Fraunhofer-Gesellschaft June 3, 2019
Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable.
a machine learning library programmed in C that can run on microcontrollers, but also on other platforms such as PCs, Raspberry PI and Android.
more about machine learning in this IMS blog
Sejnowski, T. J. (2018). The Deep Learning Revolution. Cambridge, MA: The MIT Press.
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.
Artificial intelligence meets human intelligence
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
EDUCAUSE Academic Communities: Teaching and Learning + Student Success
Tuesday, February 25, 2020, at 12:00 pm,
Miller Center, MC 205, the SCSU Professional Development Room
(how to get there? https://youtu.be/jjpLR3FnBLI )
You will receive an email from Canvas Catalog when you have been granted access to the event website. This site includes live event login details, program and speaker information, and technical requirements.
My notes from the Adobe Connect webinar
Malcolm Brown (MB) and Kathe Pelletier (KP)
John Martin, UW-Madison: Interesting that “Student Success” = retention. I feel retention = org success.
Cindy Auclair: Cindy Auclair – ASU – Retention is important that goes hand in hand with well-being.
Kathy Fernandes, CSU Chico: Not sure how one would measure Becoming a Citizen? We do have public Debates, Town Hall, etc. to engage with community.
Lisa Durff: I thought of digital citizenship
Jim J – MiraCosta: “as a part of teaching and learning” is a real gray area –
Jim J – MiraCosta: We may measure all of these, but there is very little formality around “teaching and learning”
Lisa Durff: very few measure instructor satisfaction
student success after 2017 shifts from SS and technology to SS and other issues
why tech adoption doesn’t equal digital transformation. article from Forbes. MB: it is not for sale, cannot buy. not a product, but deep and coordinated shifts: culture, workforce, technology.
ask for EDUCAUSE Academic Communities PDF document
more on Educause in this IMS blog
How Game-Based Learning Empowers Students for the Future
educators’ guide to game-based learning, packed with resources for gaming gurus and greenhorns alike.
How are schools and districts preparing students for future opportunities? What is the impact of game-based learning?
It’s 2019. So Why Do 21st-Century Skills Still Matter?
21st-century trends such as makerspaces, flipped learning, genius hour, gamification, and more.
EdLeader21, a national network of Battelle for Kids.has developed a toolkit to guide districts and independent schools in developing their own “portrait of a graduate” as a visioning exercise. In some communities, global citizenship rises to the top of the wish list of desired outcomes. Others emphasize entrepreneurship, civic engagement, or traits like persistence or self-management.
ISTE Standards for Students highlight digital citizenship and computational thinking as key skills that will enable students to thrive as empowered learners. The U.S. Department of Education describes a globally competent student as one who can investigate the world, weigh perspectives, communicate effectively with diverse audiences, and take action.
Frameworks provide mental models, but “don’t usually help educators know what to do differently,” argues technology leadership expert Scott McLeod in his latest book, Harnessing Technology for Deeper Learning. He and co-author Julie Graber outline deliberate shifts that help teachers redesign traditional lessons to emphasize goals such as critical thinking, authenticity, and conceptual understanding.
1. Wondering how to teach and assess 21st-century competencies? The Buck Institute for Education offers a wide range of resources, including the book, PBL for 21st Century Success: Teaching Critical Thinking, Collaboration, Communication, and Creativity (Boss, 2013), and downloadable rubrics for each of the 4Cs.
2. For more strategies about harnessing technology for deeper learning,listen to the EdSurge podcast featuring edtech expert and author Scott McLeod.
3. Eager to see 21st-century learning in action? Getting Smart offers suggestions for using school visits as a springboard for professional learning, including a list of recommended sites. Bob Pearlman, a leader in 21st century learning, offers more recommendations.
more on game- based learning in this IMS blog
Key Issues in Teaching and Learning
A roster of results since 2011 is here.
1. Academic Transformation
2. Accessibility and UDL
3. Faculty Development
4. Privacy and Security
5. Digital and Information Literacies
Three Models of Digital Literacy: Universal, Creative, Literacy Across Disciplines
United States digital literacy frameworks tend to focus on educational policy details and personal empowerment, the latter encouraging learners to become more effective students, better creators, smarter information consumers, and more influential members of their community.
National policies are vitally important in European digital literacy work, unsurprising for a continent well populated with nation-states and struggling to redefine itself, while still trying to grow economies in the wake of the 2008 financial crisis and subsequent financial pressures
African digital literacy is more business-oriented.
