S7SE Special Episode: Collaborating with Community Colleges
Speakers:
Shira Segal, Sarah Hansen, Patricia Foley, John Francis, Fernando Romero, Robert Bergman
Description:
MIT OpenCourseWare has been one of the pioneers of open education, leading the way by offering free materials from MIT courses as early as 2001, when no other institutions were pursuing comparably ambitious initiatives. But in subsequent years, there’s been an explosion of activity in open education, led by faculty members, instructional designers, and librarians at institutions throughout the United States and worldwide. In this episode, we hear from senior manager of MIT Open Education collaborations, Dr. Shira Segal, who talks about MIT’s efforts to team up with and learn from open education practitioners at the Maricopa County Community College District in Arizona, whose energetic promotion of open educational resources has saved students over $270 million in textbook costs, and College of the Canyons in California, a leader in the Zero Textbook Cost movement. We also hear excerpts from interviews with four instructors from those colleges, who talk about the potential benefits and unexpected challenges of using open educational resources in general, and about what they learned from their experiences in adapting OCW materials for use in their own classes.
Relevant Resources:
MIT OpenCourseWare (https://ocw.mit.edu/)
The OCW Educator Portal (https://ocw.mit.edu/educator/)
More on MIT OpenCourseWare’s collaboration with community colleges (https://openlearning.mit.edu/news/collaborating-support-community-college-faculty-teaching-mit-open-educational-resources)
Maricopa County Community College District (https://www.maricopa.edu/)
College of the Canyons (https://www.canyons.edu/)
Maricopa Community Colleges Save Students $270M in Textbooks (https://www.yourvalley.net/scottsdale-independent/stories/maricopa-community-colleges-save-students-270m-in-textbooks,620220)
OER and Zero Textbook Cost at College of the Canyons (https://www.canyons.edu/academics/onlineeducation/ztc/)
Music in this episode by Blue Dot Sessions (https://www.sessions.blue/)
Connect with Us
If you have a suggestion for a new episode or have used OCW to change your life or those of others, tell us your story. We’d love to hear from you!
Call us @ 617-715-2517
On our site (https://ocw.mit.edu/contact/)
On Facebook (https://www.facebook.com/MITOCW/)
On X (https://twitter.com/MITOCW)
On Instagram (https://www.instagram.com/mitocw/)
Stay Current
Subscribe to the free monthly "MIT OpenCourseWare Update" e-newsletter. (https://ocw.mit.edu/newsletter/)
Subscribe to Chalk Radio (https://chalk-radio.simplecast.com/)
Support OCW
If you like Chalk Radio and OpenCourseware, donate to help keep these programs going! (https://giving.mit.edu/give/to/ocw/?utm_source=ocw&utm_medium=podcast&utm_campaign=donate)
Credits
Sarah Hansen, host and producer (https://www.linkedin.com/in/sarah-e-hansen/)
Brett Paci, producer (https://twitter.com/Brett_Paci)
Dave Lishansky, producer (https://twitter.com/DaveResonates)
Show notes by Peter Chipman
MIT Economist Jon Gruber responds to YouTube comments
Economist Jon Gruber answers YouTube's burning questions about the economy, Netflix, chalkboards, the NFL, and more!
View his full Chalk Radio podcast interview here: https://www.youtube.com/watch?v=dLg9MK1hv2Y
View his Principles of Microeconomics course here: https://www.youtube.com/show/VLPLUl4u3cNGP60V7HxLYRaJMbFzP77bzEjb?sbp=Kgs4c3NqS1I3bk5ja0AB
3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
A recap of training flow is given, then the rest of the session walks through the steps of building a deep neural network in Colab.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Addresses Generative Pretrained Transformers (GPTs) version differences and nuances of training data, instruction tuning, and adapting base language learning models (LLMs).
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
A deeper dive into transformers and how to use them.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Introduces next word prediction using the transformer encoder architecture from the previous class.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
5: Deep Learning for Natural Language – The Basics
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Introduction to natural language processing, including vectorization, the bag-of-words model, and includes demonstration in CoLab.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
This session introduces various aspects of designing and training deep neural networks using the example of a model for heart disease prediction.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Covers transfer learning, convolutional neural network (CNN) models, pooling layers, and application examples, including a handbags-shoes classifier.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
6: Deep Learning for Natural Language – Embeddings
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Continues discussion of natural language processing with a focus on embeddings, including stand-alone and contextual embeddings.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
7: Deep Learning for Natural Language – Transformers
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Transformers are described via an airline travel-related example.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
11: Generative AI – Text-to-Image Models
MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
Discussion on various text-conditional diffusion models using a transformer architecture, including text-to-image and text-to-video examples.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
S7SE Special Episode: Collaborating with Community Colleges