Searching for "educational theories"

cognitive load theory

Cognitive load theory: Research that teachers really need to understand
AUGUST 2017 Centre for Education Statistics and Evaluation
https://www.cese.nsw.gov.au/images/stories/PDF/cognitive_load_theory_report_AA1.pdf
Cognitive load theory is built upon two commonly accepted ideas. The first is that there is a limit to how much new information the human brain can process at one time. The second is that there are no known limits to how much stored information can be processed at one time. The aim of cognitive load research is therefore to develop instructional techniques and recommendations that fit within the characteristics of working memory, in order to maximise learning.
Explicit instruction involves teachers clearly showing students what to do and how to do it, rather than having students discover or construct information for themselves
how working memory and long-term memory process and store information
Working memory is the memory system where small amounts of information are stored for a very short duration (RAM). Long-term memory is the memory system where large amounts of information are stored semi-permanently (hard drive)
Cognitive load theory assumes that knowledge is stored in long- term memory in the form of ‘schemas’ 2 . A schema organises elements of information according to how they will be used. According to schema theory, skilled performance is developed through building ever greater numbers of increasingly complex schemas by combining elements of lower level schemas into higher level schemas. There is no limit to how complex schemas can become. An important process in schema construction is automation, whereby information can be processed automatically with minimal conscious effort. Automaticity occurs after extensive practice
Schemas provide a number of important functions that are relevant to learning. First, they provide a system for organising and storing knowledge. Second, and crucially for cognitive load theory, they reduce working memory load. This is because, although there are a limited number of elements that can be held in working memory at one time, a schema constitutes only a single element in working memory. In this way, a high level schema – with potentially infinite informational complexity – can effectively bypass the limits of working memory

Types of cognitive load

Cognitive load theory identifies three different types of cognitive load: intrinsic, extraneous and germane load
Intrinsic cognitive load relates to the inherent difficulty of the subject matter being learnt.

subject matter that is difficult for a novice may be very easy for an expert.
Extraneous cognitive load relates to how the subject matter is taught.
extraneous load is the ‘bad’ type of cognitive load, because it does not directly contribute to learning. Cognitive load theorists consider that instructional design will be most effective when it minimises extraneous load in order to free up the capacity of working memory
Germane cognitive load refers to the load imposed on the working memory by the process of learning – that is, the process of transferring information into the long-term memory through schema construction
the approach of decreasing extraneous cognitive load while increasing germane cognitive load will only be effective if the total cognitive load remains within the limits of working memory
Explicit teaching

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more on educational theories in this IMS blog
http://blog.stcloudstate.edu/ims?s=educational+theories

IM 690 VR and AR lab part 2

IM 690 Virtual Reality and Augmented Reality. short link: http://bit.ly/IM690lab

IM 690 lab plan for March 3, MC 205:  Oculus Go and Quest

Readings:

  1. TAM:Technology Acceptances Model
    Read Venkatesh, and Davis and sum up the importance of their model for instructional designers working with VR technologies and creating materials for users of VR technologies.
  2. UTAUT: using the theory to learn well with VR and to design good acceptance model for endusers: http://blog.stcloudstate.edu/ims/2020/02/20/utaut/
    Watch both parts of Victoria Bolotina presentation at the Global VR conference. How is she applying UTAUT for her research?
    Read Bracq et al (2019); how do they apply UTAUT for their VR nursing training?

Lab work (continue):

revision from last week:
How to shoot and edit 360 videos: Ben Claremont
https://www.youtube.com/channel/UCAjSHLRJcDfhDSu7WRpOu-w
and
https://www.youtube.com/channel/UCUFJyy31hGam1uPZMqcjL_A

  1. Oculus Quest as VR advanced level
    1. Using the controllers
    2. Confirm Guardian
    3. Using the menu

Oculus Quest main

    1. Watching 360 video in YouTube
      1. Switch between 2D and 360 VR
        1. Play a game

Climbing


Racketball

View this post on Instagram

Hell yeah, @naysy is the ultimate Beat Saber queen! 💃 #VR #VirtualReality #BeatSaber #PanicAtTheDisco

A post shared by Beat Saber (@beatsaber) on

Practice interactivity (space station)

    1. Broadcast your experience (Facebook Live)
  1. Additional (advanced) features of Oculus Quest
    1. https://engagevr.io/
    2. https://sidequestvr.com/#/setup-howto

Interactivity: communication and working collaboratively with Altspace VR

https://account.altvr.com/

setting up your avatar

joining a space and collaborating and communicating with other users

  1. Assignment: Group work
    1. Find one F2F and one online peer to form a group.
      Based on the questions/directions before you started watching the videos:
      – Does this particular technology fit in the instructional design (ID) frames and theories covered
      – how does this particular technology fit in the instructional design (ID) frames and theories covered so far?
      – what models and ideas from the videos you will see seem possible to be replicated by you?
      exchange thoughts with your peers and make a plan to create similar educational product
    2. Post your writing in the following D2L Discussions thread
  2. Augmented Reality with Hololens Watch videos at computer station)
    1. Start and turn off; go through menu

      https://youtu.be/VX3O650comM
    2. Learn gestures, voice commands,
  1. Augmented Reality with Merge Cube
    1. 3D apps and software packages and their compatibility with AR
  2. Augmented Reality with telephone
  3. Samsung Gear 360 video camera
    1. If all other goggles and devices are busy, please feel welcome to use the camera to practice and/or work toward your final project
    2. CIM card and data transfer – does your phone have a CIM card compatible with the camera?
    3. Upload 360 images and videos on your YouTube and FB accounts
  4. Issues with XR
    1. Ethics
      1. empathy
        Peter Rubin “Future Presence”
        http://blog.stcloudstate.edu/ims/2019/03/25/peter-rubin-future-presence/

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Enhance your XR instructional Design with other tools: http://blog.stcloudstate.edu/ims/2020/02/07/crs-loop/

https://aframe.io/

https://framevr.io/

https://learn.framevr.io/ (free learning of frame)

https://hubs.mozilla.com/#/

https://sketchfab.com/ WebxR technology

https://mixedreality.mozilla.org/hello-webxr/

https://studio.gometa.io/landing

+++++++++++
Plamen Miltenoff, Ph.D., MLIS
Professor
320-308-3072
pmiltenoff@stcloudstate.edu
http://web.stcloudstate.edu/pmiltenoff/faculty/
schedule a meeting: https://doodle.com/digitalliteracy
find my office: https://youtu.be/QAng6b_FJqs

Educators in VR

Info on all presentations: https://account.altvr.com/channels/1182698623012438188

Charlie Fink: Setting the Table for the Next Decade in XR

Translating Training Requirements into Immersive Experience

Virtual Reality Technologies for Learning Designers

Virtual Reality Technologies for Learning Designers Margherita Berti

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Technology Acceptance and Learning Process Victoria Bolotina part 1

Technology Acceptance and Learning Process Victoria Bolotina part 2

Assessment of Learning Activities in VR Evelien Ydo part 2

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VR: So Much More Than a Field Trip Shannon Putman, Graduate Assistant/PhD Student, University of Louisville SPED special education https://account.altvr.com/events/1406092840622096803

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VR and Health Professionals Rob Theriault

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Transform Your History Lessons with AR and VR Michael Fricano II

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Transform Your History Lessons with AR and VR Michael Fricano II, Technology Integration Specialist https://www.arvreduhub.com/transform-history

Qlone App for 3D scanning

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2020 Educators in VR International Summit

The 2020 Educators in VR International Summit is February 17-22. It features over 170 speakers in 150+ events across multiple social and educational platforms including AltspaceVRENGAGErumiiMozilla Hubs, and Somnium Space.

