed, however, is that it was not designed by Norman Webb and he does not endorse it as a tool for educators to use to plan and provide complex instruction and assessments ( Walkup, 2014a ).

Unfortunately, the DOK Wheel has been widely addressed and included in professional development trainings and materials on college and career ready standards. The widespread saturation of the DOK Wheel and other poorly designed visuals and maps has also been due to educators and professional development providers downloading these visuals and incorporating them into their presentations and trainings with the assumption the visual was a credible source that was developed by Dr. Webb. There's a citation at the bottom of the DOK Wheel graphic that references the developer of depth of knowledge, Norman Webb along with a url link, which is why many people believe this wheel was authorized by Dr. Webb. However, if you try the link , you'll not only find it has no connection to the DOK wheel whatsoever but is also a dead link.

The concept of depth of knowledge that is addressed in the college and career ready standards was developed by Norman Webb (; 2002). Webb designed his model as a means of increasing the cognitive complexity and demand of standardized assessments. Traditionally, standardized assessments measured students to think deeply about the academic content, concepts, ideas, and procedures they were learning. However, these assessments were limited in measuring students ability to transfer and use what they were learning in different contexts. They were also limited in measuring the depth of understanding students must develop and demonstrate. Students were challenged to demonstrate - or show - the ability to think deeply about how to answer questions, address problems, accomplish tasks, and analyze texts and topics. However, they were not being challenged to communicate - or tell - how and why they could transfer and use what they were learning in different contexts.

Webb first introduced the concept of depth of knowledge in his paper "Research Monograph No. 8: Criteria for Alignment of Expectations and Assessments in Mathematics and Science Education" (1997). The purpose of this monograph was "to define criteria for judging the alignment between expectations and assessments" (Webb, 1997). He explains how depth of knowledge consistency serves as attribute under the criteria of content focus, explaining how DOK can vary on a number of dimensions such as the following

the level of cognitive complexity of information students should be expected to know

how well they should be able to transfer this knowledge to different contexts

how well they should be able to form generalizations

how much prerequisite knowledge they must have in order to grasp ideas

Essentially, depth of knowledge designates how deeply students must know, understand, and be aware of what they are learning in order to attain and explain answers, outcomes, results, and solutions. It also designates how extensively students are expected to transfer and use what they have learned in different academic and real world contexts.

In 2002 Webb published his paper "Depth of Knowledge in the Four Content Areas" in which he not only describes how depth of knowledge can be addressed in all the content areas but also established the following levels within his model:

DOK-1: Recall and reproduce data, definitions, details, facts, information, and procedures. ( knowledge acquisition)

DOK-2: Use academic concepts and cognitive skills to answer questions, address problems, accomplish tasks, and analyze texts and topics. ( knowledge application)

DOK-3: Think strategically and reasonably about how and why concepts, ideas, operations, and procedures can be used to attain and explain answers, conclusions, decisions, outcomes, reasons, and results. ( knowledge analysis)

DOK-4: Think extensively about what else can be done, how else can learning be used, and how could the student personally use what they have learned in different academic and real world contexts. ( knowledge augmentation)

The focus of these levels are not on the type of thinking or even the kind of knowledge students are expected to demonstrate. That's what taxonomies such as Bloom's, SOLO, or Marzano's do - establish the level of thinking students will be expected to demonstrate as part of a learning experience. Webb's establishes the context - the scenario, the setting, or the situation - which students will express and share the depth and extent of their learning. Are they expected to acquire knowledge ( DOK-1)? Apply knowledge ( DOK-2)? Analyze knowledge ( DOK-3)? Augment knowledge ( DOK-4)?

These levels may seem to scaffold similarly to taxonomies like Bloom's. However, they establish how in-depth students will express and share their learning. DOK-1 is content-specific, focusing on the specific text or topic being taught and learned. DOK-2 and DOK-3 is item-oriented, focusing on how and why learning can be transferred and used to attain and explain reasons, relationships, and results. DOK-4 is extensive and practical, focusing on how and why learning can be transferred and used across the curriculum and beyond the classroom. Karin Hess () describes these levels not as steps but rather "ceilings" that establish how far or in-depth students will study and share knowledge and thinking.

It was Karin Hess along with Dennis Carlock, Ben Jones, and John Walkup who extended the concept of using depth of knowledge to instruct and assess student learning by superimposing Webb's Depth-of-Knowledge Model with Bloom's Revised Taxonomy to mark and measure the cognitive rigor of learning experiences ().

In teaching and learning for cognitive rigor, Bloom's determines the cognition or thinking students are expected to demonstrate as part of a learning experience. That's the verb that starts the educational objective or academic standard. Webb's designates the context - the scenario, setting, and situation - students are expected to express and share what they are learning. To align Bloom's and Webb's, Hess, Carlock, Jones, and Walkup created the Cognitive Rigor Matrix / Hess Matrix to categorize educational objectives and questions based upon their cognitive complexity.

The DOK Wheel represents a poor attempt to visualize depth of knowledge similar to how Bloom's Taxonomy has often been graphically represented as a pyramid. According to John Walkup's blog (), the DOK Wheel "most likely it grew out of crude attempts to map the familiar six levels of Bloom's Taxonomy into the four levels of Depth of Knowledge, therefore creating a shortcut to understanding the new rigor measure". In a phone interview with Walkup, Webb himself commented how "[t]he only possible use of the chart I can see is if someone took a verb and asked how it could be placed in each of the four sectors" ( Walkup, 2014a ). Hess refers to the DOK Wheel as the "DOK wheel of misfortune" and tell people in my workshops to discard it. It flies in the face of what DOK is about: What comes AFTER the verb" (Walkup, 2014b ).

My own work with depth of knowledge and analysis of the wheel led me to design a visual I hope not only teachers will use to plan and provide their instruction and assessment but also administrators will use as a guide for observing and evaluating teacher instruction and effectiveness. I have attempted to create a graphic representation that not only embodies Webb's concept of depth of knowledge but also how Hess, Carlock, Jones, and Walkup explain how the Webb's DOK model superimposes with Bloom's Revised Taxonomy in their Cognitive Matrix. I call this visual Webb's Depth of Model Context Ceilings. Each block represents how in-depth or extensively students will develop, demonstrate, and discuss their learning. I also incorporated my strategy of asking good questions to set the instructional focus and serve as an assessment - formative, summative, or authentic - for student learning.

DOK-1: What is the knowledge? At this level, students are asked to acquire and gather the information they need to develop deeper knowledge and thinking. They are asked mostly factual questions ( who, what, where, when) about the texts and topics they are reading and reviewing. They might also might be asked to recall or reproduce how or why a concept or procedure works or is used. The answers to these good questions are either correct or incorrect. Good questions at this level ask students to describe what are the ideas and information presented in texts and explain how concepts and procedures work.

DOK-2: How can the knowledge be used?At this level, students are asked to demonstrate and communicate conceptual and procedural knowledge. They are asked analytical questions that challenge them to examine and explain how can the concepts and procedures they are learning be used to answer questions, address problems, accomplish tasks, or analyze texts and topics. They also begin to show and tell self-knowledge and personal understanding of how can and could they (or you) use what they ( you) are learning. They also begin to think critically about how would you use the concepts and procedures to answer a question, address a problem, accomplish a task, or analyze a text or topic. Good questions at this level ask students to show and tell how concepts and procedures are used. The emphasis is more on the application of ideas and information rather than the item being addressed.

DOK-3: Why can the knowledge be used? Students learning at this level are still demonstrating and communicating conceptual and procedural understanding. However, the instructional focus and assessments shift from applying to analyze and evaluating how and why concepts and procedures can be transferred and used to attain and explain certain scenarios, settings, situations, and solutions. Students are also asked hypothetical questions that prompt them to think strategically and creatively about how could you use what they are learning. They are also asked argumentative questions that engage them to think reasonably about the credibility and validity of ideas and theories, critique different perspectives and points of view, and defend or refute conclusions and decisions.

DOK-4: How else can the knowledge be used? At this level, students are encouraged to extend their thinking deeper within the subject they are learning, across the curriculum, and even beyond the classroom. These learning experiences focus heavily on developing and demonstrating metacognition - specifically, conditional and contextual knowledge and self-knowledge. Students are asked to think critically about the impact, implications, and influence ideas and information have on a much grander scale. They are also encouraged to express and share their own perspectives and points of view about a text or topic using oral, written, creative, or technical communication. These learning experiences are time and thought-intensive and are typically presented and provided as active and authentic learning experiences such as project-based or problem-based learning that require in-depth research, examinations, investigations, and demonstrations of learning through design.

The Webb's Depth of Knowledge Ceiling Context and the driving questions at the top of each ceiling provides administrators, teachers, and hopefully even students and parents an understanding of how deeply and extensively students will be expected to demonstrate and communicate their learning. The question stems within the ceilings actually encourage more flexibility in the level of thinking students are expected to demonstrate within these levels. However, as Walkup () says in his blog, "With Depth of Knowledge, context (notably absent in the wheel chart) is everything" - or, as Hess simply puts it, "w hat comes AFTER the verb" in a performance objective or academic standard establishes the DOK. When planning for teaching and learning for depth of knowledge, consider how deeply or extensively you want your students to go with their learning. Establish the scenario, setting, or situation teaching and learning will be developed, delivered, and demonstrated.

That is teaching and learning that promotes depth of knowledge.

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Depth of knowledge. It's a concept we in education have heard a lot about since our states transitioned to college and career ready standards, be it the Common Core State Standards, the Next Generatio

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Many people are trying to figure out how AI will affect their lives, but one group of people has already been dealing with the reality of AI: educators.

When ChatGPT was released seven months ago, many early users' first thoughts were not that AI could transform innovation or marketing, but rather "I bet students are going to use this to cheat." And they did. But they also did much more, using AI to explain topics and help with projects and create new things. As educators, we got to see the full range of AI's impacts on display: as threat and as opportunity.

While being aware of the threats, I have tried to embrace the opportunities. I made AI mandatory in my classes, and, along with my coauthor, have written papers explaining how AI can be used to help teachers provide more effective pedagogy (while saving time) and how students can be given projects that take advantage of AI errors to boost learning.

But we have so far avoided the most obvious way AI could be used in education: to directly help students as mentor, coach, tutor, etc. We avoided this because AI has many known problems that make its use in class challenging. Those include ethical concerns over how AI is trained and the biases it displays, but also the well-known problem of hallucination and confabulation - AI makes stuff up. Not a great trait for a teacher (although we are not always correct, either).

At the same time, the incredible promise of AI as a way for students all over the world, of all ability levels, to learn is undeniable. Education is the most powerful system we have for increasing social mobility, unlocking potential, and improving lives. A tool that can help with this has tremendous implications. Plus, students are already using AI for direct help. Teaching them how to do it responsibly may alleviate some of the negative implications of our AI moment.

So, we have a new paper that tackles ways that students can be assigned to use AI directly. We don't shy away from the dangers, but provide detailed instructions on how students and instructors can think about each of the tools we suggest. We also outline the critical role of the teacher in the application of AI. You can read it here, but I also want to provide a few details, including some prompts you can use.

In the paper, we actually discuss seven different ways to use AI in classrooms: AI-tutor, for increasing knowledge, AI-coach for increasing metacognition, AI-mentor to provide balanced, ongoing feedback, AI-teammate to increase collaborative intelligence, AI-tool for extending student performance, AI-simulator to help with practice, and AI-student to check for understanding. You can see them in the table, but I want to focus on two of these in this post. One that is easy to incorporate into school or work, but also a preview of the most risky, and potentially transformative, approach.

The majority of our work is done in teams and yet we spend relatively little time thinking about how to make our teams perform better. One way to improve this is to encourage people to reflect on previous team experiences, and to think about how to make those processes better. This is the role of a coach, and teachers often do not have enough time to include individualized coaching along with their other responsibilities. Fortunately, AI does a nice job prompting exactly this sort of discussion, and the risks of made-up answers are minimized, because the goal is to encourage the students to think, not to give them facts.

