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Gamification popularity is nothing new to those who are heavily involved in the learning industry. Every course seems to have even the smallest amount of gamification integrated, be it online or in-person.

Gamification is growing in both the corporate sector and in education. It is a fantastic way to bring some life to elearning since they can often be tiresome and lack any real engagement.

In addition, live training sessions benefit from gamification in that it encourages learners to think about the content from another perspective.

Did You Also Know...?

Over 75% of people are "gamers" (50% casually and 27% moderately).

Learners recall only 10% of what they read and 20% of what they hear. This number jumps to 90% if they are applying content within a simulation.

80% of learners say that their productivity would increase if their university was more game-like.

Over 60% of learners would be motivated by leader boards and increased competition among the students.

89% would be engaged with an LMS if the application had a point system.

Favorite gaming methods include level progression, scores, avatars, and virtual currency.

Least favorite gaming methods include competition, virtual gifts, real time performance feedback feeds.

Most effective gaming strategies include illustrating progress, increasing engagement through forums, creating challenges and badges, and instilling a sense of accomplishment when something is completed.

By 2015, 50% of organizations managing innovation processes will gamify select aspects of their business.

By 2015, 40% of Global 1000 companies will use gaming as the primary means to transform business operations.

53% of individuals responding to a survey on gaming say that by the year 2020 gamification will be adopted by most industries.

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Did You Know This About Gamification? | LearnDash

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Gamification popularity is nothing new to those who are heavily involved in the learning industry. Every course seems to have even the smallest amount of gamification integrated, be it online or in-person. Gamification is growing in both the corporate sector and in education.

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RESEARCH: For Young Kids, The Power of Play-Based Learning

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RESEARCH: For Young Kids, The Power of Play-Based Learning

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The mere presence of the word play in the teaching method known as play-based learning can alarm some parents of early childhood learners. Students, even our youngest students, should be "playing" at home. They come to school to learn, they might say.

That distinction-between "learning" and "play"-is a false one, according to early childhood educator and author Erika Christakis. Although kindergarten and elementary classrooms often devalue it in favor of direct instruction or seat time, play is the "defining feature" of all mammalian development, and its "signature" is apparent in the bodies and lives of little kids who experience it: "Their life expectancies are longer and their social-emotional capabilities are more robust when they have a chance to learn through play and deep relationships, and when their developing brains are given the chance to grow in a nurturing, language-rich, and relatively unhurried environment," Christakis told Edutopia in a 2019 interview.

Children aren't miniature adults. Nonetheless, a bias toward adult perspectives of childhood, with its attendant schedules and routines, has gradually exerted a stranglehold on our educational system, Christakis continues, trapping young kids in educational spaces that too often feel dreary, joyless, and alienating. "The notion that there is something of value in being a little kid-with little kid desires and, above all, needs-seems to have fallen out of favor."

Breaking the Cycle

Despite the clear benefits of play, setting aside the time for even the youngest students can seem out of step with the academic demands of the school day. Early childhood teachers are pressured to meet strict seat-time guidelines in their classrooms, and they often feel that direct instruction is the best method to achieve the many curricular objectives that parents, principals, and other leaders expect.

According to a new study, there's a middle path. A group of researchers from the University of Cambridge analyzed decades of research on "guided play"-more commonly called play-based learning-and concluded that it can have a "greater positive effect" on the acquisition of skills like math, shape knowledge, and task switching than more traditional approaches that prioritize seat time and explicit instruction.

"In redefining play as a spectrum with varying degrees of child autonomy and adult guidance, guided play has been situated as a 'middle-ground' between free play and direct instruction," the researchers concluded. The learning is inherently rich and meaningful because "play naturally cultivates their enjoyment, motivation, and agency; while the inclusion of guidance by a supportive adult extends the scope for learning beyond what the child might achieve on their own."

Incorporating key elements of play-like wonder, exploration, and student agency-into loosely structured lessons that are gently supported by teachers provides an "optimal" approach for students, according to the researchers. For Christakis, this means that play-based learning experiences should provide students with a "steady diet of free, unstructured time and access to open-ended materials" that allow them to engage in "rambling" storytelling and provide plenty of time to just "mess around and make their own rules."

Play, With an Objective in Mind

In a successful play-based learning class, teachers often have a clear "learning goal" behind the play they let students engage in ahead of time, according to the Cambridge study. A teacher should keep this goal in mind during the play and subtly guide the child toward the goal.

Don't pull the strings too tight: According to primary teacher Maggie Sabin, teachers shouldn't necessarily expect students to produce specific outputs. For example, to teach students how colors can be mixed to form new colors, you might avoid giving students instructions to mix specific colors and instead model one example and then allow them to make their own combinations. "Be well prepared and intentional in planning, but allow for flexibility and inspiration," writes Sabin.

One way to make sure that students are playing with purpose is to structure your classroom with deliberate spaces or centers containing materials, games, or objects intentionally chosen for students to engage with and make sense of.

An area in Sabin's classroom, for example, contains a "tinker tray" of items that might seem random but are related to lessons or units she is using direct instruction to guide students through. During a unit on nature and natural materials, for example, the tray is stocked with items like pebbles, leaves, or sticks that students can both practice naming and manipulating. The materials can also be used to practice early math skills through the course of play by simply asking students how many pebbles they have or how many pebbles they have left after giving some to a friend.

Providing Choice and Agency

Effective play-based learning should be child-led when possible and give students "freedom and choice over their actions and play behavior," the researchers assert. However, their findings suggest that the level of autonomy being given to students in play-based learning scenarios is often less than the amount needed to "cultivate children's agency, motivation, and curiosity."

To foster that agency, New Hampshire kindergarten teacher Jessica Arrow often starts the day by allowing students 30 to 45 minutes of "choice time" to explore various spaces in the classroom-a block center, math center, science center, art center, book nook, or dramatic play corner.

The items they encounter are related to previous lessons and the interests her students have expressed. For example, after reading the children's book Miss Maple's Seeds, Arrow said, her students became fascinated by the author's process of creating the book from her imagination. As a result, Arrow's art center included materials for students to create stories of their own and to practice speaking, listening, and writing standards in the process.

Arrow writes that their bookmaking interests eventually carried over into other areas of learning. For example, one student created a number book. After Arrow shared it with the class, number books became popular, and her students were referencing number grids and creating their own number books that helped them count and identify large numbers in the process.

"Once my students had experienced play-based learning, they were more focused, motivated, and purposeful," writes Arrow. "Most important, they were happier. Bringing play-based learning to my classroom created balance, deepened our learning, and defined our classroom community as a place where we could learn and grow together."

When to Step In

As children play, teachers should be observing closely to gather insights about the way students are learning and use open-ended questions, hints, and prompts to gently nudge students and encourage deeper thinking. You might step in "when a child appears to find an activity too difficult or too easy" so that you "can help them learn beyond what might be possible in independent play," the researchers say.

For example, when children are playing with blocks, open-ended questions can be posed to encourage problem-solving, prediction, and hypothesizing, according to veteran teacher and curriculum manager for Edmentum Winnie O'Leary. A teacher can bring awareness to math standards by asking students low-stakes questions such as "I wonder how tall this tower can get?" or "I wonder how many blocks you need to make that tower as tall as your friend's?"

Simple questions can also encourage practice recalling information and identifying shapes, objects, or colors, according to O'Leary. During a game of Go Fish, for example, you can ask, "Hey, who had the number 4 in the last round?" Or during a game of Uno you might ask, "Hmm, what color card do you need to add to the center deck?" Games involving strategy-like checkers or tic-tac-toe-are great to get students thinking critically about their objectives and how to adjust them based on what is happening during the game. Try questions like "I wonder what move you could have made to win?"

Use these strategies wisely, though, the researchers caution. In the end, hints and questions should not feel like directives.

Christakis agrees, telling Edutopia that she often coaches teachers to stay away from "checking questions" such as "What color is the apple?" or "What are you drawing?" Instead, she says, teachers should ask questions like "Tell me about your drawing."

"The open-ended response really opens up a huge space for spontaneous and deep learning," Christakis says.

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For Young Kids, The Power of Play-Based Learning

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New research shows play-based learning can be more effective than direct instruction at improving outcomes for early learners-particularly in the development of mathematical and spatial skills.

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The Difference Between Girls and Boys in Learning

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The Difference Between Girls and Boys in Learning

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www.researchgate.net needs to review the security of your connection before proceeding.

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The digital transformation of education: connecting schools, empowering learners

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The digital transformation of education: connecting schools, empowering learners

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Students: Personal Data Protection and Privacy Principles

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Students: Personal Data Protection and Privacy Principles

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These principles (the "Principles") set out a basic framework for the processing of "personal data", which is defined as information relating to an identified or identifiable natural person ("data subject"), by, or on behalf of, the United Nations System Organizations in carrying out their mandated
activities.

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Personal Data Protection and Privacy Principles

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These principles (the "Principles") set out a basic framework for the processing of "personal data", which is defined as information relating to an identified or identifiable natural person ("data subject"), by, or on behalf of, the United Nations System Organizations in carrying out their mandatedactivities.

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Research suggests that men are more competitive than women. 

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Research suggests that men are more competitive than women. 

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Do Women Shy Away from Competition? Do Men Compete Too Much? | Gender Action Portal

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Research suggests that men are more competitive than women. This gender difference appears in childhood, as evidenced by the playtime activities that girls and boys choose, and increases through puberty and adulthood. Given that in the United States, only 2.5 percent of the highest paid executives a…

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Video Games have great potential in adding the element of entertainment. They address a specific problem and promote fun learning at the same time.

With an expansive growth in the Ed-tech industry in recent years, it is quite evident that online learning is the next big thing. Technological integration with learning has shown some remarkable shifts. Learning has moved from blackboards in classrooms to desktops and mobile devices at homes.

For years, games were tools for learning and continue to do so. The rise in the use of digital devices and learning opportunities online has paved the way for Game-Based Learning. Games have become part of traditional classrooms and online teaching mediums.

Game-Based Learning in simple terms is the use of gaming with educational goals that support a child's development and growth. Game-based learning promotes analytical thinking and logical reasoning. It also enhances problem-solving and collaborative thinking among kids. Games like Sudoku, Lego Tower and Scrabble make kids learn in an engaging and interactive manner. These games promote learning and also ensure the analytical and practical application of the mind.

With Gen X known to be tech-savvy and going online to look for learning opportunities, the rise of E-Learning opportunities is set to rise in coming years. Game-based education is also known to enhance soft skills much more than traditional teaching methods.

Game Based Learning Vs Gamification: The Misconception

At first, you might want to rethink if there is actually any difference between Game Based Learning and Gamification?

Gamification is the use of game design elements, game mechanics and game thinking in non-gaming elements. The main aim of Gamification is to motivate and promote involvement in non-gaming elements. Gamification aims at rewarding users for certain behaviours. Gamification in online learning could encourage students to learn more and compete with their peers.

One great example to quote would be using gamification systems like Classcraft in classrooms. A classroom is re-imagined using technology, games and storytelling in a way that adds fun and interaction in the usual curriculum. Students create a character, play as part of a team and get rewarded with points and experience. They are then judged on factors like teamwork, uniqueness and many other life skills.

Game Based Learning is the use of games to achieve educational goals. For example, the game Sudoku that forces kids to apply their minds to solve complex problems. It is also proven to sharpen the memory of kids and improve their abstract learning. At the same time, it teaches kids many life skills like patience, focus and decision-making skills. All of these factors lead to improved and quality learning.

Both Gamification and Game Based Learning are related terms if we look at the education point of view. They vary on the purpose they serve in classrooms and now even in online classrooms!

Benefits of Game Based Learning: Backed by Science!

The clubbing of games with education not only promotes learning in a fun way but also promotes "Learning by Doing". This means that tasks in a game based app or software foster a sense of competitiveness and self-learning among kids. This is accomplished through in-game tasks which in turn benefit the kids in becoming quick learners.

Studies suggest that Game Based Learning boosts the self-confidence of children by 20%. Kids feel a sense of accomplishment while advancing to different levels in an educational based game.

Conceptual Understanding and knowledge increase by 11% in kids through Game Based Learning. This is because children learn topics better when they learn through interactive gaming. The impact this has on kids is that it increases their engagement levels. This promotes quality learning in an effective manner.

Game Based Learning is known to sharpen the memory of kids by a staggering 90%! Learning concepts through visuals leads to better retention power and sharpens the minds of kids. Visual Learning leads to a decrease in learning time, improves the retrieval of information that leads to faster concept grabbing for students.

You child gets a 20% better understanding of real-world applications through Game Based Learning. They also promote "learning by doing"

Children who undergo Game Based Learning complete 3 times more tasks. This is way more than the children who learned through traditional learning methods.

Game Based Learning has 4 times higher likelihood of improving working and analytical skills. This is possible through games that promote the use of logical reasoning and critical thinking. Games like Sudoku and Scrabble are great examples!

What does the Future of Game Based Learning hold in the Ed Tech Industry?

With the advent of online learning and the use of game based learning or gamification, learning is surely going to reach new heights. The integration of gaming to achieve educational goals is supportive of that fact.

The shift from traditional learning to online has been on the rise. Innovative and interactive means to make learning fun and knowledgeable are increasing each year. Ed tech learning is known for its personalized and effective teaching methods. With the boom in online tutoring, game based learning is something that teachers all over prefer to use even in the classrooms.

In a global survey conducted, it was found that 81% of students were more productive if their learning was more game-like and followed the gaming approach. If we go by the trends in game based learning, then 1 in every 5 teachers use games every day as part of their educational curriculum.

$341 billion dollars is the estimated global expenditure on Game Based Learning in education by 2025. Also, with an expected growth rate of 27.1% of adoption of game based learning from 2020 - 2025, it is correct to say that Game Based Learning is very soon going to become a part of each and every classroom and household!

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Why Game Based Learning Will Make Your Child Smarter?

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Video Games have great potential in adding the element of entertainment. They address a specific problem and promote fun learning at the same time. With an expansive growth in the Ed-tech industry in recent years, it is quite evident that online learning is the next big thing.

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New Vision for EducationUnlocking the Potential of Technology WEF and BCG Report

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New Vision for EducationUnlocking the Potential of Technology WEF and BCG Report

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Used the SWOT analysis framework to outline ChatGPT’s strengths and weaknesses and to discuss its opportunities for and threats to education. 

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Used the SWOT analysis framework to outline ChatGPT’s strengths and weaknesses and to discuss its opportunities for and threats to education. 

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The strengths:
using a sophisticated natural language model to generate plausible answers;

self-improving capability;

providing personalised and real-time responses.

increase access to information,

facilitate personalised and complex learning,

and decrease teaching workload, thereby making key processes and tasks more efficient.

The weaknesses

lack of deep understanding,

difficulty in evaluating the quality of responses,

a risk of bias and discrimination,

lack of higher-order thinking skills.

Threats to education:

lack of understanding of the A.I. ultimate capabilities;

threatening academic integrity,

perpetuating discrimination in education.

democratising plagiarism, and declining high-order cognitive skills.

Opportunities

Teachers may improve different effective teaching methods

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The impact of artificial intelligence on learner–instructor interaction in online learning.

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The impact of artificial intelligence on learner–instructor interaction in online learning.

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The impact of artificial intelligence on learner–instructor interaction in online learning - International Journal of Educational Technology in Higher Education

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Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors’ routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner–instructor interaction (inter alia, communication, support, and presence) has a profound impact on students’ satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner–instructor interaction, capturing students’ and instructors’ concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

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AI literacy in K‑12: a systematic literature review

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AI literacy in K‑12: a systematic literature review

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AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.

However, no evidence suggests AI alone could enhanced students grade.

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Best practices of ICT policy in education: Republic of Korea

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Best practices of ICT policy in education: Republic of Korea

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A man widely seen as the godfather of artificial intelligence (AI) has quit his job, warning about the growing dangers from developments in the field.

Geoffrey Hinton, 75, announced his resignation from Google in a statement to the New York Times, saying he now regretted his work.

He told the BBC some of the dangers of AI chatbots were "quite scary".

"Right now, they're not more intelligent than us, as far as I can tell. But I think they soon may be."

Dr Hinton also accepted that his age had played into his decision to leave the tech giant, telling the BBC: "I'm 75, so it's time to retire."

Dr Hinton's pioneering research on neural networks and deep learning has paved the way for current AI systems like ChatGPT.

In artificial intelligence, neural networks are systems that are similar to the human brain in the way they learn and process information. They enable AIs to learn from experience, as a person would. This is called deep learning.

The British-Canadian cognitive psychologist and computer scientist told the BBC that chatbots could soon overtake the level of information that a human brain holds.

"Right now, what we're seeing is things like GPT-4 eclipses a person in the amount of general knowledge it has and it eclipses them by a long way. In terms of reasoning, it's not as good, but it does already do simple reasoning," he said.

"And given the rate of progress, we expect things to get better quite fast. So we need to worry about that."

In the New York Times article, Dr Hinton referred to "bad actors" who would try to use AI for "bad things".

When asked by the BBC to elaborate on this, he replied: "This is just a kind of worst-case scenario, kind of a nightmare scenario.

"You can imagine, for example, some bad actor like [Russian President Vladimir] Putin decided to give robots the ability to create their own sub-goals."

The scientist warned that this eventually might "create sub-goals like 'I need to get more power'".

He added: "I've come to the conclusion that the kind of intelligence we're developing is very different from the intelligence we have.

"We're biological systems and these are digital systems. And the big difference is that with digital systems, you have many copies of the same set of weights, the same model of the world.

"And all these copies can learn separately but share their knowledge instantly. So it's as if you had 10,000 people and whenever one person learnt something, everybody automatically knew it. And that's how these chatbots can know so much more than any one person."

Matt Clifford, the chairman of the UK's Advanced Research and Invention Agency, speaking in a personal capacity, told the BBC that Dr Hinton's announcement "underlines the rate at which AI capabilities are accelerating".

"There's an enormous upside from this technology, but it's essential that the world invests heavily and urgently in AI safety and control," he said.

Dr Hinton joins a growing number of experts who have expressed concerns about AI - both the speed at which it is developing and the direction in which it is going.

In March, an open letter - co-signed by dozens of people in the AI field, including the tech billionaire Elon Musk - called for a pause on all developments more advanced than the current version of AI chatbot ChatGPT so robust safety measures could be designed and implemented.

Yoshua Bengio, another so-called godfather of AI, who along with Dr Hinton and Yann LeCun won the 2018 Turing Award for their work on deep learning, also signed the letter.

But Dr Hinton told the BBC that "in the shorter term" he thought AI would deliver many more benefits than risks, "so I don't think we should stop developing this stuff," he added.

He also said that international competition would mean that a pause would be difficult. "Even if everybody in the US stopped developing it, China would just get a big lead," he said.

Dr Hinton also said he was an expert on the science, not policy, and that it was the responsibility of government to ensure AI was developed "with a lot of thought into how to stop it going rogue".

Dr Hinton stressed that he did not want to criticise Google and that the tech giant had been "very responsible".

"I actually want to say some good things about Google. And they're more credible if I don't work for Google."

In a statement, Google's chief scientist Jeff Dean said: "We remain committed to a responsible approach to AI. We're continually learning to understand emerging risks while also innovating boldly."

It is important to remember that AI chatbots are just one aspect of artificial intelligence, even if they are the most popular right now.

AI is behind the algorithms that dictate what video-streaming platforms decide you should watch next. It can be used in recruitment to filter job applications, by insurers to calculate premiums, it can diagnose medical conditions (although human doctors still get the final say).

