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Peer Instruction

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Peer instruction may offer some of the richest opportunities for metacognitive teaching. Reciprocal (peer) teaching forces the instructor to use a whole series of metacognitive processes such as determining what the learner already knows, deciding what is to be taught/learned and how; monitoring comprehension and evaluating the outcome in terms of increased comprehension, which in turn encourages the instructor to reflect upon his or her own thinking processes. By asking the students to defend their answer to a question to another student you are, in effect, moving the role of "teacher" to the students.

Material Type: Activity/Lab

Author: Perry J. Samson

Instructional Design for Educators

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This course outline examines topics of importance to educators participating in instructional design projects. Topics include needs assessment, adult learning principles, learning objectives, instructional strategies, assessment, implementation and evaluation. Learners will develop a course using media and open educational resources while observing copyright and plagiarism guidelines.

Material Type: Reading, Teaching/Learning Strategy

Author: Lana Penny

The Student Handbook of Instructional Psychology & Technology

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Short Description: This handbook is written by students for students. Its goal is to provide a concise overview of information for students to understand basic concepts, methods, and theories associated with instructional psychology and technology. Content of the handbook pulls from various fields, including educational psychology, instructional design, educational technology, and learning sciences. Long Description: This handbook is a work-in-progress and will be added to by students in the Instructional Psychology & Technology program at Brigham Young University. Word Count: 10505 (Note: This resource's metadata has been created automatically as part of a bulk import process by reformatting and/or combining the information that the author initially provided. As a result, there may be errors in formatting.)

Ideation Workshop

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This workshop is focused on developing several different Ideation approaches for problem investigating and concept creation:Why-why diagramsBrainstormingMash-upsAffinity diagramsIncluded are two introduction videos for review by students online in advance, a pre-workshop exercise and a slide deck with facilitator notes for running a face-to-face workshop.

Material Type: Module

Author: John Dickinson

Generative AI and Open Educational Resources: Opportunities and Pitfalls

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This talk explores the intersection of generative AI and Open Educational Resources (OER), examining both the exciting opportunities and the inherent challenges. Generative AI offers the potential to revolutionize OER creation, adaptation, and delivery. It can assist with drafting, translation, improved accessibility, and can facilitate personalized learning experiences. However, critical issues such as access disparities, content quality concerns, amplified biases, copyright complexities, and data privacy must also be addressed. The talk calls for responsible and transparent use of AI, encouraging educators to experiment, stay informed, and prioritize an ethical approach in deploying AI tools to enhance open education. (note: the above description was generated by pasting the video's automatically-generated captions into Google's chatbot Gemini using the Advanced model on 4/26/2024) Presented by D’Arcy Hutchings, Instructional Design and Open Education Librarian, University of Alaska Anchorage

Material Type: Lecture

Author: D’Arcy Hutchings

Framework for Accessible and Equitable Artificial Intelligence (AI) in Education

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This is a practical guide to the dizzying domain of artificial intelligence within the education ecosystem, with a particular focus on the impact on equity and accessibility. AI and accessibility are beginning to have an interesting conversation. Not unlike the conversation about AI in general, the conversation about AI and accessibility in education can be found taking a techno-solutionist or techno-tragedist perspective. As we grow wary of this false dichotomy, we move toward what is much more likely to be the case: that it will be “both/and” and “neither/nor.” AI can make things better. It can benefit us all, it can address inequities, and it can lower barriers for people with disabilities in education. It can equally be used to amplify inequities (intentional and unintended), including discrimination against people who do not fit a “norm.”

Material Type: Reading

Authors: Jess Mitchell, Joseph Scheuhammer, Jutta Treviranus, Lorna Lo, Rachel Spence

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Material Type: Full Course

Authors: Jaakkola, Tommi, Mohammad, Ali, Singh, Rohit

Introduction to Machine Learning

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Course DescriptionMachine Learning is the study of how to build computer systems that learn from experience. This course will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.

Material Type: Activity/Lab, Homework/Assignment, Lecture

Author: Shumet Tadesse Nigatu

Machine Learning Module

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These are materials that may be used in a CS0 course as a light introduction to machine learning. The materials are mostly Jupyter notebooks which contain a combination of labwork and lecture notes. There are notebooks on Classification, An Introduction to Numpy, and An Introduction to Pandas. There are also two assessments that could be assigned to students. One is an essay assignment in which students are asked to read and respond to an article on machine bias. The other is a lab-like exercise in which students use pandas and numpy to extract useful information about subway ridership in NYC. This assignment uses public data provided by NYC concerning entrances and exits at MTA stations. This OER material was produced as a result of the CS04ALL CUNY OER project

Material Type: Activity/Lab, Lecture Notes

Author: Johnson Hunter R

How and Why Machines Work

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Subject studies how and why machines work, how they are conceived, how they are developed (drawn), and how they are utilized. Students learn from the hands-on experiences of taking things apart mentally and physically, drawing (sketching, 3D CAD) what they envision and observe, taking occasional field trips, and completing an individual term project (concept, creation, and presentation). Emphasis on understanding the physics and history of machines.

Material Type: Full Course

Authors: Culpepper, Martin, Smith, Joseph

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