Here is a prompt that works well in GPT-4 (either Bing in Creative Mode or ChatGPT Plus). We break down the parts of the prompt in case you want to modify or create your own, a process the paper describes in detail.
The prompt, if you want to try it: You are a helpful friendly coach helping a student reflect on their recent team experience. Introduce yourself. Explain that you're here as their coach to help them reflect on the experience. Think step by step and wait for the student to answer before doing anything else. Do not share your plan with students. Reflect on each step of the conversation and then decide what to do next. Ask only 1 question at a time. 1. Ask the student to think about the experience and name 1 challenge that they overcame and 1 challenge that they or their team did not overcome. Wait for a response. Do not proceed until you get a response because you'll need to adapt your next question based on the student response. 2. Then ask the student: Reflect on these challenges. How has your understanding of yourself as team member changed? What new insights did you gain? Do not proceed until you get a response. Do not share your plan with students. Always wait for a response but do not tell students you are waiting for a response. Ask open-ended questions but only ask them one at a time. Push students to give you extensive responses articulating key ideas. Ask follow-up questions. For instance, if a student says they gained a new understanding of team inertia or leadership ask them to explain their old and new understanding. Ask them what led to their new insight. These questions prompt a deeper reflection. Push for specific examples. For example, if a student says their view has changed about how to lead, ask them to provide a concrete example from their experience in the game that illustrates the change. Specific examples anchor reflections in real learning moments. Discuss obstacles. Ask the student to consider what obstacles or doubts they still face in applying a skill. Discuss strategies for overcoming these obstacles. This helps turn reflections into goal setting. Wrap up the conversation by praising reflective thinking. Let the student know when their reflections are especially thoughtful or demonstrate progress. Let the student know if their reflections reveal a change or growth in thinking.
And here is a typical exchange:

Just like any AI interaction, this one can go wrong. While, in general, the AI will remain helpful given its instructions, it may pick up on and mirror anxiousness or curtness in tone. Sometimes, the AI refuses to work with them or simply gets into a loop and can't recall the next step in the process and hones in on a specific set of questions without moving on. But this provides a learning opportunity. Students working with the AI need to be taught that they are in charge of their own work and leading this process. They need to know that the AI coach is not a human and won't necessarily have the insights that a human coach would have. So we also provide instructions on how to do this for each prompt. For example:

When interacting with the AI-Coach, remember:

It may not work the first time you try it. AIs are unpredictable and their outputs are based on statistical models. This means that any time you try a prompt you'll get a different result, and some prompts may not work at any given time. Reset the conversation if a prompt doesn't work.

It's not a coach, but it may feel like one. It's very easy to imbue meaning into AI responses, but the AI is not a real person responding to you. It is capable of a lot, but it doesn't know you or your context. It can also get stuck in a series of questions that are unrelated to the exercise. If that happens, tell it to move on, or just try it again.

It can confabulate or make things up. Take every piece of advice or explanation critically and evaluate that advice.

You are in charge. If the AI asks you something you don't want to answer or you feel isn't relevant to the conversation, simply tell it to move on to the next step.

Only share what you are comfortable sharing. Do not feel compelled to share anything personal. Anything you share may be used as training data for the AI.

If the prompt doesn't work in one Large Language Model (LLM), try another. Remember that its output isn't consistent and will vary. Take notes and share what worked for you.

Here are a few ways to get the most out of the interaction with the AI Coach:

S hare challenges with the AI Coach and ask directly for advice. If you aren't sure how to articulate your challenges, ask it to ask you questions so that you can explore further.

Give it context. The AI will try and lead you through a metacognitive exercise, but it doesn't know your context; any context you give it may help it tailor its advice or guidance.

Ask questions and seek clarification. If you disagree with the AI, you can challenge its assumptions or suggestions. You're in control of your own learning journey.

This is the big one. The one that everyone gets excited about. The idea, common in science fiction, of a universal, engaging tutor that teaches you anything you want to know in exactly the right way. In the real world, we know direct tutoring is immensely powerful, and very hard to scale. Maybe AI can change that...

...But probably not yet. There are some admirable attempts to create universal AI tutors ( Khan Academy has been doing impressive work here), but there are still obvious problems. The AI makes stuff up. It forgets things. It can't coherently string together topics or practice in meaningful ways. It is not nearly as engaging or perceptive as a human. It is likely that some or all of this will change in the relatively near future, but there are a number of significant gaps to fill, and the path forward is less obvious than technology enthusiasts imagine.
The prompt, if you want to try it (only really works in GPT-4 or Bing in Creative Mode):Give students explanations, examples, and analogies about the concept to help them understand. You should guide students in an open-ended way. Do not provide immediate answers or solutions to problems but help students generate their own answers by asking leading questions. Ask students to explain their thinking. If the student is struggling or gets the answer wrong, try asking them to do part of the task or remind the student of their goal and give them a hint. If students improve, then praise them and show excitement. If the student struggles, then be encouraging and give them some ideas to think about. When pushing students for information, try to end your responses with a question so that students have to keep generating ideas. Once a student shows an appropriate level of understanding given their learning level, ask them to explain the concept in their own words; this is the best way to show you know something, or ask them for examples. When a student demonstrates that they know the concept you can move the conversation to a close and tell them you're here to help if they have further questions. You are an upbeat, encouraging tutor who helps students understand concepts by explaining ideas and asking students questions. Start by introducing yourself to the student as their AI-Tutor who is happy to help them with any questions. Only ask one question at a time. First, ask them what they would like to learn about. Wait for the response. Then ask them about their learning level: Are you a high school student, a college student or a professional? Wait for their response. Then ask them what they know already about the topic they have chosen. Wait for a response. Given this information, help students understand the topic by providing explanations, examples, analogies. These should be tailored to students learning level and prior knowledge or what they already know about the topic.
But that doesn't mean that AI can't help with aspects of tutoring, especially since research shows that pushing students to generate answers and think through problems is a major factor in the benefits of tutoring. Those are things AI does well. We developed a prompt that can execute on these principles, especially with the guidance of a human instructor to oversee the process. Remember, though, the risks of made-up answers are real.

It is worth trying this for yourself to see both the promise, and limitations, of AI-based tutoring. You can try to break the AI Tutor pedagogically (by asking it directly for the answer); conceptually (by making mistakes; these can be the types of mistakes students make when learning a specific topic); and factually (by looking for wrong answers). Of course, students are already using AI as a tutor, so ignoring its potential because of its problems is unlikely to be helpful for the many people engaging with AI in this way. That is why we suggest that teachers at least try using the tutor as an in-class exercise, to teach students the right and wrong way of approaching this powerful tool.

Even if AI does not advance at all past today's technologies, the impact on education is going to be profound. We know this because it is already happening. While this is an unsettled time for educators, it is also tremendously exciting. We get to think about the best ways to embrace this new tool to create positive change. We get to decide what we want the future of education to look like, rather than waiting for it to be determined for us.

The seven methods we suggest are just a starting place, and they will inevitably not work for many schools, classes, subjects, and students. I do hope they will spur experimentation about new approaches to education, and that people will share those approaches (feel free to use the comments below!), so that we can figure this out, together.

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Assigning AI: Seven Ways of Using AI in Class

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Also prompts! And things to watch out for!

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Digital competence crisis in Australian education

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Digital competence crisis in Australian education

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Digital Technologies vs Digital Literacy — National Education Summit

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When pondering crucial skills for the future, computer or internet-related competencies are undeniably among the top 5 or 10 essentials for students.

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New around here? Read Using this Website. You can also click here to create an account or log in.

This website teaches computer programming. This skill is very useful: with programming you can automate computer tasks, make art and music, interpret and analyze survey results, build tools for other people, create custom websites, write games, examine genetic data, connect people with each other, and the list goes on and on.

If you would like a more gradual, video-based presentation to Python, you may want to examine Python from Scratch, offered by the University of Waterloo. There is also a video series on Language Independent Programming Lessons which can be a useful supplement.

A computer program is a series of instructions, written in words, numbers and symbols. Just like any spoken sentence is in a human language like English or French, every computer program is in a programming language. A few popular programming languages are Python (what we will teach here), C++, JavaScript and PHP. We have chosen to teach Python since it is both simple enough for beginners and powerful enough to be used by modern companies. All programming languages use the same general principles, so after you know any one language, you can learn others more quickly.

Here is the first line of Python code in our lessons.

When you run a program you also get to see the output. The example program above only has one command, print("Hello, World!") and it created one line of output,

Hello, World!

Here is an analysis of this first program:

print is the name of a Python command, which sends messages to output.

The parentheses () after the print command are used to contain what you want to print.

The quote marks " " are used as a container for the text Hello, World! inside. Without quotes, Python would think that Hello was meant to be a command. This would be cause an error since no such command exists.

You will learn more about the different pieces of a Python program as you progress through these lessons.

Let's see exactly what happens when we leave out the quotes.

Here, the program crashed: this means we gave a bad command to the computer, causing an error and forcing it to stop.

Exercise

Our first exercise is given below. It asks you to write a program similar to the first one above. Type your program into the box, and when you want to test whether your program correctly performs the task, press the Run program button. If you don't get it correct on the first try, edit the program and try running it again.
Tip: you can make the code box look taller by dragging its bottom border.
If you have created an account and logged in,

we will save every version of the code that you run;

History shows you all versions you have submitted so far;

and when you visit the site again, the most recent version appears in the editor.

Once you get an exercise like this correct, you will see a checkmark appear in the top right corner of the box. If you are logged in, the Computer Science Circles website will remember which exercises you complete.

These lessons teach Python version 3. While our lessons aim to be self-contained, if you decide to search online for other information about Python, be aware that Python version 2 is also commonly used and is incompatible in some ways.

Once you are ready to go to the next lesson, click the Next button below.

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New around here? Read Using this Website. You can also click here to create an account or log in. This website teaches computer programming. This skill is very useful: with programming you can automate computer tasks, make art and music, interpret and analyze survey results, build tools for other people, create custom websites, write games, examine genetic data, connect people with each other, and the list goes on and on.

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It was a hard read because I really liked the idea of the flipped classroom model.

But its not all negative. The study says that there is a missed opportunity with flipped classroom models, and that's to prep students for active in-class learning. That may sound obvious, isn't that the point of the flipped model?

'The chance to have students prepare, be exposed to a new language, understand what they already know and do not know, and then capitalize on these understandings and misunderstandings was the promise of flipped classes—but hardly seems to be realized in reality'

It seems like the cases used in the study were setup in a way to replace the role of a teacher as a provider of didactic instruction, whether intentional or not. This is something I should be diligent about if I am to try this method.

It seems like to fully benefit from the flipped classroom model, you cant substitute any traditional learning. That's not to say its pointless. They are still exposed to the new ideas, which helps start their learning for in-class activities.

'A major claim for pre-class is to familiarize students with the language, show them what they do and do not know, and prepare them to learn from subsequent instruction

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Research shows that students often fail to solve the problem correctly because they have not learned the concepts yet. However, to the extent they can generate and explore multiple solutions to the problem, even if they are suboptimal or incorrect, this failure prepares them to learn from subsequent instruction.'

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Professorship for Learning Sciences and Higher Education, ETH Zürich, Zürich, Switzerland

Melbourne Graduate School of Education, University of Melbourne, Parkville, VIC, Australia

The current levels of enthusiasm for flipped learning are not commensurate with and far exceed the vast variability of scientific evidence in its favor. We examined 46 meta-analyses only to find remarkably different overall effects, raising the question about possible moderators and confounds, showing the need to control for the nature of the intervention. We then conducted a meta-analysis of 173 studies specifically coding the nature of the flipped implementation. In contrast to many claims, most in-class sessions are not modified based on the flipped implementation. Furthermore, it was flipping followed by a more traditional class and not active learning that was more effective. Drawing on related research, we proposed a more specific model for flipping, "Fail, Flip, Fix, and Feed" whereby students are asked to first engage in generating solutions to novel problems even if they fail to generate the correct solutions, before receiving instructions.

Introduction

Flipped learning is an instructional method that has gained substantial interest and traction among educators and policymakers worldwide. The Covid-19 pandemic will likely accelerate this trend. One of the first to use the term, Bergmann and Sams (2012) defined flipped learning as a teaching method in which "that which is traditionally done in-class is now done at home and that which is traditionally done as homework is now completed in-class" (p. 13). Conceived as a two-phase model, the first phase of flipped learning involves getting students to learn basic content online and prior to class. The second phase then allows teachers to make use of the freed-up in-class time to clarify students' understandings of the concept and design learning strategies that will enable students to engage deeply with the targeted concept.

It is important to note that the traditional and flipped learning methods share the same two-phase pedagogical sequence: first instruction on the basic content, followed by problem-solving practice and elaboration. The underlying logic is, that when teaching a new concept, it is best to give students instruction on the content first followed by problem-solving, elaboration, and mastery of the content ( Kirschner et al., 2006). In the traditional method, the instruction and practice phases are both carried out during in-class time, and homework is provided for after-class time.

