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Learning that is differentiated in real time

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Learning that is differentiated in real time

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Manipulatives
Modified instructions
additional time to complete assignments
Microlearning to learn at my pace
Clarification when needed or resources such as dictionaries
Directions provided in different methods
Scaffolding lessons

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Supporting student needs, goal setting, proper intervention, project choice, assessment and feedback (constructive and purposeful)

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Customized learning

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Using technology to personalize learning based on my specific learning goals and desire outcomes. Computer algorithm dictate the type of work and level of assistance based on goals, outcomes, and performance. The software was adapts in real time - on the fly - to accommodate the student, eliminating analysis of post-performance data.

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Designing learning for different types of learners.

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Designing learning for different types of learners.

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using artificial intelligence to help facilitate zone of proximal development

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using artificial intelligence to help facilitate zone of proximal development

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Challenging and interactive.

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https://www.imaginelearning.com/

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An adaptive online program for K-12 kids- math, ELA, anything- very engaging and also very, very personalized

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https://www.cerego.com

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a self-directed learner can make any learning adaptive :-)

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a self-directed learner can make any learning adaptive :-)

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LearnSmartTextbooks - Ask you "probes" (different questions) and direct you to material in the textbook you need to cover. 

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LearnSmartTextbooks - Ask you "probes" (different questions) and direct you to material in the textbook you need to cover. 

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Maybe you already have a vision for your course, or maybe you're looking for help bringing your ideas together. Whatever your starting point, your Realizeit team will meet you where you are and be with you every step of the way. We've helped launch more than 800 courses serving over 1 million students.

We start with your learning outcomes, objectives and content preferences, and collaborate with you to create an adaptive experience. Realizeit is an open platform, so we can use original material, publisher content, open educational resources (OER) or any combination of the three. When it's time to launch, your course will be integrated into your LMS - mobile-friendly, accessible and available to your students from day one of the class.

Ready to learn more? Let's talk.
"Every vital educational component comes from me, and someone at Realizeit always had my back if I needed help."
- Andrea Leonard, University of Louisiana-Lafayette

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Realizeit - adaptive learning provider | mastery-based personalized competency-based modules

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Maybe you already have a vision for your course, or maybe you're looking for help bringing your ideas together. Whatever your starting point, your Realizeit team will meet you where you are and be with you every step of the way. We've helped launch more than 800 courses serving over 1 million students.

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Math, Literacy (k - 8)

https://www.pearsonschool.com/index.cfm?locator=PS2qJ3&acornRdt=1&acornRef=http%3A%2F%2Fwww%2Epearsonschool%2Ecom%3A80%2Fsuccessmaker

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https://www.firstinmath.com/

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Lots of video games seem to be dependent on adaptive learning

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Lots of video games seem to be dependent on adaptive learning

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I have used Pearson programs during undergrad for an Algebra course. I didn't particularly hate nor enjoy the experience. I have a neutral stance on these types of experiences

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I recall a lot of my experiences here being through the eyes of a teacher.  My students would utilize IXL in the computer lab every Wednesday.  I remember, before they would ask me why they got something wrong, the rule was to ask the computer for feedback first.  It was pretty cool.  A lot of their questions were answered and scaffolded down through the AI.

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i

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Orai is a program that helps public speaking

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Orai is a program that helps public speaking

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Orai is a public speaking app that gives instant feedback on speech: fillers, pace, energy, clarity.

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Call for Applications: Simon Initiative's LearnLab Summer School: July 29 - August 2, 2019

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Call for Applications: Simon Initiative's LearnLab Summer School: July 29 - August 2, 2019

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Monday, July 29 - Friday, August 2, 2019

An intensive five-day course that focuses on a wide range of advanced learning technologies for course development and scientific research. The summer school covers the design and implementation of course materials with advanced learning technologies. It also focuses on designing, running, and analysis of in vivo experiments.

The course is half lecture and half hands-on activities.

The course will provide both conceptual background knowledge on advanced technology for learning and hands-on experience with state-of-the-art development tools.

New track: Computational Models of Learning

Applicants choose one of five parallel tracks: Building Online Courses in the Open Learning Initiative (OLI), Intelligent Tutor Systems development (ITS), Computer Supported Collaborative Learning (CSCL), Educational Data Mining (EDM), and Computational Models of Learning (CML).

Application deadline: Midnight May 10, 2019. Notification of acceptance June 3, 2019.

For more information and to apply see: http://learnlab.org/index.php/simon-initiative-summer-school/

We invite applications for participation in an intensive 1-week summer school on advanced learning technologies and technology-enhanced learning experiments. The summer school will provide a conceptual background and considerable hands-on experience in developing, running and analyzing technology-enhanced learning experiments. Materials are drawn from research as well as abbreviated portions from our Masters of Educational Technology and Applied Learning Science (METALS) curriculum ( http://metals.hcii.cmu.edu).

The summer school is organized into five parallel tracks: Building online courses with OLI (BOLI), Intelligent Tutor Systems development (ITS), Computer Supported Collaborative Learning (CSCL), Educational Data Mining (EDM) and a new Computational Models of Learning (CML) track. The tracks will overlap somewhat but will differ significantly with respect to the hands-on activities, which make up about half the summer school. The goal for each track is described below.