The event requires no registration, and is virtual only, free, and open to the public. Platform access is required, so please install one of the above platforms to attend the International Summit. You may attend in 2D on a desktop or laptop computer with a headphone and microphone (USB gaming headphone recommended), or with a virtual device such as the Oculus Go, Quest, and Rift, Vive, and other mobile and tethered devices. Please note the specifications and requirements of each platform.

The majority of our events are on AltspaceVR. AltspaceVR is available for Samsung GearSteam Store for HTC ViveWindows Mixed Reality, and the Oculus Store for RiftGo and Quest users. Download and install the 2D version for use on your Windows desktop computer.

Charlie Fink, author, columnist for Forbes magazine, and Adjunct Faculty member of Chapman University, will be presenting “Setting the Table for the Next Decade in XR,” discussing the future of this innovative and immersive technology, at the 2020 Educators in VR International Summit. He will be speaking in AltspaceVR on Tuesday, February 18 at 1:00 PM EST /

International Summit

Setting the Table for the Next Decade in XR 1PM, Tues, Feb 18 https://account.altvr.com/events/1406089727517393133

Finding a New Literacy for a New Reality 5PM, Tues, Feb 18

https://account.altvr.com/events/1406093036194103494 schedule for new literacy

Finding a New Literacy for a New Reality

Dr. Sarah Jones, Deputy Dean, De Montfort University

This workshop with Dr. Sarah Jones will focus on developing a relevant and new literacy for virtual reality, including the core competencies and skills needed to develop and understand how to become an engaged user of the technology in a meaningful way. The workshop will develop into research for a forthcoming book on Uncovering a Literacy for VR due to be published in 2020.

Sarah is listed as one of the top 15 global influencers within virtual reality. After nearly a decade in television news, Sarah began working in universities focusing on future media, future technology and future education. Sarah holds a PhD in Immersive Storytelling and has published extensively on virtual and augmented reality, whilst continuing to make and create immersive experiences. She has advised the UK Government on Immersive Technologies and delivers keynotes and speaks at conferences across the world on imagining future technology. Sarah is committed to diversifying the media and technology industries and regularly champions initiatives to support this agenda.

Inter-cognitive and Intra-cognitive Communication in Virtual Reality

Inter-cognitive and Intra-cognitive Communication in Virtual Reality

Michael Vallance, Professor, Future University Hakodate

Currently there are limited ways to connect 3D VR environments to physical objects in the real-world whilst simultaneously conducting communication and collaboration between remote users. Within the context of a solar power plant, the performance metrics of the site are invaluable for environmental engineers who are remotely located. Often two or more remotely located engineers need to communicate and collaborate on solving a problem. If a solar panel component is damaged, the repair often needs to be undertaken on-site thereby incurring additional expenses. This triage of communication is known as inter-cognitive communication and intra-cognitive communication: inter-cognitive communication where information transfer occurs between two cognitive entities with different cognitive capabilities (e.g., between a human and an artificially cognitive system); intra-cognitive communication where information transfer occurs between two cognitive entities with equivalent cognitive capabilities (e.g., between two humans) [Baranyi and Csapo, 2010]. Currently, non-VR solutions offer a comprehensive analysis of solar plant data. A regular PC with a monitor currently have advantages over 3D VR. For example, sensors can be monitored using dedicated software such as EPEVER or via a web browser; as exemplified by the comprehensive service provided by Elseta. But when multiple users are able to collaborate remotely within a three-dimensional virtual simulation, the opportunities for communication, training and academic education will be profound.

Michael Vallance Ed.D. is a researcher in the Department of Media Architecture, Future University Hakodate, Japan. He has been involved in educational technology design, implementation, research and consultancy for over twenty years, working closely with Higher Education Institutes, schools and media companies in UK, Singapore, Malaysia and Japan. His 3D virtual world design and tele-robotics research has been recognized and funded by the UK Prime Minister’s Initiative (PMI2) and the Japan Advanced Institute of Science and Technology (JAIST). He has been awarded by the United States Army for his research in collaborating the programming of robots in a 3D Virtual World.

Create Strategic Snapchat & Instagram AR Campaigns

Create Strategic Snapchat & Instagram AR Campaigns

Dominique Wu, CEO/Founder, Hummingbirdsday

Augmented Reality Lens is popular among young people thanks to Snapchat’s invention. Business is losing money without fully using of social media targeting young people (14-25). In my presentation, Dominique Wu will show how businesses can generate more leads through Spark AR (Facebook AR/Instagram AR) & Snapchat AR Lens, and how to create a strategic Snapchat & Instagram AR campaigns.

Domnique Wu is an XR social media strategist and expert in UX/UI design.She has her own YouTube and Apple Podcast show called “XReality: Digital Transformation,” covering the technology and techniques of incorporating XR and AR into social media, marketing, and integration into enterprise solutions.

Mixed Reality in Classrooms Near You

Mixed Reality in Classrooms Near You

Mark Christian, EVP, Strategy and Corporate Development, GIGXR

Mixed Reality devices like the HoloLens are transforming education now. Mark Christian will discuss how the technology is not about edge use cases or POCs, but real usable products that are at Universities transforming the way we teach and learn. Christian will talk about the products of GIGXR, the story of how they were developed and what the research is saying about their efficacy. It is time to move to adoption of XR technology in education. Learn how one team has made this a reality.

As CEO of forward-thinking virtual reality and software companies, Mark Christian employs asymmetric approaches to rapid, global market adoption, hiring, diversity and revenue. He prides himself on unconventional approaches to building technology companies.

Designing Educational Content in VR

Designing Educational Content in VR

Avinash Gyawali, VR Developer, Weaver Studio

Virtual Reality is an effective medium to impart education to the student only if it is done right.The way VR is considered gimmick or not is by the way the software application are designed/developed by the developers not the hardware limitation.I will be giving insight about the VR development for educational content specifically designed for students of lower secondary school.I will also provide insights about the development of game in unity3D game engine.

Game Developer and VR developer with over 3 years of experience in Game Development.Developer of Zombie Shooter, winner of various national awards in the gaming and entertainment category, Avinash Gyawali is the developer of EDVR, an immersive voice controlled VR experience specially designed for children of age 10-18 years.

8:00 AM PST Research Virtual Reality Technologies for Learning Designers Margherita Berti ASVR

Virtual Reality Technologies for Learning Designers

Margherita Berti

Virtual Reality (VR) is a computer-generated experience that simulates presence in real or imagined environments (Kerrebrock, Brengman, & Willems, 2017). VR promotes contextualized learning, authentic experiences, critical thinking, and problem-solving opportunities. Despite the great potential and popularity of this technology, the latest two installations of the Educause Horizon Report (2018, 2019) have argued that VR remains “elusive” in terms of mainstream adoption. The reasons are varied, including the expense and the lack of empirical evidence for its effectiveness in education. More importantly, examples of successful VR implementations for those instructors who lack technical skills are still scarce. Margherita Berti will discuss a range of easy-to-use educational VR tools and examples of VR-based activity examples and the learning theories and instructional design principles utilized for their development.

Margherita Berti is a doctoral candidate in Second Language Acquisition and Teaching (SLAT) and Educational Technology at the University of Arizona. Her research specialization resides at the intersection of virtual reality, the teaching of culture, and curriculum and content development for foreign language education.