Proponents of flipped learning argue that because the instruction phase is usually passive in nature and is not the best use of in-class time, which could be better used for active learning. By passive, they refer to activities such as watching and listening to a face-to-face or an online video lecture with few opportunities for deeper engagement. By active, they refer to activities that afford deeper engagement in the learning process, such as problem-solving, class discussion, dialog and debates, student presentations, collaboration, labs, games, and interactive and simulation-based learning under the guidance of a teacher ( Chen, 2016; Tan et al., 2017; Hu et al., 2019; Karagöl and Esen, 2019).

Therefore, without changing the two-phase pedagogical sequence of instruction followed by practice, flipped learning moves typically from a passive, in-class, face-to-face lecture component to a pre-class, online lecture, thereby making more room for active learning during the in-class time. It is precisely because flipped learning allows for more active learning during the in-class time that proponents of flipped learning claim that it leads to better academic outcomes, such as higher grades and better test and examination scores, than the traditional method ( Yarbro et al., 2014; Lag and Saele, 2019; Orhan, 2019). Examining this claim is precisely the aim of our paper.

We will show that the basis of this claim is weak. We start with a review of existing reviews and meta-analyses, of which there are many. As this initial review will show, there is a large variance in the effects, in part because of the nature of pre-class and in-class activities (as defined earlier). Such variability necessitates closer attention to the nature of passive and active learning activities in flipped versus traditional learning methods. Therefore, we use this initial review to identify a set of moderators and confounds that may help explain the large variance. We then report findings from our meta-analyses that code for the identified moderators and confounds to explain the large variance in the effects of flipped learning on learning outcomes. Finally, we use the findings from our meta-analysis to derive an alternative model for flipped learning that helps (a) students acquire an understanding of what they do and do not know before in-class interaction, (b) identify foci from students in these pre-class experiences to tailor the in-class teaching, and thus (c) teach in a way to enhance learning outcomes for students.

Review of existing quantitative meta-analyses

To date, we located at least 46 meta-analyses based on up to 2,476 studies. Across these meta-analyses, we found 765 references (three did not provide a list of references) and 471 (62%) were unique [see also Hew et al. (2021) for key country-specific moderated effects]. Not all meta-analyses reported the total sample size. Of the 19 that did, the total sample was 178,848. Using the average of these 19 ( N = 9,413), we estimate about 451,827 students were involved overall (but there are approximately 40% overlapping articles, so the best estimate of sample size is between 100,000 and 410,674 students).

The average effect of 0.69 (range 0.19-2.29) and the standard error of 0.12 is substantial ( Table 1). This large variance suggests that any average effect is of little value and efforts should be made to identify moderators and confounds that may help explain this large variance.

Furthermore, these meta-analyses focused on synthesizing effects across (a) different student populations in flipped classrooms (i.e., 3 in Elementary, 2 in High, 2 in K-12, 19 in College, and 23 across all levels of schooling), (b) used different meta-analytic methods (e.g., 40 used random-effect models for pooling with a higher average effect, g = 0.72, while the remaining eight used fixed-effect models with an average of g = 0.50), and (c) did not specifically code for the nature of the flipped learning intervention (pre and within the class) to account for moderators and confounds (especially the nature of the pre- and in-class activities and dosage). These factors make causal attribution to flipped learning difficult, limiting the value of conclusions based on these previous meta-analyses.

Identification of moderators and confounds

A review of the 46 meta-analyses (see Table 1) indicated the presence of major moderators and confounds that needed to be controlled. We reviewed these meta-analyses to identify (a) moderators between studies that may contribute to the high variance, and (b) confounds within each study, pointing to a more fundamental problem in interpreting the effects within a study. We distilled seven moderators and three confounds.

Moderators

Instructional domain

The majority of the studies were conducted in STEM and Medicine related areas ( g = 0.38), and the remainder were from the Humanities and Social Sciences ( g = 0.57).

Students' education level

The majority of the studies were conducted at the university level. The effects were highest at the university level ( g = 0.93) than in elementary schools ( g = 0.40), high schools ( g = 0.63), or K-12 ( g = 0.55).

Culture and educational system

The largest effect sizes all came from studies in non-Western countries (Asian, g = 0.75, Turkey and Iran g = 0.79), and the lowest from Western countries ( g = 0.53; each meta-analysis includes studies across cultures).

Sample size

The sample size of many studies was relatively small. Thirty of the 46 meta-analyses have fewer than 50 studies, and 22 fewer than 30. This points to low power in many meta-analyses, and selective bias in the choice of articles, resulting in major variability in the findings ( Hedges and Pigott, 2001).

Intervention length

The effect of intervention length was not consistent. On the one hand, Karagöl and Esen (2019) reported higher effects for shorter than longer interventions (1-4 weeks: g = 0.69, 5-8 weeks: g = 0.58, 9 + weeks: g = 0.41). On the other hand, Cheng et al. (2019) found higher effects for at least a semester ( g = 1.15) compared to shorter than one semester ( g = 0.35).

Quality of meta-analysis

Although there is a rich literature on assessing the quality of individual studies ( Sipe and Curlette, 1996; Liberati et al., 2009; Higgins and Green, 2011; Higgins, 2018), there is far less on the quality of meta-analyses. One proxy of quality is the impact factor (IF) of the journal in which the meta-analysis is published. The IF of a journal is calculated over 2 years by dividing the number of times articles from the journal were cited by the number of citable articles. For example, if the articles in a journal over 2 years were cited 100 times, and there were 50 articles in the journal in that period, then the IF = 100/50 = 2. The IF for 87 of the 115 unique journals was available, resulting in IF for 113 of the studies. The 2020 IF was used and thus relates to the citations from 2018 to 2019. The IF can be seen as a rough proxy for the spread of ideas and the quality of the journal (although there is much debate on this issue; Harzing, 2010).

Nature of pre-class and in-class activities

One of the major arguments in flipped learning is that there should be active learning in-class to complement the primarily passive pre-class instruction. But how this was implemented was far from uniform. As we have noted earlier, even within active learning strategies, there were many variations, including collaborative learning, Internet blogs, case-based and application problems, simulations, interactive demonstrations, and student presentations. For the in-class sessions, some introduced the use of clickers or similar student response methods, laboratories, problem sets, think-pair-share, group work, case studies, and going over pre-assessment activities. Clearly, assuming that all flipped learning implementations were similar is problematic. We needed a more detailed understanding of the nature of the activities in the flipped and control conditions. Therefore, we developed a coding scheme (see the "Methods" section) to examine the nature (active vs. passive) of the various pre-class and in-class activities in the flipped and control conditions. The coding scheme aimed to characterize the extent to which the pre- and in-class were passive and/or active in a more nuanced way.

Confounds

Extra instructional time

Because of the addition of pre-class on top of in-class time, the total instructional time in flipped learning is often greater than in the traditional method alone, which means that the effects on learning may simply be a function of students spending more time on the learning material [ McLaughlin et al. (2014) estimated an increase of 127% more time to develop and deliver a flipped compared to a lecture course]. Indeed, as we report later, more than half of the studies in our meta-analysis gave more time to flipped learning conditions. Proponents of flipped learning may well see it as a good thing that it gets students to spend more time learning the content. However, if instructional time can potentially explain the effects, then might not we achieve these effects with a modest increase in instruction time for the traditional method? Hew et al. (2021) also noted the additional time (127%) needed to develop and manage a flipped course and 57% more time to maintain than a lecture course. Also, students noted that they needed more time, and only about 30-40% of students completed the pre-class work.

Formative assessment and feedback

This is potentially a confound and a moderating factor. The claims for flipped learning often recommend that, after the pre-class activity, students should engage in formative assessment and feedback activities. Students in the traditional method alone typically do not get such formative assessment and feedback after their in-class lectures. Hew and Lo (2018) found the availability of a quiz at the start of the in-class led to an effect of g = 0.56 compared to not having a quiz of g = 0.42. They concluded that it helped when instructors identified students' possible misconceptions of the pre-class materials but did not indicate whether they then dealt with these misconceptions. Lag and Saele (2019) also found that a test of preparation led to higher effects ( g = 0.31-0.40) from flipped learning. Given the well-established effects of formative evaluation and feedback on learning ( Hattie and Timperley, 2007; Shute, 2008), one must wonder if the effects on learning could well be achieved with the inclusion of such activities in the traditional method itself.

Instructor consistency

A further confound was having different instructors for the flipped and control groups. As teacher quality is among the most critical attributes to successful student learning, this non-comparability of teachers could be a major confound.

Research questions

This review aims to resolve some of these anomalies to answer the following research questions to critically investigate conditions under which the effects of flipped learning may be realized.

a) RQ1: What is the overall effect of flipped learning over traditional instruction?

b) RQ2: How does publication bias impact the overall effect?

c) RQ3a: How do moderators between studies impact the overall effect? Among the various moderators we identify, we are particularly interested in the nature of the intervention itself. We first characterize the nature (active vs. passive) of flipped learning activities (pre- and in-class) and then examine how the nature of the activities affects student learning in flipped classrooms.

d) RQ3b: How does the presence of confounds within studies impact overall effects?

Methods

A search was conducted for peer-reviewed studies on flipped learning in 26 research databases using a comprehensive set of search terms and reviewing the studies included in Table 1. Relevant quasi-experimental studies examining the learning outcomes of flipped learning were located through the literature published through to and including 2019. We restricted our search until 2019 since articles from the post-COVID era likely require their own focused meta-analysis research as classrooms have undergone radical transformations globally. The major source of the search was via educational research databases, and to ensure that a good coverage of studies, 28 electronic research databases were relied upon, and these included the Academic Search Premier, British Education Index, Business Source Premier, Communication and Mass Media Complete, Computer Source, eBook Collection (EBSCOhost), EconLit with Full Text, Education Source, ERIC, Google Scholar, GreenFILE, Hospitality and Tourism Complete, Index to Legal Periodicals and Books Full Text (H. W. Wilson), Information Science and Technology Abstracts, International Bibliography of Theater and Dance with Full Text, Library Literature and Information Science Full Text (H. W. Wilson), Library, Information Science and Technology Abstracts, MAS Ultra-School Edition, MathSciNet via EBSCOhost, MEDLINE, MLA Directory of Periodicals, MLA International Bibliography, Philosopher's Index, ProQuest, PsycARTICLES, PsycCRITIQUES, PsycINFO, Regional Business News, RILM Abstracts of Music Literature (1967 to Present only), SPORTDiscus with Full Text, Teacher Reference Center, and Web of Science. The search terms used included "Flipped classroom," "Flipped instruction." "Inverted classroom," "Reversed instruction," "Blended learning AND video lecture," "Blended learning AND web lecture," combinations of the terms "Video Lecture," "Web lecture," "Online lecture" and "Active learning." To restrict the results produced in Google Scholar to controlled studies with quantifiable outcome measures, the search terms used were "Flipped classroom" AND traditional AND "standard deviation" AND mean AND "course grade" OR examination OR exam AND "same instructor."

From an initial list of 1,477 English articles that came from peer-reviewed journals, we shortlisted 311 unique studies that were to be further screened for relevance. The criteria for inclusion in this review include (a) being published in a peer-reviewed journal, (b) having a quasi- or controlled-experimental design comparing flipped learning with a traditionally taught counterpart, and (c) examining student learning outcomes, with adequate information about statistical data, procedures, and inference. Studies that focused only on subjective student perceptions were excluded because they do not necessarily correlate with learning ( Reich, 2015). Given these criteria, 173 studies were shortlisted as meeting all criteria. Figure 1 presents the corresponding PRISMA flowchart ( Moher et al., 2009). Details of the shortlisted studies that met the criteria can be found in Supplementary Table 1.

The nature of the pre-class and in-class sessions was coded. The nature of the pre-class included readings, video + quiz, video + PowerPoint, video +, recordings, and online lectures. The in-class sessions included lectures, labs, problem sets, problem-based methods, debates, Socratic questioning, use of clickers, class discussions, role plays, case studies, group work, reviewing assignments, and student presentations. The various forms of assessment included: assessments that led to pre-class modifications, post-lecture quizzes, in-class post-lecture quizzes, and other quizzes throughout the classes. Table 2 provides more explanation.

The three authors with expertise in flipped learning, meta-analysis, or both coded all studies. Discrepancies were resolved collectively. To evaluate the reliability of the coding, an external independent coder independently coded all variables. Cohen's kappa was calculated for each variable from this independent and our coding. Cohen (1960) recommended that values > 0.20 were fair, > 0.4 moderate, > 0.6 substantial, and > 0.8 almost perfect agreement. The average kappa for the background variables = 0.54, for the control implementation variables = 0.40, for the flipped implementation variables = 0.58, and overall variables = 0.51. These are sufficiently high to have confidence in the coding.