The summer school involves intensive mentoring by LearnLab researchers. The mentoring starts by e-mail before the summer school, in order to select a subject domain and task for the project, where appropriate. It continues during the summer school with a good amount of one-on-one time during the hands-on sessions. The mentors are assigned based on your interests as stated in the application. All participants will have the opportunity to interact with all course instructors, but will interact more frequently with their designated mentor.

Building online courses with OLI - OLI Track: In the OLI (Open Learning Initiative) track, you will focus on elements of effective course design including a connection between learning objectives and learning outcomes. Participants will create OLI courseware and be able to continue to use OLI tools and techniques after the summer school. Participants identify a course module that they would like to create and the expected learning outcomes. The learning outcomes will be refined making them precise and measurable. Course content, activities, and assessments to support these outcomes will be developed.

ITS track:In the intelligent tutor system development track, you will learn to implement a prototype computer-based tutor, using authoring tools developed by LearnLab researchers, such as CTAT (the Cognitive Tutor Authoring Tools) or TuTalk. CTAT supports the creation of intelligent tutoring systems. TuTalk is used to develop tutorial dialogue systems that interact with students in natural language.

EDM/track:If you are in the educational data mining track, you will learn to analyze an educational dataset using data mining tools and methods. The dataset used in hands-on activities could be one of the data sets currently in LearnLab's Data Shop or you could bring your own. This also includes qualitative datasets.

CSCL Track:If you are in the Computer Supported Collaborative Learning track, you will learn to implement automatic support for collaborative learning that could be integrated with an existing environment, such as the Virtual Math Teams online learning environment.

Computational Models of Learning (CML) Track: A key focus of this theme will be to explore the relationship between human and machine learning and how each can inform the other. We will provide an overview of techniques for modeling human learning and differentiate descriptive versus generative models. Attendees will increase their awareness of approaches for building generative models of learning and the use cases for these models. The emphasis will be on prescriptive, generative models of human learning that do not require a priori data. You will build your own generative models of learning to predict and explain human learning and behavior.

The summer school will last five days. Each day includes lectures, discussion sessions, and laboratory sessions where the participants will work on developing a small prototype system or a small prototype experiment in an area of math, science, or language learning. The participants will use state-of-the-art tools including the Cognitive Tutor Authoring Tools and other tools for course development, environments for computer supported collaborative learning, natural language dialog, semi-automated coding of verbal data, and LearnSphere for storage of student interaction data analysis of student knowledge and performance.

On the last day, student teams will present their accomplishments to the rest of the participants. Participants are expected to do some preparation before the summer school starts.

For those who would like to get more information prior to submitting an application, these papers provide background about the topics, technology, and tools that will be discussed during the summer school.

Course Instructors

The primary course instructors may include:

Dr. Kenneth R. Koedinger
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Vincent Aleven
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Carolyn Penstein Rosé
Language Technologies Institute
Human-Computer Interaction Institute
Carnegie Mellon University

Mr. Norman Bier
Simon InitiativeCarnegie Mellon University

Dr. John Stamper
Human-Computer Interaction Institute
Carnegie Mellon University

Dr. Lauren Herckis
Simon Initiative/Human-Computer Interaction Institute
Carnegie Mellon University

All instructors have considerable experience in research and development in technology-based learning experiments, computer-supported collaborative learning, intelligent tutoring systems and tutorial dialogue systems. Members of the team have taught this summer school for many years. Key instructors have also taught similar material as semester-long courses.

Required Background

The course is intended for anyone with the educational zeal who would like to learn how to create technology-enhanced learning experiments or with the appropriate computational background to actually build an intelligent tutoring system. This could include seasoned learning engineers, learning experience researchers, edtech researchers, advanced graduate students, computationally sophisticated teachers and commercial or military instructional developers. Please contact us when in doubt. In the past, people with a variety of backgrounds have attended the summer school, including those with backgrounds in psychology, education, human-computer interaction, computer science, as well as instructors working in a wide range of domains.

Applications

To apply, please visit our online application page

Important Dates

The deadline for applications is May 10, 2019.

Admission decisions will be made by June 3, 2019.

Costs

The fee for attending the summer school is $950.00. The fee for Graduate Students is $500.00; proof of current enrollment is required for this rate. A limited number of partial scholarships for full-time graduate students are available. See the application for information about how to request a scholarship. The fee includes a continental breakfast and lunch, but not lodging or travel. Please make checks payable to Carnegie Mellon University.

Participants will be responsible for paying for their own travel, additional meals and lodging. Dorm rooms at the Carnegie Mellon University campus are available for a low rate (typically around $80/night for a single room). Rooms may be shared further reducing this cost.

Academic credit is not available, although participants will receive a certificate verifying their participation. 30 hours of Act 48 credit is available for K12 teachers.

For more information and to apply see: http://learnlab.org/index.php/simon-initiative-summer-school/

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Call for Applications: Simon Initiative's LearnLab Summer School: July 29 - August 2, 2019

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Monday, July 29 - Friday, August 2, 2019 An intensive five-day course that focuses on a wide range of advanced learning technologies for course development and scientific research. The summer school covers the design and implementation of course materials with advanced learning technologies. It also focuses on designing, running, and analysis of in vivo experiments.