Digital Literacy for St. Cloud State University
In a high-poverty Colorado school trying to turn itself around, individualized approach brings wide-ranging benefits.
Personalized learning gives students a sense of control over chaotic lives
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more on personalized learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=personalized+learning
Could the forces that fought the Common Core bring down personalized learning? https://t.co/9ZrjwSw0M5 @DianeRavitch @carolburris @AnthonyCody @leoniehaimson @ECEPolicyWorks @ProfessorJVH @BadassTeachersA @marla_kilfoyle @ProfessorJVH @YohuruWilliams
— Network4PublicEd (@Network4pubEd) December 21, 2018
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more on personalized learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=personalized+learning
Emerging Technologies for Lifelong Learning:
Intro to #EmTechMOOC and EmTechWIKI from SUNY
“… open-access resource… to identify the value and implications of using established and emerging technology tools for personal and professional growth…strategies to … keep pace with technology change.
“… #EmTechMOOC, – Coursera Massive Open Online Course
“…EmTechWIKI …socially-curated discovery engine to discover tools, tutorials, and resources. The WIKI can be used as a stand-alone resource, or it can be used together with #EmTechMOOC. Anyone is welcome to add or edit WIKI resources.”
” – excerpt from https://www.coursera.org/learn/emerging-technologies-lifelong-learning,
Guests
Roberta (Robin) Sullivan, Online Learning Specialist, Center for Educational Innovation, University at Buffalo
Plamen Miltenoff was selected to serve on the Editorial Review Board (ERB) for the International Journal of Innovative Teaching and Learning in Higher Education (IJITLHE).
“Your term as a board member starts January 2019 and you will be expected to review up to 4 articles a year for the next 2 years. IGI Global advises to have up to 5 reviewers per article, so if the journal were to receive numerous submissions, I may need some volunteers to review additional articles. If that situation were to take place and you would like to be contacted to review more articles, please let me know and I will make a note.”
https://elearningindustry.com/instructional-design-models-and-theories
1. Virtual Reality, or VR
Ultimately, the key question to consider when adopting anything new is whether it will help you achieve the desired outcome. VR shouldn’t be incorporated into learning just because it’s a common buzzword. Before you decide to give it a go, consider how it’s going to help your learner, and whether it’s truly the most effective or efficient way to meet the learning goal.
2. Gamification
considering introducing an interactive element to your learning, don’t let this deter you—just ensure that it’s relevant to the content and will aid the learning process.
3. Artificial Intelligence, or AI
If you are confident that a trend is going to yield better results for your learners, the ROI you see may well justify the upfront resources it requires.
Again, it all comes down to whether a trend is going to deliver in terms of achieving an objective.
4. Microlearning
The theory behind microlearning makes a lot of sense: organizing content into sections so that learning can fit easily with modern day attention spans and learners’ busy lifestyles is not a bad thing. The worry is that the buzzword, ‘microlearning’, has grown legs of its own, meaning the industry is losing sight of its’ founding principles.
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more on elearning in this IMS blog
https://blog.stcloudstate.edu/ims?s=elearning
what will be like the learning place of the future
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.
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more on personalized learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=personalized+learning
I’m sorry. But I have a rant about “personalized learning” https://t.co/lgVgCZBae7
— Audrey Watters (@audreywatters) November 17, 2018
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more about personalized learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=personalized+learning
Kahoot presents Star Wars-based quizzes for different disciplines
https://create.kahoot.it/pages/ebe8eef7-a483-4392-97c9-44aea89f137a
An excellent opportunity to gamify your classes.
If you are not a Kahoot user yet, please consider: a) the Kahoots (quizzes) can be an excellent conversation starter (vs. assessment tool) b) the Kahoots can be modified to your liking (you can change the content)
here some screen-sharing capture to get a taste of the excitement:
Engineering
Astronomy
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