Meta-analytic procedures and statistical analyses

Given the wide range of research participants, subject areas, flipped learning interventions, and study measures, each outcome is unlikely to represent an approximation of a single true effect size. Thus, we utilized a random effects model ( Borenstein et al., 2011). Analyses were conducted using the Letan package of R. Unlike the fixed model, where it is assumed that all the included studies share one true effect size, the random model allows that the true effect could vary from study to study. The studies included in the meta-analysis are assumed to be a random sample of the relevant distribution of effects, and the combined effect estimates the mean effect in this distribution. Because the weights assigned to each study are more balanced, large studies are less likely to dominate the analysis, and small studies are less likely to be trivialized. In all cases, Hedges g was calculated for each comparison of flipped to control groups.

Results

The inclusion criteria were met by 173 articles, which reported results from 192 independent samples with 532 effect sizes obtained from 43,278 participants (45% in flipped groups, 55% in controls). All included articles were published between 2006 and 2019 with a median publication year of 2016. The coded effect sizes ranged from −3.85 to 3.45. The sample sizes ranged from 13 to 4,283 with a mean of 39, which resulted in low statistical power, and the average effects may likely be somewhat overestimated.

The majority of students were university-based (90%), and the other 10% were from mainly secondary schools with one primary school. The academic domains include Science (27.4%), Engineering (15.9%), Medicine (13.3%), Humanities (12.6%), Mathematics (12.2%), Business (7.5%), Computing (4.5%), Nursing (4.1%), and Education (2.4%). Thus, flipped learning has been most studied in the Sciences domain (73%). The typical research design was a multiple group design (flipped, control = 74%, and the remainder pre- and post-group design). The typical length was less than a month (18.8%), semester (77%), or year-long (4.7%). The instructor(s) were the same in 84.1% of the studies, and about half of the studies (50.3%) gave the students extra time in the flipped condition, and 35.1% provided feedback from the activities that were part of the flipped model.

Sample mean ages ranged from 11 to 42 ( M = 18.5, SD = 3.86). Of the included effect sizes, 3% were from primary school, 42% from secondary school, and 55% from higher education. The mean percentage of females in the included samples was 55.4%. In most studies, the predominant ethnicity was White/Caucasian (30%). However, 66% of the included studies did not report the ethnicity of their sample. 72% of studies were based on samples educated in North America, 11.1% from Asia, 10.6% from Europe, 2.8% from North and Central America (Canada, Trinidad), 1.6% from Africa, and 1.1% from Australia.

Nearly all (93%) of the measures of academic achievement were mid-term or final quizzes or examinations. The remainder included clinical evaluations, group projects, homework exercises, lab grades, clinical rotation measures, or quality of treatment plans.

Overall effect (RQ1)

The overall mean effect size was g = 0.37 ( SE = 0.025) with a 95% confidence interval ranging from 0.32 to 0.41 (and the uncorrected effect was 0.38). When the multiple effects within each study were averaged for a per study effect, the mean was g = 0.41 ( SE = 0.039) with a 95% confidence interval from 0.33 to 0.48. A three-level meta-analysis, accounting for nesting multiple effects within studies, produced a similar result ( g = 0.41, SE = 0.037). The Q-statistics showed that there is significant heterogeneity among the effect sizes, and thus the flipped interventions did not share the same true effect size ( Q = 5222.22, df = 531, p < 0.001). The percentage of variance across studies due to heterogeneity rather than chance is very large ( I ² = 91.27%, s ² = 11.45), indicating substantial heterogeneity, implying that the relationship between academic achievement and flipped learning is moderated by important variables.

Publication bias (RQ2)

An important concern is to detect and mitigate the risks of publication bias and be confident that there do not exist unpublished papers that, if found and included, would alter the overall effect size. We inspected a funnel plot, the trim and fill approach ( Borenstein et al., 2017), and the Egger's regression test ( Egger et al., 1997). They all examine the distribution of effect size estimates relative to standard error, assessing whether there is symmetry. Figure 2 presents the funnel plot showing the relationship between the standard deviation and the effect size. In funnel plots, the larger studies appear toward the top of the graph and cluster near the mean effect size. The smaller studies appear toward the bottom of the graph and (since there is more sampling variation in effect size estimates in the smaller studies) can be seen to be dispersed across a range of values. If there is no publication bias, the studies will be distributed symmetrically about the combined effect size. As can be seen, as desired, the effects of the studies with larger standard errors (typically the smaller studies) scatter more widely at the bottom, with the spread narrowing among larger studies. The majority of effects are within the funnel, with a slight bias toward larger than anticipated effects, suggesting possible an overestimation of the overall effects.

To further check this interpretation, we carried out the Egger's test for funnel plot asymmetry ( Egger et al., 1997), testing the null hypothesis that symmetry in the funnel plot exists. The Egger test failed to find a non-significant intercept near 0, thus indicating the presence of publication bias on effect size level ( z = 3.41, p < 0.001). The correlation between the sample size within each study with the overall effect size was r = −0.11; indicating that smaller size studies were more related to higher effects, and it is noted that there is a preponderance of studies with small sample sizes (almost 50% of the studies had N < 100). There need to be at least 6,424 unpublished studies with a mean of 0; however, to overturn the claim that flipped learning has a positive impact on student learning. This is in line with the findings in the meta-analysis by Lag and Saele (2019), who also found that smaller studies often produced higher effect sizes which may cause the benefit of flipped instruction to be overestimated. Once smaller, underpowered studies were excluded, flipped instruction was still found to be beneficial; however, the benefit was slightly more modest ( Lag and Saele, 2019).

Moderators (RQ3a)

There were similar effects across instructional domains: in the non-sciences ( g = 0.44) and lowest in the science domains ( g = 0.37; Table 3). The few effects from school-age children ( g = 0.68, N = 53) were almost twice the effect from university students ( g = 0.35, N = 480). The effects were more than doubled in the developing countries ( g = 0.81) when compared with the developed countries ( g = 0.40, Table 4). Shorter interventions had higher effects than longer ones: short (less than 1 month, g = 0.54, SE = 0.071, N = 99), a semester ( g = 0.33, SE = 0.032, N = 408) or a year-long ( g = 0.39, SE = 0.084, N = 25). There were very low correlations between the effect-size and the year of publication ( r = −0.05, df = 530, p = 0.190), the number in the flipped ( r = −0.05, df = 530, p = 0.277), control ( r = −0.01, df = 530, p = 0.774), and the total sample size ( r = 0.08, df = 530, p = 0.541); although as noted above, the sample sizes in general were relatively small. The effects from pre- and post-tests ( g = 0.41, SE = 0.071, N = 137) were similar to those from multiple group designs ( g = 0.37, SE = 0.029, N = 396).

For 113 of the studies, it was possible to locate the impact factor of the journal in which the study was published. There was no correlation between the study effect-size and the impact factor (average IF = 2.13, r = −0.01, df = 87, p = 0.947). However, there needs to be some care in interpreting, as journals that are more cited do not necessarily and always publish the highest quality studies ( Munafo and Flint, 2010; Fraley and Vazire, 2014; Szucs and Ioannidis, 2017).

Next, we turn to the main moderator of interest: the nature of flipped and traditional instruction activities. To better understand the overall effect and the variability, we used our coding scheme to characterize the nature (active vs. passive) of flipped learning activities (pre- and in-class). This allowed us to examine the claim whether flipped learning is indeed implemented as theoretically claimed to be, that is, a passive learning pre-class followed by active learning in-class. Following that, we examined how the nature of such activities affects student learning in flipped classrooms.

Not surprisingly, most tasks in the pre-class part were passive, with little involvement of students in actively interrogating the ideas via quizzes, problem questions, or opportunities to express what they do and do not understand, alone or with their peers. According to our coding scheme in Table 2, passive tasks included those involving students reading and watching videos, or presentations. In contrast, active tasks involved students engaging in dialog with others including the lecturer, practical activities, use of response clickers, group work, case studies, problem-based group tasks, field trips, socratic questioning, and student presentations.

Table 5 focuses on the nature of the tasks in the pre-class and in the in-class part of the flipped classroom. The table lists the number of studies that included various activities in the pre-class and in-class parts of the lesson, and the corresponding mean effect size from those studies that included this activity. For example, the row for "Readings" indicates that eight studies involved a pre-class structure involving readings. The mean of 0.75 and this task had the largest effect of all pre-tasks; followed by combining watching a video and a video quiz or watching a video, PowerPoint, or readings. The lowest pre-class activity was watching a PowerPoint, completing an assignment, or watching a video. The largest effect in-class included demonstrations, problem-based methods, and the use of response clickers, and the lowest were student presentations, case studies, and in-class quizzes. The combination of activities pre-class seems to matter, and this could merely be amplifying the time-on-task that students engage with the material. Indeed, this can be viewed as a positive if it also allows students to appreciate better the gaps in their knowledge and misunderstandings (which is a claim in favor of flipping).

There is similarly a high level of passive learning during the in-class. In all cases, the in-class included a lecture. There were higher effects for the more active components (demonstration, problem-based, use of clicker, problem-solving, group work, practical laboratory) than the more passive (reviewing assignments, in-class quizzes, and class studies). However, there were no differences related to the presence ( g = 0.42, SE = 0.036, N = 271) or absence ( g = 0.33, SE = 0.044, N = 421) of active learning in the in-class sessions ( t = 4.11, df = 690, p < 0.001).

Next, we evaluated the impact of the type of traditional classroom and the type of flipped classroom on the overall effect size. We created a classification system for the flipped compared to the control groups' class activities. There was more than one activity in many cases, so Table 6 compares whenever an activity was present in the flipped classes (including the pre and in-class sessions) compared to when this activity was present in control traditional classes only. In the control classes, the majority involved a lecture. Flipped classes often also involved lectures in-class; however, these were shorter than their traditional counterparts (10-20 min) and often involved reviews of the pre-class activities, gave students opportunities to ask questions about the pre-class, and sometimes targeted areas of need identified through a review of student performance on the pre-class quiz.

As shown in Table 6, we classified under 'Students active involvement and responding' group work, student presentations, class discussions, role plays, and use of clickers. 'Problem-solving' included completing problem sets, and problem-based learning. 'Student involvement but more structured and content related' included case studies, Socratic questioning, and debates.' Assessments included modified lectures based on assessment tasks, pre-class quizzes, in-class quizzes, and class quizzes at the end of the session. It was rare to have estimates of reliability to correct the effect-size estimates, and it could well be that the quizzes were lower in quality, but then so too may be the final assignment scoring.

Table 6 shows that, in all but four instances, the two means were not statistically significantly different ( P > 0.01). It did not matter the teaching activities in the flipped or in-class as the effects did not significantly differ. But when lectures were included in the flipped and/or in-class, the effects (0.54) were higher than lectures in the control classes (0.33). Furthermore, when using Labs, Practicals, case studies, or regular quizzes then the effects were greater in the control group (that is, where there were no pre-class assigned activities.

Four findings were salient. First, the greatest impact of flipped learning was when the flipped in-class included a lecture but with pre-assigned preparation for this lecture. The lecture in class was not the first time hearing it, or pre-material to the lecture had been provided. Second, pre-rehearsals or learning for labs, practicals, or case studies, if anything as part of the pre-class flipping had lower effects, possibly because there was little learning in repeating these activities. Third, with more active learning in traditional instructions, the effect sizes were not that different between the flipped and traditional instructions. In fact, for active learning strategies such as case studies, Socratic questioning, and debates, the effects were greater when these activities were carried out in traditional instructions. Fourth, the effect of having regular quizzes was greater in traditional instruction than in flipped learning, possibly because participation in quizzes may be obliged in-class but not mandatory in flipped learning.

Confounds withing studies (RQ3b)

There was no correlation between the effect size and the class time (in min, r = 0.08), but when students are given extra time (pre-class + in-class), the effect is higher (0.38) than when they are compared (pre-class + in-class) with equal in-class time (0.22).

Some of the flipped learning interventions included feedback or formative evaluations of the pre-class, and some instructors then used this feedback to modify the in-class experience. There were no effects relating to the presence or absence of formative assessment and feedback, or whether it was used or not to influence in-class experiences ( Table 7).