What we are seeing now though is the rise of AGI - artificial general intelligence - which can be trained to do a number of things within a remit. So for example, ChatGPT can only offer text answers to a query, but the possibilities within that, as we are seeing, are endless.

But the pace of AI acceleration has surprised even its creators. It has evolved dramatically since Dr Hinton built a pioneering image analysis neural network in 2012.

Even Google boss Sundar Pichai said in a recent interview that even he did not fully understand everything that its AI chatbot, Bard, did.

Make no mistake, we are on a speeding train right now, and the concern is that one day it will start building its own tracks.

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AI 'godfather' Geoffrey Hinton warns of dangers as he quits Google

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Artificial intelligence pioneer Geoffrey Hinton quits Google, saying he now regrets his work.

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Digital etiquette, or netiquette as it is sometimes called, is a basic set of rules you should follow to make the Internet a better place for yourself and for others. Just as there are rules of etiquette in real life, face-to-face encounters, there are also rules you should follow while online.

How do you practice good digital etiquette? It's easier than you might think. Simply treat others as you would like to be treated, with courtesy and respect. It is also important to know your audience and to communicate accordingly.

Watch the video below for a brief introduction to digital etiquette.

The rules of digital etiquette vary depending upon the particular technology you are using; however, in general, there are several core principles that apply regardless of the technology being used.

Kent State University identifies nine, which are listed below. You can read more about each rule by clicking here.

1. Avoid language that may come across as strong or offensive.
2. Keep writing to a point and stay on topic.
3. Read first, write later.
4. Review, review, then send.
5. Remember that an online classroom is still a classroom.
6. Know the language of the Internet.
7. Consider the privacy of others.
8. If possible, keep attachments small.
9. Do not send, forward, or post inappropriate material.

Watch the two videos below to learn more about how to behave appropriately when using digital technology.

1. Not texting during class
2. Not answering the phone while you are talking to someone
3. Not using offensive or inflammatory language

1. Using all caps
2. Posting inappropriate messages or media on social networks
3. Cyber bullying

Digital technologies are increasing in number and in importance. Learning how to conduct oneself online is essential to becoming a successful 21st century citizen. Thus, as educators, we have an obligation to teach digital etiquette to our students. To this end, m any schools implement what is known as an acceptable use policy, or AUP. An AUP delineates what is considered acceptable and unacceptable with regard to the use of school technology resources. Does your school have an AUP? If not, consider the example below as you draft your own AUP.

Take the Brain POP Digital Etiquette Quiz by clicking here.

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Digital etiquette, or netiquette as it is sometimes called, is a basic set of rules you should follow to make the Internet a better place for yourself and for others. Just as there are rules of etiquette in real life, face-to-face encounters, there are also rules you should follow while online.

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Digital Manners: Navigating Relationships with New Technology

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Digital Manners: Navigating Relationships with New Technology

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Navigating Relationships with New Technology

Somewhere between the kids on Tic Toc and the grandparents on Facebook more and more of our lives have moved into the digital world. Whether it is the devices in our hands, on our bodies and in our homes or the virtual spaces where we meet, argue, play and learn, new communication technology is changing the ways we relate to each other. What would Emily Post say about all this? Well, once upon a time you needed an etiquette book to get the best advice from Emily Post, now it is a Siri search away. As we all navigate this new space; digital natives, millennials, gen X, boomers and traditionalists alike, we can learn from the shifts in communication that have occurred before and from what has stayed constant as times have changed. Good etiquette serves relationships and new technology can too, as long as we use it well.

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Somewhere between the kids on Tic Toc and the grandparents on Facebook more and more of our lives have moved into the digital world. Whether it is the devices in our hands, on our bodies and in our homes or the virtual spaces where we meet, argue, play and learn, new communication technology is changing the ways we relate to each other.

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What is Netiquette? 20 rules Internet Etiquette Rules

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What is Netiquette? 20 rules Internet Etiquette Rules

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What is Netiquette? 20 Internet Etiquette Rules

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What is netiquette? ✓ With these 20 rules, you and your children will learn the right behavior on the internet and in online communication. ✓

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Common Sense Education provides educators and students with the resources they need to harness the power of technology for learning and life. Find a free K-12 Digital Citizenship curriculum, reviews of popular EdTech apps, and resources for protecting student privacy.

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After a photographer left his camera equipment out for a group of wild macaques to explore, the monkeys took a series of photos, including selfies. Once the photos were posted publicly, legal disputes arose around who should own the copyrights -the human photographer who engineered the situation, or the macaques who snapped the photos. This unique case raises the increasingly pertinent question as to whether non-humans-whether they be monkeys or artificial intelligence machines-can claim copyrights to their creations. Join Jon Lovvorn, Lecturer on Law and the Policy Director of Harvard Law School's Animal Law & Policy Program, as he hosts a discussion panel featuring the General Counsel of PETA, which sued on behalf of the monkey, and experts on copyright, cyber law, and intermediary liability issues.

This event is supported by the Ethics and Governance of Artificial Intelligence Initiative at the Berkman Klein Center for Internet & Society. In conjunction with the MIT Media Lab, the Initiative is developing activities, research, and tools to ensure that fast-advancing AI serves the public good. Learn more .

Notes from the Talk

Can non-humans hold copyrights? Jon Lovvorn (Policy Director of the Harvard Animal Law and Policy Program) explored this question in a conversation with Jeff Kerr (General Counsel to PETA), Tiffany Li (attorney and Resident Fellow at Yale Law School's Initiative on Intermediaries and Information), Chris Bavitz (Managing Director of Harvard Law School's Cyberlaw Clinic), and Kendra Albert (Clinical Instructional Fellow at the Cyberlaw Clinic).

Lovvorn opened the conversation by questioning the dualistic nature of law. As he explained, the law divides everything into a binary of persons vs. things. However, scientific knowledge about animals' sentience, intelligence, and creativity shows that animals do not fit will into this binary. Likewise, artificial intelligence is another category that seems to blur the boundaries between persons and things.

Next, Kerr introduced the famed "monkey selfie case." David Slater, nature photographer, traveled to Indonesia in 2008, where he left his camera equipment with a group of crested macaques. One of the macaques, Naruto, took several images, including the notorious "monkey selfies." Slater later published a book, which included some of the images. In 2011, the images were uploaded onto Wikimedia Commons. Slater complained, arguing that his copyright was violated, and legal disputes ensured. In 2015, PETA filed a lawsuit on behalf of Naruto, requesting that he be given copyright over the photographs. The court ruled that copyright does not extend to animals. PETA filed an appeal. In 2017, PETA, Slater, and his publishing company reached an agreement that Slater would donate 25% of future proceeds to charities that will protect the Indonesian crested macaques.

Each of the panelists responded to Kerr's overview. Bavitz suggested that aside from one person or one monkey owning the images, there exists a viable third option: no one owns it. Within the US context, he explained, copyright supposedly creates incentive. How does this map onto a world of non-human actors? Albert added that in addition to its incentivizing potential, copyright is also a form of speech regulation. What types of harm might result from this speech being owned when it was not previously? Yi emphasized that US precedent has repeatedly voted against giving copyright to nonhuman authors. However, in the future, with continued advancements in AI and deeper understanding of neuroscience, we will likely have to reconsider our laws. Finally, Lovvorn reiterated the inefficacy of law's duality, pointing out that "animals" and "other creations of nature" are excluded from owning copyright, but that in contexts outside of the law, humans are typically considered both of these things.
notes by Donica O'Malley
About Jon Lovvorn

Jonathan Lovvorn is the first Policy Director of the Harvard Animal Law & Policy Program. Mr. Lovvorn is also a Lecturer on Law at Harvard Law School and will teach our inaugural course on Farmed Animal Law & Policy this Fall. He previously taught the HLS seminar in Wildlife Law in both the Fall 2015 and Fall 2016 terms. In addition to teaching at Harvard, Mr. Lovvorn has taught Animal Law and Wildlife Law at a number of other law schools, including New York University, Georgetown, George Washington University, and most recently Yale. He also has authored several articles concerning animal law and environmental policy, most recently publishing Climate Change Beyond Environmentalism in the Georgetown Environmental Law Review, which focuses on the intersectional threats of climate change to animals, people, and the environment. For several years Mr. Lovvorn has been serving as Senior Vice President & Chief Counsel for the Humane Society of the United States, where he founded and managed the nation's largest animal protection litigation program. He has argued dozens of successful cases on behalf of both animals and the environment, authored or co-authored hundreds of state and federal animal protection reform laws, and served as the primary legal strategist for most of the major animal protection ballot measures enacted over the last 15 years.

About Jeff Kerr

As general counsel to PETA and its international affiliates for nearly 25 years, Jeff Kerr built and leads the world's largest legal team working for animal rights. His team was named Corporate Counsel magazine's 2017 Best Legal Department, and his high-profile cases-including the 13 th Amendment case Tilikum v. SeaWorld,the first two successful constitutional challenges to "ag-gag" laws, and the "Monkey Selfie" copyright case-have made headlines around the world and sparked a global conversation about the legal rights of animals. Jeff's undergraduate degree is from George Mason University, where he was a Weber scholar, and he received his law degree from the University of Virginia School of Law, which will honor him this weekend with its Shaping Justice Award for Extraordinary Achievement for his career fighting for and advancing the cause of animal rights.

About Tiffany Li

Tiffany C. Li is an attorney and Resident Fellow at Yale Law School's Information Society Project, where she leads the Wikimedia/Yale Law School Initiative on Intermediaries and Information. She is an expert on privacy, intellectual property, and law and policy at the forefront of new technological innovations. Li is also an Affiliate Scholar at Princeton's Center for Information Technology Policy. She has been honored as a Transatlantic Digital Debates Fellow (Global Public Policy Institute/New America Foundation), a Fellow of Information Privacy (International Association of Privacy Professionals), and a Fellow and Founding Member of the Internet Law and Policy Foundry. Li is a licensed attorney and has CIPP/US, CIPP/E, CIPT, and CIPM certifications from the International Association of Privacy Professionals (IAPP). She holds a J.D. from Georgetown University Law Center, where she was a Global Law Scholar, and a B.A. in English from University of California Los Angeles, where she was a Norma J. Ehrlich Alumni Scholar.

About Chris Bavitz

Christopher T. Bavitz is Managing Director of Harvard Law School's Cyberlaw Clinic, based at the Berkman Klein Center for Internet & Society. He is also a Clinical Professor of Law at HLS, where he co-teaches the Counseling and Legal Strategy in the Digital Age seminar and teaches the seminar, Music & Digital Media. Chris concentrates his practice on intellectual property and media law, particularly in the areas of music, entertainment, and technology. He oversees many of the Clinic's projects relating to copyright, speech, and advising of startups, and he serves as the HLS Dean's Designate to Harvard's Innovation Lab. Prior to joining the Clinic, Chris served as Senior Director of Legal Affairs for EMI Music North America. From 1998-2002, Chris was a litigation associate at Sonnenschein Nath & Rosenthal and RubinBaum LLP / Rubin Baum Levin Constant & Friedman, where he focused on copyright and trademark matters. Chris received his B.A., cum laude, from Tufts University in 1995 and his J.D. from University of Michigan Law School in 1998.

About Kendra Albert

Kendra Albert is a Clinical Instructional Fellow at the Cyberlaw Clinic and was formerly an associate at Zeitgeist Law PC, a boutique technology law firm in San Francisco. They received their JD from Harvard Law School in 2016. Kendra is also a Fellow at the Berkman Klein Center for Internet and Society and a writer and speaker on a diverse set of internet issues. Their work has been published in the Green Bag, the Harvard Law Review Forum, and WIRED. Kendra's undergraduate degree is from Carnegie Mellon University, where they studied lighting design and history. Before starting law school, Kendra worked as a research associate at the Berkman Klein Center for Internet and Society, where they helped found Perma.cc. They also served as the first head teaching fellow for CopyrightX, Professor William Fisher's open online copyright course. During law school, they spent time at the Electronic Frontier Foundation, Cloudflare, and Public Citizen. With EFF, they co-filed and received a DMCA 1201 exemption request for video game archiving and play.

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The "Monkey Selfie" Case: Can Non-Humans Hold Copyrights?

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Can non-human animals own copyrights? Can artificial intelligence machines? Join the Berkman Klein Center, the Harvard Law School Animal Law & Policy Program, and the HLS Student Animal Legal Defense Fund for a discussion of the "Monkey Selfie" case and the issues it raises around untraditional definitions of who can be considered a creator under the law.

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L2 Learner Cognitive Psychological Factors About Artificial Intelligence Writing Corrective Feedback.

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L2 Learner Cognitive Psychological Factors About Artificial Intelligence Writing Corrective Feedback.

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Attitude of Prospective Teachers towards Digital Game-Based Learning (DGBL).

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Attitude of Prospective Teachers towards Digital Game-Based Learning (DGBL).

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AI In The Classroom: Pros, Cons And The Role Of EdTech Companies.

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AI In The Classroom: Pros, Cons And The Role Of EdTech Companies.

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Council Post: AI In The Classroom: Pros, Cons And The Role Of EdTech Companies

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Let us explore the pros and cons of AI as a tool in the classroom, address the concern that AI could be a threat to teachers’ and instructors’ jobs, and discuss the role of edtech companies in advancing this AI use case.

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Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. 

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Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. 

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Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study

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Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study

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Like the rest of the internet, I've been playing with ChatGPT, the new AI chatbot released by OpenAI, and I've been fascinated by how much it does well and how it still gets a lot wrong.

ChatGPT is a foundation model, that is, a deep learning model (also called a neural network) that is trained on so much data and with so many parameters that it is qualitatively different from models you could feasibly train yourself. I wanted to know what data ChatGPT is trained on, but it turns out information is not readily available.

My conclusion, after reading up on all this, is that ChatGPT is multilingual but monocultural - but that by using it, we're all helping to train it to align its values with our own.

Let me explain.

What is ChatGPT trained on?

The basics are clear. ChatGPT is based on the GPT models (GPT-1, GPT-2, GPT-3, and the current GPT-3.5 series), which are trained on data scraped from the web and some books. I'll discuss them in more detail below.

In addition, as described in Ouyang et.al. 2022 or for non-scholars, here, ChatGPT is based on InstructGPT, which was fine-tuned by humans who wrote "desired responses" to prompts that the model was then trained on. After that, human labellers rated GPT-3's responses (presumably similarly to the way ChatGPT asks us to label its responses). A model was trained on the labeled responses to predict what the humans would prefer, and that gave us InstructGPT, which is what ChatGPT is based on.

Here's OpenAI's visual explanation of the process for the value alignment training.

The team describes InstructGPT (which ChatGPT is based on) as aligned with the values of the 40 contractors they initially hired to test it. It is also "biased towards the cultural values of English-speaking people".

More generally, aligning model outputs to the values of specific humans introduces difficult choices with societal implications, and ultimately we must establish responsible, inclusive processes for making these decisions.
OpenAI: "Aligning Language Models to Follow Instructions" (2022)

The model card for InstructGTP explains that it still has issues. For instance, and rather seriously, it makes up "facts". Unfortunately, it's really good at making its "facts" sound quite convincing.
In this blog post, I'll explain more about the data its trained on, how it all works, and how you and I are training the model each time we use it.
How deep learning models make sense of the world

First of all: what is the data the GPT-series of AI models were trained on? Here is the table from the paper that introduced GTP-3 in 2020 (Brown et.al 2020).

I'll return to each of these datasets below, but first I need to explain tokens and vectors and latent space.

Models like GPT-3 count things in tokens. A token is the smallest semantic unit for a machine learning unit, like a phoneme is the smallest unit of sound that can distinguish one word from another in spoken language. Often a token corresponds to a word, although it gets more complicated. The basic GPT-3 model is trained on unlabelled data, so it figures out what a token is itself. A model like GPT-3 calculates how tokens (let's just say words) relate to each other by assigning each word a vector. For example, in a specific model trained on Wikipedia and newswire data, McCoy and Ullman explain that "the word 'dog' is represented as the vector [0.308, 0.309, 0.528, ?0.925, ....]". If you plot that into a coordinate system, then words that often co-occur with "dog" in the training data will be positioned close to "dog". This "map" of how words are related to each other is also called the "vector space" or "latent space" or even just "space".

Remember those x/y coordinate grids we drew in 6th grade? It's kind of like that. Except instead of two dimensions (an x-axis and a y-axis) there are literally billions of axes, or parameters.

Once GPT-3 is trained, it doesn't "know" anything about its training data any more. All it knows is those coordinates. Dog is [0.308, 0.309, 0.528, ?0.925, ....], and that .... stands for a lot more numbers. It also knows what other words (or tokens) "dog" is close to. All those tokens and their coordinates across billions of different parameters make up the "latent space" of the model.

OK, so back to the table about the data GPT-3 was trained on.

The Common Crawl is a lot of scraped web data. WebText2 is webpages that have been shared in Reddit posts that have received at least three upvotes. Books1 and Books2 are not specified, but people have suggested the Gutenberg library, BookCorpus (free, self-published books) and libgen as possibilities. Finally, the Wikipedia means the English-language Wikipedia, not all of them. The Quantity (tokens) shows how much is in each dataset, but they're not equally weighted. Here is a table showing the relative weighting.

OpenAI are pretty vague about what, exactly, some of those datasets are, but here's what I've found out so far:

Common Crawl (filtered)

The Common Crawl is an open repository of web crawl data in more than 40 languages. In the paper Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus, Jesse Dodge and co-authors (including Margaret Mitchell who was fired from Google's AI ethics team last year with Timnit Gebru but now works for Hugging Face) document a version of the Common Crawl that is filtered in the way described for the GPT-2 training data.

Dodge et.al. analyse three levels of the Common Crawl dataset, the metadata, like what domains data is from and when it was created or collected, the text itself, and what is missing or not included.

At the metadata level, they found that US domains dominate, with far more content than domains with many native English speakers like India or Pakistan.

"51.3% pages are hosted in the United States. The countries with the estimated 2nd, 3rd, 4th largest English speaking populations-India, Pakistan, Nigeria, and The Philippines-have only 3.4%, 0.06%, 0.03%, 0.1% the URLs of the United States, despite having many tens of millions of English speakers."
(Dodge et al., 2021, p. 4)

They found a surprising amount of patent data, and a lot of this is machine-translated, because various countries require patents to be in their languages. There are even patents run through OCR, so quite a bit of text is machine-generated in one way or another. Finally, they found that filters that remove words that are on a banned words list "disproportionately remove documents in dialects of English associated with minority identities (e.g., text in African American English, text discussing LGBTQ+ identities)." (Dodge et al., 2021, p. 2) You can take a look at the "bad words list" yourself. It's clear that most of these words are on it so porn can be filtered out, and there are some slurs and swearwords on there as well. This means that texts representing minorities is missing. Removing sex words also means that non-offensive material about queer culture, including legal documents about same sex marriage, have been filtered out.

OK, so there is some bias there, and a crawl of "all the web" is bound to have a lot of not exactly high quality language. The next corpus is meant to remedy that.

WebText2

WebText2 is a corpus of websites that have been linked to Reddit posts that have three or more upvotes. The idea is that having three upvotes from Reddit ensures that the webpages have a certain level of quality. The exact corpus used to train GPT-3 is not available, but it has been recreated and can be downloaded as OpenWebText2, which also has instructions for how to recreate the dataset.