Wed 11:00 AM PST Special Event Gamifying the Biblioverse with Metaverse Amanda Fox VR Design / Biblioverse / Training & Embodiment ASVR

Gamifying the Biblioverse with Metaverse

Amanda Fox, Creative Director of STEAMPunks/MetaInk Publishing, MetaInk Publishing

There is a barrier between an author and readers of his/her books. The author’s journey ends, and the reader’s begins. But what if as an author/trainer, you could use gamification and augmented reality(AR) to interact and coach your readers as part of their learning journey? Attend this session with Amanda Fox to learn how the book Teachingland leverages augmented reality tools such as Metaverse to connect with readers beyond the text.

Amanda Fox, Creative Director of STEAMPunksEdu, and author of Teachingland: A Teacher’s Survival Guide to the Classroom Apolcalypse and Zom-Be A Design Thinker. Check her out on the Virtual Reality Podcast, or connect with her on twitter @AmandaFoxSTEM.

Wed 10:00 AM PST Research Didactic Activity of the Use of VR and Virtual Worlds to Teach Design Fundamentals Christian Jonathan Angel Rueda VR Design / Biblioverse / Training & Embodiment ASVR

Didactic Activity of the Use of VR and Virtual Worlds to Teach Design Fundamentals

Christian Jonathan Angel Rueda, research professor, Autonomous University of Queretaro (Universidad Autónoma de Querétaro)

Christian Jonathan Angel Rueda specializaes in didactic activity of the use of virtual reality/virtual worlds to learn the fundamentals of design. He shares the development of a course including recreating in the three-dimensional environment using the fundamentals learned in class, a demonstration of all the works developed throughout the semester using the knowledge of design foundation to show them creatively, and a final project class scenario that connected with the scenes of the students who showed their work throughout the semester.

Christian Jonathan Angel Rueda is a research professor at the Autonomous University of Queretaro in Mexico. With a PhD in educational technology, Christian has published several papers on the intersection of education, pedagogy, and three-dimensional immersive digital environments. He is also an edtech, virtual reality, and social media consultant at Eco Onis.

Thu 11:00 AM PST vCoaching Closing the Gap Between eLearning and XR Richard Van Tilborg XR eLearning / Laughter Medicine ASVR

Closing the Gap Between eLearning and XR

Richard Van Tilborg, founder, CoVince

How we can bridge the gap between eLearning and XR. Richard Van Tilborg discusses combining brain insights enabled with new technologies. Training and education cases realised with the CoVince platform: journeys which start on you mobile and continue in VR. The possibilities to earn from your creations and have a central distribution place for learning and data.

Richard Van Tilborg works with the CoVince platform, a VR platform offering training and educational programs for central distribution of learning and data. He is an author and speaker focusing on computers and education in virtual reality-based tasks for delivering feedback.

 

Thu 12:00 PM PST Research Assessment of Learning Activities in VR Evelien Ydo Technology Acceptance / Learning Assessment / Vaping Prevention ASVR
Thu 6:00 PM PST Down to Basics Copyright and Plagiarism Protections in VR Jonathan Bailey ASVR

 

Thu 8:00 PM PST Diversity Cyberbullying in VR John Williams, Brennan Hatton, Lorelle VanFossen ASVR

IM 690 VR and AR lab

IM 690 Virtual Reality and Augmented Reality. short link: http://bit.ly/IM690lab

IM 690 lab plan for Feb. 18, MC 205:  Experience VR and AR

What is an “avatar” and why do we need to know how it works?

How does the book (and the movie) “Ready Player One” project the education of the future

Peter Rubin “Future Present” pictures XR beyond education. How would such changes in the society and our behavior influence education.

Readings:

each group selected one article of this selection: http://blog.stcloudstate.edu/ims/2020/02/11/immersive-reality-and-instructional-design/
to discuss the approach of an Instructional Designer to XR

Announcements:
http://blog.stcloudstate.edu/ims/2020/02/07/educators-in-vr/

http://blog.stcloudstate.edu/ims/2020/01/30/realities360-conference/

Translating Training Requirements into Immersive Experience

Virtual Reality Technologies for Learning Designers

Virtual Reality Technologies for Learning Designers

Inter

Inter-cognitive and Intra-cognitive communication in VR: https://slides.com/michaelvallance/deck-25c189#/

https://www.youtube.com/channel/UCGHRSovY-KvlbJHkYnIC-aA

People with dementia

https://docs.google.com/presentation/d/e/2PACX-1vSVNHSXWlcLzWZXObifZfhrL8SEeYA59IBdatR1kI7Q-Hry20AHtvLVTWQyH3XxBQ/pub?start=false&loop=false&delayms=60000&slide=id.p1

Free resources:
http://blog.stcloudstate.edu/ims?s=free+audio, free sound, free multimedia

Lab work:

  1. Video 360 as VR entry level
    1. During Lab work on Jan 28, we experienced Video 360 cardboard movies
      let’s take 5-10 min and check out the following videos (select and watch at least three of them)

      1. F2F students, please Google Cardboard
      2. Online students, please view on your computer or mobile devices, if you don’t have googles at your house (you can purchase now goggles for $5-7 from second-hand stores such as Goodwill)
      3. Both F2F and online students. Here directions how to easily open the movies on your mobile devices:
        1. Copy the URL and email it to yourself.
          Open the email on your phone and click on the link
          If you have goggles, click on the appropriate icon lower right corner and insert the phone in the goggles
        2. Open your D2L course on your phone (you can use the mobile app).
          Go to the D2L Content Module with these directions and click on the link.
          After the link opens, insert phone in the goggles to watch the video
      4. Videos:
        While watching the videos, consider the following objectives:
        – Does this particular technology fit in the instructional design (ID) frames and theories covered, e.g. PBL, CBL, Activity Theory, ADDIE Model, TIM etc. (http://blog.stcloudstate.edu/ims/2020/01/29/im-690-id-theory-and-practice/ ). Can you connect the current state, but also the potential of this technology with the any of these frameworks and theories, e.g., how would Google Tour Creator or any of these videos fits in the Analysis – Design – Development – Implementation – Evaluation process? Or, how do you envision your Google Tour Creator project or any of these videos to fit in the Entry – Adoption – Adaptation – Infusion – Transformation process?

– how does this particular technology fit in the instructional design (ID) frames and theories covered so far?
– what models and ideas from the videos you will see seem possible to be replicated by you?