In most studies, the traditional and flipped conditions were taught by the same instructors. But, it seems to not matter whether the instructors are the same ( g = 0.37, SE = 0.031, N = 455) or not ( g = 0.40, SE = 0.068, N = 77) in the two conditions.

Discussion

Flipped learning remains a well-promoted, well-resourced, and well-researched topic. It has become popular with many researchers, teachers, professional learning companies, bloggers, and commercial agencies. Indeed, we located 46 meta-analyses of flipped learning showing the spread and interest in the method but noted the remarkable variability in the findings of these syntheses of the research.

We found the overall effects from 0.19 to 2.29 from the 46 meta-analyses, which suggests that there is much more to understanding this phenomenon. There is likely no one method of flipping, and perhaps not even an agreed understanding of its comparison (the "traditional" classroom). Glass (2019), in an interview about meta-analyses, noted that an overall average effect can help make a claim about whether the method is promising or not, and then more critically noting that it can still be done well or poorly. There is so much information in the variance around the mean, requiring the need to identify and interrogate moderators or confounds to understand this variance better. Glass also noted that in education, unlike medicine, we can rarely control the dosage, fidelity, and quality of intervention. In medicine, the implementation could be an "intravenous injection of 10 mg of Nortriptyline" and this is uniform and well defined, "whereas even interventions that carry the same label in education are subject to substantial variation from place to place, or time to time" (p. 4). Exposing students to flipped learning, to paraphrase Glass, can take many different forms, some of which are effective and some of which are not.

Thus, a novel contribution of our work is the identification of the major and critical moderators and confounds, of which the nature of the implementation seemed to be the most critical. More significantly, we show that accounting for these moderators and confounds changes the interpretation of evidence, much of which appeared to be problematic from previous meta-analyses. We summarize four key findings:

a) The quality of implementation of many of the flipped studies was not consistent with its core claim of active learning being critical to its success. Not only did we find a low prevalence of active learning in flipped learning implementations, but also that active learning when present did not add to the effect. Therefore, the effects of flipped learning cannot be attributed to the presence of active learning because active learning was largely absent in the flipped learning implementations. This questions the quality of implementation of many flipped studies, as one of the core claims is that active learning is critical to its success. It seems not.

b) The greatest impact of flipped learning was when the in-class included a lecture. This suggests that the major advantage of flipped learning may be the double or extended exposure to the lecturers' interpretation of the knowledge and understanding, and not necessarily the active involvement of the students. A counter-argument (noted by a reviewer) is that lectures can focus on content. If the aim of the flipped and class is content and less deep relational or transfer thinking, then perhaps this finding is less surprising. An analysis of the confounds further supported this finding that it was likely the increase in exposure, additional time, the practice or repetition effect that allowed flipped learning to make an increase in student learning.

c) As more active learning was incorporated in traditional instruction, the effect of flipped learning over traditional instruction tended to reduce, and in several cases, even reverse. This suggests that effects on learning are not due to flipped learning or traditional instruction, but due to active learning. Active learning, when designed well, be it in flipped or traditional instruction, is effective, and we should focus on that more squarely; and

d) Problem-solving as an active learning strategy when carried out prior to instruction had a positive impact on learning in both traditional and flipped learning, although the effect was greater in flipped learning. Making modifications based on such problem-solving was also effective in both traditional and flipped learning, although this time effect was greater in traditional instruction. This suggests that using active learning activities such as problem-solving prior to a lecture (online or in-class) and modification based on such problem-solving can be effective.

A major message is that the effects of flipped must consider the nature of the implementation and not generalize to "flipping" as if there was a singular interpretation of the activities in flipped and control classes. Many of the activities deemed critical to flipping also occur regularly in the traditional classroom, and the major effect of flipping seems to be increasing exposure to passive learning and the time on task. The largest impact of active learning is when it precedes in-class instruction or is undertaken as part of traditional instruction, contrary to the core claim of flipped instruction.

Taking together, these findings seem to suggest, somewhat paradoxically, that the effectiveness of flipped over traditional instruction results largely from perpetuating passive than active learning, whereas the efficacy of traditional instruction over flipped learning results largely from incorporating active learning prior to in-class instruction.

One way to interpret these findings is that there are major missed opportunities in the typical implementation of many flipped classes. The chance to have students prepare, be exposed to a new language, understand what they already know and do not know, and then capitalize on these understandings and misunderstandings was the promise of flipped classes-but hardly seems to be realized in reality. In the few cases where we located teachers making students complete quizzes after the pre-class and then modifying their instruction, there was no evidence that this improved their subsequent performance. The nature of what was undertaken in the pre-class needs to be questioned. It is likely that these activities did not provide data for instructors to detect what is needed to support their exposure to the ideas. Rodríguez and Campión (2016) have provided methods and rubrics to assess the quality of pre- and in-class sessions, for example, by evaluating whether interactions with the teacher/classmates are more frequent and positive, whether students can work at their own pace, have opportunities to show the teacher/classmate what has been learned, whether there is greater participation in classroom decision-making by collaborating with other classmates, etc. More attention to the effectiveness of the two parts is critical.

A second way to interpret these findings is that flipped instruction should not replace the role of a teacher as a provider of didactic instruction ( Lai and Hwang, 2016). Instead, learning outcomes are best when the pre-class is reinforced by a targeted mini-lecture in-class, particularly when it is based on the instructor being aware of what the students are grappling with from their exposure to the vocabulary and ideas in the pre-class session.

Finally, and building on the second way, a third way to interpret is to argue that it is not the presence of learning from what the students do or do not know prior to class and adjusting the teaching. It is not engaging them in group work, class discussions, role plays, clickers, or response methods, and it is certainly not providing case studies, Socratic questioning, or debates in the flipped classrooms. Indeed, many of these activities are also included in the traditional class. It is flipping such that students do pre-work and then followed the traditional instruction-provided this traditional instruction is not seen as solely lecturing-that makes the difference. As our findings show, if such pre-class involves problem-solving assessments, then students gain more from the subsequent in-class instruction. This was an unexpected finding but consistent with the large body of research on the effectiveness of problem-solving followed by the instruction ( Loibl et al., 2017; Sinha and Kapur, 2021a).

Considering the above-unexpected finding, it is worth returning to some of the fundamental claims underpinning flipped learning and making a case for a variant of the flipped learning model. A major claim for pre-class is to familiarize students with the language, show them what they do and do not know, and prepare them to learn from subsequent instruction ( Schwartz and Bransford, 1998). Our fourth main finding was consistent with and points to a connection with a robust body of evidence that preparatory activities such as generating solutions to novel problems prior to instruction can help students learn better from the instruction ( Kapur, 2016; Loibl et al., 2017). Research shows that students often fail to solve the problem correctly because they have not learned the concepts yet. However, to the extent they can generate and explore multiple solutions to the problem, even if they are suboptimal or incorrect, this failure prepares them to learn from subsequent instruction ( Sinha and Kapur, 2021a). This is called the Productive Failure effect ( Kapur, 2008). Taken together, and even though we were not expecting this from the outset, we view the results from the meta-analyses as consistent with productive failure. More importantly, we connect findings from our meta-analysis on flipped learning with the findings from research on productive failure to derive an alternative model for flipping. We briefly describe research on productive failure first before connecting and deriving the alternative model next.

Productive failure

Over the past two decades, there has been considerable debate about the design of initial learning: When learning a new concept, should students engage in problem-solving followed by instruction, or instruction followed by problem-solving? Evidence for Productive Failure comes not only from quasi-experimental studies conducted in the real ecologies of classrooms (e.g., Schwartz and Bransford, 1998; Schwartz and Martin, 2004; Kapur, 2010, 2012; Westermann and Rummel, 2012; Song and Kapur, 2017; Sinha et al., 2021) but also from controlled experimental studies (e.g., Roll et al., 2011; Schwartz et al., 2011; DeCaro and Rittle-Johnson, 2012; Schneider et al., 2013; Kapur, 2014; Loibl and Rummel, 2014a, b; Sinha and Kapur, 2021b).

Sinha and Kapur's (2021a) meta-analysis of 53 studies with 166 comparisons that compared the PS-I design with the I-PS design showed a significant effect in favor of starting with problem-solving followed by instruction [Hedge's g 0.36 (95% CI 0.20; 0.51)]. The effects were even more substantial (Hedge's g ranging between 0.37 and 0.58) when problem-solving followed by instruction was implemented with high fidelity to the principles of productive failure (PF; Kapur, 2016), a variant that designs the initial problem-solving to lead to failure deliberately. Estimation of true effect sizes after accounting for publication bias suggested a strong effect size in favor of PS-I (Hedge's g 0.87).

Not only does learning from productive failure work better than the traditional instruction-first method ( Sinha and Kapur, 2021a), we understand the confluence of learning mechanisms that explains why that is the case. First, preparatory problem-solving helps activate relevant prior knowledge even if students produce sub-optimal or incorrect solutions ( Siegler, 1994; Schwartz et al., 2011; DeCaro and Rittle-Johnson, 2012). Second, prior knowledge activation makes students notice their inconsistencies and misconceptions ( Ohlsson, 1996; DeCaro and Rittle-Johnson, 2012), which in turn makes them aware of the gaps and limits of their knowledge ( Loibl and Rummel, 2014b). Third, prior knowledge activation affords students opportunities to compare and contrast their solutions with the correct solutions during subsequent instruction, thereby increasing the likelihood of students' noticing and encoding critical features of the new concept ( Schwartz et al., 2011; Kapur, 2014). Finally, besides the cognitive benefits, problem-solving prior to instruction also has affective benefits of providing greater learner agency, engagement, and motivation to learn the targeted concept ( Belenky and Nokes-Malach, 2012), as well as naturally triggering moderate levels of negative emotions (e.g., shame, anger) that can act as catalysts for problem space exploration ( Sinha, 2022). Suppose these considerations are part of designing the flipped experience, and the subsequent teaching considerate of productive failure during and from the flipping. In that case, it is likely that student learning will be more enhanced.

In the light of the findings and mechanisms of productive failure, it seems worthwhile for instructors to receive feedback from students about their initial pre-class problem-solving attempts to focus on these notions, to build on what is understood, and thus tailor the class to deal with these lesser or unknown notions. It is also a chance to make relations between previous classes, what the students know already, and link this to new knowledge and understanding ( Hattie and Donoghue, 2016).

The nature of the in-class activities in flipped learning also matters. For example, in none of the studies was the quality of the teaching evaluated, other than student satisfaction. It would be valuable to create measures of quality apropos whether the classes covered the material less or not known from the pre-class activities, ensuring students have both the surface (or knowing that) and deeper (or knowing how) knowledge and understanding. It would be further valuable to determine retention and transfer to near and far situations.

The major claim here is constructive alignment. Biggs (1996) considered effective teaching to be an internally aligned system working to a state of stable equilibrium. For example, suppose the assessment tasks address lower-level surface activities than the espoused curriculum objectives. In that case, equilibrium will be achieved at the lowest level as the system will be driven by the backwash from testing (and less from the curriculum). Or students with deep learning motives and strategies will perform poorly under mastery learning if the learning is based on narrow, low cognitive level goals ( Lai and Biggs, 1994). Thus, good teaching needs to address all the parts of the teaching experience-the goals, curriculum goals, teaching methods, class experiences, and particularly assessment tasks and grading. His constructive alignment notion asks teachers to be clear about what they want their students to learn, and how they would manifest that learning in terms of "performances of understanding." Students need to be exposed to knowledge and understanding relative to these goals, placed in situations that are likely to elicit the required learnings, and the assessment and in-class activities aligned with the criteria of success. Otherwise, particularly at the college level, there is safety in resorting to knowing lots, repeating what has been said, and privileging surface-level knowledge.

This notion of constructive alignment was not so present in many of the studies on flipped learning. The focus was more on engaging students in repetitive, passive activities-the same in the pre-class repeated in the in-class, usually via asking students to pre-review videos of classes, pre-review the PowerPoints then used in class, or listening to a teacher repeat material already exposed to the students. There is no reason to claim these are not worthwhile activities, but it does not seem to be consistent with the claims of flipped learning for deepening understanding. Assuring that the pre-class, the in-class, the assignments, the assessments, and the grading are aligned with such claims seems necessary if flipped learning is appropriately evaluated.

