more on brain and learning in this IMS blog:
more on brain and learning in this IMS blog:
more on learning and the brain in this blog:
more on storytelling in this blog:
Brain breaks and focused attention practices help students feel relaxed and alert and ready to learn.
more on mindfulness in this IMS blog
12:00 PM – 1:00 PM
Centennial Hall – 100
Anyone interested in
new methods for teaching
Kyle Bowen, Director, Teaching and Learning with Technology https://members.educause.edu/kyle-bowen
Jennifer Sparrow, Senior Director of Teaching and Learning With Tech, https://members.educause.edu/jennifer-sparrow
Malcolm Brown, Director, Educause, Learning Initiative
more in this IMB blog on Jennifer Sparrow and digital fluency: http://blog.stcloudstate.edu/ims/2018/11/01/preparing-learners-for-21st-century-digital-citizenship/
Feb 5, 2018 webinar notes
creating a jazz band of one: ThoughSourus
Eureka: machine learning tool, brainstorming engine. give it an initial idea and it returns similar ideas. Like Google: refine the idea, so the machine can understand it better. create a collection of ideas to translate into course design or others.
influencers and microinfluencers, pre- and doing the execution
place to start explore and generate content.
a machine can construct a book with the help of a person. bionic book. machine and person working hand in hand. provide keywords and phrases from lecture notes, presentation materials. from there recommendations and suggestions based on own experience; then identify included and excluded content. then instructor can construct.
Design may be the least interesting part of the book for the faculty.
multiple choice quiz may be the least interesting part, and faculty might want to do much deeper assessment.
use these machine learning techniques to build assessment. how to more effectively. inquizitive is the machine learning
students engagements and similar prompts
presence in the classroom: pre-service teachers class. how to immerse them and practice classroom management skills
First class: marriage btw VR and use of AI – an environment headset: an algorithm reacts how teachers are interacting with the virtual kids. series of variables, oppty to interact with present behavior. classroom management skills. simulations and environments otherwise impossible to create. apps for these type of interactions
facilitation, reflection and research
AI for more human experience, allow more time for the faculty to be more human, more free time to contemplate.
Jason: Won’t the use of AI still reduce the amount of faculty needed?
Christina Dumeng: @Jason–I think it will most likely increase the amount of students per instructor.
Andrew Cole (UW-Whitewater): I wonder if instead of reducing faculty, these types of platforms (e.g., analytic capabilities) might require instructors to also become experts in the various technology platforms.
Dirk Morrison: Also wonder what the implications of AI for informal, self-directed learning?
Kate Borowske: The context that you’re presenting this in, as “your own jazz band,” is brilliant. These tools presented as a “partner” in the “band” seems as though it might be less threatening to faculty. Sort of gamifies parts of course design…?
Dirk Morrison: Move from teacher-centric to student-centric? Recommender systems, AI-based tutoring?
Andrew Cole (UW-Whitewater): The course with the bot TA must have been 100-level right? It would be interesting to see if those results replicate in 300, 400 level courses
Recording available here
Todd Rose, the director of the Mind, Brain, and Education program at the Harvard Graduate School of Education, has emerged as a central intellectual figure behind the movement. In particular, his 2016 book, “The End of Average,” is seen as an important justification for and guide to the personalization of learning.
what Rose argues against. He holds that our culture is obsessed with measuring and finding averages—averages of human ability and averages of the human body. Sometimes the average is held to be the ideal.
The jaggedness principle means that many of the attributes we care about are multi-faceted, not of a whole. For example, human ability is not one thing, so it doesn’t make sense to talk about someone as “smart” or “dumb.” That’s unidimensional. Someone might be very good with numbers, very bad with words, about average in using space, and gifted in using of visual imagery.
Since the 1930s, psychologists have debated whether intelligence is best characterized as one thing or many.
But most psychologists stopped playing this game in the 1990s. The resolution came through the work of John Carroll, who developed a third model in which abilities form a hierarchy. We can think of abilities as separate, but nested in higher-order abilities. Hence, there is a general, all-purpose intelligence, and it influences other abilities, so they are correlated. But the abilities nested within general intelligence are independent, so the correlations are modest. Thus, Rose’s jaggedness principle is certainly not new to psychology, and it’s incomplete.