Assignment: Use Google Cardboard to watch at least three of the following options
YouTube:
Elephants (think how it can be used for education)
https://youtu.be/2bpICIClAIg
Sharks (think how it can be used for education)
https://youtu.be/aQd41nbQM-U
Solar system
https://youtu.be/0ytyMKa8aps
Dementia
https://youtu.be/R-Rcbj_qR4g
Facebook
https://www.facebook.com/EgyptVR/photos/a.1185857428100641/1185856994767351/

From Peter Rubin’s Future Presence: here is a link http://blog.stcloudstate.edu/ims/2019/03/25/peter-rubin-future-presence/ if you want to learn more
Empathy, Chris Milk, https://youtu.be/iXHil1TPxvA
Clouds Over Sidra, https://youtu.be/mUosdCQsMkM

  1. Assignment: Group work
    1. Find one F2F and one online peer to form a group.
      Based on the questions/directions before you started watching the videos:
      – Does this particular technology fit in the instructional design (ID) frames and theories covered. e.g. PBL, CBL, Activity Theory, ADDIE Model, TIM etc. (http://blog.stcloudstate.edu/ims/2020/01/29/im-690-id-theory-and-practice/ ). Can you connect the current state, but also the potential of this technology with the any of these frameworks and theories, e.g., how would Google Tour Creator or any of these videos fits in the Analysis – Design – Development – Implementation – Evaluation process? Or, how do you envision your Google Tour Creator project or any of these videos to fit in the Entry – Adoption – Adaptation – Infusion – Transformation process?
      – how does this particular technology fit in the instructional design (ID) frames and theories covered so far?
      – what models and ideas from the videos you will see seem possible to be replicated by you?
      exchange thoughts with your peers and make a plan to create similar educational product
    1. Post your writing in the following D2L Discussions thread: https://stcloudstate.learn.minnstate.edu/d2l/le/4819732/discussions/threads/43483637/View
  1. Lenovo DayDream as VR advanced level
    1. Recording in DayDream
      https://skarredghost.com/2018/08/17/how-to-shoot-cool-screenshots-videos-lenovo-mirage-solo-and-save-them-on-pc/
    2. Using the controller
      https://support.google.com/daydream/answer/7184597?hl=en
    3. Using the menu
    4. Watching 360 video in YouTube
      1. Using keyboard to search
      2. Using voice command to search
    5. Using Labster. https://www.labster.com/
      1. Record how far in the lab you managed to proceed
    6. Playing the games
      1. Evaluate the ability of the game you watched to be incorporated in the educational process

Assignment: In 10-15 min (mind your peers, since we have only headset), do your best to evaluate one educational app (e.g., Labster) and one leisure app (games).
Use the same questions to evaluate Lenovo DayDream:
– Does this particular technology fit in the instructional design (ID) frames and theories covered, e.g. PBL, CBL, Activity Theory, ADDIE Model, TIM etc. (http://blog.stcloudstate.edu/ims/2020/01/29/im-690-id-theory-and-practice/ ). Can you connect the current state, but also the potential of this technology with the any of these frameworks and theories, e.g., how would Google Tour Creator or any of these videos fits in the Analysis – Design – Development – Implementation – Evaluation process? Or, how do you envision your Google Tour Creator project or any of these videos to fit in the Entry – Adoption – Adaptation – Infusion – Transformation process?
– how does this particular technology fit in the instructional design (ID) frames and theories covered so far?
– what models and ideas from the videos you will see seem possible to be replicated by you?

+++++++++++
Plamen Miltenoff, Ph.D., MLIS
Professor
320-308-3072
pmiltenoff@stcloudstate.edu
http://web.stcloudstate.edu/pmiltenoff/faculty/
schedule a meeting: https://doodle.com/digitalliteracy
find my office: https://youtu.be/QAng6b_FJqs

instructional design and models

Instructional Design Models and Theories

https://elearningindustry.com/instructional-design-models-and-theories

Instructional Design Models and Theories History*

  1. 1903 – Ivan Pavlov discovers Classical Conditioning Theory, while conducting research on the digestive system of dogs.
  2. 1910 – Thorndike introduces its Laws and Connectionism Theory, which are based on the Active Learning Principles.
  3. 1922 – Max Wertheimer, Kurt Koffka and Wolfgang Köhler introduce Gestalt Psychology.
  4. 1932 – Psychologist Frederic Bartlett proposes the Schema Theory.
  5. 1937 – B.F. Skinner introduces the Operant Conditioning Theory.
  6. 1937 – May and Doob publish Competition and Cooperation, where the Cooperative and Collaborative Learning Theory is launched, discussed and analyzed.
  7. 1950s – The Information Processing Theory emerges.
  8. 1950s – Computer-based Instruction is used in educational and training environments.
  9. 1954 – Skinner introduces the Programmed Instruction Educational Model.
  10. 1960s – The Inquiry-based Learning Model is developed, based on constructivist learning theories.
  11. 1961 – Jerome Bruner introduces the Discovery Learning Model.
  12. 1960s – Howard Barrows introduces Problem-based Learning (PBL) in the medical education program at McMaster University in Canada.
  13. 1963 – David Ausubel publishes his findings on the Subsumption Theory.
  14. 1962 – The Keller Plan revolves around the Individualized Instruction Model and is used in educational environments throughout the United States.
  15. 1971 – Allan Paivio hypothesized about the Dual Coding Theory; a theory of cognition.
  16. 1974 – Merlin Wittrock publishes the Generative Learning Theory.
  17. 1978- Vygotsky’s Sociocultural Learning Theory influences the West.
  18. 1979 – Charles Reigeluth introduces the Elaboration Theory.
  19. 1980 – Reginald Revans introduces the Action Learning Model.
  20. 1983 – David Merrill introduces the Component Display Theory and Instructional Model.
  21. 1983 – J. M. Keller’s ARCS Model of Motivation is published.
  22. 1988 – Spiro, Feltovich, and Coulson introduce their Cognitive Flexibility Theory.
  23. 1989 – Brown, Collins, Duguid and Newman introduce their Situated Cognition Theory and the Cognitive Apprenticeship Model.
  24. 1990 – The Cognition & Technology Group at Vanderbilt University develops the Anchored InstructionEducational Model.
  25. 1990s – Multimedia and CD-ROMs are introduced in educational environments.
  26. 1991 – Lave and Wenger introduce the Communities of Practice Model and the Situated Learning Theory in “Situated learning: legitimate peripheral participation”.
  27. 1991 – Hudspeth and Knirk publish the case-based Learning Model in Performance Improvement Quarterly.
  28. 1992 – Roger C. Schank releases a technical report, introducing the Goal-based Scenario Model.
  29. 1993 – The first Computer-supported Intentional Learning Environments (CSILEs) prototype is used in a university setting.
  30. 1995 – Saltzberg and Polyson publish Distributed Learning on the World Wide Web, which outlines the Distributed Learning Model.
  31. 1995 – Dodge and March develop WebQuest.
  32. 1996 – Professor Joseph R. Codde publishes a report that outlines Contract Learning.
  33. 2007 – M. Lombardi publishes a report, outlining the Authentic Learning Model.

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more on ID in this IMS blog
http://blog.stcloudstate.edu/ims?s=instructional+design

IRDL proposal

Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations

The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.

 

 

Research Literature

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013  https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.

In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).

The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).

Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).

De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).

The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics.  The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).

Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).

Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?

Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.

Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success.  Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).

Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.

Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).

The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.

Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.

 

Method

 

The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data.  Send survey URL to (academic?) libraries around the world.

Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.

The study will include the use of closed-ended survey response questions and open-ended questions.  The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.

Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17).  Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).

 

Sampling design

 

  • Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
  • 1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
  • data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

Contact libraries. Follow up and contact again, if necessary (low turnaround) – month

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

While it has been widely acknowledged that Big Data (and its handling) is changing higher education (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).

 

This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.

Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.

 

 

References:

 

Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.

Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125

Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5

Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.

Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576

Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132

De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823

Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503

Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115

Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009

Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121

Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006

Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/

Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.

Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746

Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015

Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222–228. https://doi.org/10.1080/12460125.2014.888848

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

Reilly, S. (2013, December 12). What does Horizon 2020 mean for research libraries? Retrieved November 7, 2017, from http://libereurope.eu/blog/2013/12/12/what-does-horizon-2020-mean-for-research-libraries/

Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1

Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194

Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187

Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.

Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010

Weiss, A. (2018). Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals. Rowman & Littlefield Publishers. Retrieved from https://rowman.com/ISBN/9781538103227/Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals

West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.

Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1

Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157

 

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more on big data





flipped classroom resources

More on flipped classroom in this IMS blog:

http://blog.stcloudstate.edu/ims/?s=flipped&submit=Search

what is it?

  • The flipped classroom is a pedagogical model in which the typical lecture and homework elements of a course are reversed.
EDUCAUSE Learning Initiative 7 Things You Should Know About Flipped Classrooms – eli7081.pdf. (n.d.). Retrieved March 23, 2016, from https://net.educause.edu/ir/library/pdf/eli7081.pdf
  • Flipped classroom is an instructional strategy and a type of blended learning that reverses the traditional educational arrangement by delivering instructional content, often online, outside of the classroom.

Flipped classroom. (2016, March 22). In Wikipedia, the free encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Flipped_classroom&oldid=711368580

  • In essence, “flipping the classroom” means that students gain first exposure to new material outside of class, usually via reading or lecture videos, and then use class time to do the harder work of assimilating that knowledge, perhaps through problem-solving, discussion, or debates.
Flipping the Classroom | Center for Teaching | Vanderbilt University. (n.d.). Retrieved March 23, 2016, from https://cft.vanderbilt.edu/guides-sub-pages/flipping-the-classroom/

flipped classroom

 

flipped classroom

flipped classroom

The Flipped Class: Overcoming Common Hurdles by Edutopia:
http://www.edutopia.org/blog/flipped-learning-toolkit-common-hurdles-jon-bergmann

platforms like Blackboard and Canvas are playing a bigger role in the flipped learning environment. Other viable options include Google’s Classroom, which “automates” the sharing process but isn’t necessarily an organizational tool.
McCrea, B. (2016). 6 Flipped Learning Technologies To Watch in 2016. THE Journal. Retrieved from https://thejournal.com/articles/2016/03/16/6-flipped-learning-technologies-to-watch-in-2016.aspx

Pros:

  • Helps kids who were absent, stay current.

  • Helps kids who don’t get the lesson the first time in class.

  • Good resource for teacher assistants or student support staff who may not know the curriculum or may not know what to focus on.

  • Can attach Google spreadsheets or other online quizzes to check for comprehension, along with the video link sent to students

Pros and Cons of The Flipped Classroom. (n.d.). Retrieved March 23, 2016, from http://www.teachhub.com/pros-and-cons-flipped-classroom
  • Students have more control
  • It promotes student-centered learning and collaboration
  • Access = easier for parents to see what’s going on
  • It can be more efficient
Acedo, M. (2013, November 27). 10 Pros And Cons Of A Flipped Classroom. Retrieved from http://www.teachthought.com/learning/blended-flipped-learning/10-pros-cons-flipped-classroom/
an example of a positive take:
  • Myth #1 – Proponents of the Flipped Classroom Methodology Dislike Lectures
  • Myth #2 – Flipping Your Class Means Getting Rid of Lecturing
  • Myth #3 – Flipping Your Class Will Mean That Students Will Stop Coming to Class
  • Myth #4 – Flipping Your Class Will Require Lots of Technical Knowledge
  • Myth #5 – Flipping Your Class Will Require Huge Amounts of Time
  • Myth #6 – Students Will Not Like the Flipped Class, and Your Teaching Evaluations Will Suffer
Kim, J. (n.d.). 6 Myths of the Flipped Classroom | Inside Higher Ed. Retrieved March 23, 2016, from https://www.insidehighered.com/blogs/technology-and-learning/6-myths-flipped-classroom

Cons:

  • I have a long way to go in my skill set in making the videos interesting (they, to me anyway, are really boring to watch).
  • I’m not sure how much they (the videos) are being utilized. There are just certain items that are learned better through direct one on one contact.
  • I know as I’m teaching, I get direct feedback from my students by looking at their faces and gauging comprehension. I, as a teacher, don’t get that feedback as I’m designing and creating my videos.”
Pros and Cons of The Flipped Classroom. (n.d.). Retrieved March 23, 2016, from http://www.teachhub.com/pros-and-cons-flipped-classroom
  • It can create or exacerbate a digital divide
  • It relies on preparation and trust
  • Not naturally a test-prep form of learning
  • Time in front of screens–instead of people and places–is increased
Acedo, M. (2013, November 27). 10 Pros And Cons Of A Flipped Classroom. Retrieved from http://www.teachthought.com/learning/blended-flipped-learning/10-pros-cons-flipped-classroom/
an example of negative take:
  • I dislike the idea of giving my students homework.
  • A lecture by video is still a lecture.
  • I want my students to own their learning.
  • My students need to be able to find and critically evaluate their own resources
Wright, S. (2012, October 8). The Flip: End of a Love Affair. Retrieved March 23, 2016, from http://plpnetwork.com/2012/10/08/flip-love-affair/

Research:

Zuber, W. J. (2016). The flipped classroom, a review of the literature. Industrial & Commercial Training, 48(2), 97-103. doi:10.1108/ICT-05-2015-0039 http://www.emeraldinsight.com/doi/full/10.1108/ICT-05-2015-0039

although learning styletheories serve as a justification for different learning activities it does not provide the necessarytheoretical framework as to how the activities need to be structured (Bishop and Verleger, 2013). p. 99

One observation from the literature is there is a lack of consistency of models of the FCM (Davieset al.,2013, p. 565) in addition to a lack of research into student performance, (Findlay-Thompson andMombourquette, 2014, p. 65; Euniceet al., 2013) broader impacts on taking up too much of thestudents’time and studies of broader student demographics. In another literature review of the FCM,Bishop and Verleger concur with the observation that there is a lack of consensus as to the definitionof the method and the theoretical frameworks (Bishop and Verleger, 2013). p. 99

The FCM isheavily reliant on technology and this is an important consideration for all who consideremploying the FCM. p. 101

Flipped Classrooms’ may not have any impact on learning:
http://blog.stcloudstate.edu/ims/2013/10/23/flipped-classrooms-may-not-have-any-impact-on-learning/

Gross, B., Marinari, M., Hoffman, M., DeSimone, K., & Burke, P. (2015). Flipped @ SBU: Student Satisfaction and the College Classroom. Educational Research Quarterly, 39(2), 36-52.
we found that high levels of student engagement and course satisfaction characterised the students in the flipped courses, without any observable reduction in academic performance.

Hotle, S. L., & Garrow, L. A. (2016). Effects of the Traditional and Flipped Classrooms on Undergraduate Student Opinions and Success. Journal Of Professional Issues In Engineering Education & Practice, 142(1), 1-11. doi:10.1061/(ASCE)EI.1943-5541.0000259
It was found that student performance on quizzes was not significantly different across the traditional and flipped classrooms. A key shortcoming noted with the flipped classroom was students’ inability to ask questions during lectures. Students in flipped classrooms were more likely to attend office hours compared to traditional classroom students, but the difference was not statistically significant.

Heyborne, W. H., & Perrett, J. J. (2016). To Flip or Not to Flip? Analysis of a Flipped Classroom Pedagogy in a General Biology Course. Journal Of College Science Teaching, 45(4), 31-37.
Although the outcomes were mixed, regarding the superiority of either pedagogical approach, there does seem to be a trend toward performance gains using the flipped pedagogy. We strongly advocate for a larger multiclass study to further clarify this important pedagogical question.