Instead, we created a new web scrape which emphasizes document quality. To do this we only scraped web pages which have been curated/filtered by humans. Manually filtering a full web scrape would be exceptionally expensive so as a starting point, we scraped all outbound links from Reddit, a social media platform, which received at least 3 karma. This can be thought of as a heuristic indicator for whether other users found the link interesting, educational, or just funny.
Radford et.al. 2019, page 3.

Unfortunately Reddit users are not a representative sample of humanity, so there is likely to be bias in this too. And three upvotes is not a lot. But OpenAI must trust this dataset, because WebText2 is the most heavily weighted sample of all five samples used to train GPT-3.

Books1 and Books2

The description of these datasets in the original paper is disappointingly vague: "two internet-based books corpora (Books1 and Books2)."

Presumably the reason that OpenAI is so vague about what, exactly, these two datasets are is that it's a bit dodgy in terms of copyright. I assume (hope?) that at least one of these is the Gutenberg library, which is books in the public domain. But if it is, why not just say so?

Many assume that one of these is BookCorpus, which consists of 11038 books that were self-published on Smashwords and are available for free. BookCorpus was definitely used for training GPT-1 and BERT (another large language model). The BookCorpus dataset is available from Hugging Face, and Jack Bandy and Nicholas Vincent have published a paper retrospectively documenting it.

The biggest issues Bandy and Vincent identify in the BookCorpus dataset are

Even though the books are free, the licence doesn't really permit this use. It's legally dubious.

There are lots of duplicates (particularly of romance novels) and some authors have published hundreds of novels in the dataset, so it's not exactly representative.

There's a skewed religious orientation - Christianity is overrepresented.

Another group of authors found that there is a lot of toxic language in BookCorpus (Gehman et.al. 2020). That's honestly not surprising given that "toxic language" includes flirtation, and anything sexual, threatening or insulting. I mean, this is literature. You want that kind of stuff in literature. But the context is crucial. Does the language model recognise this context?

Here is an example of a book published a few days ago that is currently available for free on Smashwords.

As a human looking at that I am pretty sure you won't be surprised that the first two paragraphs of the novel are, well, in the style you'd expect:

"Not many people went up against me or any of the members of my family, but when
someone actually did, I found that shit entertaining as hell.

Staring down at the girl yelling at me, it was almost a shame that I was going to have to sick D.J., Maggie, or Lennon on her. Though our code of honor might not always be so honorable, the rules when it came to the fairer sex had been ingrained in us since birth. While us guys handled any males that
were stupid enough to tangle with our family, we left any girls that were just as stupid to my sisters."
(Enticing the Enemy by M.E. Clayton)

Imagine you're a neural network inputting this data and assigning values to the tokens so you can organise them in your vector space. The token " girl" is close to " yelling" and to " stupid ". So yelling and stupid are probably also related to each other.

The idea is, of course, that with enough data you end up with so many parameters that you don't need to worry about this kind of gender stereotypes, because there'll be enough good stuff to even it out. Maybe.

InstructGPT solves it by having humans label responses, and I'm guessing most of the humans would have labelled output that assumed girls are stupid and yell all the time as bad and thereby trained the model to avoid that.

Here is the full model card Bandy and Vincent made for BookCorpus:

Wikipedia

The final dataset specified in Brown et.al. 2020 as one of the datasets GPT-3 was trained on is English-language Wikipedia pages. The Wikipedia has a lot of great information, but we know there is a strong bias in who edits it. A 2015 analysis of differences between articles about women and men found clear differences not only in coverage and interlinking but also in how women are described. Interestingly, given that GPT-3 is only trained on the English Wikipedia, the English and Russian language versions have the strongest gender bias.

What does this mean?

AI is getting very, very good. It still has issues, but it now generates convincing language. It is trained on somewhat dubious data that we know is biased - Wikipedia, self-published novels and pages linked from Reddit in particular. But with the addition of labelled value alignment it does a much better job of avoiding the most obvious toxicity and bias, though it still often fabricates information. It seems to use some templates to deal with potentially difficult questions related to bias or values or violence. Also answers tend to follow US-centric genres like the three paragraph essay. This "pay for essay" website has a decent explanation of how the three paragraph essay formula works.

ChatGPT is multilingual but monocultural

I was surprised at how good ChatGPT is at answering questions in Norwegian. Its multi-lingual capabilty is potentially very misleading, because it is trained on English-language texts, with the cultural biases and values embedded in them, and then aligned with the values of a fairly small group of US-based contractors.

This means

1. ChatGPT doesn't know much about Norwegian culture. Or rather, whatever it knows about Norwegian culture is presumably mostly learned from English language sources. It translates that into Norwegian on the fly.

2. ChatGPT is explicitly aligned with US values and laws. In many cases these are close to Norwegian and European values, but presumably this will not always be the case.

3. ChapGPT frequently uses US genres and templates to answer questions, like the three paragraph essay or standard self-help strategies.

Customised values: we're training AI to align with our values

But this won't last. By playing with ChatGPT, we're training it to align more with our values. We're giving OpenAI a vast, human-labelled dataset showing what responses we like and don't like. InstructGPT was trained by 40 human contracters in the USA. ChatGPT is being trained by thousands and thousands of people all over the world.

Right now, ChatGPT is free for use, and there's a thumbs up or thumbs down option for every answer it gives. If you click the icon, it asks for additional feedback.

InstructGPT was "value aligned" using labels from just 40 contractors (Ouyang 2022, page 2). ChatGPT is providing vastly more data. Although it's free to use ChatGPT, you do have to create an account. OpenAI knows my email and the country I am connecting from, so they can assume my judgements about how ChatGPT responds to me align with "Norwegian values". OpenAI also knows what device, browser and operating system I am using, which can be a proxy for class and socio-economic status.

Presumably OpenAI will fine-tune their AI to learn what people in different countries and using different devices and browsers prefer. Perhaps they'll align future GPT values with those of "Mac OS users in Norway who sometimes connect using an iPhone that is recent but not this year's model". It'll be like customised advertising only it's an artificial friend, a companion, a conversation partner. (I wrote about how apps are like our companions now a few years ago, and I'm working on a piece about our companionable relationships with AIs.)

Perhaps we don't need to create a "Norwegian AI" to get Norwegian values. OpenAI will do it for us, learning our preferences, adding parameters to the vectors of each token in our semantic constructs of reality. It will align with us perfectly.

References

Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. 2020. "Language Models Are Few-Shot Learners." arXiv. http://arxiv.org/abs/2005.14165. ( This is the paper that introduced GPT-3)

Bandy, Jack, and Nicholas Vincent. 2021. "Addressing 'Documentation Debt' in Machine Learning Research: A Retrospective Datasheet for BookCorpus." arXiv. http://arxiv.org/abs/2105.05241.

Dodge, Jesse, Maarten Sap, Ana Marasovi?, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021. "Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus." arXiv. http://arxiv.org/abs/2104.08758.

Gao, Leo, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, et al. 2020. "The Pile: An 800GB Dataset of Diverse Text for Language Modeling." arXiv. http://arxiv.org/abs/2101.00027.

Gehman, Samuel, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020. "RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models." arXiv. http://arxiv.org/abs/2009.11462.

McCoy, John P., and Tomer D. Ullman. 2018. "A Minimal Turing Test." Journal of Experimental Social Psychology 79 (November): 1-8. https://doi.org/10.1016/j.jesp.2018.05.007.

Ouyang, Long, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, et al. 2022. "Training Language Models to Follow Instructions with Human Feedback." arXiv. http://arxiv.org/abs/2203.02155.

Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. "Language Models Are Unsupervised Multitask Learners". OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

Rettberg, Jill Walker. 2018. "Apps as Companions: How Quantified Self Apps Become Our Audience and Our Companions." In Self-Tracking: Empirical and Philosophical Investigations, edited by Btihaj Ajana, 27-42. Basingbroke: Palgrave Macmillan. https://doi.org/10.1007/978-3-319-65379-2_3.

Solaiman, Irene, and Christy Dennison. 2021. "Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets." In Advances in Neural Information Processing Systems, edited by M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, 34:5861-73. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2021/file/2e855f9489df0712b4bd8ea9e2848c5a-Paper.pdf. (This paper explains how you can improve the bias and values in GPT-3 by adding a relatively small "value-targeted dataset")

Wagner, Claudia, David Garcia, Mohsen Jadidi, and Markus Strohmaier. 2015. "It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia." arXiv. http://arxiv.org/abs/1501.06307.

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ChatGPT is multilingual but monocultural, and it's learning your values

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Like the rest of the internet, I've been playing with ChatGPT, the new AI chatbot released by OpenAI, and I've been fascinated by how much it does well and how it still gets a lot wrong...

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A week ago, I asked ChatGPT for a list of Latinx Georgia state legislators. It responded that, as of 2021, no Latinxs officials were elected in the state. This claim is incorrect. When I re-confronted the AI phenomena, it later listed State Representative Pedro "Pete" Marin and Rep. Marvin Lim as the only two Latinx Georgia legislators. This time, only the latter was incorrect. Despite citing "news articles and official government websites" as its source, the instance made me entirely question the factual reliability and accuracy of ChatGPT.

ChatGPT has taken the world by storm with its ability to generate human-like responses at lightning speed. The AI tool has been praised as a revolutionary tool for students and professionals, branded as the future of college essay writing rooted in its instant access to vast information. It's no surprise the popular AI language model caught the attention of the Wheel's Editorial Board.

The Feb. 1 , "ChatGPT threatens the integrity of learning, but we have the final say," lauds ChatGPT as a "foundational source for summarized research," arguing the tool could help students save time with assignments. There is no denying that ChatGPT is impressive; however, as with any technology, there are numerous concerns about the accuracy and authenticity of the information and sources provided. The Board also failed to warn students, and all ChatGPT users, about the false information the AI tool incorporates when elaborating on certain writing samples or responding to specific research efforts.

It is easy to see why people have turned to ChatGPT for assistance with research, writing and other "busy-work" tasks. Chat GPT will acknowledge that it does not have access to certain publications when asked to summarize specific scholarly articles. Thus, the tool is restrained by the data it has been trained on and has access to. If the data ChatGPT draws from is biased, incomplete or inaccurate, it only makes sense for the results it generates to be as well.

This trend of inaccuracy has also been analyzed and scrutinized by a graduate student at George Mason University, Will Hickman. In his efforts to challenge the growing AI tool, Hickman asked ChatGPT a vast amount of research questions, which he said "false and misleading references." The author found an insurmountable amount of reasoning and factual errors, including references to nonexistent papers, sources utterly unrelated to the question and incorrect authors for the sources it provided.

Fallacious arguments and references for research are only a glimpse into the inherently unreliable nature of this AI tool. Fueled by the fear of mass misinformation, NewsGuard a study that the latest version, GPT-4, is "easier to convince to spread misinformation" despite being 40% "more likely to produce factual responses" than its previous model. The results also included the concerning theory that ChatGPT could be used to "spread misinformation at an unprecedented scale."

ChatGPT is undeniably a powerful tool with the potential to revolutionize the way we access and dissect information. In the age of misinformation, however, it is crucial to be cautious when using the tool and verifying the accuracy of the information it provides. Students and all ChatGPT users should be aware of these limitations and actively combat inaccurate claims when using the chatbot.

There is always room to improve language models like ChatGPT. However, one thing is for sure: all ChatGPT users should be wary of the very real possibility of misinformation created by excessive confidence in the "revolutionary" AI mechanism.
Sara Perez (24c) is from Managua, Nicaragua.

Sara Perez (24C) is from Managua, Nicaragua, possibly majoring in political science and mathematics. Outside of the Wheel, she is involved in 4 Justice and Emory's debate team. You can also catch her drinking insane amounts of coffee and rewatching the same shows for the nth time.

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Don't blindly trust ChatGPT | The Emory Wheel

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A week ago, I asked ChatGPT for a list of Latinx Georgia state legislators. It responded that, as of 2021, no Latinxs officials were elected in the state. This claim is incorrect. When I re-confronted the AI phenomena, it later listed State Representative Pedro "Pete" Marin and Rep.

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NEITHER WE NOR ANY OF OUR AFFILIATES OR LICENSORS WILL BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL, OR EXEMPLARY DAMAGES, INCLUDING DAMAGES FOR LOSS OF PROFITS, GOODWILL, USE, OR DATA OR OTHER LOSSES, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

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Toward AI-Powered Approximations of Practice: Using Automated Evaluation of Teacher Attention to and Use of Student Ideas During Argumentation-Focused Science Discussions

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Toward AI-Powered Approximations of Practice: Using Automated Evaluation of Teacher Attention to and Use of Student Ideas During Argumentation-Focused Science Discussions

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Critical Appraisal of Teacher Education Programmes in Achieving Curriculum Goals

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Critical Appraisal of Teacher Education Programmes in Achieving Curriculum Goals

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Buzzwords get a bad rap-especially business buzzwords. From freemium to hyper local, these terms are used so often that they essentially lose all meaning. A quick Google search yields list upon list of "the top 100 worst business buzzwords"-and nearly all of the lists include synergy.

Like so many other business buzzwords, synergy has been used so frequently that it doesn't always pack the punch it used to. But every word has a purpose behind its creation-and synergy is no exception. In this article, we'll dig into the true definition of synergy, and how to use this term-not as a buzzword-but as a driver for team growth and impact.

What is synergy?

Synergy is when two or more things-organizations, departments, or even teams-work together to produce something of value. This commonly misused buzzword takes inspiration from the Ancient Greek philosophy that "the whole is greater than the sum of its parts." In fact, the word synergy comes from the Latin synergia, which was derived from the Greek word, synergos. Synergos means "to work together" or "to collaborate."

At its core, synergy is about helping you effectively connect, communicate, and collaborate with cross-functional partners. So why is this word so often ridiculed?

History behind the synergy buzzword

The concept of synergy in business achieved popularity in the 1990s, when corporate executives and investment bankers used corporate synergy to gain buy-in for proposed mergers and acquisitions (M&As).

Corporate synergy describes the expected additional value companies achieve by merging. In other words, two companies working together under a merger or acquisition can produce more value than the sum of their individual effects. This is largely due to economies of scale. Larger, merged businesses not only support one another, but they also achieve cost reductions that ultimately lead to higher profitability.

Sometimes, corporate synergy doesn't just describe the M&A process. It's also used when a company cross-sells another company's work, or lends team members for cross-business product development, for example.

In practice, corporate synergy-and especially financial synergy, which is when two companies merge finances-is hard to achieve. Integrating two businesses and the entirety of what those businesses represent-including finances, employees, products, culture, and practices-takes a lot of time and effort. Without the right change management process, the M&A process can fall short of its intended benefits. This is called negative synergy.

The hype for corporate synergy in relation to mergers and acquisitions-combined with the potential negative effects when combined action didn't work out-contributed to the modern-day association with synergy as a buzzword.
Read: What is change management? 6 steps to build a successful change management process
At its core, synergy describes a way to work together to produce great results. Though this term was co-opted by corporate executives, it doesn't refer to mergers and acquisitions as a rule. After all, the term comes from ancient Greek and was used in practice as early as the 1600s.

Team synergy takes the idea that the whole is greater than the sum of its parts and applies it to teamwork. This positive synergy enables team members to be their full selves at work-with their unique life experiences, perspectives, talents, and communication styles. In fact, each individual's unique perspective is exactly what enables a team to get their best work done. By leaning into each team member's strengths-while also giving them opportunities to learn from one another-your team can achieve much more together than they would be able to do on their own.

The difference between diversity and synergy

Diversity describes how similar or different your team is. The more varied experiences, backgrounds, perspectives, and beliefs you have on your team, the more diverse your team is.

But we know that simply valuing diversity isn't enough. That's where business initiatives like Diversity and Inclusion programs (D&I) come into play. Committing to a diverse team means doing the work to build a more equitable and inclusive environment.

Team synergy focuses on the "doing the work" part of diversity. In order to achieve team synergy, you can't just have a diverse team, you also need to empower collaboration and communication between team members in order to build something amazing together. When team members can be their full selves at work, they can unlock better collaboration and synergy.
Read: Asana's approach to Diversity, Inclusion, and Equity
Ways to build team synergy

The team synergy effect leads to effective teamwork and high-impact results across the team. But like all interpersonal skills, building team synergy takes time and effort. To build team synergy, try these three strategies:

1. Start with communication

The core of any strong working group is communication. This is especially true for diverse groups. The value of a diverse group comes from the differences of opinion and experiences that team members bring to the table-but team members need to feel comfortable expressing themselves in order to share those experiences. With effective workplace communication, team members can express themselves freely and accurately, and more effortlessly achieve synergy.

To start building good workplace communication skills:

Establish where your team should communicate, and about what. When team members understand where to communicate, the barrier to doing so is lower. If you haven't already, set and share a communication plan with your entire team.

Prioritize two-way communication. Part of being a collaborative team member, especially in a diverse group, is listening to other people's ideas instead of just trying to put your own ideas out there. To build these skills, encourage team members to practice active listening.

Differentiate between facts and stories. "Facts vs. stories" is a conscious leadership technique. "Facts" are observable details like who attended a meeting. A "story," on the other hand, is your interpretation of the situation. By separating stories from facts, you can avoid acting on stories until you're able to validate them.

Read: 12 tips to effective communication in the workplace
2. Foster trust and collaboration

In addition to knowing how to communicate effectively, team members also need to feel comfortable doing so. Make sure you're making space for team members to bring their full selves to work by modeling team collaboration best practices.

Invite co-creation. A key part of team synergy is working on things together to produce something each individual wouldn't be able to do on their own. To do this, don't just tell team members to work together on a project. Instead, hold brainstorming sessions, invite discussion, and open the door to disagreement. Co-creation means building an idea together, not chipping away towards a goal separately.

Encourage open communication. Team members should feel comfortable saying what they feel, giving their opinion on projects, or disagreeing with other team members. We often treat disagreements like a bad thing, but healthy disagreement is actually key to good team collaboration.

Lead by example. It takes time to build team collaboration-but the best way to get started is to model the behavior you want to see in your team. Make sure you're always inviting co-creation and welcoming new opinions, and you'll start to see your team do the same.

Read: 10 easy steps to boost team collaboration
To foster collaboration:

3. Set group norms intentionally

Group norms are the unspoken rules that guide how team members interact. Even if you don't set group norms, they will naturally develop as your team works together. Left unchecked, group norms can lead to some bad practices that make team members uncomfortable and, ultimately, lead to bad group dynamics.

But by proactively setting group norms, you make it easier for your team to collaborate. Bringing these "unspoken rules" out into the open reduces guesswork and uncertainty, so team members can spend less time worrying and more time getting their collaborative, high-impact work done.

To avoid that, proactively set group norms. For examples of how team leads set group norms, read our article on tips to create group norms for high-performance teams, with examples from 7 Asana managers.
For more tips on how to enable great teamwork, read our article on 45 team building games to improve communication and camaraderie.
More than a buzzword

Synergy isn't just the word of the day or a boring buzzword. With effective team synergy, you can empower a diverse team to work together effortlessly-and get their highest-impact work done.

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How to Build Team Synergy, Definition and Examples [2024] * Asana

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Get the true definition of synergy, and how to use this term-not as a buzzword-but as a driver for team growth and impact.

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Exploring the AI competencies of elementary school teachers in South Korea.

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Exploring the AI competencies of elementary school teachers in South Korea.

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Highlights

Applications of artificial intelligence in education (AIEd) are emerging and new to researchers and practitioners.

AIEd research is more scattered and less organised.

This review is to understand the opportunities and challenges of AIEd in real examples in educational domains and learning outcomes.