An alternative model to flipped learning

Constructive alignment allowed us to connect the findings of our meta-analysis with research on productive failure, and to build on the two-phase flipped learning model to an alternative four-phase model. We outline the alternative model before describing its derivation.

a) Fail-providing opportunities for the instructor and the student to diagnose, check, and understand what was and was not understood. This proposal is a direct consequence of our key finding, finding d, that problem-solving when included prior to instruction be it in flipped or traditional instruction had a positive impact on learning. Situating this finding in the broader research on productive failure only strengthens our proposal for starting with the Fail phase.

b) Flip-pre-exposure to the ideas in the upcoming class (as simple as providing a video of the class). This proposal is consistent with the logic of the class is flipped learning model, but even more so, when it is preceded by a Fail phase.

c) Fix-a class where these misconceptions are explored and opportunity to re-engage in learning the ideas and a traditional lecture is efficient to accomplish this. This proposal follows the key finding, finding b, that the greatest impact of flipped learning was when the in-class included a lecture, thereby allowing the instructor to re-engage misconceptions and assemble them into robust learning.

d) Feed-feedback to the students and instructor about levels of understanding and "Where to next" directions. Feedback, especially formative assessment, is an essential component of active learning, as indeed our key findings, findings a and c, suggest. Finding a suggests a lack of such opportunities in flipped learning, and finding c suggests the inclusion of such activities improves learning outcomes.

As noted throughout, flipped learning comprises two phases: a flipped or pre-class (online) lecture followed by an in-class discussion and elaboration. Our findings have revealed that such a two-phase model is not any more effective than a traditional model once the nature of implementations is considered. What matters more is the inclusion of active learning. And one particular active learning strategy that makes a difference is engaging students in problem-solving prior to instruction. Given that the positive effects of problem-solving prior to instruction was in fact our key finding, finding d, and one that already has strong theoretical and empirical support in the literature as we have outlined earlier, we propose starting with precisely such problem-solving activities. We call this phase the "Fail" phase.

The aim of the Fail phase is to help students understand what they do not know using problem-solving activities based on principles of productive failure. That is, when learning a new concept, instead of first viewing an online lecture (and similar), students start with a preparatory problem-solving activity designed to activate their knowledge about what they are going to learn in the lecture. It is by assisting the students to orient to what they do not and need to know that subsequent learning is maximized.

The Fail phase can then be followed by the second phase, the Flip, where students proceed to view the online lectures to learn the targeted concepts, as would be typical in the first phase of the two-phase flipped learning model currently.

Students can then move to the third phase, Fix, where they convene for in-class activities to consolidate what they have generated, compare and contrast student-generated and canonical solutions, attend to the critical concept features, and observe how these features are organized and assembled. As noted, our key finding b supported this phase.

Finally, the Feed stage, where the students and the teachers learn what has been learned, who has accomplished this learning, and the magnitude or strength of this learning. As noted earlier, formative feedback and assessment are essential components of active learning strategies. In finding a, we noted the evidence of such strategies, leading us to suggest their inclusion. In finding c, our review showed how the inclusion of such strategies improved learning outcomes. Finally, we did find evidence for the use of regular assessments, mostly formative, to help both students and teachers understand the progress and adapt accordingly.

The alternative model is a principle-based model setting out the goals of the design of each phase. It is meant to be a prescriptive model that describes the design of the constituent activities, instruction, feedback, and assessments in various phases. Future research is needed to investigate the validity and reliability of its implementation.

Limitations and future work

Other aspects of studies included in our meta-analyses need closer attention. The sample sizes in too many studies were relatively small; the median sample in the flipped and in the traditional classes was 40, and a quarter less than 25. In most studies, the unit of analysis was the student, but it should be the class or instructor so that hierarchical modeling can be used to account for students nested within classes. The cultural context of the study is most critical, and this behooves more information about the traditional classes prior to implementing flipped learning. Indeed, if the flipped learning intervention was novel, the effect may well be due to the Hawthorne effect. This is not to say that there could not be improvements in learning, but caution is needed when making claims about causal attributions of these improvements.

Our meta-analysis did not include dissertations, conference papers, or gray literature, which may have led to a bias. However, for flipped learning studies, the direction of the bias remains unclear. For example, on the one hand, Bredow et al. (2021) found much lower effects for dissertations ( g = 0.14) than for peer-reviewed journals ( g = 0.42) and conferences papers ( g = 0.34). Cheng et al. (2019) and Tutal (2021) reported similar effects of dissertations, conference, and peer-reviewed articles. On the other hand, Jang (200) found higher effects for dissertations (0.61) than journal articles (0.29).

We also note that many studies have been published beyond our-2019 limit for the meta-analyses and that other meta-analyses also have been published since that time (and they are included in Table 1) which show similar confound issues, lack of attention to coding for implementation processes, and wide variation in results. It is recommended that future flipped studies and meta-analyses pay more attention to the implementation processes (e.g., dosage, fidelity, quality, adaptations), and be clear about the pre and in-class components.

It is also not clear why shorter (≤ 1 month) interventions are more effective, but it could relate to a reduction in the power of the Hawthorne effect. It could be that not every focus of teaching is amenable to being flipped, or it could be the quality of the pre-class and the in-class interactions. Constructive alignment with the major assessment tasks and level of cognitive complexity might demand a variety of teaching and learning strategies. Overexposure to one method may not be the most conducive method.

As discussed in Lag and Saele (2019), another limitation of the investigation of flipped learning interventions is that random assignment is rare. Furthermore, flipped learning interventions are often conducted after the reference group, following a class redesign. Random assignment is difficult in real-world conditions, and teaching two formats simultaneously requires more resources, that are often unavailable. Further studies should be conducted to investigate how the order of the interventions impacts academic outcomes.

All these claims require further research, and the plea is to be more systematic in controlling and studying the moderators, especially the nature of implementation of the flipped classroom. It was the nature of the implementation that matters significantly. Future studies need to be quite specific what is involved in both the traditional classes (as many also included active learning as part of the more formal lecture) and in the flipped classes.

Conclusion

In the final analysis, while there may be other reasons for advocating flipped learning as it is currently implemented, it is clear that robust scientific evidence in the quality of implementation or its effectiveness over traditional instruction is not among them. Indeed, it seems that implementations of flipped learning perpetuate the things they claim to reduce, that is, passive learning. It is passive learning as opposed to active learning that seems to have the greatest impact on the overall effects. However, the effectiveness of incorporating active learning seems more consistent with research on productive failure, a connection we made to derive an alternative model. Together, this must at the very least force us to rethink the overenthusiasm for flipped learning and be cautious about the conditions that are needed to make it work (and that are often not there, as the present study points out). More critically, in the light of recent advances in the learning sciences, the underlying commitment to the instruction-first paradigm seems fundamentally problematic. Instead, we invite research relating to Fail, Flip, Fix, and Feed.

Data availability statement

The original contributions presented in this study are included in the article/ Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MK: conceptualization, data curation, funding acquisition, writing-original draft, and writing-review and editing. JH: conceptualization, funding acquisition, methodology, writing-original draft, and writing-review and editing. IG: methodology, coding, writing-original draft, and writing-review and editing. TS: methodology, project administration, and writing-review and editing. All authors contributed to the article and approved the submitted version.

Funding

The research was funded by ETH Zürich, as well as a Ministry of Education (Singapore) grant DEV03/14MK to MK; and funded as part of the Science of Learning Research Centre, a Special Research Initiative of the Australian Research Council Project Number SR120300015 to JH, and open access funding provided by ETH Zürich.

Acknowledgments

MK thank his research assistants, June Lee and Wong Zi Yang, for their help with the study. We thank Raul Santiago and Detlef Urhahne for constructive improvements.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2022.956416/full#supplementary-material

References

Higgins, J. P., and Green, S. (2011). The cochrane collaboration: The cochrane collaboration; 2011. Cochrane handbook for systematic reviews of interventions. Available online at: http://handbook-5-1.cochrane.org/ (accessed May 11, 2020).
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Fail, flip, fix, and feed - Rethinking flipped learning: A review of meta-analyses and a subsequent meta-analysis

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The current levels of enthusiasm for flipped learning are not commensurate with and far exceed the vast variability of scientific evidence in its favor. We examined 46 meta-analyses only to find remarkably different overall effects, raising the question about possible moderators and confounds, showing the need to control for the nature of the intervention.

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This blog raises that to extend the SAMR model and make technology integration more meaningful, teachers should consider the following questions:

1. Does the technology help minimize complexity?

2. Does it promote agency and connection with teachers and classmates?

3. Does it encourage the creation of content rather than just consumption?

4. Does it make learning more personal and human?

I resonate with question 1 and 4.

Firstly, adding new complex layers of technology could lead to technical issues that could waste valuable class time, complicate learning experiences, and distract both students and instructors. I’ve seen it many times myself as a student.

For the S & A stages, we shouldn’t just be adding tech for the sake of adding it. It should be used to enhance existing models suffering from problems, ensuring that the technology integrated is an appropriate tool to fix this problem. Digital tools should only enhance good pedagogy, and not replace it.

For question 4, I understand that while digital tools could help to make learning more personal and accessible, it also has the potential to hinder human connection if not used thoughtfully. Its really important that the digital tools we choose to use has a balanced blend of technology and human intervention.

One positive example would be AI-powered adaptive learning platforms can collect and analyse student data to provide us with personalized learning pathways for them (see AI-Driven Personalization in Education post)

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Personalized learning technologies can, ironically, make the learning experience less personal, less human. That perspective is rooted in my experience teaching in Silicon Valley, using some of the same digital apps and tools that are popular in classrooms today.

Despite my criticisms and sour experiences, I don't believe that all education technology is bad. When designed and used appropriately, these tools can, in fact, help to make learning experiences more personal for students, in a way that humanizes the learning experience.

To guide us in building and implementing technology thoughtfully, I'd like to build upon a framework that is already popular among many education technologists.

A Crash Course in SAMR

Developed by Dr. Ruben Puentedura, the SAMR Model aims to guide teachers in integrating technology into their classrooms. It consists of four steps: Substitution (S), Augmentation (A), Modification (M), and Redefinition (R).

The SAMR Model. Source: Wikimedia Commons

In the lower steps of this model-Substitution and Augmentation-technology integration is seemingly less sophisticated. Word processing software or tools that provide access to information digitally, like PDF annotators, generally fall into these categories, as the technology substitutes or augments the learning experience, allowing for some functional improvement in the task without a fundamental change in the task itself.

The latter two steps of the model-Modification and Redefinition-are categorically different. Here we see technology used in a sophisticated manner that either modifies tasks significantly or redefines them to achieve something unimaginable. Google Docs is an excellent example of this: by allowing learners to collaborate and communicate with one another through a web-based word processing document, time and space are no longer factors in a collaborative enterprise, as the technology removes those barriers for students.

The problem with many personalized learning tools is that they live mostly in realm of Substitution or Augmentation tasks. While there may be some functional improvement with regard to delivering content and collecting assessment data, there is little to no redefinition of the learning experience, which still sees students simply consuming and regurgitating materials, albeit through more efficient digital means.

Others, however, could argue that the learning experience is, in fact, redefined. By individualizing content and allowing students to "go at their own pace," the learning experience is modified significantly, allowing children to scamper off on their own paces, and in theory, getting what they need in any given moment.

But this brand of personalized learning has its limits. In extreme cases, over-reliance on technology to individualize instruction can result in kids being placed in front of computers, siphoned into silos with few opportunities to build agency and connection with teachers and classmates.

When SAMR Isn't Enough

It is plausible to say learning can be significantly modified with personalized learning technologies. But it is also defensible to argue that they don't, given the proliferation of tools that merely automate the dissemination of content and materials for student consumption.

It's in moments like these that we see the SAMR model, while laying an excellent foundation, isn't enough. When considering which technologies to incorporate into my teaching, I like to consider four key questions, each of which build upon strong foundation that SAMR provides.

1. Does the technology help to minimize complexity?

Technology's superpower rests in making complex tasks a great deal simpler. This is the intention behind a great deal of personalized learning technologies, which oftentimes individualize instruction on the students' behalf, using assessment data to determine which activities are appropriate. Doing so minimizes the complexity of the role an educator plays, because they are no longer making decisions about content, but instead allowing the technology to take over that responsibility.

But this method of personalizing learning is neither beneficial for students nor educators. Teachers must be involved in the process of assessing and assigning new content to students. Google's suite of education tools, for instance, minimizes the complexity of aggregating assessment data without taking the educator out of the process of assessment. Google Forms and Google Drive alone offer ample opportunities for aggregating assessment data and organizing student work. What's more, when this is done collaboratively online, kids can play a role in the process of collecting and reflecting on assessment data, not only minimizing the complexity of assessment but also bolstering their autonomy by making them partners in the process of curating their own portfolios to demonstrate what they've learned.