Tomory, A., & Watson, S. (2015). Flipped Classrooms for Advanced Science Courses. Journal Of Science Education & Technology, 24(6), 875-887. doi:10.1007/s10956-015-9570-8

 

Helping a psychology student from Edinburgh Napier with his essay:

To what extent have the new generation of psychodynamic psychoanalysts addressed the issues raised by the ferocious critiques of Freud’s work that have emerged?

Almond, R. (2006). How do we bridge the gap? Commentary on Luyten, Blatt, and Corveleyn. Journal of the American Psychoanalytic Association, 54(2), 611–618.

Ambrosio, J. (2010). A Fearsome Trap: The will to know, the obligation to confess, and the Freudian subject of desire. Educational Philosophy & Theory, 42(7), 728–741.

Appelbaum, J. (2013). Psychoanalysis and philosophy: Nurturing dialogues. The American Journal of Psychoanalysis, 73(2), 117–120.

Auxéméry, Y. (2015). « Névrose » et « Psychose »: Quelles définitions pour la psychiatrie contemporaine ? = “Neurosis” and “psychosis”: What definitions for contemporary psychiatry? Annales Médico-Psychologiques, 173(8), 643–648. http://doi.org/10.1016/j.amp.2014.12.015

Brookes, C. E. (2015). Review of From classical to contemporary psychoanalysis: A critique and integration. Psychodynamic Psychiatry, 43(4), 507–512.

Cohen, J. (2007). Interdisciplinary Psychoanalysis and the Education of Children: Psychoanalytic and Educational Partnerships. Psychoanalytic Study of the Child, 62, 180–207.

Cooper-White, P. (2002). “Higher Powers and Infernal Regions”: Models of Mind in Freud’s Interpretation of Dreams and Contemporary Psychoanalysis, and Their Implications for Pastoral Theology. Pastoral Psychology, 50(5), 319–343.

Cooper-White, P. (2014). Review of Psychoanalysis, monotheism and morality: The Sigmund Freud Museum Symposia, 2009–2011. Journal of the American Psychoanalytic Association, 62(6), 1163–1170. http://doi.org/10.1177/0003065114558924

Cortina, M. (2012). Review of Psychoanalysis and motivational systems. A new look. Psychodynamic Psychiatry, 40(2), 357–364.

Daldin, H. (1988). The fate of the sexually abused child. Clinical Social Work Journal, 16(1), 22–32.

Dimen, M. (2014). Inside the Revolution: Power, Sex, and Technique in Freud’s “‘Wild’ Analysis”. Psychoanalytic Dialogues, 24(5), 499–515.

Eckardt, M. H. (2003). Evolution of psychoanalysis. Journal of the American Academy of Psychoanalysis, 31(4), 726–728.

Eliman, S. J. (2005). Rothstein as a Self and Object Freudian. Psychoanalytic Dialogues, 15(3), 459–471.

Friedman, N. (1976). From the experiential in therapy to experiential psychotherapy: A history. Psychotherapy: Theory, Research & Practice, 13(3), 236–243. http://doi.org/10.1037/h0088347

Golding, R. (1982). Freud, psychoanalysis, and sociology:  some observations on the sociological analysis of the individual. British Journal of Sociology, 33(4), 545–562.

Gordon, P. (2014). Radical analyses? Psychodynamic Practice, 20(1), 68–74.

Granqvist, P. (2006). On the relation between secular and divine relationships: An emerging attachment perspective and a critique of the “depth” approaches. International Journal for the Psychology of Religion, 16(1), 1–18. http://doi.org/10.1207/s15327582ijpr1601_1

Greenwood, D. (2010). Embracing the “allegiance effect” as a positive quality in research into the psychological therapies-exploring the concept of “influence”. European Journal of Psychotherapy & Counselling, 12(1), 41–54.

Hansen, I. A. (2007). Buddhist Influences on the Idea of the Unconscious. Psychological Perspectives, 50(2), 181–197.

Jeffrey M JJ Jackson. (2008). Philosophy as Melancholia: Freud, Kant, Foucault. Psychoanalysis, Culture & Society, 13(3), 299–315.

Jones, G. Y. (1999). “Beyond the Oedipal Complex”:  Freud and Lacan revisited. Psychodynamic Counselling, 5(4), 453.

Kerr, J. (2012). Review of From classical to contemporary psychoanalysis: A critique and integration. Psychoanalytic Review, 99(5), 785–792.

Knoblauch, S. H. (2005). Body Rhythms and the Unconscious: Toward an Expanding of Clinical Attention. Psychoanalytic Dialogues, 15(6), 807–827.

Kronemyer, D. E. (2011). Freud’s Illusion: New Approaches to Intractable Issues. International Journal for the Psychology of Religion, 21(4), 249–275.

Kruger, L.-M. (2006). A tribute to 150 years of Sigmund Freud: Not mastering the mind: Freud and the “Forgotten Material” of Psychology. Psycho-Analytic Psychotherapy in South Africa, 14(2), 1–12.

Lee, N.-N. (2014). Sublimated or castrated psychoanalysis? Adorno’s critique of the revisionist psychoanalysis: An introduction to “The Revisionist Psychoanalysis”. Philosophy & Social Criticism, 40(3), 309–338.

Lenthall, A. (2007). Review of Psychotherapy and phenomenology: On Freud, Husserl and Heidegger. Psychodynamic Practice: Individuals, Groups and Organisations, 13(4), 423–427. http://doi.org/10.1080/14753630701609939

Lothane, Z. (2006). Freud’s legacy–is it still with us? Psychoanalytic Psychology, 23(2), 285–301. http://doi.org/10.1037/0736-9735.23.2.285

Metcalf, R. (2000). THE TRUTH OF SHAME-CONSCIOUSNESS IN FREUD AND PHENOMENOLOGY. Journal of Phenomenological Psychology, 31(1), 1–18.

Mook, B. (2007). Review of Psychotherapy and phenomenology. The Humanistic Psychologist, 35(4), 401–403. http://doi.org/10.1080/08873260701593417

Moyaert, P. (2013). The Death Drive and the Nucleus of the Ego: An Introduction to Freudian Metaphysics. Southern Journal of Philosophy, 51, 94–119.

Noys, B. (2009). Revolution in Psychology: Alienation to Emancipation The Lacanian Left: Psychoanalysis, Theory, and Politics. Historical Materialism, 17(1), 183–190.

Palombo, J. (2013). The Self As a Complex Adaptive System Part I: Complexity, Metapsychology, and Developmental Theories. Psychoanalytic Social Work, 20(1), 1–25.

Pavón-Cuéllar, D. (2014). The Freudo-Marxist Tradition and the Critique of Psychotherapeutic Ideology. Psychotherapy & Politics International, 12(3), 208–219.

Pedroni, I. (2015). Finding New Ways of Belonging Through Religious Experience in the Framework of a Therapeutic Encounter. International Journal of Psychoanalytic Self Psychology, 10(4), 343–354.

Price, M. (1995). The illusion of theory: Discussion of R. D. Chessick’s “Poststructural psychoanalysis or wild analysis?.” Journal of the American Academy of Psychoanalysis, 23(1), 63–70.

Robinson, P. (1985). FREUD UNDER SIEGE. Halcyone (01986449), 7, 1–15.

Rogers, R. (1989). Review of The Psychotic Core. Psychoanalytic Psychology, 6(3), 367–373. http://doi.org/10.1037/h0079741

Samuels, R. (1998). Passing beyond ego psychology: Freud, Lacan and the end of analysis. Clinical Studies: International Journal of Psychoanalysis, 4(1), 93–104.