The results presented  details of 13 roles of AI technologies in the domains, and outcomes of AIEd.

Numbers of challenges provided and their recommendations for future research in AIEd.

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Author links open overlay panel Thomas K.F. Chiu a , , , ,

Show more

Highlights

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Applications of artificial intelligence in education (AIEd) are emerging and new to researchers and practitioners.

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AIEd research is more scattered and less organized.

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This review is to understand the opportunities and challenges of AIEd in four educational domains and two learning outcomes.

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The results presented 13 roles of AI technologies in the domains, and seven outcomes of AIEd.

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We provided 10 challenges and their recommendations for future research in AIEd.

Abstract

Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012-2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.

Keywords

Artificial intelligence in education

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Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education

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Author links open overlay panel Thomas K.F. Chiu a , , , , Show more * Applications of artificial intelligence in education (AIEd) are emerging and new to researchers and practitioners. * AIEd research is more scattered and less organized. * This review is to understand the opportunities and challenges of AIEd in four educational domains and two learning outcomes.

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The Role of Artificial Intelligence (AI) in Teacher Education: Opportunities & Challenges.

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The Role of Artificial Intelligence (AI) in Teacher Education: Opportunities & Challenges.

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Didactic Activities On Artificial Intelligence: The Perspective Of STEM Teachers. 

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Didactic Activities On Artificial Intelligence: The Perspective Of STEM Teachers. 

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ChatGPT threatens the integrity of learning, but we have the final say.

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ChatGPT threatens the integrity of learning, but we have the final say.

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The definition of cheating in academic settings has been altered with the emergence of AI technology. The pressure to succeed academically, along with the ease of obtaining answers through technology, has led to an increase in students turning to cheating as a means of achieving their goals. Now, AI-powered tutoring systems and online homework help services can provide students with answers to questions they should be solving on their own.

The paragraph you just read was completely simulated by ChatGPT, an online AI platform that can a range of material - college essays, poems and cover letters - based on user input such as a question or command: "write an essay about frogs."

If you feel cheated by this revelation, you stand in solidarity with many current educators and writers. Content writers, who create material for blogs and product merchandising, risk their jobs to ChatGPT, which offers the benefits of an insurmountable informational database, free of cost. Educators are tasked with wholly changing how they , with the platform being able to instantly concoct entire essays or identify a singular mistake in lines of code. Additionally, emails, cover letters and news reports can be easily at the cost of authenticity; globally, people are contending with how this new development in AI could revolutionize the concept of work as we know it.

The rise in popularity of this platform is astounding. Netflix hit a million users in four years; Instagram accomplished the same in two months. The same feat took ChatGPT Much of the traffic was generated by intellectual curiosity, but as more individuals became aware of the software's capabilities, ChatGPT swiftly joined the arsenal of cheating tools used by students. With over ten million queries each day, it's not a stretch to imagine millions of students are using the tool to fabricate essays or look up homework answers. In fact, despite being released in November, the software has already been banned from some public school systems.

Studies indicate that cheating during high school and undergraduate studies to dishonesty and other ethical concerns in the workplace. Cheating leads to an incompetent absorption of knowledge, the easily accessible gateway of ChatGPT undermining creative ability and intellectual capacity. While some schools and universities are to ban ChatGPT, this fundamentally neglects to consider that students can use hotspots and VPNs to access the platform on campus and in class. Moreover, ChatGPT is here to stay, having already workplaces by taking over menial tasks, such as email writing, accounting and building presentations. Institutions won't be able to truly curb cheating, they have to accept that students will decide as to how much they want to integrate the software into their education; writing an essay with the platform is only cheating oneself of knowledge.

For starters, ChatGPT's resources can be beneficial for learning. Instead of trying to condemn the technology altogether, we must discern the potential usefulness of AI. At Emory University, different professors have varying ideas concerning how or whether to integrate ChatGPT. For instance, in certain computer science courses, professors might encourage students to run their code through the software, yet are opposed to students using the software to completely create said code.

Associate Professor of Political Science Eric Reinhardt, for example, has decided to prohibit submissions produced entirely by softwares like ChatGPT, but allow students to use such tools for polishing and editing already "student-written" work. Citing his goal "to guide how students might ethically and educationally use this tool," Reinhardt requires that if students do use this software, they should attach a log of queries fed into the service as well as their respective outputs to the end of their assignment.

There is a naive optimism in the hope that humans will prioritize human intellect over the regurgitated and robotic thought of AI. AI may be able to replicate or assist with certain creative tasks, but it will never fully replace the unique and unpredictable nature of human creativity. If current students set the precedent of choosing to invest in their own intellect, ChatGPT and its successors can be integrated into society without completely overhauling the authenticity of its functioning.

One of the sentences in the last paragraph was written by ChatGPT. Can you guess which?
The above editorial represents the majority opinion of the Wheel's Editorial Board. The Editorial Board is composed of Isabelle Bellott-McGrath, Rachel Broun, Evelyn Cho, Ellie Fivas, Marc Goedemans, Aayam Kc, Elyn Lee, Saanvi Nayar, Shruti Nemala, Nushrat Nur, Sara Perez and Kayla Robinson.

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ChatGPT threatens the integrity of learning, but we have the final say | The Emory Wheel

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The definition of cheating in academic settings has been altered with the emergence of AI technology. The pressure to succeed academically, along with the ease of obtaining answers through technology, has led to an increase in students turning to cheating as a means of achieving their goals.

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The role and challenges of education for responsible AI.

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The role and challenges of education for responsible AI.

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Creating a Vertically Aligned Curriculum

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Creating a Vertically Aligned Curriculum

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There's a certain flow to a well-designed curriculum . It connects not only between units in a class, but also over years of education and between the subjects students study. When students learn skills from the foundation up, and when teachers reinforce those skills across grades and classes, that's called vertical alignment. This is different from horizontal alignment, which describes consistent standards. What students learn between classes, schools, districts, and states or provinces should, ideally, be equal-horizontally aligned.

Vertical alignment is the how and when of what's taught. It's about linking lessons, skills, and assessments together as a holistic experience. It helps prioritize, focus, reinforce, and place learning in new contexts. It helps schools make better decisions faster-and helps learners apply those skills in new ways.

Teaching a vertically aligned curriculum involves comparing and coordinating moving pieces, from grade-level standards to unit content. Here's what a vertically aligned curriculum looks like and how it brings cohesion into the classroom.

6 Ways Vertical Alignment Supports Student, Teacher, and School Success

1. Reduces unnecessary repetition

Many subjects share the same foundational knowledge. But when teachers design curriculums independently, they don't know what's going on in other classrooms. It's quite common for students to re-learn the same material across grades and subjects.

Vertical alignment requires teachers to think beyond their classroom walls and collaborate with their colleagues. Together, they can organize around a curriculum that focuses on developing those skills further, rather than repeating them over and over at the same level.

Quite the opposite of repetition, you also don't want to miss teaching any core skills. A vertically aligned curriculum helps you identify where students are currently struggling and where they need extra attention.

Similarly, when you compare your curriculums side-by-side, you can see where you're thin in lessons, or where timing doesn't match up between the basics and the next level of learning.

Imagine your students are learning about Africa in geography class. Now imagine they're learning African folklore in English Language Arts, studying the animals of the Serengeti in science, and solving word problems that reflect life on the continent in math.

That connectivity gives students more depth because they can apply their knowledge in many ways, from many different perspectives.

Vertical alignment is an exercise in coordination and planning. Curriculum maps need to be built ahead of time to make sure students learn skills in the right order.

Not only does it let teachers plan around what students should know before they arrive for the day's class, it provides a way for students (and their families) to know ahead of time what they'll be learning and track progress throughout the year.

5. Sparks professional growth

Student development depends on teacher development. Vertical alignment brings teachers together to talk about what's working (and what's not working) in their classrooms.

The nature of collaboration means teachers get to see what other teachers are doing. It gives them opportunities to share ideas and pick up new approaches to try.

6. Makes decisions faster

Are students struggling with one topic in particular? Is there a gap that plagues your learning experience? Vertical alignment doesn't just help suss out where you can improve as an institution-it helps you prioritize those gaps so you can focus on filling them.

It means being nimbler, too. Taking out the guesswork and looking at the big picture helps drive direction to inform decision-making.

How to Start Vertically Aligning Your Curriculum

There are five main steps to creating a vertically aligned curriculum.

1. Bring your data together

Administration, regulators, and teachers alike need to share what's happening, identify areas where there are common goals, and find gaps in the current process. For that, you'll want to compare some key data points :

Targeted standards . Knowing what your objectives are before you begin (and when you need to reach them) will help you map the best way to get there, at the right time.

Unit content . Go beyond borders to examine in detail what students are already learning from across the spectrum of your school.

Assessments . Alignment applies to the way you measure student learning, too, to reflect a logical and achievable progression between grades.

Course materials and resources . Textbooks don't always support a vertically aligned agenda, so it's important that in-class materials line up between grades and subjects, too.

Student performance . The best way to identify gaps is by looking at trends. If students hit a wall during that week of trigonometry, there's probably something you can improve.

2. Study the data

Bringing all your school's data together will result in a lot of information, so take the time to look at what it's telling you. Step back, look at what you're working with, and resist the urge to begin making decisions right away. Instead, encourage everyone to think about high-level connections between skills, knowledge, and subject areas.

3. Build curriculum maps

It's time to put pen to paper to put together the first version of your new curriculum maps. Like any masterpiece, the first draft-which this is-won't be perfect. That's okay. This is the starting point from which you'll build something much better.

4. Hold workshops and collaboration sessions

It'll take a couple of sessions and iterations, but you're working towards an important goal: writing a fully connected curriculum. Compare your new curriculum maps side-by-side, with the same data points as above, and align as much as you can.

5. Keep communication open

A curriculum isn't a one-and-done thing. It ebbs and flows, and topics change, so your team will sometimes need to make changes-and keep everyone else on the same page. Save your curriculum maps in a shared place and encourage professional learning communities where teachers can share tools and resources. Professional learning communities are also an important part of professional development.

While designing such a large and cohesive curriculum may sound complex on the surface, a methodical approach helps to make its creation approachable and iterative. Keeping your goals in mind and putting student learning front and center will help you find your way every time.

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Creating a Vertically Aligned Curriculum

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There's a certain flow to a well-designed curriculum . It connects not only between units in a class, but also over years of education and between the subjects students study. When students learn skills from the foundation up, and when teachers reinforce those skills across grades and classes, that's called vertical alignment.

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Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. 

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Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. 

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Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence

Abstract

The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT's output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.

Artificial Intelligence and the Field of Education

Artificial intelligence (AI) is playing a crucial role in the ever-increasing digitisation of society. AI's capability to automate tasks, process large quantities of data, and provide predictive insights will increasingly revolutionise various aspects of our daily lives (Yang, 2022). Previously, AI has been described as technology that has the capacity to mimic human-like responses, such as reasoning, exercising judgement, and exhibiting intentionality (Shubhendu & Vijay, 2013). Technological advancements such as machine learning and neural networks have sparked further discussion about how to define AI (Wang, 2019). The challenge ... "is to specify the parameters of artificiality, or the ways in which computers are unlike human intelligence. They are much less than human intelligence-they can only calculate. And they are much more-they can calculate larger numbers and faster than humans. We have cause to be in awe at the super-human brilliance of their feats of calculation" (Cope et al., 2021, p. 1230). Discourse about the potential societal impact of AI has recently been attracting significant attention. For instance, there have been increasing concerns about significant job losses and debates about classifying AI output as art (e.g. WALL-E 2, Midjourney) (Pavlik, 2023). Discussions around the use of AI, like in other fields, are mostly in the early stages. Celik ( 2023) claims that educators have not maximised the potential of AI in teaching and learning contexts. It has even been described as the "Cinderella of the AI story", alluding to its underdeveloped and often ignored status in education (Lameras & Arnab, 2021). Despite calls for further uptake, some educators are resistant to using software that collects large amounts of student data and, moreover, sceptical of companies that present technology as a panacea for all educational issues (Stockman & Nottingham et al., 2022). This research focuses on an AI platform called ChatGPT. It has garnered worldwide attention and captured the public zeitgeist.

ChatGPT

Large language models are a form of generative AI that produces human-like language (OpenAI, 2023a). OpenAI trains its text-generating models using machine learning algorithms on large amounts of text, including books, news articles, websites, and Wikipedia (Scharth, 2022). By processing terabytes of data, models acquire the patterns and structure of language, enabling them to provide users with relevant and meaningful content in response to their requests. When I asked ChatGPT to describe itself (prompt- Describe ChatGPT), it generated the following italicised text:

ChatGPT is a language model developed by OpenAI. It is a variant of the GPT (Generative Pre-training Transformer) model, which is trained on a massive amount of text data to generate human-like text. It can be used for a variety of natural language processing tasks, such as language translation, text summarization, question answering, and also for chatbot applications. It can understand and respond to human inputs in a conversational way, making it well suited for interactive applications. ChatGPT can be fine-tuned on specific data sets and task, which means it can be customized to perform specific task and generate outputs that are more accurate and useful.

ChatGPT can... "write stories, give life advice, even compose poems and code computer programs" (Scharth, 2022, para. 2). There have been reports however that it... "produces fake citations and reproduces biases that exist in the literature" (Gleason, 2022, para. 7). According to cited ChatGPT output, "AI systems can perpetuate biases that are present in the data they are trained on. This can lead to unequal and unfair outcomes for students, particularly if the bias is related to factors such as race, gender, or socio-economic status" (Zhai, 2022, p. 7). OpenAI ( 2023b) acknowledge in their advice to educators that... "ChatGPT may produce content that perpetuates harmful biases and stereotypes, sometimes in subtle ways... the model is generally skewed towards content that reflects Western perspectives and people" (para. 18).

ChatGPT has gone viral on social media, and anecdotal evidence suggests that students are already familiar with it. It is safe to assume that undergraduate students are using ChatGPT or a similar software. Given ChatGPT's apparent usage and stakeholder concerns, it is crucial to evaluate its output about science education. As far as I am aware, there has been no prior research that has examined the text generated by ChatGPT in relation to science education topics or its potential applications to create science teaching resources. Also, there is little written about its utilisation as a research tool, especially in STEM education contexts. This presents as a significant and timely research gap to address.

Methodology

Inspired by Pavlik's ( 2023) exploration of ChatGPT in the field of journalism and media studies, research question 1 of this study asks: How did ChatGPT answer questions related to science education? For example, what are key characteristics of effective science teaching? Should I use chalk and talk to teach science or embrace more student-centred pedagogies? What happens if a student is failing in science, should I care?

Research question 2 of this study asks: What are some ways educators could utilise ChatGPT in their science pedagogy? For example, how might it design a science unit using the 5Es model, or how could it design a rubric to use in my science classroom? How might it generate a multiple-choice quiz with an answer key?

Research question 3 of this study asks: How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? "All tools do more than human minds and bodies can achieve unaided, which is why we create and use them" (Cope et al., 2021, p. 1230). Positioning the AI as a research tool, I will reflect on its use in the present study.

This is exploratory research; the intention is to document my experiences with ChatGPT and to reflect on its possible implications. This exploratory study applies a self-study methodology to investigate the technology (Hamilton et al., 2009). Through this study, I acknowledge that aspects of my educator and researcher identities have influenced the kinds of AI prompts I am interested in posing. As part of my exploration, I critically analyse ChatGPT output about science education, comparing it to key themes in the research. Moreover, I am eager to investigate the practical applications of using it for the classroom. Anecdotally, educators are already utilising ChatGPT to teach science. As discussed earlier, I am also interested in the application of ChatGPT as a tool for researchers.

The text italicised below has been directly copied from ChatGPT. It is important to remember that the following results are a moment-in-time response, meaning the same questions could be entered into ChatGPT and you may get a different response. The responses below were collected in January 2023.

Results

[ChatGPT Prompt 1]: What Are Key Characteristics of Effective Science Teaching?

Let us compare ChatGPT's response above to seminal research discussing characteristics of effective science teaching. Hackling and Prain ( 2005) report six elements including:

1. 1.
Students experience a curriculum that is relevant to their lives and interests.

2. 2.
Classroom science is linked with the broader community.

3. 3.
Students are actively engaged with inquiry, ideas, and evidence.

4. 4.
Students are challenged to develop and extend meaningful conceptual understandings.

5. 5.
Assessment facilitates learning and focuses on outcomes that contribute to scientific literacy.

6. 6.
Information and communication technologies are exploited to enhance the learning of science with opportunities to interpret and construct multimodal representations (p. 19).

Both lists stress the importance of inquiry-based learning, real-world connections, differentiation of instruction, the use of technology to enhance science learning, the importance of effective assessments, and student-led, collaborative science learning. The first list by ChatGPT mentions the importance of professional development while the second list by Hackling et al. makes no explicit mention of this. It does however stress the importance of connecting science learning with the broader community, which is less obvious in the ChatGPT response (e.g. real world connection). Overall, there seems strong alignment between the two lists.

[ChatGPT Prompt 2]: Should I Use Chalk and Talk to Teach Science or Embrace More Student-Centred Pedagogies?

Both "chalk and talk" teaching, where the teacher primarily lectures and writes on the board, and student-centred pedagogies, which put the focus on the student's own learning and discovery, have their own advantages and disadvantages.

"Chalk and talk" teaching can be effective for introducing new concepts and providing a clear structure for the material. However, it can also be less engaging for students and may not allow for enough student interaction and active learning.

On the other hand, student- centred pedagogies such as inquiry-based learning, project-based learning and problem-based learning, allow students to take an active role in their own learning, encouraging them to ask questions, explore, and discover new concepts on their own. This type of teaching has been shown to be more engaging for students and can lead to better retention of the material.

It is important to note that a good science teaching should aim to be a balance between the teacher-centred and student-centred pedagogies.

It can be beneficial to use a variety of teaching methods in the classroom, such as incorporating hands-on activities, group work, and discussions to supplement traditional lectures. This can help to keep students engaged and promote active learning. Additionally, allowing for student choice and incorporating real-world connections can help to make the material more relevant and interesting to students.

The response balances the different pedagogical approaches to teaching science. Its output defines "chalk and talk" as a process whereby the teacher primarily lectures and writes on the board... 'chalk and talk' teaching can be effective for introducing new concepts. The former is more closely aligned with a teaching model that emphasises direct instruction (Kirschner et al., 2006). The output also details the benefits of inquiry-based, student-centred models of science education (Rannikmäe et al., 2020), stating that this type of teaching has been shown to be more engaging for students and can lead to better retention of the material. Later, ChatGPT discusses the importance of aiming for a balance between teacher-centred and student-centred pedagogies. Where is the evidence base to support a balance? I also wonder about the importance of context? (e.g. individual student needs, school profile/priorities). We will elaborate on these points later.

[ChatGPT Prompt 3]: What Happens if a Student Is Failing in Science, Should I Care?

Yes, as a teacher it's important to care if a student is failing in science because it can have a significant impact on their academic and future career opportunities. Failing in science can indicate that a student may be struggling with the material and may need additional support or different teaching methods.

It's important to identify the reasons why the student is failing and address the underlying issues. This could include providing additional support such as tutoring, adjusting teaching methods to better suit the student's learning style, or providing opportunities for hands-on, inquiry-based learning.

It's also important to communicate with the student's parents or guardians and work together to develop a plan to support the student's success in science. Additionally, creating a positive and encouraging classroom environment can help to motivate the student to improve their performance in science.

