It’s tempting right? I mean, you are paying something to do your work for you! Unfortunately, as it may be the easy way out, it could get you in trouble, and by trouble I mean, Instagram might end up deleting or banning your account – and that is probably the last thing you would want to happen.
It may take a big chunk of your time each day, but if you want your account to grow, focus on being genuine, providing value and engaging with your audience in an authentic manner!
Daniel J. Siegel is clinical professor of psychiatry at the UCLA School of Medicine, the founding co-director of the UCLA Mindful Awareness Research Center, and executive director of the Mindsight Institute. He is also the author of several books, including the New York Times bestsellers “Brainstorm” and, with Tina Payne Bryson, “The Whole-Brain Child” and “No-Drama Discipline.”
Tina Payne Bryson is a pediatric and adolescent psychotherapist and the Founder and Executive Director of The Center for Connection in Pasadena, California. She is also is the co-author, with Daniel J. Siegel, of the New York Times bestsellers “The Whole-Brain Child” and “No-Drama Discipline.”
why instructional design doesn’t typically work with students, or anyone’s learning for that matter, when you teach with PowerPoint—as well as how you can avoid it. It all begins with a little concept called “cognitive load.”
Cognitive load describes the capacity of our brain’s working memory (or WM) to hold and process new pieces of information. We’ve all got a limited amount of working memory, so when we have to handle information in more than one way, our load gets heavier, and progressively more challenging to manage.
In a classroom, a student’s cognitive load is greatly affected by the “extraneous” nature of information—in other words, the manner by which information is presented to them (Sweller, 2010). Every teacher instinctively knows there are better—and worse—ways to present information.
A study in Australia in the late 1990s (the 1999 Kalyuga study) compared the learning achievement of a group of college students who watched an educator’s presentation involving a visual text element and an audio text element (meaning there were words on a screen while the teacher also talked) with those who only listened to a lecture, minus the pesky PowerPoint slides.
Researchers including John Sweller and Kimberly Leslie contend that it would be easier for students to learn the differences between herbivores and carnivores by closing their eyes and only listening to the teacher. But students who close their eyes during a lecture are likely to to called out for “failing to paying attention.”
Richard Mayer, a brain scientist at UC Santa Barbara and author of the book Multimedia Learning, offers the following prescription: Eliminate textual elements from presentations and instead talk through points, sharing images or graphs with students
a separate Australian investigation by Leslie et al. (2012), suggest that mixing visual cues with auditory explanations (in math and science classrooms, in particular) are essential and effective. In the Leslie study, a group of 4th grade students who knew nothing about magnetism and light learned significantly more when presented with both images and a teacher’s explanation than a separate group which received only auditory explanation.
Limit yourself to one word per slide. If you’re defining words, try putting up the vocabulary word and an associated set of images—then challenge students to deduce the definition.
Honor the “personalization principle,” which essentially says that engaging learners by delivering content in a conversational tone will increase learning. For example, Richard Mayer suggests using lots of “I’s” and “you’s” in your text, as students typically relate better to more informal language.
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.
Badging programs are rapidly gaining momentum in higher education – join us to learn how to get your badging efforts off the ground.
Key Considerations: Assessment of Competencies
During this session, you will learn how to ask the right questions and evaluate if badges are a good fit within your unique institutional context, including determining ROI on badging efforts. You’ll also learn how to assess the competencies behind digital badges.
Key Technology Considerations
This session will allow for greater understanding of Open Badges standards, the variety of technology software and platforms, and the portability of badges. We will also explore emerging trends in the digital badging space and discuss campus considerations.
Key Financial Considerations
During this hour, we will take a closer look at answering key financial questions surrounding badges:
What does the business model look like behind existing institutional badging initiatives?
Are these money-makers for an institution? Is there revenue potential?
Where does funding for these efforts come from?
Partnering with Industry
Badging can be a catalyst for partnerships between higher education and industry. In this session, you will have the opportunity to learn more about strategies for collaborating with industry in the development of badges and how badges align with employer expectations.
Branding and Marketing Badges
Now that we have a better idea of the “why” and “what” of badges, how do we market their value to external and internal stakeholders? You’ll see examples of how other institutions are designing and marketing their badges.
Alongside your peers and our expert instructors, you will have the opportunity to brainstorm ideas, get feedback, ask questions, and get answers.