Schafer, R. (1970). Requirements for a critique of the theory of catharsis. Journal of Consulting and Clinical Psychology, 35(1, Pt.1), 13–17. http://doi.org/10.1037/h0029613

Schafer, R. (1975). Psychoanalysis without psychodynamics. The International Journal of Psychoanalysis, 56(1), 41–55.

Shafranske, E. P. (1992). A Psychoanalytic Response to Hood. International Journal for the Psychology of Religion, 2(3), 161.

SHILL, M. (2011). Intersubjectivity and the Ego. Psychoanalytic Social Work, 18(1), 1–22.

Thomas, A. (2007). Practical Irrationality, Reflexivity and Sartre’s Regress Argument. Teorema, 26(3), 113–121.

Tillman, J. G. (1998). Psychodynamic psychotherapy, religious beliefs, and self-disclosure. American Journal of Psychotherapy, 52(3), 273.

Wax, M. L. (1995). Method as Madness: Science, hermeneutics, and art in psychoanalysis. Journal of the American Academy of Psychoanalysis, 23(4), 525–543.

Webster, J. (2013). Critique and cure: A dream of uniting psychoanalysis and philosophy. The American Journal of Psychoanalysis, 73(2), 138–157. http://doi.org/10.1057/ajp.2013.1

Weinberger, J., & Westen, D. (2001). Science and psychodynamics: From arguments about Freud to data. Psychological Inquiry, 12(3), 129–132. http://doi.org/10.1207/S15327965PLI1203_02

Zepf, S. (2012). Repression and Substitutive Formation: The Relationship Between Freud’s Concepts Reconsidered. Psychoanalytic Review, 99(3), 397–420.

Zepf, S. (2013). Abwehr, Verdrängung und Ersatzbildung: Die Beziehung zwischen Freuds Konzepten neu organisiert. Defence, Repression and Substitutive Formation: The Relationship between Freud’s Reorganized Concepts, 29(4), 499–515.

Academic Journal

By: Freeman, Tabitha. Studies in Gender & Sexuality. Spring2008, Vol. 9 Issue 2, p113-139. 27p. DOI: 10.1080/15240650801935156., Database: EBSCO MegaFILE

Subjects: ESSAY (Literary form); PSYCHOANALYSIS; FATHERHOOD; OEDIPUS complex; PARENT & child; FATHER & child; PATRILINEAL kinship

Add to folder

handbook of mobile learning

Routledge. (n.d.). Handbook of Mobile Learning (Hardback) – Routledge [Text]. Retrieved May 27, 2015, from http://www.routledge.com/books/details/9780415503693/

Crompton, H. (2013). A historical overview of mobile learning: Toward learner-centered education. Retrieved June 2, 2015, from https://www.academia.edu/5601076/A_historical_overview_of_mobile_learning_Toward_learner-centered_education

Crompton, Muilenburg and Berge’s definition for m-learning is “learning across multiple contexts, through social and content interactions, using personal electronic devices.”
The “context”in this definition encompasses m-learnng that is formalself-directed, and spontaneous learning, as well as learning that is context aware and context neutral.
therefore, m-learning can occur inside or outside the classroom, participating in a formal lesson on a mobile device; it can be self-directed, as a person determines his or her own approach to satisfy a learning goal; or spontaneous learning, as a person can use the devices to look up something that has just prompted an interest (Crompton, 2013, p. 83). (Gaming article Tallinn)Constructivist Learnings in the 1980s – Following Piage’s (1929), Brunner’s (1996) and Jonassen’s (1999) educational philosophies, constructivists proffer that knowledge acquisition develops through interactions with the environment. (p. 85). The computer was no longer a conduit for the presentation of information: it was a tool for the active manipulation of that information” (Naismith, Lonsdale, Vavoula, & Sharples, 2004, p. 12)Constructionist Learning in the 1980s – Constructionism differed from constructivism as Papert (1980) posited an additional component to constructivism: students learned best when they were actively involved in constructing social objects. The tutee position. Teaching the computer to perform tasks.Problem-Based learning in the 1990s – In the PBL, students often worked in small groups of five or six to pool knowledge and resources to solve problems. Launched the sociocultural revolution, focusing on learning in out of school contexts and the acquisition of knowledge through social interaction

Socio-Constructivist Learning in the 1990s. SCL believe that social and individual processes are independent in the co-construction of knowledge (Sullivan-Palinscar, 1998; Vygotsky, 1978).

96-97). Keegan (2002) believed that e-learning was distance learning, which has been converted to e-learning through the use of technologies such as the WWW. Which electronic media and tools constituted e-learning: e.g., did it matter if the learning took place through a networked technology, or was it simply learning with an electronic device?

99-100. Traxler (2011) described five ways in which m-learning offers new learning opportunities: 1. Contingent learning, allowing learners to respond and react to the environment and changing experiences; 2. Situated learning, in which learning takes place in the surroundings applicable to the learning; 3. Authentic learning;

Diel, W. (2013). M-Learning as a subfield of open and distance education. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.

  1. 15) Historical context in relation to the field of distance education (embedded librarian)
  2. 16 definition of independent study (workshop on mlearning and distance education
  3. 17. Theory of transactional distance (Moore)

Cochrane, T. (2013). A Summary and Critique of M-Learning Research and Practice. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
( Galin class, workshop)

P 24

According to Cook and Sharples (2010) the development of M learning research has been characterized by three general faces a focus upon Devices Focus on learning outside the classroom He focus on the mobility of the learner

  1. 25

Baby I am learning studies focus upon content delivery for small screen devices and the PDA capabilities of mobile devices rather than leveraging the potential of mobile devices for collaborative learning as recommended by hope Joyner Mill Road and sharp P. 26 Large scale am learning project Several larger am learning projects have tended to focus on specific groups of learners rather than developing pedagogical strategies for the integration of am mlearning with him tertiary education in general

27

m learning research funding

In comparison am learning research projects in countries with smaller population sizes such as Australia and New Zealand are typiclly funded on a shoe string budget

28

M-learning research methodologies

I am learning research has been predominantly characterized by short term case studies focused upon The implementation of rapidly changing technologies with early adopters but with little evaluation reflection or emphasis on mainstream tertiary-education integration

 

p. 29 identifying the gaps in M learning research

 

lack of explicit underlying pedagogical theory Lack of transferable design frameworks

 

Cochrane, T. (2011).Proceedings ascilite 2011 Hobart:Full Paper 250 mLearning: Why? What? Where? How? http://www.ascilite.org/conferences/hobart11/downloads/papers/Cochrane-full.pdf
(Exploring mobile learning success factors http://files.eric.ed.gov/fulltext/EJ893351.pdf
https://prezi.com/kr94rajmvk9u/mlearning/
https://thomcochrane.wikispaces.com/MLearning+Praxis

Pachler, N., Bachmair, B., and Cook, J. (2013). A Sociocultural Ecological Frame for Mobile Learning. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
(Tom video studio)

35 a line of argumentation that defines mobile devices such as mobile phones as cultural resources. Mobile cultural resources emerge within what we call a “bile complex‘, which consist of specifics structures, agency and cultural practices.