The second (Context Principle) of Rose’s principles holds that personality traits don’t exist, and there’s a similar problem with this claim: Rose describes a concept with limited predictive power as having none at all. The most commonly accepted theory holds that personality can be described by variation on five dimensions
Rose’s third principle (pathways principle) suggests that there are multiple ways to reach a goal like walking or reading, and that there is not a fixed set of stages through which each of us passes.
Rose thinks students should earn credentials, not diplomas. In other words, a school would not certify that you’re “educated in computer science” but that you have specific knowledge and skills—that you can program games on handheld devices, for example. He think grades should be replaced by testaments of competency (my note: badges); the school affirms that you’ve mastered the skills and knowledge, period. Finally, Rose argues that students should have more flexibility in choosing their educational pathways.
more on personalized learning in this IMS blog
How deep learning―from Google Translate to driverless cars to personal cognitive assistants―is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies.
Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.
Machine learning is a very large field and goes way back. Originally, people were calling it “pattern recognition,” but the algorithms became much broader and much more sophisticated mathematically. Within machine learning are neural networks inspired by the brain, and then deep learning. Deep learning algorithms have a particular architecture with many layers that flow through the network. So basically, deep learning is one part of machine learning and machine learning is one part of AI.
December 2012 at the NIPS meeting, which is the biggest AI conference. There, [computer scientist] Geoff Hinton and two of his graduate students showed you could take a very large dataset called ImageNet, with 10,000 categories and 10 million images, and reduce the classification error by 20 percent using deep learning.Traditionally on that dataset, error decreases by less than 1 percent in one year. In one year, 20 years of research was bypassed. That really opened the floodgates.
The inspiration for deep learning really comes from neuroscience.
AlphaGo, the program that beat the Go champion included not just a model of the cortex, but also a model of a part of the brain called the basal ganglia, which is important for making a sequence of decisions to meet a goal. There’s an algorithm there called temporal differences, developed back in the ‘80s by Richard Sutton, that, when coupled with deep learning, is capable of very sophisticated plays that no human has ever seen before.
there’s a convergence occurring between AI and human intelligence. As we learn more and more about how the brain works, that’s going to reflect back in AI. But at the same time, they’re actually creating a whole theory of learning that can be applied to understanding the brain and allowing us to analyze the thousands of neurons and how their activities are coming out. So there’s this feedback loop between neuroscience and AI
Wednesday, 11/21/2018 – Wednesday, 12/12/2018
Looking for a beginner’s crash course in game making software and process? Games can be an excellent teaching resource, and game development is easier than ever. Whether you’re looking to develop your own teaching resources or run a game-making program for users, this course will give you the information you need to choose the most appropriate software development tool, structure your project, and accomplish your goals. Plain language, appropriate for absolute beginners, and practical illustrative examples will be used. Participants will receive practical basic exercises they can complete in open source software, as well as guides to advanced educational resources and available tutorials.
This is a blended format web course:
The course will be delivered as 4 separate live webinar lectures, one per week on Wednesday November 21 and then repeating Wednesdays, November 28, December 5 and December 12 at Noon Central time. You do not have to attend the live lectures in order to participate. The webinars will be recorded and distributed through the web course platform for asynchronous participation. The web course space will also contain the exercises and discussions for the course.
Who Should Attend
Library staff looking to develop educational games or run game making programs for users (including tween or teen users).
Ruby Warren believes in the power of play, and that learning is a lot more effective when it’s interactive. She is the User Experience Librarian at the University of Manitoba Libraries, where she recently completed a research leave focused on educational game prototype development, and has been playing games from around the time she developed object permanence.
Moodle and Webinar login info will be sent to registrants the week prior to the start date.
more on games and libraries in this IMS blog
More on the brain in this IMS blog
September 5, 2018 Siddharth (Sid) Pai
more on AI in this IMS blog
(thank you Mike Pickle: https://www.facebook.com/groups/190982954245635/permalink/2110516852292226/)
“rosehip neurons,” were found in the uppermost layer of the cortex, which is home to many different types of neurons that inhibit the activity of other neurons.
the set of genes expressed in these inhibitory rosehip neurons doesn’t closely match any previously identified cell in the mouse, suggesting they have no analog in the rodent often used as a model for humans,
The locations of their points of contact on other neurons suggest they’re in a powerful position to put the brakes on other incoming, excitatory signals—by which complex circuits of neurons activate one another throughout the brain.
more on learning and the brain in this IMS blog
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