Middle Eastern nations offer yet another variation, with a strong focus on media literacy. As with other regions, this can be a response to countries with strong state influence or control over local media. It can also represent a drive to produce more locally-sourced content, as opposed to consuming material from abroad, which may elicit criticism of neocolonialism or religious challenges.
p. 14 Digital literacy for Humanities: What does it mean to be digitally literate in history, literature, or philosophy? Creativity in these disciplines often involves textuality, given the large role writing plays in them, as, for example, in the Folger Shakespeare Library’s instructor’s guide. In the digital realm, this can include web-based writing through social media, along with the creation of multimedia projects through posters, presentations, and video. Information literacy remains a key part of digital literacy in the humanities. The digital humanities movement has not seen much connection with digital literacy, unfortunately, but their alignment seems likely, given the turn toward using digital technologies to explore humanities questions. That development could then foster a spread of other technologies and approaches to the rest of the humanities, including mapping, data visualization, text mining, web-based digital archives, and “distant reading” (working with very large bodies of texts). The digital humanities’ emphasis on making projects may also increase
Digital Literacy for Business: Digital literacy in this world is focused on manipulation of data, from spreadsheets to more advanced modeling software, leading up to degrees in management information systems. Management classes unsurprisingly focus on how to organize people working on and with digital tools.
Digital Literacy for Computer Science: Naturally, coding appears as a central competency within this discipline. Other aspects of the digital world feature prominently, including hardware and network architecture. Some courses housed within the computer science discipline offer a deeper examination of the impact of computing on society and politics, along with how to use digital tools. Media production plays a minor role here, beyond publications (posters, videos), as many institutions assign multimedia to other departments. Looking forward to a future when automation has become both more widespread and powerful, developing artificial intelligence projects will potentially play a role in computer science literacy.
6. Integrated Planning and Advising Systems for Student Success (iPASS)
7. Instructional Design
8. Online and Blended Learning
In traditional instruction, students’ first contact with new ideas happens in class, usually through direct instruction from the professor; after exposure to the basics, students are turned out of the classroom to tackle the most difficult tasks in learning — those that involve application, analysis, synthesis, and creativity — in their individual spaces. Flipped learning reverses this, by moving first contact with new concepts to the individual space and using the newly-expanded time in class for students to pursue difficult, higher-level tasks together, with the instructor as a guide.
Let’s take a look at some of the myths about flipped learning and try to find the facts.
Myth: Flipped learning is predicated on recording videos for students to watch before class.
Fact: Flipped learning does not require video. Although many real-life implementations of flipped learning use video, there’s nothing that says video must be used. In fact, one of the earliest instances of flipped learning — Eric Mazur’s peer instruction concept, used in Harvard physics classes — uses no video but rather an online text outfitted with social annotation software. And one of the most successful public instances of flipped learning, an edX course on numerical methods designed by Lorena Barba of George Washington University, uses precisely one video. Video is simply not necessary for flipped learning, and many alternatives to video can lead to effective flipped learning environments [http://rtalbert.org/flipped-learning-without-video/].
Myth: Flipped learning replaces face-to-face teaching.
Fact: Flipped learning optimizes face-to-face teaching. Flipped learning may (but does not always) replace lectures in class, but this is not to say that it replaces teaching. Teaching and “telling” are not the same thing.
Myth: Flipped learning has no evidence to back up its effectiveness.
Fact: Flipped learning research is growing at an exponential pace and has been since at least 2014. That research — 131 peer-reviewed articles in the first half of 2017 alone — includes results from primary, secondary, and postsecondary education in nearly every discipline, most showing significant improvements in student learning, motivation, and critical thinking skills.
Myth: Flipped learning is a fad.
Fact: Flipped learning has been with us in the form defined here for nearly 20 years.
Myth: People have been doing flipped learning for centuries.
Fact: Flipped learning is not just a rebranding of old techniques. The basic concept of students doing individually active work to encounter new ideas that are then built upon in class is almost as old as the university itself. So flipped learning is, in a real sense, a modern means of returning higher education to its roots. Even so, flipped learning is different from these time-honored techniques.
Myth: Students and professors prefer lecture over flipped learning.
Fact: Students and professors embrace flipped learning once they understand the benefits. It’s true that professors often enjoy their lectures, and students often enjoy being lectured to. But the question is not who “enjoys” what, but rather what helps students learn the best.They know what the research says about the effectiveness of active learning
Assertion: Flipped learning provides a platform for implementing active learning in a way that works powerfully for students.
9. Evaluating Technology-based Instructional Innovations
What is the total cost of my innovation, including both new spending and the use of existing resources?
What’s the unit I should measure that connects cost with a change in performance?
How might the expected change in student performance also support a more sustainable financial model?
The Exposure Approach: we don’t provide a way for participants to determine if they learned anything new or now have the confidence or competence to apply what they learned.
The Exemplar Approach: from ‘show and tell’ for adults to show, tell, do and learn.