Next Steps and the Road Ahead: Where Badging in Higher Ed is Going
Most institutions are getting into the badging game, and we’ll talk about the far-reaching considerations in the world of badging. We’ll use this time to engage in forward-thinking and discuss the future of badging and what future trends in badging might be.
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.
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
Untether instructors from the room’s podium, allowing them control from anywhere in the room;
Streamline the start of class, including biometric login to the room’s technology, behind-the-scenes routing of course content to room displays, control of lights and automatic attendance taking;
Offer whiteboards that can be captured, routed to different displays in the room and saved for future viewing and editing;
Provide small-group collaboration displays and the ability to easily route content to and from these displays; and
Deliver these features through a simple, user-friendly and reliable room/technology interface.
Key players from Crestron, Google, Sony, Steelcase and Spectrum met with Indiana University faculty, technologists and architects to generate new ideas related to current and emerging technologies. Activities included collaborative brainstorming focusing on these questions:
What else can we do to create the classroom of the future?
What current technology exists to solve these problems?
What could be developed that doesn’t yet exist?
top five findings:
Screenless and biometric technology will play an important role in the evolution of classrooms in higher education. We plan to research how voice activation and other Internet of Things technologies can streamline the process for faculty and students.
The entire classroom will become a space for student activity and brainstorming; walls, windows, desks and all activities are easily captured to the cloud, allowing conversations to continue outside of class or at the next class meeting.
Technology will be leveraged to include advance automation for a variety of tasks, so the faculty member is released from duties to focus on teaching.
The technology will become invisible to the process and enhance and customize the experience for the learner.
Virtual assistants could play an important role in providing students with a supported experience throughout their entire campus career.
In September 2015, the back-then library dean (they change every 2-3 years) requested a committee of librarians to meet and discuss the remodeling of Miller Center 2018. By that time the SCSU CIO was asserting the BYOx as a new policy for SCSU. BYOx in essence means the necessity for stronger (wider) WiFI pipe. Based on that assertion, I, Plamen Miltenoff, was insisting to shift the cost of hardware (computers, laptops) to infrastructure (more WiFi nods in the room and around it) and prepare for the upcoming IoT by learning to remodel our syllabi for mobile devices and use those (students) mobile devices, rather squander University money on hardware. At least one faculty member from the committee honestly admitted she has no idea about IoT and respectively the merit of my proposal. Thus, my proposal was completely disregarded by the self-nominated chair of the committee of librarians, who pushed for her idea to replace the desktops with a cart of laptops (a very 2010 idea, which by 2015 was already passe). As per Kelly (2018) (second article above), it is obvious the failure of her proposal to the dean to choose laptops over mobile devices, considering that faculty DO see mobile devices completely replacing desktops and laptops; that faculty DO not see document cameras and overhead projectors as a tool to stay.
Here are the notes from September 2015 http://blog.stcloudstate.edu/ims/2015/09/25/mc218-remodel/
As are result, my IoT proposal as now reflected in the Johnston (2018) (first article above), did not make it even formally to the dean, hence the necessity to make it available through the blog.
The SCSU library thinking regarding physical remodeling of classrooms is behind its times and that costs money for the university, if that room needs to be remodeled again to be with the contemporary times.
With a growing body of research proving yoga’s healing benefits, it’s no wonder more doctors—including those with traditional Western training—are prescribing this ancient practice to their patients.
Yoga therapy is now recognized as a clinically viable treatment, with established programs at major health care centers, such as The University of Texas MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, Cleveland Clinic, and many others. In 2003, there were just five yoga-therapy training programs in the International Association of Yoga Therapists (IAYT) database. Today, there are more than 130 worldwide, including 24 rigorous multi-year programs newly accredited by IAYT, with 20 more under review. According to a 2015 survey, most IAYT members work in hospital settings, while others work in outpatient clinics or physical therapy, oncology, or rehabilitation departments (and in private practice).
Some therapists focus on physical mechanics, while others bring in Ayurvedic healing principles and factor in diet, psychological health, and spirituality to create a holistic, customized plan.
“Researchers take blood samples before and after yoga practice to see which genes have been turned on and which were deactivated,” says Khalsa. “We’re also able to see which areas of the brain are changing in structure and size due to yoga and meditation.” This kind of research is helping take yoga into the realm of “real science,” he says, by showing how the practice changes psycho-physiological function.