In addition, it's important to recognize that students may fail for a variety of reasons, such as lack of interest, lack of prior knowledge or skills, or other personal issues. Therefore, it's important to be understanding and supportive and not to blame the student for their failure but instead, help them to overcome the obstacles and achieve their goals.

There are several helpful suggestions in the ChatGPT response that align with the research literature. For instance, ensuring more opportunities for student-centred learning (Kang & Keinonen, 2017), working with guardians or caregivers to support student learning (Archer et al., 2013; Gorard & See, 2009), and fostering a supportive learning environment (Du & Wong, 2019).

[ChatGPT Prompt 4]: Create a Teaching Unit Using the 5Es Model that Is Challenging for Students Who Have a Strong Understanding of Renewable and Non-renewable Energy Sources at a Year 7 Level. Also Provide Support and Scaffolding for Students Who Are Struggling with the Material

Engage:

Begin the unit by posing a real-world problem related to energy sources such as "How can we ensure a sustainable future for our community?"

Show a video or pictures of a community facing energy challenges, such as a power outage during a heatwave, or an oil spill

Encourage students to brainstorm and share their prior knowledge about energy sources

Explore:

Divide the class into small groups and assign each group a different renewable or non-renewable energy source to research.

Provide students with a set of guiding questions to help them focus their research. (e.g. What are the advantages and disadvantages of this energy source? How is it produced? What are the environmental impacts?)

Give students time to conduct their research and gather information.

Students can use a variety of resources such as books, articles, videos, and websites to research their topic.

Provide additional support and resources for students who are struggling with the material, such as providing a more simplified version of the guiding questions or offering extra time for research.

Explain:

Have each group present their findings to the class, using visual aids and hands-on activities to help explain the concepts

Encourage class discussion and questions

Provide additional support and resources for students who are struggling with the material, such as providing a graphic organiser or a template for the presentation

Elaborate:

Students will work in groups again to create a proposal for a sustainable energy plan for their community.

Provide a list of criteria that their plan must meet such as cost-effectiveness, environmental impact, and reliability.

Provide additional support and resources for students who are struggling with the material, such as providing a sample proposal or a step-by-step guide to creating the plan.

Evaluate:

Have students self-assess their understanding and participation in the unit using a rubric

Have students peer-assess the presentations and plans of other groups

Have the teacher provide feedback and grades on the final plan.

Finalise the unit with a class debate about which energy source is the best for the community

This teaching unit allows students to actively engage in the learning process, explore and research the subject, explain their findings, elaborate a plan, evaluate their understanding and participation and debate about the best energy source for the community. The unit provides support and scaffolding for students who are struggling with the material, while also challenging students who have a strong understanding of the topic to think critically and apply their knowledge to real-world scenarios.

The 5Es model (Bybee, 2002) is a popular teaching model used by schools and influential in pre-service teacher courses globally. One might imagine how it could be used by an educator as a catalyst for ideas, to move beyond a blank page, when designing a science unit. Example learning experiences in the output above seem mostly underpinned by a social-constructivist pedagogy (e.g. students sharing prior knowledge, working in groups to create a proposal, classroom debates, and opportunities for students to assess their own and peer presentations). As discussed, context is crucial. I can see it as a helpful tool for teachers as a starting point who then should consider their own teaching contexts (e.g. student needs, school profile, curriculum, access to resources). It is important that educators are critical about the ChatGPT output, deleting parts that are not helpful and building on elements that are. While the unit certainly needs refining, I suspect many educators will find this feature in ChatGPT helpful. Early career teachers and pre-service teachers may find the use of ChatGPT especially helpful for teaching resources because they are less likely to have an extensive bank of trusted resources.

[ChatGPT Prompt 5]: Create a Rubric that Year 7 Students Can Use to Self-assess Their Learning About Renewable and Non-renewable Energy Sources (Note I Asked ChatGPT a Follow-Up Formatting Request: Make This Rubric Easier to Copy to Word in a Tidy Format)

Table 1 Rubric output generated by ChatGPT

This rubric can be used by students to self-assess their learning about renewable and non-renewable energy sources, it can be used to evaluate their understanding of the concepts, research, critical thinking, and participation. Each criteria will have a scale of four levels: exemplary, proficient, developing and emerging. The students can check the level that they think they have reached on each criteria and then have a teacher or peer review and feedback on their self-assessment.

I see some potential for the use of ChatGPT to help with rubric design (Table 1). As you can see from the question I asked ChatGPT, it self-identified rubric criteria (e.g. understanding, research, participation). The rubric categories make a distinction between levels (e.g. exemplary, proficient) but are too vague to identify specific indicators in each category. This might suit some educators. Others might add further detail and use the ChatGPT response as a starting point, in a similar way to the unit design discussed earlier.

[ChatGPT Prompt 6]: Generate a Quiz on the Concept on Renewable and Non-renewable Energy Sources at a Year 7 Level and Include Answer Key

Renewable and Non-Renewable Energy Sources Quiz

What is a renewable energy source?

What are the three main types of renewable energy sources?

What are the advantages of using renewable energy sources?

What is a non-renewable energy source?

What are the three main types of non-renewable energy sources?

Answer key:

This quiz assesses the student's understanding of the concept of Renewable and Non-Renewable Energy Sources, it includes multiple-choice questions that test their knowledge of the definition of Renewable and Non-renewable energy sources.

Discussion

RQ1: How Did ChatGPT Answer Questions Related to Science Education?

I asked ChatGPT a series of questions broadly related to science education. It is worth acknowledging that its capacity to emulate human-like responses is nothing short of extraordinary. Broadly speaking, the ChatGPT response commonly aligned with key research themes in the literature. As discussed, the AI output in both prompts 1 and 3 highlighted strong synergies between the ChatGPT response and key themes in the research. Despite this, a major criticism of its current design is the absence of evidence to support its output. As it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed, without a proper grounding in evidence or presented with sufficient qualifications. The response to prompt 2 highlights the problematic absence of evidence, where the AI stated it was important to aim for a balance between teacher-centred and student-centred pedagogies. As stated earlier, where is the recognition of context? Where is the evidence base? Even when ChatGPT is prompted to provide references, it has been reported that... "it continually provides false and misleading references. To make matters worse, it will often provide correct references to papers that do exist and mix these in with incorrect references and references to non-existent papers... The question is, when does it give good answers and when does it give garbage answers?" (Buchanan, 2023, para. 1-3). Science educators, who prioritise evidence-based explanations in their own teaching, may find the current design of ChatGPT problematic.

Beyond its narrow framing of truth, its output is based on... " argumentum ad populum-it considers to be true what is repeated the most" (Darics & Poppel, 2023, para. 4). As discussed, the model is generally skewed towards content that reflects Western perspectives and people. Whose voices are silenced by the algorithm? Who is the author, and what is their bias? These are critical questions for educators, as well as students, to think carefully about.

Although this paper does not extensively probe the ethical implications related to ChatGPT, it may be valuable to discuss the matters listed below with students who are exploring its use or AI in general. One consideration is the potential environmental impact of AI platforms. Although the information is not readily available on ChatGPT, machine learning models require substantial processing power, and data centres hosting cloud networks must be effectively cooled (Boudreau, 2023; Wu et al, 2022). It was also reported that Kenyan workers were paid about $2 per hour to work as content moderators for systems associated with the creation of ChatGPT, sifting through disturbing content like sexual abuse, hate speech, and violence (Perrigo, 2023). The use of large language models such as ChatGPT also raises questions about the potential for copyright infringement when generated text resembles or copies existing content (Karim, 2023). Apart from these broader concerns, there are other considerations for how students use AI. For instance, is it okay for students to reference ChatGPT verbatim in an assessment? Given my previous discussion about its lack of evidence, I have instructed students in my classes not to. Students may first generate essay text in ChatGPT and subsequently insert key references mentioned in class. I do not know how I feel about this, it does not quite sit well with me. Instead of allowing research to drive the argument, it seems more like an essay hack. Matching an AI-generated narrative with research to legitimise it.

I am interested in the potential of ChatGPT to be used as (1) a learning scaffold for learning new concepts (before supporting students to engage with more traditional stimuli, such as an academic journal or textbook) and its potential to (2) help students who are not strong writers. For instance, I have modelled prompts students can enter to get a broad overview of a concept (e.g. ChatGPT prompt- Imagine I am an undergrad student, make some bullet points about [phenomena]). I suspect that for students who find it difficult to write, it will be helpful in overcoming writer's block. To demonstrate to students the advantages of using ChatGPT to improve their writing, again, I have modelled prompts (e.g. ChatGPT prompt- rewrite: [paste your text here]). It is important for stakeholders to carefully consider how AI impacts the design of, and completion of, assessments and pre-service teacher programmes more broadly. Prioritising student's critical thinking, critiquing ethical issues related to the use of AI systems, modelling its responsible use, and being clear about expectations for its use in assessments seems like a good place to start a broader conversation. Beyond large language models, educators need to consider generative AI more broadly (e.g. image, audio, video etc.). The ability to think critically as an educator is now more important than ever, an essential element of a science teacher's toolkit. In an age of social media echo chambers, climate change scepticism, and uncertainty about sources of evidence and "truth(s)", the emergence of generative AI introduces further complexity.

RQ2: What Are Some Ways Educators Could Utilise ChatGPT in Their Science Pedagogy?

In this part of the study, I was interested in exploring how educators might draw on its use of ChatGPT. Its output to prompts 4, 5, and 6 illustrates ways ChatGPT can be helpful to generate ideas when designing science units, rubrics, and quizzes. I was particularly impressed by its capacity to generate a science unit underpinned by the 5Es model, even if some of the output seemed a little generic and in need of further refinement. The output embedded the science topic (renewable and non-renewable energy sources) within a pedagogical framework (the 5Es). Again, however, educators need to critically evaluate any resources and adapt it to their specific context. Teacher's expertise, experience, and understanding of their students remain key to making sound pedagogical decisions. AI does not replace the expertise of the science teacher (yet).

RQ3: How Has ChatGPT Been Utilised in This Study, and What Are My Reflections About its Use as a Research Tool?

As part of my research exploration, I was interested in using ChatGPT as a research tool in the present study. It has been reported that some scientists are already.... "using chatbots as research assistants- to help organize their thinking, generate feedback on their work, assist with writing code and summarize research literature" (Nature, 2023, p. 612). The large bulk of its use in this research was assistance with editing. There were sentences that I asked ChatGPT to rewrite (ChatGPT prompt- rewrite: [paste sentence]) at different stages of the paper to help with phrasing, flow, and word choice. Researchers who tend to write excessively long or complicated sentences could use ChatGPT to clarify their message. Certain sentences, however, had better phrasing prior to being entered into ChatGPT, while others were improved after a rewrite by the AI. When composing this paper, I kept a browser window open, experimenting with the possibilities of making my research narrative clearer.

There is presently a debate among journal editors, researchers, and publishers regarding the role of such AI tools in published literature and whether it is acceptable to attribute authorship to the bot (Stokel-Walker, 2023). Nature, along with all Springer Nature journals, has formulated two key principles to their existing guidelines for authors in response to the rise of ChatGPT. The first principle is that no large language model will be recognised as an accredited author because attribution carries accountability for the work, which AI tools cannot take such responsibility (Nature, 2023). "If ChatGPT deserves authorship, Microsoft Word deserves it, too, for providing us with the platform to organise and write documents more efficiently.... Excel, R, or Python deserve to be co-authors for calculating statistics or analysing data for a quantitative scientific publication" (Karim, 2023, para. 5). The second principle added to Nature's author guidelines is that researchers need to disclose their use of large language models in the methods or acknowledgements sections (Nature, 2023). Similar to how journals require statements about data availability or ethical research, authors may soon have the option to disclose their use of large language models or AI during the journal submission process. I suspect formatting guidelines about the use of ChatGPT input and output will become clearer soon as well. The addition of AI to the research process commonly means new rules and processes for investigators. Ultimately, transparency and clearer guidelines about the use of AI platforms in research are essential for advancing scientific knowledge.

Conclusion

The emergence of generative AI is already having far-reaching implications for science educators. It seems like we are in the early stages of a seismic shift. This article is in no way intended to be a comprehensive discussion of its use, merely an exploratory study that hopefully acts as a catalyst for a broader conversation-how do generative AI tools, such as ChatGPT, fit into our research and teaching pedagogy? How do we feel about them? What feels right, what does not? How will generative AI platforms evolve? And what are the potential future implications for science educators? At this stage, I have more questions than answers.

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References

Bybee, R. W. (2002). Learning science and the science of learning: Science educators' essay collection. NSTA Press.

Nature. (2023). Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. https://www.nature.com/articles/d41586-023-00191-1

Rannikmäe, M., Holbrook, J., & Soobard, R. (2020). Social constructivism-Jerome Bruner. In B. Akpan & T. J. Kennedy (Eds.), Science education in theory and practice: An introductory guide to learning theory (pp. 259-275). Springer Nature.

Chapter Google Scholar

Stockman, C., & Nottingham, E. (2022). Surveillance capitalism in schools: What's the problem? Digital Culture & Education, 14(1), 1-15.

Google Scholar

Wu, C. J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., & Hazelwood, K. (2022). Sustainable AI: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, 4, 795-813.

Google Scholar

Zhai, X. (2022). ChatGPT User Experience: Implications for Education. https://doi.org/10.2139/ssrn.4312418

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Grant Cooper

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Cooper, G. Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. J Sci Educ Technol 32, 444-452 (2023). https://doi.org/10.1007/s10956-023-10039-y
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Keywords

Generative artificial intelligence and science education

Large language models

ChatGPT

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# data[].attributes.attachment_link.title
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence - Journal of Science Education and Technology

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The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy?

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A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools.

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A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools.

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Application of the Educational Game to Enhance Student Learning.

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Application of the Educational Game to Enhance Student Learning.

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Great statistical data.

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Introduction

Gamification in education has become the focus of attention in recent years. While “gamification is the practice of using game design elements, game mechanics and game thinking in non-game activities to motivate participants” (Al-Azawi et al., 2016, p. 133), educational gamification is a teaching method that requires learners to participate in competitions according to preset rules (Fitzgerald, 1997). It has been an interdisciplinary and prevalent tool for educators to utilize in teaching in the past few years (Robson et al., 2016). In view of the rapid development of technology, learners may expect teachers/lecturers to employ this tool in lessons/lectures (Rondon et al., 2013). In the field of education, researchers have been eager to find new strategies to enrich students’ learning experiences, especially in this technology-driven world in which educational games are one of them (Minovic et al., 2012a).

Effectiveness of Educational Games

A systematic literature review had been conducted by Calderón and Ruiz (2015) who found that 53 educational games research literature had adopted different methods to assess the effectiveness of diverse educational games in the period between November 2013 and April 2015, compared to 18 and 20 games used in health and wellness, and the professional learning and training domain, respectively. They also reported that 60% of these 53 studies examined the effectiveness of using educational games in higher education setting, compared to only 40% in primary or secondary school settings, indicating that teachers in higher education are more likely to combine educational games with traditional teaching methods into students’ learning experiences—a sign of creativity of embracing the new strategy to enrich students’ learning experience.

Gamification has become popular in education in recent years. Its advantages include, but not limited to, giving students the opportunity to experience learning in a multi-sensory, active and experimental environment. Specifically, learners can use these educational games for experimental learning to develop their decision-making and problem-solving skills in a dynamic learning environment (Adachi and Willoughby, 2013). In addition, students can receive feedback/results immediately to get answers, instead of receiving delayed feedback from traditional assessment methods (e.g., tests and examinations). Moreover, some educational gamification may help to reduce limitations, including time and place, as portable devices can enable students to study and/or learn anytime and anywhere. These user-friendly tools can make difficult subjects easier to understand and memorize (Hanus and Fox, 2015). In other words, with the use of educational games, the learning process is considered to be more interesting (Calliari, 1991), motivating (Sun-Lin and Chiou, 2019), achieving knowledge retention (Gros, 2007), increasing attention (Prensky, 2003), and can even enhance peer communication and social skills (Liao et al., 2011).

Although extensive research supports the use of educational games to help improve students’ learning experience, it has been found that young students are more likely to be inattentive (Wickens, 1974), and the use of educational games, among the many other solutions, to alleviate the issue may become helpful. Research shows that students’ attitudes toward learning, class attendance and mood were more positive when compared to primarily using traditional methods of teaching and learning (Vernon and Blake, 1993). It is believed that in the learning process, when students have the opportunity to participate in decision-making, they are more likely to enjoy learning because they regard “learning” as “playing” (Zapalska et al., 2012). Even though as children grow older, their attention and concentration may correspondingly increase (Wickens, 1974), it is important to ensure that students’ learning motivation can remain at a high level. In other words, by using educational games during lectures or teaching, students will be required to focus on the teaching content in order to successfully complete the tasks in the game, whilst their learning motivation may remain high due to the inclusion of game elements in the learning process.

However, the advantages of using gamification in education does not necessarily mean it would receive overwhelming advocacy, as it had produced some mixed results (Hanus and Fox, 2015). Whitton (2007) even documented that their participants did not find educational games motivating or interesting, elucidating that since individuals normally play games for entertainment purposes, the deployment of gamification in educational environments/settings may not produce the same motivational effect. If individuals have the option of either playing educational or casual games, they are more likely to choose the latter (Karakus et al., 2008), because they may perceive educational games to be dreary (Kinzie and Joseph, 2008). Furthermore, participating in educational games may even have detrimental effects. According to Dominguez et al. (2013), the utilization of education games was associated with a decline in academic performance.

While the fun side of gamification has counterbalancing effect to learning motivation is not yet known, an educational game is fun and/or motivating, and ultimately effective for learning, will highly depend on learners’ personal differences and learning preferences. It is therefore essential to investigate factors affecting the effectiveness of educational games to enhance student learning.

When May Educational Games Be Played?

The effectiveness of educational games is undoubtedly significant to scholars, but it is equally important to consider how to make the best use of these games. Most educational games are played on computers during classes, but not many are played on mobile and tablet devices. This may limit the usage of these educational games outside of traditional classroom. As a result, students may not be able to take the full benefit of these games. It is worth noting that learning and studying can take place outside the classroom, thus the availability of these portable tools could meet the needs of the students. It is believed that an educational game that enables students to “play” and study conveniently and effectively may be more beneficial for students, particularly in this fast-evolving modern society where individuals spend a vast majority of time on their mobile phones. A study conducted by Hanson et al. (2010) found that college students spend approximately 20 hours on their mobile phones per week, for example, when they are traveling and waiting. The high usage of mobile phones may indicate that if educational games are also available on mobile phones, other than on computers, the opportunity to learn outside of the classroom would increase as individuals may use the educational games applications more often due to their convenience. Thus, the educational game which is available on mobile devices was one of the criteria in the current study.

Introduction of Educational Game: “PaGamO”

“PaGamO” is an online gamification learning platform for individuals to learn and compete with each other by participating in a game. Individuals are required to answer questions correctly in the game to successfully build, occupy and conquer territories. It is believed that with such a popular and unique method of motivating students to learn, students would be more motivated and entertained in the learning process. In addition, “PaGamO” provides information for teachers to monitor the learning progress of a large group of students, as shown in the statistics of completion rate and percentage-correctness rates.

With the increasing popularity of “PaGamO,” the game is now widely used for different purposes, including but not limited to, language learning, license examinations and other professional training. With over 500,000 players and over US $6 million being invested into the game (Business Next, 2015), we may wish to understand the factors behind its quick success ever since its start-up, and more prominently, its effectiveness for learning.

