Infographic: How does the brain retain information?

Toward a science of learning — from InsideHigherEd.com by Diana Chapman Walsh

In travels around the country, I’ve been seeing signs of a trend in higher education that could have profound implications: a growing interest in learning about learning. At colleges and universities that are solidly grounded in a commitment to teaching, groups of creative faculty are mobilizing around learning as a collective, and intriguing, intellectual inquiry.

This trend embraces the advances being made in the cognitive sciences and the study of consciousness. It resides in the fast-moving world of changing information technology and social media. It recognizes and builds upon new pedagogies and evolving theories of multiple ways of knowing and learning. It encompasses but transcends the evolution of new and better measures of student learning outcomes.

I know that this essay is loaded with fighting words. But I believe we need, and are now beginning to see, ways to reframe the problem of learning outcomes, ways that might galvanize positive energy and support within a faculty. Imagine “the administration” saying to faculty, in effect: We want you to be learning all you can about who your students are now, how they learn and what they need to know in order to be successful in a world that is changing faster than we can imagine much less anticipate. And we want you to have the resources and collegial connections you will need to make the pursuit of that question an exciting and fruitful complement to your scholarship. From learning science there are stunning advances that need translation before they can be brought successfully into classrooms, findings and possibilities that at least some faculty might find inherently fascinating if they were approached right, offered a supportive culture with meaningful incentives and rewards and scholarly payoffs.

Elementary, my dear Watson: Jeopardy computer offers insight into human cognition — from Sentient Developments by George Dvorsky

Also see:

  1. Massive parallelism:
    Exploit massive parallelism in the consideration of multiple interpretations and hypotheses.
  2. Many experts:
    Facilitate the integration, application and con-textual evaluation of a wide range of loosely coupled probabilistic question and content analytics.
  3. Pervasive confidence estimation:
    No single component commits to an answer; all components produce features and associated confidences, scoring different question and contentinterpretations. An underlying confidence processing substratelearns how to stack and combine the scores.
  4. Integrate shallow and deep knowledge:
    Balance the use of strict semantics and shallow semantics, leveraging many loosely formed ontologies.

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IBM's Watson -- incredible AI!

Smartphones as Cognitive Prosthetics — from insidedigitalmedia.com by Phil Leigh

From DSC:
Watch the Digimarc Discover video below — then think about how this might apply to anytime, anywhere learning!!! Wow!

Enable your phone to see and hear with Digimarc Discover; recognize all forms of media in your surroundings to enrich and simplify your life..

Digimarc Discover

Apollo Group joins learning technology partnership with Stanford University — from businesswire.com
Partnership brings together academic researchers and select industry partners to study interactive communications and technology; Apollo Group’s Dr. Tracey Wilen-Daugenti to serve as visiting scholar

Through the partnership, Apollo Group and Dr. Wilen-Daugenti will work with Stanford University faculty and researchers studying basic issues about the design and use of modern technologies and their impact on today’s learner. Apollo Group will also participate with Stanford faculty members and graduate students to explore the role technology can play in higher education organizations, with a specialization in distance learning.

“Technology today is changing at an extremely fast pace, which impacts both enterprise and educational institutions, requiring them to keep up with the latest trends,” said Dr. Wilen-Daugenti. “Students are increasingly exposed to the latest technology in their lives, and seek access to it in their work and education environments. Through this partnership and visiting scholar program, we hope to address this issue and find ways for higher learning institutions to more readily use technology to address the needs of today’s students.”

An introduction to Robert Gagnes' 9 events -- by Christopher Pappass

Modality & Redundancy eLearning Principles — from Anne Negus via Wanza Wiley
Here are some of the slides from that narrated presentation:

The Modality Principle

The Redundancy Principle

I just listened to a presentation by Dr. Ruth Clark entitled, “Efficiency in Learning: Applying Cognitive Load Theory to Distance Learning”. Below are my notes from her presentation.


Besides our long-term memory we have a working memory — which is where the action is and where cognitive load theory focuses

  • 7  +- 2 chunks
    • George Miller’s work in the 1950’s re: the limitations of working memory
    • Cognitive load theory is an update to George’s work
    • The concept of “chunking” and the capacity of short term memory. Miller (1956) presented the idea that short-term memory could only hold 5-9 chunks of information (seven plus or minus two) where a chunk is any meaningful unit. A chunk could refer to digits, words, chess positions, or people’s faces. The concept of chunking and the limited capacity of short term memory became a basic element of all subsequent theories of memory.
    • So segmenting of content is good – chunking it up — as information should be presented in small digestible units
    • A digestible unit of information contains no more than nine separate items of information.
    • By chunking information the author improves the reader’s comprehension and ability to access and retrieve the information.
    • [Search for items related to “Information Processing Theory” and George Miller for more information]
  • Working memory has a limited capacity
  • Great for processing – not great for holding information
  • Prior knowledge is key here
  • Gets slower as trying to hold more information in working memory
  • Our challenge as instructional designers is how to optimize cognitive load that maximizes learning
  • More complex/difficult subject matter or more novice the learning à more cognitive load
  • Intrinsic (imposed by content; how complex is the content?) + Extraneous  / Extrinsic (irrelevant & want to minimize this)  + Germaine (good stuff; relevant; want to maximize this)
  • Intrinsic + extraneous + germaine = additive cognitive load
  • Giving learners orientation gives better learning; establish context
  • Use audio to explain visuals when appropriate – uses both auditory information track and visual information track
  • Modality effect
    • Better learning if a visual is explained by words expressed in audio (except if different language)
  • Redundancy effect
    • Don’t want to use the same text w/ same audio at the same time – less is more – if have a picture of something, with text next to it, plus having someone say that text is too much info – too much cognitive load
  • Proximity effect
    • Placement of text and visuals
    • Keep visuals next to the relevant text/explanation of that visual
    • Avoid splitting attention
  • Germaine load
    • Use examples – but also add self-explanation questions to examples to encourage deeper mental processing and not blowing the example off
  • Some more tips
    • Watch the pacing of the presented materials
    • Provide control to user
    • Don’t put items on screen unless serving a purpose
    • Don’t put background music if trying to concentrate on learning something
    • Motion – careful when use it
    • If dealing with experts, don’t have to worry as much about cognitive load burdens; allow control/freedom
  • Didn’t sound like Ruth supported learning styles too much – believes that we place too much emphasis on them; prior knowledge is the key according to Ruth
  • Some synchronous, web-based communication and collaboration tools can cause cognitive overloads – as the interface can split our attention. We try to absorb information that is flowing at us from the various areas of the interface:
    • Chat
    • An attendee list of members
    • The presentation area/PPTs
    • Audio
    • Motion w/ application sharing
    • etc.

Clark Training & Consulting’s blog –> http://clarktraining.com/blog/

Ruth Clark's Training & Consulting site

The value of multimedia in learning — Patti Shank (2005, from Adobe’s Media Center)

  

The value of multimedia in learning -- by Patti Shank (2005)

“With this in mind, here are a few sites that feature cognitive psychology podcasts, research, articles and news. I’m even sneaking in a few brain science sites for the true believers.”

“And just in case you’re new to this field, cognitive psychology is the discipline that examines our mental processes, such as attention, perception, memory and learning. Cognitive psychology uses an information-processing model to explain mental operations in computational terms. Your resources are below. Enjoy.”

© 2024 | Daniel Christian