The Tutorial Approach: Getting a group that can meet at the same time and place can be challenging. That is why many faculty report a preference for self-paced professional development.build in simple self-assessment checks. We can add prompts that invite people to engage in some sort of follow up activity with a colleague. We can also add an elective option for faculty in a tutorial to actually create or do something with what they learned and then submit it for direct or narrative feedback.
The Course Approach: a non-credit format, these have the benefits of a more structured and lengthy learning experience, even if they are just three to five-week short courses that meet online or in-person once every week or two.involve badges, portfolios, peer assessment, self-assessment, or one-on-one feedback from a facilitator
The Academy Approach: like the course approach, is one that tends to be a deeper and more extended experience. People might gather in a cohort over a year or longer.Assessment through coaching and mentoring, the use of portfolios, peer feedback and much more can be easily incorporated to add a rich assessment element to such longer-term professional development programs.
The Mentoring Approach: The mentors often don’t set specific learning goals with the mentee. Instead, it is often a set of structured meetings, but also someone to whom mentees can turn with questions and tips along the way.
The Coaching Approach: A mentor tends to be a broader type of relationship with a person.A coaching relationship tends to be more focused upon specific goals, tasks or outcomes.
The Peer Approach:This can be done on a 1:1 basis or in small groups, where those who are teaching the same courses are able to compare notes on curricula and teaching models. They might give each other feedback on how to teach certain concepts, how to write syllabi, how to handle certain teaching and learning challenges, and much more. Faculty might sit in on each other’s courses, observe, and give feedback afterward.
The Self-Directed Approach:a self-assessment strategy such as setting goals and creating simple checklists and rubrics to monitor our progress. Or, we invite feedback from colleagues, often in a narrative and/or informal format. We might also create a portfolio of our work, or engage in some sort of learning journal that documents our thoughts, experiments, experiences, and learning along the way.
The Buffet Approach:
10. Open Education
11. Learning Analytics
12. Adaptive Teaching and Learning
13. Working with Emerging Technology
In 2014, administrators at Central Piedmont Community College (CPCC) in Charlotte, North Carolina, began talks with members of the North Carolina State Board of Community Colleges and North Carolina Community College System (NCCCS) leadership about starting a CBE program.
Building on an existing project at CPCC for identifying the elements of a digital learning environment (DLE), which was itself influenced by the EDUCAUSE publication The Next Generation Digital Learning Environment: A Report on Research,1 the committee reached consensus on a DLE concept and a shared lexicon: the “Digital Learning Environment Operational Definitions,
CAST’s 4th Annual UDL Symposium:Empowering Learners
This year’s UDL Symposium was an opportunity to come together as a community to explore the promise of Universal Design for Learning for empowering learners. By engaging in sessions designed to encourage critical conversations, problem-solving, and hands-on exploration, participants considered empowerment through a UDL lens. Participants left with a deeper understanding of the important role empowerment plays in learning and with concrete examples of ways to leverage UDL for these critical aims.We hope our participants left with the confidence and the motivation to apply their learning to their practice—and with a new network of colleagues to encourage and support their efforts.
Beyond The Buzz Phrase: Social Learning And LMS Gamification In Real Life
When: Thursday 26 July 2018, 11:00 PM – 12:00 PM
Adobe’s Senior Learning Evangelist, Katrina Marie Baker in our webinar, and find out how you can easily transform your learning by taking a deep dive into the 2 smartest learning breakthroughs of the decade: Social Learning & Gamification 🚀
During this session, you will:
• Learn how to blend social learning into existing courses using an LMS
• Discover how gamification can be aligned with your business objectives
• Come upon the latest learning tech tips to help you drive engagement
• See examples of how gamification and social learning can be both employed in Captivate Prime LMS
Are social learning and gamification the new fashion that will dominate the future of eLearning? Let’s find out together!
Save your spot here now http://ow.ly/EqYP30kV0qQ
more on social learning in this IMS blog
Microlearning: The Emerging Instructional Design Strategy in Elearning
BY SYED AMJAD ALI NOVEMBER 8, 2017
Microlearning is a learning strategy that involves bite-sized learning nuggets (small and focused segments) designed to meet a specific learning outcome. To put it simply, the learning content is chunked to reduce learner’s cognitive overload making it easy for learners to absorb and recall.
An effective microlearning course:
- Provides deeper learning on a specific concept or a performance objective
- Is bite-sized, effectively chunked and easily digestible
- Designed for exact moment-of-need – Right information at right time
- Ideal for extended performance support providing a better mobile learning experience
- Focused on a single performance objective, concept or idea
- Is usually 4 to 5 minutes in length, or shorter
Adobe is trying to reshape an old theory: chunking
by calling it “microlearning”?
What do you think?
more on instructional design in this IMS blog