One of the advantages of the “PaGamO” is that it can be accessed via computer, mobile phone and tablet applications. Students can not only review their knowledge prior to their academic tests/examinations, they can also use educational games as study tools in their spare time. If these mobile educational games are developed and used appropriately, they may help enhance students’ learning motivation and change their perceptions of learning, i.e., learning can be fun and interesting. In this rapidly changing world, traditional education system may not be as effective as before, hence researchers are very eager to explore alternative learning strategies (Minovic et al., 2012b).

Thus, the principal researcher decided to use the educational game, i.e., the “PaGamO,” in the current study. For students in the current study, other assessments (such as quizzes and tests) that were conducted in class accounted for a larger proportion of their final grades. The principal researcher decided that the “PaGamO” assessment scores would contribute to only 5% of the final grade, so students could try this educational game as a study tool and/or as a supplement to the learning materials in the lectures. After the selection of the educational game, the next step was to design the appropriate instructional strategies according to the theory of enhancing students’ learning.

Educational Games and Motivation

In order to let educational game developers understand how to motivate and encourage individuals to use educational games, it is important to first understand the reasons behind the popularity of recreational games. According to the Interactive Software Federation of Europe (2016), individuals from the Great Britain, France, Germany and Spain spent more than 6 hours every week playing video games, and their gaming activity further increased in 2017 (Interactive Software Federation of Europe, 2016, 2017). Those players are believed to play games for enjoyment without any pressure from outside in participation. Precisely, players are attracted to those games while no one else “force” them or encourage them to play. One explanation is that playing games is intrinsically satisfying for them, as players can experience a sense of enjoyment and fun (Malone and Lepper, 1987). For some, they are willing to pay an absurd amount of money to participate in these video games. Scholars from the education sector are very interested in studying the sources of motivation that lead individuals to spend time and money on these entertainments. It would be fascinating to see if educational games, like popular recreational video games, can enhance motivation for learners. As such, the Self-Determination Theory (SDT; Deci and Ryan, 2004), a widely used theory in motivation, is adopted to aid the discussion in this study. It will also be appropriate to use theoretical reasoning to elucidate the sources of motivation to participate in recreational gamification.

Self-Determination Theory

Motivation is defined as a decisive psychological process that can stimulate human behavior and guide our behavior to specific situations (Reeve et al., 2004). Self-Determination Theory (SDT; Deci and Ryan, 2004) meticulously explains about the sources of motivation in some specific situations, in this context, it is to play educational games. This theory comprises of three different types of motivation, namely “intrinsic motivation,” “extrinsic motivation,” and “amotivation.” “Intrinsic motivation” refers to internal feelings, such as enjoyment, pleasure or interest, which drive the individuals to participate in a particular activity. For example, a game player may play a recreational game because he enjoys it. Conversely, “extrinsic motivation” consists of external rewards or outside pressure that encourage or push individuals to behave in a certain way. For example, a player may play a game because he wants to win a medal. The behavior is merely directed by the external reward rather than intrinsic feelings. It is worth noting that over-reliance on extrinsic rewards can weaken the existing intrinsic motivation because individuals are likely to focus on the extrinsic reward as a source of motivation (Lepper and Henderlong, 2000). Finally, “amotivation” indicates that individuals are not motivated at all to complete a particular activity. In the educational context, individuals may not be psychologically driven to participate in certain educational activities, for example, to play educational games, because they are simply not motivated.

It is believed that the adherence to a particular activity will be longer if we engage with intrinsic motivation (Ryan et al., 1997). Intrinsic motivation can be enhanced when three basic psychological needs are met, namely “autonomy,” “competence” and “relatedness.” How the three needs are relevant to the current study will be discussed in detail in later part of this paper. It is suggested that when one of them is achieved, it is likely to increase a sense of intrinsic motivation in oneself. If three of the needs are all satisfied, the level of motivation may be further enhanced (Deci and Ryan, 2004). However, it is worth noting that the three basic psychological needs may offset each other and can be an impediment of one or another needs. Specifically, one of the needs can be fulfilled at the expense of the others, or two needs can be met through the same task (Ryan and Deci, 2000). For example, collaboration in a group may yield a sense of relatedness, but at the expense of one’s need for autonomy because other group members may not approve certain decisions, therefore freedom can be limited. On the other hand, two psychological needs (relatedness and competence) can be satisfied when a group has successfully mastered a particular task together. It is therefore recommended that when teachers are supporting one of the psychological needs, they should be wary of not thwarting the other needs (Van Roy and Zaman, 2017).

Need for Autonomy

In education, “autonomy” refers to the absence of external pressure to force the learners to act or behave in a certain way during the learning process. Individuals would have freedom of choosing whether or not they will take certain actions, and when those actions are being taken. For example, if individuals can choose when to exercise during the week, instead of having a fixed schedule of running on every Monday, Wednesday, and Friday, they are more likely to enjoy exercising because they would enjoy a sense of autonomy. In the educational context, when students are being put in situation where it is controlling, such as having very strict parents and teachers, the level of autonomous motivation is likely to descend (Rigby and Ryan, 2011). Moderate level of choice and freedom have been found to be fundamental toward autonomous motivation (Deterding, 2015). However, Rigby and Ryan (2011) believed that individuals can still have a sense of autonomy even when only one single option is provided. For example, if students are being instructed to write an essay on a particular topic, and if that topic aligns with their interest or strength, that will allow the students to have a sense of volition.

With regard to the current study, the authors believe that a sense of autonomy was achieved through the freedom of: (1) choosing when to play the game, rather than forcing them to play in class, and (2) choosing whether to use the “PaGamO” as a study tool or not. Although some may argue that students’ sense of autonomy may diminish as the “PaGamO” scores would contribute only 5% to the students’ final academic grade, the researchers believe that the small fraction would not negatively affect students’ sense of autonomy. On the contrary, this could encourage students to participate in the game, and students could possibly become more intrinsically motivated once they have explored the interesting and motivating side of the game.

Need for Relatedness

The need for relatedness can be achieved when individuals feel that they belong to a particular group (Deci and Ryan, 2004). When humans have the opportunity to share experiences, the positive feelings will be strengthened and fostered (Rigby and Ryan, 2011). Although students may be involved in school settings where they can become competitive, they all share a common goal—to achieve academic success. If teamwork is encouraged, it would enable students to share their knowledge and experiences, resulting in stronger bonds, higher sense of relatedness and motivation to complete a common task together (Carr and Walton, 2014). The benign competition against other groups would perhaps drive students to improve as a group to fulfill a sense of relatedness and competence.

In the current study, 4–5 students formed a team. The “PaGamO” also allowed students to send messages among themselves to discuss their team tactics and share their academic knowledge. Taking the advantage of using social features, the students were encouraged to work together to complete the “missions”—to facilitate interaction among them and lead to better assessment grades (De-Marcos et al., 2016).

Need for Competence

The “need for competence” refers to the feeling of being able to successfully master a particular task/activity (Deci and Ryan, 2004). Students who experience a sense of competence are more likely to persist in the face of challenges, and have better academic results than those who are incompetent (Rigby and Ryan, 2011). However, it is not just a matter of making the activities/tasks easy to accomplish, so that students can experience a sense of mastery. Teachers should ensure that the tasks are being set at an “appropriately difficult level” where they are challenging, yet attainable (Peng et al., 2012), as students may feel bored if they can do the task effortlessly, or may become anxious if it is too difficult (Csikszentmohalyi, 1990).

In the current study, with over 50 students in the sample, it is not feasible to ensure that the difficulty of the questions is challenging yet attainable for every student. Therefore, the researchers included a set of easy, medium and hard questions in the “PaGamO” sessions, with a large proportion being at medium level. Students were also encouraged to seek advice from the principal researcher if they would perceive the questions to be too challenging, i.e., tutorial sessions. Thus, students would feel more comfortable and competent when they thoroughly understand the content, and would be able to master the “hard” questions.

Ways to Enhance Intrinsic Motivation

One of the factors that Malone and Lepper (1987) mentioned to increase intrinsic motivation is curiosity. If learners are curious and have a desire for knowledge, they are more likely to pay more attention to the new information in order to integrate into their existing knowledge. Curiosity is induced when there is a gap between our perceived discrepancies or conflicts from our personal expectations and knowledge (Loewenstein, 1994). As human beings, we are curious about our surroundings and the world in order to make sense of the things that we are not certain and/or could not explain. On the other hand, as Garris et al. (2002) argued, whether an individual is motivated to learn can be determined by the complexity of the information. For example, if the information is considered to be slightly discrepant/easy, the learner may simply disregard the message. On the contrary, if the new information has a high level of discrepancy compared with our existing knowledge, or if the content is too difficult, it may confuse the learner, and he/she may lose motivation or interest to learn the new knowledge. In this sense, it should be taken into consideration when designing the questions for students in educational games. The questions should be set at a level where students can feel challenged, yet attainable.

In addition, many children and adolescents do not participate in outdoor sports or physical activities, but they play computer and video games in their spare time (Mumtaz, 2001), so educators are interested in combining educational content with educational games to enhance students’ intrinsic motivation for learning (Prensky, 2001). Prensky has provided some suggestions to keep players intrinsically challenged: (1) to ensure that individuals feel challenged, and experience a sense of mystery and control while playing; (2) when designing the game, ensure that the level of difficulty of the game should neither be too easy nor too difficult, otherwise both may affect the player’s motivation; (3) the player’s score should be provided to the player so that he/she can track the progress while pursuing the goal; and (4) the purpose of playing the game must be relevant and meaningful to the player—gaining specific knowledge from participating in the game should provide a purpose for the learner to be adherent to the game. This study has followed each of the aforementioned guidelines with an aim to enhance intrinsic motivation in learning.

Objective

This study uses a combination of gamification and traditional learning methods, while implementing the elements of self-determination theory to design the instructional strategies to motivate students. The aim is to study whether the use of educational game “PaGamO” could enhance students’ learning ability and understand students’ perceptions of educational games.

Materials and Methods

Participants

The participants consisted of 56 college students (32 men, 24 women, Mage = 21 years, SD = 1.72) majoring in physical education and recreation management and taking the motor learning and development course. Each group consisted of 4–5 students; a total of 12 groups were formed. In addition, a total of five students, who had achieved low, medium, and high scores (score range from 19 to 94 out of 100 points) in the tasks using “PaGamO,” had participated in focus-group interviews, further enabling the researchers to collect students’ opinions on “PaGamO” in exhaustive detail. According to Morgan (1988), a small focus-group encourages participants to make more contributions.

Procedure

Since the educational game “PaGamO” was newly introduced to the researchers in this study, a meeting was held with the Senior Project Officer (SPO) of the University’s Centre for Holistic Teaching and Learning in order to gain a thorough understanding on how the educational game functions. SPO provided clear instructions and recommendations to ensure that the game could run and be managed smoothly and effectively.

Then, the principal researcher/lecturer of the course designed a list of 100 multiple-choice (MC) questions based on the course materials. Afterward, the researchers checked the quality and the accuracy of the questions and answers. They also discussed the difficulty of the questions and ensured that the questions were comprised of different levels, namely simple, medium, and difficult. After successfully uploading the questions to the “PaGamO” system, the SPO and researchers introduced the game instructions to students in detail. Technical support was also provided to ensure that all students could download and install the “PaGamO” game effectively.

In order to increase the sense of competitiveness while playing the game, 4–5 students formed their own team. A total of 12 teams were eventually formed. Teams and individuals with the highest scores at the end of the study would be awarded. Meanwhile, the principal researcher would send weekly reminder to students to participate in the game. Upon completion of each section, students would receive individual and team scores.

Students played the “PaGamO” game in four sections according to the theme of the module. As the principal researcher decided to encourage and increase the motivation among the students, the latter were informed that 20 of the 100 questions in “PaGamO” would be included in the final examination. In addition, the final “PaGamO” score would contribute 5% to the students’ final grade. Students were given 7–10 days to complete the questions (the length of the completion time depended upon the number of questions in different sections). All tasks of the four sections were required to be completed within 5 weeks. The principal researcher also encouraged students to collaborate with their team-mates and/or search for answers in handouts and textbooks, that aimed to stimulate their critical thinking and make it easier for them to absorb what they have learned.

Data Collection and Analysis

A mixed-method approach was used to thoroughly assess the effectiveness of “PaGamO.” The current study extracted primary data via the use of questionnaires and was supplemented by interview in which feedback and answers were thematically analyzed.

Quantitative Method

Students’ perceptions and opinions of “PaGamO” were evoked immediately after the completion of their final examination through a questionnaire which consisted of two sections. Questions on Section 1 were modified from the questionnaire of Riemer and Schrader (2015) entitled “Students’ attitudes, perceptions and intentions to learn with different types of serious games.” For example, the question “I would like to use serious games regularly for learning” was changed to “I would like to use ‘PaGamO’ regularly for learning.” The reason for changing the wording was to ensure that students were indicating their level of agreement or disagreement based on the “PaGamO,” instead of general educational games. Questions on Section 2 specifically focused on “PaGamO,” such as “Would you like the game ‘PaGamO’ to be played in other courses?” and “What are your motives to play ‘PaGamO’?”

Qualitative Method

To help answer the research question (In what ways the application of the educational game to enhance student learning?), a group of students were subsequently invited as members of a focus-group to elaborate their thoughts on “PaGamO.” In the questionnaire, a student strongly agreed to the statement “I would like to use ‘PaGamO’ regularly for learning.” In order to understand the reasons behind this response, the breadth and depth of the information was gathered through exhaustive interviews with the students. For example, the students elaborated and insisted, “‘PaGamO’ can help me revise for the final exam for this module”. This extensive information enabled researchers to better understand this circumstance.

Prior to the interviews, trust and rapport were built in order that participants would provide truthful and exhaustive data for precise and valid analysis (Hodge et al., 2014). In addition, the researchers also ensured that the questions in the interview guide were open-ended and non-leading (Patton, 2002). Semi-structured interviews lasted 30 minutes on average. The audio recording was used to assist the note-taking in order to ensure the accuracy of the latter. After that, the transcript of the interview was translated back to back to confirm accuracy.

Some of the literatures identified by Boyle et al. (2016) have examined the effectiveness of educational games on learning using randomized control trial (RCT) and quasi-experimental design. However, as the students participated in this study were taking a university course, and they were assessed as a whole class, it would not be feasible to allocate them into experimental and control groups. Furthermore, it would be meaningless to conduct a pre-test as our participants would have limited knowledge in the motor development and learning topics.

Results and Discussion

Students’ “PaGamO” Score and Examination Score

There were 48.3% of the students completed a total of four sections of the game, and eight of them scored above 80 points out of 100. The final examination consisted of three parts, namely, MC questions (20%), fill in the blanks (20%), and long questions (60%). A statistical analysis was conducted to examine whether there was relationship between the educational game “PaGamO” score and the final examination score. The quantitative data was supported by qualitative findings received from focus-group interviews.

A simple regression was conducted to examine the relationship between the “PaGamO” (“PaGamO” score) and the MC and final examination scores. Regression analysis indicated that “PaGamO” score was a significant (p < 0.01) predictor of MC score in the final examination. A medium positive correlation (β = 0.354) was found. The R ² suggests that 12.6% of MC score was explained by “PaGamO” score. For the final examination score, the “PaGamO” score was not a predictor (p = 0.065). The R ² suggests that only 6.3% of final examination score was explained by “PaGamO” score. One possible explanation was that the format of the question in “PaGamO” was MC questions, while there were other questions in different formats in the final examination. Therefore, it could be more challenging if students were unable to transfer and apply their knowledge from one format to another while answering the questions. Also, they had never seen the long questions before the final examination, unlike the questions in MC which primarily were included in the “PaGamO” game.

Some students had a low “PaGamO” score, but had a high MC 20 mark on the final examination were observed. We assumed that students tended to use their existing knowledge to conduct self-tests rather than searching for answers directly using their lecture notes. These assumptions are supported by students in the focus group:

“At the beginning, I don’t use notes because I want to see how much I know, but for the questions that I did wrongly, probably when I was wrong twice, I would use the notes in the third time. Because if you get the notes out at the beginning, you won’t remember the answer (for the final exam), but when you are wrong, you will know what you have done wrong.”—Student A.

“When I played it the first time, I did not revise for it, I based on my memory only and tested myself to see how much I remember. For second time, I did do a bit of revision, because I want to get higher scores.”—Student B.

“PaGamO” could be an effective tool for studying and students seemed to have learnt by adopting the “trial and error” strategy to strengthen their memory on the subject. Another reason for scoring the high MC mark was possibly because the students had been informed that 20% of the questions in “PaGamO” would repeat in the MC section in the final examination.

One of the main purposes for using educational games is to provide an alternative learning option for students, with an aim to help them study more effectively in an entertaining manner. A student said, “I think this app is quite good, because it is a game, it allows me to revise in a relaxing way, unlike how I normally revise which I just try to remember everything” (Student C). When nearing the end of the semester, with assignment deadlines and examination periods approaching, it could become a stressful period for students. The quote aforementioned suggests that students perhaps can study effectively “in a relaxing way” rather than simply over-loading their memory. The elements of the game could be new, exciting and challenging for students to learn and study. This method was also welcomed by other students from the same class, “I can play education game in this subject, I think it’s quite special” (Student D) and “it’s better than keep on revising my notes again and again, PaGamO is a more interesting way to revise, so why not use it.” (Student E). Hence, the findings support that the educational game can enhance students to study the course materials and could be effective in learning.

Students’ Motives to Play “PaGamO”

Motives for participating in the “PaGamO” were mainly intrinsic as suggested in the top 10 motives of the list, i.e., (1) fun, (2) self-learning, (3) want to get a higher grade in the final examination, (4) challenging, (5) want to get a higher score in the game, (6) enjoyment, (7) I can choose when to play, (8) self-achievement, (9) want to win, and (10) the game has high relevance to my learning. The findings are consistent with previous research (Dominguez et al., 2013; Sun-Lin and Chiou, 2019) and they support the gamified design to make learning tasks more interesting and fun, thereby attracting students’ attention and motivating them to complete the learning tasks.

In addition, not only were the students driven by both intrinsic and extrinsic motives to play the “PaGamO,” there was also the social factor involved. Student A remarked:

“The game is simple and it has ranking, that’s why it is competitive, it is fun. It is challenging… you have to compete with others, so that you have the motivation to play, because you would want to compare with others, if you can see the ranking, the motivation level is higher.”

Herodotou et al. (2014) argued that the theory of self-determination did not well predict individuals’ motivations for playing entertainment games. In their research, they found a weak association between basic psychological needs and game playing. The researchers believe that one of the main factors motivating individuals to play casual games is the social interaction between players, who would cooperate and/or compete with each other. In the current study, the view provided by Student A supports the discovery of Herodotou and the research team, suggesting that social interaction and competitiveness are key to game players’ participation in the game.

Students’ Perceptions of Educational Games

In order to study students’ cognitive perception and intention to use the “PaGamO,” 15 statements on the 7-point Likert scale were listed and utilized for in-depth analysis. The top five statements are: (1) “In my opinion, the use of ‘PaGamO’ enables me to better prepare for the final examination” (M = 5.04, SD = 1.41), (2) “In my opinion, the use of ‘PaGamO’ enables me to understand learning contents” (M = 4.8, SD = 1.19), (3) “In my opinion, the use of ‘PaGamO’ allows me to apply knowledge” (M = 4.75, SD = 1.08), (4) “In my opinion, by using ‘PaGamO,’ I can learn easily” (M = 4.67, SD = 1.28), and (5) “In my opinion, using ‘PaGamO’ enhances my learning performance” (M = 4.65, SD = 1.35) (see Table 1).