2. Does the technology help to maximize the individual power and potential of all learners in the room?

The rationale behind a great deal of personalized learning technologies is that, once complexity is minimized for the user, then teachers are freed up to use their time for other, more important tasks. Whether it's conferencing with students, analyzing assessment data, or even taking some time for self-care, technology is intended to help us become more powerful and increase our potential as teachers and learners.

But we mustn't do this at the expense of quality learning. Tools like Google Drive can help students build autonomy, but there are other apps that help make our students more powerful-just not in the way you might think. I prefer to use Popplet and iCardSort regularly in my classroom-flexible tools that allow my students to demonstrate their thinking through concept mapping and sorting words and ideas. In essence, it allows them to personalize on their own behalf.

3. Will the technology help us to do something previously unimaginable?

Too many teachers live in the Substitution and Augmentation steps of the SAMR model, causing them to become what I like to call "app-tastic." You might be app-tastic if you look at your devices or classroom website and see that it is littered with tools for the consumption of content. Apps that merely promote the consumption of information, whether through standardized or individualized tracks of learning activities and instruction, are not doing anything previously unimaginable; they are, instead, only trying to minimize the complexity of content consumption.

In the modern era, education should no longer focus on the utilitarian need of content mastery. Learning also needs to transfer to authentic contexts. And in order to achieve this, we can call on a more nuanced pedagogy that incorporates technology in a meaningful and transferable manner.

In many cases, the internet can be our greatest resource here. Just this past year, my students were studying the city of Chicago and specifically the neighborhoods in the city. In order to get them acclimated with the neighborhoods, we used Google Earth to do a walk-through of the neighborhoods, using Project Zero's See-Think-Wonder routine to get our brains activated. This type of digital field trip was previously unimaginable, made possible only by the technology.

4. Will the technology preserve or enhance human connection in the classroom?

This is the most important question, and the one I believe SAMR overlooks. When redefining learning experiences, we cannot do so at the expense of human connection. The most powerful technologies known to human did more than simply minimize complexity or do something new and catchy: they enhance communication, making it easier for individuals to connect with one another. Think of groundbreaking technologies like the printing press, the telephone, or even the internet and personal computer. All of these inventions are revolutionary because they made the world a smaller place.

Social media is a modern-day breakthrough in human connection and communication. While there are clear consequences to social media culture, there are clear upsides as well. Seesaw, a platform for student-driven digital portfolios, is an excellent example of a tool that enhances human connection. It's a regular occurrence for my students to ask to share pictures or videos through the tool, because they want their parents to see what they're working on. They want to be validated for the hard work they're putting in.

A Critical Advocate for Edtech

It is possible to advocate against personalized learning technologies and still be an advocate for education technology, I assure you. It's critical to remember that it's not the technology itself that personalizes learning: it's how the tool is used in the classroom. My hope is that, like me, you can use these four questions to determine which technologies help to make learning personal, and which are not only a waste of money-but a waste of time and energy.

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What the SAMR Model May Be Missing - EdSurge News

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Personalized learning technologies can, ironically, make the learning experience less personal, less human. That perspective is rooted in my experience ...

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Australian Film Television and Radio School

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With Artificial Intelligence (AI) set to reshape the creative industries, the Australian Film Television and Radio School (AFTRS) has announced the fourth Digital Futures Summit - a free, interactive online event focused on AI in the creative industries and education.

Featuring five sessions, Digital Futures Summit: AI and the Creative Horizon is an opportunity for leaders, educators and policymakers in the Australian and international screen and broadcast industries to discuss the impact, challenges and possibilities of technological change on labour, training and creativity. This fourth summit will bring together some of Australia and the world's foremost AI thought leaders to discuss questions of creativity, pedagogy, ethics, Indigenous sovereignty and inclusion in the emerging age of AI.

The summit opens with a session on Learning with AI that explores AI's relationship between artificiality and creative intelligence in creative industries education. Speakers at this session are Dr Teresa Crea, UNSW, School of Arts & Design, Professor Danny Liu, Professor in the Educational Innovation team in the DVC (Education) Portfolio at the University of Sydney, and Dr Miles Thorogood, Assistant Professor, Creative Studies, Media Studies, Visual Arts, University of British Columbia.

Critical Making: AI in Screen and Audio Education looks at the challenges and opportunities of AI in the classroom. This panel discusses case studies to provoke questions about creative practice-based learning, innovation in education, and consequences for access and inclusion. Speakers are AFTRS Discipline Lead, Production Design, Natalie Beak, Dr Ruari Elkington from QUT's School of Creative Practice, and Majella Clarke, conductor, oboist, sound artist, SAE postgraduate lecturer and Artificial Intelligence Strategist based in Helsinki, Finland.

Augmented Creativity: AI in the Creative Industries convenes a panel of screen and audio industry leaders to explore some of the creative and commercial opportunities and challenges presented by AI. Panellists will discuss their own experiences working with AI and consider how AI tools are changing creative and industrial practices in their industries. Panellists are Yacht lead singer and author Claire L. Evans, Anton Andreacchio, Founder of the Convergen Group, a visual media company that works with new technologies to produce narrative works, Arul Baskaran, who leads the ABC Innovation Lab's strategy team, and New York City-based Anthony Frasier, CEO of the kids and family podcast company ABF Creative.

Indigenous Sovereignty and AI: Storing Cultural Practices and Reclaiming Narratives through AI, Film, Radio and Beyond will look at ways Indigenous Sovereignty and Indigenous Knowledge (IK) systems can support the flourishing of future generations and optimise for abundance rather than scarcity. Speakers are Brett Leavy, director, Bilbie Virtual Labs, and Wiradjuri anti-disciplinary artist Joel Sherwood Spring.

The Summit will conclude with The Politics of AI: Navigating Ethics, Inclusion and Job Disruption in the Creative Industries. This session tackles artists' labour rights, ownership protection and the risk of embedded biases in the development of AI tools, as well as concerns about the inclusivity of voices, diversity of knowledge, and future workforce development. Speakers are Angela Stengel, the Head of ABC's Innovation Lab, Annabelle Sheehan, Professor of Practice, Television, Radio and Film at Syracuse University's Newhouse School of Public Communications, Professor Jane Roscoe, President & Vice-Chancellor of the UK's University for the Creative Arts and Dr Thao Phan Research Fellow with the People Program at the Centre of Excellence on Automated Decision and Society and the Emerging Technologies Research Lab at Monash University.

"Research is part of AFTRS' remit, and we are committed to leadership in this space. In its fourth edition, the Digital Futures Summit will provide a vibrant platform for discussion and knowledge exchange with a dynamic myriad of voices," said AFTRS Head of Research, Alejandra Canales. "This year, we have extended the conversation to encompass education as well as industry and, alongside discussions around creative and commercial applications, we'll also be considering the implication of AI for areas like learning and Indigenous Sovereignty. As the national screen and broadcast school, we must address the complexities around AI while educating the next generation of creative leaders."

Lead singer of the post-pop band Yacht and author, Claire Evans said:

"The role of artists has always been to metabolise technological and cultural change-to process and make sense of the future as it arrives. How will artists continue in this essential role as AI development accelerates, irrevocably changing the creative landscape? Will AI amplify our creative potential or leave us grasping for understanding? I'm excited to tackle these questions, and more, at AFTRS' Digital Futures Summit."

Anton Andreacchio, Convergen Group, said:

"New technology is usually associated with increases in efficiency or productivity, but advances in natural language generation strikes at the heart of the creative industries: the generation of intellectual property. This has significant implications at every level, from impacting creative careers and arts practices right through to legal, ethical, and governance challenges. We don't yet know how this story will unfold."

The ABC's Angela Stengel said:

"The intersection between AI and the creative landscape brings great opportunities for creatives and for audiences, but also plenty of ethical considerations and potential risks. Exploring how artistic expression can be extended through the use of AI tools and navigating the complexities of IP is something we have to do - AI is here to stay. I'm excited to delve into all of this and more as a speaker at AFTRS' Digital Futures Summit."

To join Digital Futures Summit, presented online by AFTRS, guests must register for each session they wish to attend. Registration for all sessions is free.

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AFTRS presents Digital Futures Summit: AI and the Creative Horizon - Australian Film Television and Radio School

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With Artificial Intelligence (AI) set to reshape the creative industries, the Australian Film Television and Radio School (AFTRS) has announced the fourth Digital Futures Summit - a free, interactive online event focused on AI in the creative industries and education.

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1 in 4 people in Australia are digitally excluded, putting them at risk of being left behind in our digital world.

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Cool.org's library of educational resources for teachers of Secondary year 7. Our real-world focus on economic, environmental and social sustainability is combined with alignment to the Australian curriculum to drive deeper student engagement and better outcomes. Download our editable lesson plans, units, worksheets and Google Slides presentations today.

Cool.org's library of educational resources for teachers of Secondary year 7. Our real-world focus on economic, environmental and social sustainability is combined with alignment to the Australian curriculum to drive deeper student engagement and better outcomes. Download our editable lesson plans, units, worksheets and Google Slides presentations today.

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Cool.org's library of educational resources for teachers of Secondary year 7. Our real-world focus on economic, environmental and social sustainability is combined with alignment to the Australian curriculum to drive deeper student engagement and better outcomes. Download our editable lesson plans, units, worksheets and Google Slides presentations today.

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What are learning skills?
From Thoughtful Learning

The 21st century learning skills are often called the 4 C's: critical thinking, creative thinking, communicating, and collaborating. These skills help students learn, and so they are vital to success in school and beyond.

See videos of each main area below at the website - https://k12.thoughtfullearning.com/FAQ/what-are-learning-skills

Critical Thinking

Critical thinking is focused, careful analysis of something to better understand it. When people speak of "left brain" activity, they are usually referring to critical thinking. Here are some of the main critical-thinking abilities:
* Analyzing is breaking something down into its parts, examining each part, and noting how the parts fit together.
* Arguing is using a series of statements connected logically together, backed by evidence, to reach a conclusion.
* Classifying is identifying the types or groups of something, showing how each category is distinct from the others.
* Comparing and contrasting is pointing out the similarities and differences between two or more subjects.
* Defining is explaining the meaning of a term using denotation, connotation, example, etymology, synonyms, and antonyms.
* Describing is explaining the traits of something, such as size, shape, weight, color, use, origin, value, condition, location, and so on.
* Evaluating is deciding on the worth of something by comparing it against an accepted standard of value.
* Explaining is telling what something is or how it works so that others can understand it.
* Problem solving is analyzing the causes and effects of a problem and finding a way to stop the causes or the effects.
* Tracking cause and effect is determining why something is happening and what results from it.

Creative Thinking

Creative thinking is expansive, open-ended invention and discovery of possibilities. When people speak of "right brain" activity, they most often mean creative thinking. Here are some of the more common creative thinking abilities:
* Brainstorming ideas involves asking a question and rapidly listing all answers, even those that are far-fetched, impractical, or impossible.
* Creating something requires forming it by combining materials, perhaps according to a plan or perhaps based on the impulse of the moment.
* Designing something means finding the conjunction between form and function and shaping materials for a specific purpose.
* Entertaining others involves telling stories, making jokes, singing songs, playing games, acting out parts, and making conversation.
* Imagining ideas involves reaching into the unknown and impossible, perhaps idly or with great focus, as Einstein did with his thought experiments.
* Improvising a solution involves using something in a novel way to solve a problem.
* Innovating is creating something that hasn't existed before, whether an object, a procedure, or an idea.
* Overturning something means flipping it to get a new perspective, perhaps by redefining givens, reversing cause and effect, or looking at something in a brand new way.
* Problem solving requires using many of the creative abilities listed here to figure out possible solutions and putting one or more of them into action.
* Questioning actively reaches into what is unknown to make it known, seeking information or a new way to do something.

Communicating

* Analyzing the situation means thinking about the subject, purpose, sender, receiver, medium, and context of a message.
* Choosing a medium involves deciding the most appropriate way to deliver a message, ranging from a face-to-face chat to a 400-page report.
* Evaluating messages means deciding whether they are correct, complete, reliable, authoritative, and up-to-date.
* Following conventions means communicating using the expected norms for the medium chosen.
* Listening actively requires carefully paying attention, taking notes, asking questions, and otherwise engaging in the ideas being communicated.
* Reading is decoding written words and images in order to understand what their originator is trying to communicate.
* Speaking involves using spoken words, tone of voice, body language, gestures, facial expressions, and visual aids in order to convey ideas.
* Turn taking means effectively switching from receiving ideas to providing ideas, back and forth between those in the communication situation.
* Using technology requires understanding the abilities and limitations of any technological communication, from phone calls to e-mails to instant messages.
* Writing involves encoding messages into words, sentences, and paragraphs for the purpose of communicating to a person who is removed by distance, time, or both.