36 pedagogy looks for learning in the context of identify formation of learners within a wider societal context However at the beginning of the twentieth first century and economy oriented service function of learning driven by targets and international comparisons has started to occupy education systems and schools within them Dunning 2000 describes the lengthy transformation process from natural assets Land unskilled labor to tangible assets machinery to intangible created assets such as knowledge and information of all kinds Araya and Peters 2010 describe the development of the last 20 years in terms of faces from the post industrial economy to d information economy to the digital economy to the knowledge economy to the creative economy Cultural ecology can refer to the debate about natural resources we argue for a critical debate about the new cultural resources namely mobile devices and the services for us the focus must not be on the exploitation of mobile devices and services for learning but instead on the assimilation of learning with mobiles in informal contacts of everyday life into formal education

37

Ecology comes into being is there exists a reciprocity between perceiver and environment translated to M learning processes this means that there is a reciprocity between the mobile devices in the activity context of everyday life and the formal learning

45

Rather than focusing on the acquisition of knowledge in relation to externally defined notions of relevance increasingly in a market-oriented system individual faces the challenge of shape his/her knowledge out of his/her own sense of his/her world information is material which is selected by individuals to be transformed by them into knowledge to solve a problem in the life world

Crompton, H. (2013). A Sociocultural Ecological Frame for Mobile Learning. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.

p. 47 As philosophies and practice move toward learner-centered pedagogies, technology in a parallel move, is now able to provide new affordances to the learner, such as learning that is personalized, contextualized, and unrestricted by temporal and spatial constrains.

The necessity for m-learning to have a theory of its own, describing exactly what makes m-learning unique from conventional, tethered electronic learning and traditional learning.

48 . Definition and devices. Four central constructs. Learning pedagogies, technological devices, context and social interactions.

“learning across multiple contexts, through social and content interactions, using personal electronic devices.”

It is difficult, and ill advisable, to determine specifically which devices should be included in a definition of m-learning, as technologies are constantly being invented or redesigned. (my note against the notion that since D2L is a MnSCU mandated tool, it must be the one and only). One should consider m-learning as the utilization of electronic devices that are easily transported and used anytime and anywhere.

49 e-learning does not have to be networked learning: therefore, e-learnng activities could be used in the classroom setting, as the often are.

Why m-learning needs a different theory beyond e-learning. Conventional e-learning is tethered, in that students are anchored to one place while learning. What sets m-learning apart from conventional e-learning is the very lack of those special and temporal constrains; learning has portability, ubiquitous access and social connectivity.

50 dominant terms for m-learning should include spontaneous, intimate, situated, connected, informal, and personal, whereas conventional e-learning should include the terms computer, multimedia, interactive, hyperlinked, and media-rich environment.

51 Criteria for M-Learning
second consideration is that one must be cognizant of the substantial amount of learning taking place beyond the academic and workplace setting.

52 proposed theories

Activity theory: Vygotsky and Engestroem

Conversation theory: Pask 1975, cybernetic and dialectic framework for how knowledge is constructed. Laurillard (2007) although conversation is common for all forms of learning, m-learning can build in more opportunities for students to have ownership and control over what they are learning through digitally facilitated, location-specific activities.

53 multiple theories;

54 Context is central construct of mobile learning. Traxler (2011) described the role of context in m-learning as “context in the wider context”, as the notion of context becomes progressively richer. This theme fits with Nasimith et al situated theory, which describes the m-learning activities promoting authentic context and culture.

55. Connectivity
unlike e-learning, the learner is not anchored to a set place. it links to Vygotsky’s sociocultural approach.
Learning happens within various social groups and locations, providing a diverse range of connected  learning experiences. furthermore, connectivity is without temporal restraints, such as the schedules of educators.

55. Time
m-larning as “learning dispersed in time”

55. personalization
my note student-centered learning

Moura, A., Carvalho, A. (2013). Framework For Mobile Learning Integration Into Educational Contexts. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.

p. 58 framework is based on constructivist approach, Activity theory, and the attention, relevance and confidence satisfaction (ARCS) model http://www.arcsmodel.com/#!
http://torreytrust.com/images/ITH_Trust.pdf

to set a didacticmodel that can be applied to m-learning requires looking at the characteristics of specific devi

https://www.researchgate.net/profile/Nadire_Cavus/publication/235912545_Basic_elements_and_characteristics_of_mobile_learning/links/02e7e526c1c0647142000000.pdf
https://eleed.campussource.de/archive/9/3704

Instructional Design

7 Things You Should Know About Developments in Instructional Design

http://www.educause.edu/library/resources/7-things-you-should-know-about-developments-instructional-design

Please read the entire EducCause article here: eli7120

discussion of IMS with faculty:

  • pedagogical theories
  • learning outcome
  • design activities
  • students’ multimedia assignments, which lead to online resources
  • collaboration with other departments for the students projects
  • moving the class to online environment (even if kept hybrid)

What is it?

the complexity of the learning environment is turning instructional design into a more dynamic activity, responding to changing educational models and expectations. Flipped classrooms, makerspaces, and competency-based learning are changing how instructors work with students, how students work with course content, and how mastery is verified. Mobile computing, cloud computing, and data-rich repositories have altered ideas about where and how learning takes place.

How does it work?

One consequence of these changes is that designers can find themselves filling a variety of roles. Today’s instructional designer might work with subject-matter experts, coders, graphic designers, and others. Moreover, the work of an instructional designer increasingly continues throughout the duration of a course rather than taking place upfront.

Who’s doing it?

The responsibility for designing instruction traditionally fell to the instructor of a course, and in many cases it continues to do so. Given the expanding role and landscape of technology—as well as the growing body of knowledge about learning and about educational activities and assessments— dedicated instructional designers are increasingly common and often take a stronger role.

Why is it significant?

The focus on student-centered learning, for example, has spurred the creation of complex integrated learning environments that comprise multiple instructional modules. Competency-based learning allows students to progress at their own pace and finish assignments, courses, and degree plans as time and skills permit. Data provided by analytics systems can help instructional designers predict which pedagogical approaches might be most effective and tailor learning experiences accordingly. The use of mobile learning continues to grow, enabling new kinds of learning experiences.

What are the downsides?

Given the range of competencies needed for the position, finding and hiring instructional designers who fit well into particular institutional cultures can be challenging to the extent that instructors hand over greater amounts of the design process to instructional designers, some of those instructors will feel that they are giving up control, which, in some cases, might appear to be simply the latest threat to faculty authority and autonomy. My note: and this is why SCSU Academic Technology is lead by faculty not IT staff. 

Where is it going?

In some contexts, instructional designers might work more directly with students, teaching them lifelong learning skills. Students might begin coursework by choosing from a menu of options, creating their own path through content, making choices about learning options, being more hands-on, and selecting best approaches for demonstrating mastery. Educational models that feature adaptive and personalized learning will increasingly be a focus of instructional design. My note: SCSU CETL does not understand instructional design tendencies AT ALL. Instead of grooming faculty to assume the the leadership role and fill out the demand for instructional design, it isolates and downgrades (keeping traditional and old-fashioned) instructional design to basic tasks of technicalities done by IT staff.

What are the implications for teaching and learning?

By helping align educational activities with a growing understanding of the conditions,
tools, and techniques that enable better learning, instructional designers can help higher education take full advantage of new and emerging models of education. Instructional
designers bring a cross-disciplinary approach to their work, showing faculty how learning activities used in particular subject areas might be effective in others. In this way, instructional
designers can cultivate a measure of consistency across courses and disciplines in how educational strategies and techniques are incorporated. Designers can also facilitate the
creation of inclusive learning environments that offer choices to students with varying strengths and preferences.

More on instructional design in this IMS blog:

http://blog.stcloudstate.edu/ims/2014/10/13/instructional-design/

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