Table 1. Students’ perceptions and intentions to use the educational game.

Since “In my opinion, the use of ‘PaGamO’ enables me to better prepare for the final examination” was the highest ranked statement, it was deemed essential to ensure that further elaboration was heard from the students. When being asked how “PaGamO” was helpful in their final examination, Student C recalled, “in ‘PaGamO,’ when you are wrong, you are more likely to remember it better, and when the questions come up again in the exam, then you won’t be wrong again.” Similar responses were also heard from Student D, who said, “after answering the questions, I can remember it very well.” It was evident that the “trial-and-error” approach had helped students memorize the content successfully who subsequently could apply the knowledge in the final examination.

Furthermore, based on the 15 statements of “Students’ attitudes, perceptions and intentions to learn with different types of serious games” questionnaire of Riemer and Schrader (2015), three factors were also examined and the findings are as follows: (1) perceived potential to support performance which includes items on beliefs about whether the game promotes specific learning performance, such as the final examination (range = 8–35, M = 23.82, SD = 5.48); (2) perceived potential to support performance which includes items on beliefs about the overall efficiency and effectiveness of academic performance through the use of games (range = 6–35, M = 22.89, SD = 6.27); (3) usage intention, address the intention to use or avoid using serious games for learning (range = 5–35, M = 19.38, SD = 5.38). Moreover, independent t-test results of the three factors show that men and women have similar views on “PaGamO.”

However, as mentioned earlier, perceptions of whether “PaGamO” is interesting and motivating largely depend on individual differences and learning preferences. As Student E recalled:

“PaGamO” doesn’t help that much… after I complete it, I won’t actually remember what we have answered… I don’t actually like to use these methods (educational games) to revise. Traditional learning and revision will help me more, it is better to have quizzes in classes than “PaGamO.”

Consistent with previous literature by Whitton (2007), and Chen et al. (2019), the citations above suggest that not all students would find educational gamification fun and motivating. Some students may prefer to use traditional learning and teaching methods as a result of, perhaps, its being a habitual behavior. Furthermore, the game design may also affect students’ perceptions of “PaGamO.”

“PaGamO” is less fun compared to the one I played before, in “PaGamO,” you can only upgrade your land or rob other people’s lands, but the one that I played before had different subjects, it had some words that come out and you select the right ones, but “PaGamO” only had A, B, C, like exams.—Student C.

As such, it is important to ensure that the game design is fun enough to keep students engaged. MC questions may appear too similar to the examination in the current study. Although the researcher is convinced that if MC questions can mimic the format of the examination, students may prepare better for the examination. However, Student C’s view suggests that when educational gamification is too similar to the formats of the examination, it can become demotivating and tedious. Since it is not clear which question format is best for students’ learning, further research is necessary.

Technological Devices for Playing Educational Games

In relation to the devices used by students to play “PaGamO,” it was reported that 42 students used mobile phones, 12 used tablets and 2 used computers. The use of a mobile phone enabled students to play “PaGamO” conveniently, which could increase the frequency of game participation. Student A and C mentioned, respectively, “I think this game is quite good, because it is portable, you can bring it anywhere (mobile), so when you are free, you can take it out and play the game” and “because it is more convenient, you can simply press the app button, and you can play.” The convenience undoubtedly enabled students to learn and/or study more often without having to carry textbooks/lecture materials around. They could simply open the “PaGamO” application on their mobile phones and immediately start playing and studying.

Likewise, despite not many students used computers to play “PaGamO,” one student pointed out its convenience, as student B recalled, “When I open the ‘PaGamO’ link, I don’t normally close it, so when I turn on my computer, I can play the game.” The choice of devices used to play “PaGamO” would depend upon personal preferences and the availability of devices. This game also allowed students to have freedom to decide when to play and on what devices to play.

Average Time Spent on “PaGamO” for Each Session

For each “PaGamO” session, 62.5% of students used “PaGamO” for less than an hour, while 32.7 and 1.8% of them spent between 1 hours and within 2 hours, and between 2 hours and within 3 hours, respectively. Studying can be mentally demanding and dull for students as they would have to maintain high concentration during the process. One of the purposes of using “PaGamO” was to enable students to study while “playing” and make it more interesting rather than boring. Notwithstanding the time spent on “PaGamO” was not considerably long, “PaGamO” can be considered effective and successful in helping students to prepare for their final examination as the average score for MC was 16.12 out of 20 points (SD = 2.99). When asked about how much time was spent on “PaGamO,” Student E revealed that “around 35–40 min, including time spent on viewing notes.” It was sufficient for students to study the key points of the subject within an hour.

Other Key Factors Extracted From Questionnaire Data and Interviews

There were 48.3% of students completed all four sessions of the game. While the researchers have been studying the effectiveness of the game and students responses, adding to the fact that educational games have just been developed and are still in the early stage in higher education, the present full-participation rate is acceptable.

However, the researchers understood that there were still areas for further enhancement in educational game, such as the number of the questions (a few respondents reported that 100 questions were insufficient) and the format of the question (students recommended different formats to be used along with the MC questions). Additionally, when asked if students would like to play “PaGamO” in other modules, 56% said they would welcome this idea, while some mentioned that it would be difficult to play “PaGamO” in two or more subjects simultaneously.

In summary, by using gamification as a tool for learning and studying, students did find “PaGamO” effective for their learning experience. The motives of using “PaGamO” came from both intrinsic and extrinsic motives, with more students toping to play “PaGamO” for “Fun.” In addition, the use of mobile phones to play “PaGamO” was indeed more popular than using computers and tablet devices because of its convenience. Furthermore, the combination of gamification and traditional learning methods could enhance students learning.

Recommendations for Further Research

The sample size of the present study was 56. As mentioned earlier, this is an exploratory study which aims to examine the application of educational game on students’ learning. Considering the cost of the game accounts, administration time, and manpower, it would not have been appropriate to widely utilize the educational game before assessing its suitability and effectiveness. As the results of the current research are positive, it is expected that a larger sample size can be used in future to investigate students’ attitudes, perceptions and willingness to learn.

During the research period, only three courses (computer sciences, music, and physical education) had utilized this educational game as a supplementary tool to integrate into the traditional teaching method in the University. At the same time, different course instructors adopted different approaches to design the questions and the degree of involvement of students was varied. It is suggested that, in future, course instructors of different subjects should discuss and standardize the research procedures, so as to conduct comparative study to facilitate the investigation of the effectiveness of educational games on student learning and students’ perceptions on educational games for different disciplines.

Due to the experimental nature of the study, only MC questions were used in the educational game. To avoid the limitation, in future, more formats of questions could be explored, such as using fill-in-the-blanks and short questions. This strategy can help students learn by mimicking real test scenarios under less stressful situations.

It is also feasible that, as researchers in this study introduced the use of educational game prior to students’ final examination, future studies may explore the long-term effects of gamification on students’ knowledge retention and the applicability of information in other contexts.

Conclusion

The results of the current study show that the combination of gamification and traditional learning methods can enhance students’ learning motivation and learning effects. The higher the “PaGamO” score, the higher the MC score is observed. Student learning is also influenced by the content of the game, the format of the question, the instructional strategies, and the motivation for participation. In addition, due to its convenience, playing educational game on mobile devices is more popular than using computers and tablet devices. In conclusion, with the development of educational technology, educational games are becoming more and more popular, and further research is therefore recommended.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

SC: design of the study, data collection, data analysis, and writing of the manuscript. KN: data collection, assist in data analysis, and draft of the manuscript. Both authors contributed to the article and approved the submitted version.

Funding

This research was funded by the University Grants Committee of Hong Kong, under the Funding Scheme for Teaching and Learning Related Projects (2016–2019, Triennium; HKBU4/T&L/16-19).

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.

Acknowledgments

We would like to thank our colleagues at the Centre for Holistic Teaching and Learning of Hong Kong Baptist University for supporting this study.

References

Adachi, P. J., and Willoughby, T. (2013). More than just fun and games: The longitudinal relationships between strategic video games, self-reported problem-solving skills, and academic grades. J. Youth Adolescence 42, 1041–1052. doi: 10.1007/s10964-013-9913-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Al-Azawi, R., Al-Fatma, F., and Al-Blushi, M. (2016). Educational gamification vs. game based learning: Comparative study. Int. J. Innovat. Manag. Technol. 7, 132–136. doi: 10.18178/ijimt.2016.7.4.659

CrossRef Full Text | Google Scholar

Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., et al. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Comput. Educ. 94, 178–192. doi: 10.1016/j.compedu.2015.11.003

CrossRef Full Text | Google Scholar

Calderón, A., and Ruiz, M. (2015). A systematic literature review on serious games evaluation: An application to software project management. Comput. Educ. 87, 396–422. doi: 10.1016/j.compedu.2015.07.011

CrossRef Full Text | Google Scholar

Carr, P. B., and Walton, G. M. (2014). Cues of working together fuel intrinsic motivation. J. Exp. Soc. Psychol. 53, 169–184. doi: 10.1016/j.jesp.2014.03.015

CrossRef Full Text | Google Scholar

Chen, S. W., Yang, C. H., Huang, K. S., and Fu, S. L. (2019). Digital games for learning energy conservation: A study of impacts on motivation, attention, and learning outcomes. Innovat.Educ. Teach. Int. 56, 66–76. doi: 10.1080/14703297.2017.1348960

CrossRef Full Text | Google Scholar

Csikszentmohalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper & Row.

Google Scholar

Deci, E. L., and Ryan, R. M. (eds) (2004). Handbook of self-determination research. Rochester: University Rochester Press.

Google Scholar

De-Marcos, L., Garcia-Lopez, E., and Garcia-Cabot, A. (2016). On the effectiveness of game-like and social approaches in learning: Comparing educational gaming, gamification & social networking. Comput. Educ. 95, 99–113. doi: 10.1016/j.compedu.2015.12.008

CrossRef Full Text | Google Scholar

Deterding, S. (2015). The lens of intrinsic skill atoms: A method for gameful design. Hum. Comput. Interact. 30, 294–335. doi: 10.1080/07370024.2014.993471

CrossRef Full Text | Google Scholar

Dominguez, A., Saenz-De-Navarrete, J., De-Marcos, L., Fernandez-Sanz, L., Pages, C., and Martinez-Herraiz, J. J. (2013). Gamifying learning experiences: Practical implications and outcomes. Comput. Educ. 63, 380–392. doi: 10.1016/j.compedu.2012.12.020

CrossRef Full Text | Google Scholar

Fitzgerald, K. (1997). “Instructional methods: Selection, use, and evaluation,” in Nurse as educator: Principles of teaching and learning, ed. S. Bastable (Burlington, MA: Jones and Bartlett Learning), 261–286.

Google Scholar

Garris, R., Ahlers, R., and Driskell, J. E. (2002). Games, motivation, and learning: A research and practice model. Simulat. Gaming 33, 441–467. doi: 10.1177/1046878102238607

CrossRef Full Text | Google Scholar

Gros, B. (2007). Digital games in education: The design of games-based learning environments. J. Res. Technol. Educ. 40, 23–38. doi: 10.1016/j.compedu.2013.06.005

CrossRef Full Text | Google Scholar

Hanson, T. L., Drumheller, K., Mallard, J., McKee, C., and Schlegel, P. (2010). Cell phones, text messaging, and Facebook: Competing time demands of today’s college students. College Teach. 59, 23–30. doi: 10.1080/87567555.2010.489078

CrossRef Full Text | Google Scholar

Hanus, M. D., and Fox, J. (2015). Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Comput. Educ. 80, 152–161. doi: 10.1016/j.compedu.2014.08.019

CrossRef Full Text | Google Scholar

Herodotou, C., Kambouri, M., and Winters, N. (2014). Dispelling the myth of the socio-emotionally dissatisfied gamer. Comput. Hum. Behav. 32, 23–31. doi: 10.1016/j.chb.2013.10.054

CrossRef Full Text | Google Scholar

Hodge, K., Henry, G., and Smith, W. (2014). A case study of excellence in elite sport: Motivational climate in a world champion team. Sport Psychol. 28, 60–74. doi: 10.1123/tsp.2013-0037

CrossRef Full Text | Google Scholar

Karakus, T., Inal, Y., and Cagiltay, K. (2008). A descriptive study of Turkish high school students’ game-playing characteristics and their considerations concerning the effects of games. Comput. Hum. Behav. 24, 2520–2529. doi: 10.1016/j.chb.2008.03.011

CrossRef Full Text | Google Scholar

Kinzie, M. B., and Joseph, D. R. (2008). Gender differences in game activity preferences of middle school children: implications for educational game design. Educ. Technol. Res. Dev. 56, 643–663. doi: 10.1007/s11423-007-9076-z

CrossRef Full Text | Google Scholar

Lepper, M. R., and Henderlong, J. (2000). “Turning ‘play’ into ‘work’ and ‘work’ into ‘play’: 25 years of research on intrinsic versus extrinsic motivation,” in Intrinsic and extrinsic motivation, eds C. Sansone and J. M. Harackiewicz (Cambridge,MA: Academic Press), 257–307. doi: 10.1016/b978-012619070-0/50032-5

CrossRef Full Text | Google Scholar

Liao, C. C., Chen, Z. H., Cheng, H. N., Chen, F. C., and Chan, T. W. (2011). My-Mini-Pet: A handheld pet-nurturing game to engage students in arithmetic practices. J. Comput. Assisted Learn. 27, 76–89. doi: 10.1111/j.1365-2729.2010.00367.x

CrossRef Full Text | Google Scholar

Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychol. Bull. 116, 75–98. doi: 10.1037/0033-2909.116.1.75

CrossRef Full Text | Google Scholar

Malone, T., and Lepper, M. (1987). “Making learning fun: A taxonomy of intrinsic motivations of learning,” in Aptitude, learning and instruction: Vol 3: Cognitive and affective process analyses, eds R. E. Snow and M. J. Farr (Lawrence: Erlbaum Associates), 223–253.

Google Scholar

Minovic, M., Milovanovic, M., Kovacevic, I., Minovic, J., and Starcevic, D. (2012b). “Motivational and cognitive aspects of applying educational games as a learning tool,” in Handbook of Research on Serious Games as Educational, Business and Research Tools, ed. M. M. Cruz-Cunha (Pennsylvania: IGI Global), 892–917. doi: 10.4018/978-1-4666-0149-9.ch046

CrossRef Full Text | Google Scholar

Minovic, M., Štavljanin, V., and Milovanovic, M. (2012a). Educational games and IT professionals: Perspectives from the field. Int. J. Hum. Capital Inform. Technol. Profess. 3, 25–38. doi: 10.4018/jhcitp.2012100103

CrossRef Full Text | Google Scholar

Morgan, D. L. (1988). Focus groups as qualitative research. Newbury Park, CA: Sage.

Google Scholar

Mumtaz, S. (2001). Children’s enjoyment and perception of computer use in the home and the school. Comput. Educ. 36, 347–362. doi: 10.1016/S0360-1315(01)00023-9

CrossRef Full Text | Google Scholar

Patton, M. Q. (2002). Qualitative Research & Evaluation Methods, 3rd Edn. Thousand Oaks: Sage Publications, Inc.

Google Scholar

Peng, W., Lin, J. H., Pfeiffer, K. A., and Winn, B. (2012). Need satisfaction supportive game features as motivational determinants: An experimental study of a self-determination theory guided exergame. Media Psychol. 15, 175–196. doi: 10.1080/15213269.2012.673850

CrossRef Full Text | Google Scholar

Prensky, M. (2001). Digital Game-Based Learning. New York, NY: McGraw-Hill.

Google Scholar

Reeve, J., Deci, E. L., and Ryan, R. M. (2004). “Self-determination theory: A dialectical framework for understanding socio-cultural influences on student motivation,” in Big theories revisited, eds D. M. Mclnerney and S. Van Etten (Charlotte: Information Age Press), 31–60.

Google Scholar

Riemer, V., and Schrader, C. (2015). Learning with quizzes, simulations, and adventures: Students’ attitudes, perceptions and intentions to learn with different types of serious games. Comput. Educ. 88, 160–168. doi: 10.1016/j.compedu.2015.05.003

CrossRef Full Text | Google Scholar

Rigby, S., and Ryan, R. M. (2011). Glued to games: How video games draw us in and hold us spellbound. Westport: Praeger.

Google Scholar

Robson, K., Plangger, K., Kietzmann, J. H., McCarthy, I., and Pitt, L. (2016). Game on: Engaging customers and employees through gamification. Business Horizons 59, 29–36. doi: 10.1016/j.bushor.2015.08.002

CrossRef Full Text | Google Scholar

Rondon, S., Sassi, F. C., Furquim, and de Andrade, C. R. (2013). Computer game-based and traditional learning method: A comparison regarding students’ knowledge retention. BMC Med. Educ. 13:30. doi: 10.1186/1472-6920-13-30

PubMed Abstract | CrossRef Full Text | Google Scholar

Ryan, R. M., and Deci, E. L. (2000). The darker and brighter sides of human existence: Basic psychological needs as a unifying concept. Psychol. Inquiry 11, 319–338. doi: 10.1207/S15327965PLI1104_03

PubMed Abstract | CrossRef Full Text | Google Scholar

Ryan, R. M., Frederick, C. M., Lepes, D. D., Rubio, N. N., and Sheldon, K. M. (1997). Intrinsic motivation and exercise participation. Int. J. Sport Psychol. 28, 335–354.

Google Scholar

Sun-Lin, H. Z., and Chiou, G. F. (2019). Effects of gamified comparison on sixth graders’ algebra word problem solving and learning attitude. Educ. Technol. Soc. 22, 120–130.

Google Scholar

Van Roy, R., and Zaman, B. (2017). “Why gamification fails in education and how to make it successful: introducing nine gamification heuristics based on self-determination theory,” in Serious games and edutainment applications, eds M. Ma and A. Oikonomou (London: Springer), 485–509. doi: 10.1007/978-3-319-51645-5_22

CrossRef Full Text | Google Scholar

Whitton, N. (2007). “Motivation and computer game based learning,” in Proceedings of the Australian Society for Computers in Learning in Tertiary Education, (Singapore), 1063–1067.

Google Scholar

Wickens, C. D. (1974). Temporal limits of human information processing: A developmental study. Psychol. Bull. 81, 739–755. doi: 10.1037/h0037250

CrossRef Full Text | Google Scholar

Zapalska, A., Brozik, D., and Rudd, D. (2012). Development of active learning with simulations and games. US-China Educ. Rev. 2, 164–169.

Google Scholar

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Application of the Educational Game to Enhance Student Learning

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The purpose of this study is to investigate the use of an educational game to enhance student learning effectiveness. This study consisted of 56 college students majoring in physical education and recreation management (32 men, 24 women, age M = 21 years, SD = 1.72). Students used the educational co…

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University's Students' perceptions on AI assistant tools. No details about how AI helped them improve their grade, but more about how they feels.

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Drawbacks in ChatGPT.

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This discovery and the examples provided are by graduate student Will Hickman.

Although many academic researchers don't enjoy writing literature reviews and would like to have an AI system do the heavy lifting for them, we have found a glaring issue with using ChatGPT in this role. ChatGPT will cite papers that don't exist. This isn't an isolated phenomenon - we've asked ChatGPT different research questions, and it continually provides false and misleading references. To make matters worse, it will often provide correct references to papers that do exist and mix these in with incorrect references and references to nonexistent papers. In short, beware when using ChatGPT for research.