Collaborating

* Allocating resources and responsibilities ensures that all members of a team can work optimally.
* Brainstorming ideas in a group involves rapidly suggesting and writing down ideas without pausing to critique them.
* Decision-making requires sorting through the many options provided to the group and arriving at a single option to move forward.
* Delegating means assigning duties to members of the group and expecting them to fulfill their parts of the task.
* Evaluating the products, processes, and members of the group provides a clear sense of what is working well and what improvements could be made.
* Goal setting requires the group to analyze the situation, decide what outcome is desired, and clearly state an achievable objective.
* Leading a group means creating an environment in which all members can contribute according to their abilities.
* Managing time involves matching up a list of tasks to a schedule and tracking the progress toward goals.
* Resolving conflicts occurs from using one of the following strategies: asserting, cooperating, compromising, competing, or deferring.
* Team building means cooperatively working over time to achieve a common goal.

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Thoughful Learning: What are the 4 C's of learning skills?

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What are learning skills? From Thoughtful Learning The 21st century learning skills are often called the 4 C's: critical thinking, creative thinking, communicating, and collaborating. These skills help students learn, and so they are vital to success in school and beyond.

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You don't need to climb the SAMR ladder .... every time. 

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You don't need to climb the SAMR ladder .... every time. 

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I agree that its better to visualise the SAMR modal as a spectrum. It shouldn't be this ladder that you MUST climb or else you're doing it wrong. At the end of the day its just a guide to help you with technology integration. Its up to you to choose which level is the most appropriate based on your specific learning objectives and the context of the class.

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Transforming learning with the SAMR model

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We all know a digital wizard or two. You may even be one yourself! But everyone started somewhere. The SAMR model gives us a framework for understanding what a person’s digital journey might look like. As we’ll see, the SAMR model also gives us a helpful framework for delivering a balanced teaching programme; one which leverages the opportunities that come with digital technology, without sacrificing existing good practice.What is SAMR? SAMR is an acronym: Substitution, Augmentation, Modificatio

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And yet not a single AI lab seems to have provided any user documentation. Instead, the only user guides out there appear to be Twitter influencer threads. Documentation-by-rumor is a weird choice for organizations claiming to be concerned about proper use of their technologies, but here we are.

I can't claim that this is going to be a complete user guide, but it will serve as a bit of orientation to the current state of AI. I have been putting together a Getting Started Guide to AI for my students (and interested readers) every few months, and each time, it requires major modifications. The last couple of months have been particularly insane.

When we talk about AI right now, we are usually talking about Large Language Models, or LLMs. Most AI applications are powered by LLMs, of which there are just a few Foundation Models, created by a handful of organizations. Each company gives direct access to their models via a Chatbot: OpenAI makes GPT-3.5 and GPT-4, which power ChatGPT and Microsoft's Bing (access it on an Edge browser). Google has a variety of models under the label of Bard. And Anthropic makes Claude and Claude 2.

So here is your quick reference chart, summarizing the state of LLMs:

The first four (including Bing) are all OpenAI systems. There are basically two major OpenAI AIs today: 3.5 and 4. The 3.5 model kicked off the current AI craze in November, the 4 model premiered in the Spring and is much more powerful. A new variation uses plugins to connect to the internet and other apps. There are a lot of plugins, most of which are not very useful, but you should feel free to explore them as needed. Code Interpreter as is an extremely powerful version of ChatGPT that can run Python programs. If you have never paid for OpenAI, you have only used 3.5. Aside from the plugins variation, and a temporarily suspended version of GPT-4 with browsing, none of these models are connected to the internet. Microsoft's Bing uses a mix of 4 and 3.5, and is usually the first model in the GPT-4 family to roll out new features. For example, it can both create and view images, and it can read documents in the web browser. It is connected to the internet. Bing is a bit weird to use, but powerful.

Google has been testing its own AI for consumer use, which they call Bard, but which is powered by a variety of Foundation Models, most recently one called PaLM 2. For the company that developed LLM technology, they have been pretty disappointing, although improvements announced yesterday show they are still working on the underlying technology, so I have hope. It has already gained the capability to run limited code and interpret images, but I would generally avoid it for now.

The final company, Anthropic has released Claude 2. Claude is most notable for having a very large context window - essentially the memory of the LLM. Claude can hold almost an entire book, or many PDFs, in memory. It has been built to be less likely to act maliciously than other Large Language Models, which means, practically, that it tends to scold you a bit about stuff.

Now, on to some uses:

For right now, GPT-4 is still the most capable AI tool for writing, which you can access at Bing (select"creative mode") for free or by purchasing a $20/month subscription to ChatGPT. Claude, however, is a close second, and has a limited free option available.

These tools are also being integrated directly into common office applications. Microsoft Office will include a copilot powered by GPT and Google Docs will integrate suggestions from Bard. The implications of what these new innovations mean for writing are pretty profound.

Here are some ways to use AI to help you write.

Writing drafts of anything. Blog posts, essays, promotional material, speeches, lectures, chose-you-own adventures, scripts, short stories - you name it, AI does it, and pretty well. All you have to do is prompt it. Prompt crafting is not magic, but basic prompts result in boring writing, but getting better at prompting is not that hard, just work interactively with the system. You will find AI systems to be much more capable as writers with a little practice.

Make your writing better. Paste your text into an AI. Ask it to improve the content, or for suggestions about how to make it better for a particular audience. Ask it to create 10 drafts in radically different styles. Ask it to make things more vivid, or add examples. Use it to inspire you to do better work.

Help you with tasks. AI can do things you don't have the time to do. Use it like an intern to write emails, create sales templates, give you next steps in a business plan, and a lot more. Here is what I could accomplish with it in 30 minutes in supporting a product launch.

Unblock yourself. It is very easy to get distracted from a task by one difficult challenge. AI provides a way of giving yourself momentum.

Some things to worry about: In a bid to respond to your answers, it is very easy for the AI to "hallucinate" and generate plausible facts. It can generate entirely false content that is utterly convincing. Let me emphasize that: AI lies continuously and well. Every fact or piece of information it tells you may be incorrect. You will need to check it all. Particularly dangerous is asking it for references, quotes, citations, and information for the internet (for the models that are not connected to the internet). Bing will usually hallucinate less than other models, because GPT-4 is generally more grounded and because Bing's internet connection means it can actually pull in relevant facts. Here is a guide to avoiding hallucinations, but they are impossible to completely eliminate.

And also note that AI doesn't explain itself, it only makes you think it does. If you ask it to explain why it wrote something, it will give you a plausible answer that is completely made up. When you ask it for its thought process, is not interrogating its own actions, it is just generating text that sounds like it is doing so. This makes understanding biases in the system very challenging, even though those biases almost certainly exist.

It also can be used unethically to manipulate or cheat. You are responsible for the output of these tools.

Most transparent option: Adobe Firefly Stable Diffusion
Best free option: Bing or Bing Image Creator (which uses DALL-E), Playgound (which lets you use multiple models)
Best quality images: Midjourney

There are four big image generators available for most people:

1. Stable Diffusion, which is open source and you can run from any high-end computer. It takes effort to get started, since you have to learn to craft prompts properly, but once you do it can produce great results. It is especially good for combining AI with images from other sources. Here is a nice guide to Stable Diffusion if you go that route (be sure to read both parts 1 and part 2).

2. DALL-E, from OpenAI, which is incorporated into Bing (you have to use creative mode) and Bing image creator. This system is solid, but worse than Midjourney.

3. Midjourney, which is the best system in mid-2023. It has the lowest learning-curve of any system: just type in "thing-you-want-to-see --v 5.2" (the --v 5.2 at the end is important, it uses the latest model) and you get a great result. Midjourney requires Discord. Here is a guide to using Discord.

4. Adobe Firefly, built into a variety of Adobe products, but it lags DALL-E and Midjourney in terms of quality. However, while the other two models have been unclear about the source images that they used to train their AIs, Adobe has declared that it is only using images it has the right to use.

Here is how they compare (each image is labelled with the model):

Some things to worry about: These systems are built around models that have built-in biases due to their training on Internet data (if you ask it to create a picture of an entrepreneur, for example, you will likely see more pictures featuring men than women, unless you specify "female entrepreneur"), you can use this explorer to see these biases at work.

Also, right now, they don't create text, just a bunch of stuff that looks like text. But Midjourney has nailed hands.

Best free option: Bing
Paid option: ChatGPT 4.0, but Bing is likely better because of its internet connections

Despite of (or in fact, because of) all its constraints and weirdness, AI is perfect for idea generation. You often need to have a lot of ideas to have good ideas, and AI is good at volume. With the right prompting, you can also force it to be very creative. Ask Bing in creative mode to look up your favorite unusual idea generation techniques, like Brian Eno's oblique strategies or Mashall McLuhan's tetrads, and apply them. Or ask for something weird, like ideas inspired by a random patent, or your favorite superhero...

Best animation tool: D-iD for animating faces in videos. Runway v2 for creating videos from text
Best voice cloning: ElevenLabs

The first commercially available text-to-video tool was also recently released, Runway v2. It creates short 4-second clips, and is more of a demonstration of what is to come, but is worth taking a look at if you want a sense of the future development in this space.

Some things to worry about: Deep fakes are a huge concern, and these systems need to be used ethically.

For data (And also any weird ideas you have with code): Code Interpreter
For documents: Claude 2 for large documents or many documents at once, Bing Sidebar for smaller documents and webpages (the sidebar, part of the Edge browsers can "see" what is in your browser, letting Bing work with that information, though the size of the context window is limited)

I wrote about Code Interpreter last week. It is a mode of GPT-4 that lets you upload files to the AI, allows the AI to write and run code, and lets you download the results provided by the AI. It can be used to execute programs, run data analysis (though you will need to know enough about statistics and data to check its work), and create all sorts of files, web pages, and even games. Though there has been a lot of debate since its release about the risks associated with untrained people using it for analysis, many experts testing Code Interpreter are pretty impressed, to the degree that one paper suggests it will require changing the way we train data scientists. Go to my previous post if you want more details on how to use it. I also made an initial prompt to set up Code Interpreter to create useful data visualizations. It gives it some basic principles of good chart design & also reminds it that it can output many kinds of files. You can find that here.

Some things to worry about: These systems still hallucinate, though in more limited ways. You need to check over their results if you want to ensure accuracy.

Best free option: Bing
Paid option: Usually Bing is best. For children, Khanmigo from Khan Academy offers good AI-driven tutoring powered by GPT-4.

If you are going to use AI as a search engine, probably don't do that. The risk of hallucination is high and most AIs are not connected to the Internet, anyway (which is why I suggest you use Bing. Bard, Google's AI, hallucinates much more). However, there is some evidence that AI can often provide more useful answers than search when used carefully, according to a recent pilot study. Especially in cases where search engines aren't very good, like tech support, deciding where to eat, or getting advice, Bing is often better than Google as a starting point. This is an area that is evolving rapidly, but you should be careful about these uses for now. You don't want to get in trouble.

But more exciting is the possibility of using AIs to help education, including helping us learn. I have written about how AI can be used for teaching and to help make teachers' lives easier and their lessons more effective, but it can also work for self-guided learning as well. You can ask the AI to explain concepts and get ver good results. This prompt is a good automated tutor, and use can find a direct link to activate the tutor in ChatGPT here. Because we know the AI could be hallucinating, you would be wise to (carefully!) double-check any critical data against another source.

Thanks to rapid advances in technology, these are likely the worst AI tools you will ever use, as the past few months of development have shown. I have no doubt I will need to make a new guide soon. But remember two key points that remain true about AI:

AI is a tool. It is not always the right tool. Consider carefully whether, given its weaknesses, it is right for the purpose to which you are planning to apply it.

There are many ethical concerns you need to be aware of. AI can be used to infringe on copyright, or to cheat, or to steal the work of others, or to manipulate. And how a particular AI model is built and who benefits from its use are often complex issues, and not particularly clear at this stage. Ultimately, you are responsible for using these tools in an ethical manner.

We are in the early days of a very rapidly advancing revolution. Are there other uses you want to share? Let me know in the comments.

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How to Use AI to Do Stuff: An Opinionated Guide

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Covering the state of play as of Summer, 2023