Below, we've shown some examples of the issues we've seen with ChatGPT. In the first example, we asked ChatGPT to explain the research in experimental economics on how to elicit attitudes towards risk. While the response itself sounds like a decent answer to our question, the references are nonsense. Kahneman, Knetsch, and Thaler (1990) is not about eliciting risk. "Risk Aversion in the Small and in the Large" was written by John Pratt and was published in 1964. "An Experimental Investigation of Competitive Market Behavior" presumably refers to Vernon Smith's "An Experimental Study of Competitive Market Behavior", which had nothing to do with eliciting attitudes towards risk and was not written by Charlie Plott. The reference to Busemeyer and Townsend (1993) appears to be relevant.

Although ChatGPT often cites non-existent and/or irrelevant work, it sometimes gets everything correct. For instance, as shown below, when we asked it to summarize the research in behavioral economics, it gave correct citations for Kahneman and Tversky's "Prospect Theory" and Thaler and Sunstein's "Nudge." ChatGPT doesn't always just make stuff up. The question is, when does it give good answers and when does it give garbage answers?

Strangely, when confronted, ChatGPT will admit that it cites non-existent papers but will not give a clear answer as to why it cites non-existent papers. Also, as shown below, it will admit that it previously cited non-existent papers, promise to cite real papers, and then cite more non-existent papers.

We show the results from asking ChatGPT to summarize the research in experimental economics on the relationship between asset perishability and the occurrence of price bubbles. Although the answer it gives sounds coherent, a closer inspection reveals that the conclusions ChatGPT reaches do not align with theoretical predictions. More to our point, neither of the "papers" cited actually exist.

Immediately after getting this nonsensical answer, we told ChatGPT that neither of the papers it cited exist and asked why it didn't limit itself to discussing papers that exist. As shown below, it apologized, promised to provide a new summary of the research on asset perishability and price bubbles that only used existing papers, then proceeded to cite two more non-existent papers.

Tyler has called these errors " hallucinations " of ChatGPT. It might be whimsical in a more artistic pursuit, but we find this form of error concerning. Although there will always be room for improving language models, one thing is very clear: researchers be careful. This is something to keep in mind, also, when serving as a referee or grading student work.

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ChatGPT Cites Economics Papers That Do Not Exist

# data[].attributes.attachment_link.description
This discovery and the examples provided are by graduate student Will Hickman. Although many academic researchers don't enjoy writing literature reviews and would like to have an AI system do the heavy lifting for them, we have found a glaring issue with using ChatGPT in this role.

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The Use Of Digital Tools And AI In Primary School Education

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The Use Of Digital Tools And AI In Primary School Education

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A broad and general use of digital tools. Nothing a great details or specific about AI used on pupils, but for school and IT staff.

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Impact of education on security practices in ICT

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Impact of education on security practices in ICT

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Great for reference and brainstorming ideas for decision making and ICT policy at school.

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Absolutely functional strategies in this article, the ones that have been proven to work in schools. No fuss and get the real work done.

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Gather a room of teachers together and they will give you as many teaching strategies as there are topics in the primary curriculum.
This article introduces the 13 most important teaching strategies you should be using - the ones that have been proven to work in schools like yours and the ones that we use every week with the 7000 pupils we teach in our one-to-one interventions.
We also include the 6 learning strategies that your pupils should also know about to enable them to move their own learning forwards.
Teaching and learning strategies taken from the best of UK education research

One of the great advances that has been made in the last 5 years in the education sector in the UK is the increasing influence of large scale research and evidence informing what we know about how children learn, and consequently how we can better teach them.

Whether that's a greater awareness of the EEF Toolkit and their randomised controlled trials, the growing ResearchEd movement, or even just the vibrant #edutwitter community, many more teachers now are taking their professional development into their own hands.

More teachers than ever are aware of John Hattie's 'Index of Teaching and Learning Strategies', and wary of implementing new instructional strategies unthinkingly; no-one wants to be stung by another 'learning styles' waste of effort.

But even when you know there may be better and more efficient ways to develop your students' understanding, choosing which of these teaching strategies to start with can be a challenge. This article aims to guide you through this.

What are teaching strategies?

Teaching strategies are methods and techniques that a teacher will use to support their pupils or students through the learning process; a teacher will chose the teaching strategy most suitable to the topic being studied, the level of expertise of the learner, and the stage in their learning journey.

In one lesson a teacher may use many different teaching strategies with different end goals. The most effective teaching strategies are those proven to work over large scale trials. There is no requirement for a teaching strategy to be innovative although of course some of them are.

List of teaching strategies for primary school

These are the 13 most important teaching strategies that you and all the teachers in your school should at least have in your teaching toolkit.

No-one is suggesting you will use them in every lesson, but an awareness of what they are and the results you can expect is essential to move your practice on.

And of course if you're a school leader, or responsible for CPD in your school, knowing what's current and evidence-backed is key so that you can make an informed choice the next time somebody suggests implementing a new or innovative teaching technique. One way to stay up-to-date is by reading educational blogs and listening to the best educational podcasts.

1. Know Your Pupils and Develop Their Respect

This may sound basic, but the basis of all good teaching is an understanding of your pupils and their learning needs. Allied to this is the respect you are held in by your pupils. The relationship between teacher and student is a vital element of the learning experience. Take time to get to know a new class from the first day, understand what motivates them their barriers to learning. This is an often overlooked teaching strategy.

All our one-to-one tutors are made aware before working with a pupil if they have any special educational needs, and take the time to get to know each pupil throughout the 1-to-1 lessons by asking about their hobbies and interests or the kinds of things they've been learning in school.

Read more: How to Support a Child with Autism in the Classroom

In this way, whenever possible a tutor can personalise a lesson or a teaching technique as appropriate to the child with a real world example.

2. Appropriate use of Summative and Formative Assessments

The first stage here is making sure you know the difference between formative and summative assessment. It may sound obvious but you'd be surprised how many teachers don't use each appropriately.

To cover them quickly:

Summative assessment refers to an assessment that takes place after a block of work has been completed, whether this is a term or a year. They are best thought of as assessments of learning.

Formative assessments are those that take place day-to-day and are used to gauge pupils' understanding of a topic - they are assessments for learning. Formative assessment is often used in a diagnostic capacity, to help us identify whether pupils are struggling with a topic in the moment. This then guides and adapts our instruction during the lesson, to better meet children's needs.

Diagnosis of children's gaps using formative assessments

We advocate the use of these kinds of diagnostic assessment to identifies a child's misconceptions. Usually this is best achieved through a set of multiple choice questions.

As well as the correct answer, we can include multiple distractors - answers that are incorrect based on a misconception a child may have e.g. around multiplying. If a child chooses an incorrect answer therefore, we can easily identify exactly where their thinking has gone wrong.

You can download our sample diagnostic quizzes (all including distractors and explanations of them) for free.

For pupils on our one-to-one maths interventions, we use a diagnostic quiz a the start of the intervention which is responsive based on answers to an earlier question. This helps us more clearly identify not just misconceptions and weaknesses, but also where a child's strengths lie and what therefore needs less time spent on.

3. Teach the Vocabulary

With the new focus in the curriculum on knowledge organisers, there's no excuse for children being without the relevant topic vocabulary. They need the words to be able to create the thoughts and the sentences to confidently speak about a given topic.

This is why our tutors will always talk through any specialist maths words at the start of a lesson with their pupils, explaining any new terms and checking for understanding of previously covered ones.

We recommended co-creating your maths vocabulary lists with your pupils. This Maths Vocabulary List is a great start.

4. Explicit Instruction

Also known as direct instruction, this teaching strategy is highly teacher-led, and focuses on frequent questioning and guided practice to help pupils learn a topic.

The backbone of explicit instruction is the use of the worked example in an Example-Problem Pair. This involves demonstrating a worked example in its entirety in silence alongside a problem that pupils will then attempt.

Silence is important in order to ensure pupils' attention is not split between the example and the spoken explanation, making it more likely that both will be more fully absorbed and retained.

A pupil on a Third Space Learning's online maths intervention programme will necessarily have all other distractions eliminated so they can focus entirely on the information on their screen and what the tutor is asking them to do; tutors can present a worked example in real time in a learning environment without any visual or auditory disruptions.

5. Effective Questioning Techniques

While we are all aware of the importance of questioning as a tool to gauge pupils' understanding of a topic, there are definite techniques to improve the efficacy of your questioning in the classroom.

Questions such as "Are you sure?" and "How do you know?" encourage pupils to engage in some basic critical thinking to establish how confident they are in an answer and why, while others such as "Is there another way?" help to highlight where multiple methods to derive a solution may exist.

Our tutors encourage pupils to verbalise their reasoning and ask questions to ensure pupils have really got to grips with the topic at-hand: "How do you know that answer is right?", "Can you tell me how else you could work it out?" or "What do you need to do first to answer this question?" are all questions that come up frequently during our lessons!

Goal free problems are another questioning strategy worth considering using in your classes.

6. Deliberate Practice

One of the most effective ways of introducing new concepts to a class, Deliberate Practice involves breaking learning down into a series of sub skills, each of which is deliberately practiced in turn.

The 5 steps involved in deliberate practice are:

You can find a full explanation of each of these stages in our blog post on deliberate practice in education.

But as an example, when teaching long multiplication method at KS2 we might use deliberate practice thus:

Identify (isolate) each specific sub skill involved in the long multiplication method;

Practise (develop) each of these one by one;

assess pupils' use of these skills before moving on;

have pupils put them all together for a final performance - in this case a full long multiplication problem;

return to this topic in later weeks and months to check pupils still retain those skills.

At the beginning of every Third Space Learning intervention session pupils are given a warm-up question related to a previously covered topic; this enables tutors to check that they have retained the relevant skills (and where they have not, to return to the topic).

7. Differentiation

Far more than simply "splitting the whole class into small groups based on attainment", positive and effective differentiation at the primary school level can be difficult to achieve - poor differentiation strategies risk actually widening the attainment gap we're attempting to close.

But there are plenty of impactful differentiation strategies; techniques such as interleaving and phased learning, as well as the use of maths manipulatives and formative assessment, are among those proven to have a beneficial impact on pupils when properly employed.

As we've already discussed, formative assessment is a significant aspect of how Third Space's tutors gauge pupil progress. But we also make use of several other differentiation strategies during lessons, such as spaced practice, interleaving and a mixture of direct instruction and inquiry-based learning.

8. Reinforcing Effort/Providing Recognition

Helping pupils make a link between putting effort into a task and receiving recognition is an important step in developing a classroom environment that fosters active learning.

Encouraging pupils to put more effort into activities only goes so far without something to provide them with the motivation to do so. Praise and recognition are motivators that pupils are already familiar with; shifting them from being correct to giving full effort can be highly effective.

Third Space Learning's tutors establish an effort-focused environment right from the first session, encouraging pupils to talk through their answers and celebrate their mistakes as learning opportunities, ensuring they approach each intervention as another chance to try.

Since we began our intervention programmes, our tutors have celebrated and rewarded pupil effort by awarding over 32 MILLION Effort Points!

Literally 'thinking about thinking', metacognition has been recognised by the EEF as one of the most effective, lowest cost teaching strategies there is, with pupil making an average of seven months' additional progress.

Metacognition in primary schools often incorporates some of the other effective teaching strategies, such as questioning in the classroom - "How do you know?" not only asks pupils to justify their solutions, but has them thinking about their own thought processes for deriving that solution.

Teaching pupils how to learn maths, plan, monitor and self-evaluate their learning also improves pupil motivation and encourages them to work harder in lessons. Metacognition is also one of the most effective intervention strategies for schools.

All our tutors are trained to use a variety of metacognitive strategies as standard during sessions, and we provide pupils with numerous moments for self-reflection both during and after sessions.

10. Personalised Learning

It might sound obvious, but pupils are more likely to engage with learning when is more targeted to them and appeals to their interests! This may be difficult to achieve early on - especially with a full class of 30 pupils - but as familiarity and rapport builds throughout the year it should become easier to make activities and even questions more personalised to individual children.

At Third Space, we've built our online interventions on personalisation; all our pupils undertake an Initial Diagnostic Assessment when they begin their programmes, which identifies their strengths and weaknesses in maths and allows us to design a lesson plan that helps them make progress where they need it.

Our tutors then build on this by adding a personal touch to those lessons, incorporating pupils' interests into lessons with ease thanks to our online platform.

11. Collaborative Learning

Also referred to as 'cooperative learning', the idea of having pupils work in groups for certain classroom activities won't be new to most teachers.

But the EEF notes that the impact of group work can vary widely, and that to make it most effective teachers should focus on well-structured tasks that promote talk and interaction between pupils.

The concept of 'competitive' collaborative learning (where groups of students compete against one another) has been shown to have some impact, but caution is advised in case pupils focus more on the competition rather than the learning.

12. Explicitly Teach Thinking Skills & Problem Solving Techniques

Mathenatical problem solving techniques don't always come naturally to pupils; while metacognitive strategies such as those mentioned above make it more likely that pupils will be able to apply critical thinking to a problem, there is no set way to ensure that this will happen.

Research into the topic suggests that context-agnostic deployment of problem solving techniques only occurs once pupils have secure domain knowledge, and the opportunity to practise.

Without these, pupils often fall into the trap of attributing importance to the so-called 'surface features' of a problem, which we naturally discount as irrelevant to the actual maths involved.

That said, explicit teaching of thinking skills is still of considerable importance; once domain knowledge has been (relatively) secured, teaching pupils how to recognise and focus on the 'deep structure' of problems enables them to apply their knowledge more effectively.

Try using several 'Same Surface Different Deep' SSDD problems in lessons - where the surface features of the questions are the same, but the deep structure varies (e.g. division, multiplication, addition etc.).

These questions eliminate any confusion regarding surface features and thus allow pupils to focus on differentiating the deep structures.

In the run-up to the SATs, your Year 6 pupils may unintentionally revert to focusing on the 'surface features' of questions when encountering the reasoning papers.

To help combat this, make sure you and they are aware of the different types of maths reasoning questions in year 6 to help them identify not just the maths problem solving techniques they need to know, but the types of questions that are likely to require certain techniques.

13. Modelling and Scaffolding

You may already be familiar with the "I do, We do, You do" method of scaffolding, but it's worth taking some time to dive into why it's as effective as it is.

Modelling is one of the most important factors in ensuring student learning of a particular topic, but it is most impactful when it can introduce new concepts without increasing pupils' cognitive load - hence the 'I, We You' approach.

By building from teacher-led, to joint construction, to independent working, we create a structure that presents learning as less of a step-change and more of an actual process. It also allows us greater flexibility; more time can be spent on one stage e.g. joint construction is it becomes necessary.

Gradual scaffolding with support slides

All Third Space Learning intervention lessons make use of this kind of gradual scaffolding method, and our online platform lends even more flexibility to the scaffolding structure - tutors can pull from a bank of 'support slides' if spending more time on a concept or process is necessary.

Other Teaching Strategies To Consider

The teaching strategies above form the basis of our one-to-one lessons, but some other teaching practices you'll need to consider in your own classroom include : Use of education technology
Knowing when and where to bring technology into the classroom is a delicate balancing act. While children tend to react well to technology-based lessons, there's always the risk that they focus on the tech over the learning.
Read more: How To Do An Education Technology Audit In Your School

Behaviour management
Effective classroom management is its own topic, but there's no doubt that a well-behaved class (not necessarily a perfectly-behaved one) is far more likely to engage with lessons.

Inquiry-based learning
While triggering the curiosity of your pupils for a topic is the necessary first step in inquiry-based learning, presenting them with the opportunity to research and report on the topic is where learning really occurs.

How you manage class discussions e.g. 'Think, pair, share'
Somewhat related to behaviour management, class or group discussions are most beneficial when they have a clear, well-established structure to them. Students should not only feel they have the opportunity to share their thoughts, but understand that others' thoughts also have value.

Feedback process and understanding
The single most impactful teaching strategy when used correctly, feedback (whether from the teacher or another source e.g. peer marking) needs to be specific, encouraging and actionable - pupils need to be able to understand where they could improve, and how.
Read more: Why My School Banned Marking

Growth Mindset
Separate from praise and rewarding effort, developing growth mindset can be a very valuable tool in developing pupils' resilience and fostering a more positive attitude towards both maths specifically and learning in general.
Read more: How To Get Growth Mindset Right In Primary Schools

Read more

List of Learning Strategies

Last, but not least, central to any good teaching practice is explicitly teaching learners of all ages about learning strategies. These are the 6learning strategies we think every student should be familiar with by the end of Year 6.

1. Spaced Practice

Ensuring that learned material is revisited at regular intervals instead of all at once much later on, when more of it is likely to be forgotten. For older students, study calendars can be of immense help in the run-up to major milestones such as the KS2 SATs.

2. Retrieval Practice

Retrieval is the process of recalling information purely from memory, without the aid of learning materials. Effective retrieval practice helps embed information more thoroughly in our minds, since we no longer need context to recall it.

3. Elaboration

Tied into questioning in the classroom, elaboration puts the onus on pupils to do more than 'just' recalling information. The use of open ended questions such as "How did I get that answer" help pupils to make connections between the things they've learnt rather than seeing them as several unrelated facts.

4. Interleaving

A wealth of research is now available that suggests the benefits of interleaving - mixing practice of different skills with one another - in helping pupils better identify the necessary strategies to solve different problems. Interleaving with connected topics (e.g. division and multiplication) amplifies this effect. Our Year 6 maths revision programme is built on this.

5. Concrete Examples

As maths educators the Concrete Pictorial Abstract Approach is embedded into the way we teach. Within our online tuition, we have many different ways of explaining a topic and as an extension we may ask pupils to come up with their own concrete examples for concepts, based on the examples we've used in explaining the concept to them.

6. Dual Coding

Combining words and visuals in teaching materials. This isn't referring to speech (as we established earlier), but having pupils create some kind of visual aid (e.g. a sketch, a diagram) to accompany written text can help them reinforce the concept in their brain in two different ways, making it easier to recall. Anyone who's created slides for their pupils will be familiar with the challenge of imparting information through words + pictures.

References

Do you have pupils who need extra support in maths?
Every week Third Space Learning's maths specialist tutors support thousands of pupils across hundreds of schools with weekly online 1-to-1 lessons and maths interventions designed to plug gaps and boost progress.

Since 2013 we've helped over 150,000 primary and secondary school pupils become more confident, able mathematicians. Learn more or request a personalised quote for your school to speak to us about your school's needs and how we can help.

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The 13 Teaching Strategies Proven To Work In Primary Schools

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Find out which evidence based teaching strategies are the most effective in schools; and which are the top learning strategies for your pupils to know.

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An overview of what are the skills that industries looking for and what can educators close these gaps.

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Jobs and Skills

Education 4.0 involves a new type of learning Image: Photo by Jeswin Thomas

President and Chief Executive Officer, JA Worldwide

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Problem-solving, collaboration and adaptability are the three critical skills that Education 4.0 must impart to students. Image: JA Worldwide

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Education 4.0: Here are 3 skills that students will need for the jobs of the future

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Jobs and Skills Education 4.0 involves a new type of learning Image: Photo by Jeswin Thomas President and Chief Executive Officer, JA Worldwide Stay up to date: Jobs and Skills Follow Problem-solving, collaboration and adaptability are the three critical skills that Education 4.0 must impart to students.