The theoretical categorizing of digital learning environments with a example of the grand theories applied to a case study of the spread of misconceptions.

1. The Power of
Massive Informal Learning
Environments
Donny Tusler, M.Ed. Ph.D Student; dkt124@psu.edu
Nicole Wang, MS.Ed, IEDP, Ph.D Student; nicolewang12@gmail.com
Penn State University; World Campus, Learning Design & College of Education

2. Overview
What is M.I.L.Es? p-M.I.LEs?
Visual Model of digital environments with technology
Theories
Case Study
Visualization

3. Three Digital Learning environments
Formal
LMS
Non-formal
MOOCs
Informal
Social Networks

4. What is a Massive Informal Environment(s)?
M.I.L.Es
The digital learning networks consisting of informal connected learning,
composed of the creation and or organization of knowledge and content,
through the dissemination of knowledge through social networks. These social
networks result in both synchronous and asynchronous interactions with no
measurable learning outcome or structure.

5. Personal Massive Learning Environments
A Personal Massive Learning Environment (p-M.I.L.Es) is the organization of
learning portals and affinity spaces, connecting each p-MILEs with the Massive
Learning Environment; this includes social media platforms and applications.
The tools (such as mobile devices, computers, etc.) and software platforms
allow one to create personalized digital learning networks, which varies in level
of engagement, seamlessness, and connectivity to M.I.L.E

6. Leveraging M.I.L.Es
Transformation of API data from p-MILEs to
collect and track the big data. This big data will
show metadata, personal interests, time,
click-by-click, levels of engagement, and more.
- Presented NMC Summer 2016
- Donny Tusler and Nicole Wang

7. Model is Evolving
“The idea is simple yet potentially transformative: analytics provides a new model for college and university leaders to improve
teaching, learning, organizational efficiency, and decision making and, as a consequence, serve as a foundation for systemic
change.” (Siemens, G., & Long, P. 2011.p. 32)

8. Learning Theories & Perspectives
Constructivism
Constructionism
Sociocultural
Cognitivism
Behaviorism
Connectivism
The Shift- Karl Fisch- The impact of digital or ethereal
dimension or non-tangible.
● 2008- 2.7 billion google searches/month
● 2016- >100 billion google searches/month
● Each search is manifestation of a question or a
potential learning moment.
Bell- “Life-Wide Learning”
Self directed learning based on interest

9. Case Study
About: I make comedy videos. I also
post stuff that I find funny around the
internet. I do not profit from any of
the direct posts on this page. You
have an issue or believe something
was not credited correctly then let
me know in a message!
Observed a Social Experiment
conducted by this comedy fan page.

10. Qualities of the photo
Original Photo:
Director present- photoshopped
Posing
HighResolution
At least one highly recognizable
actor.

11. Case Study
Levels of learning- not trackable, but through comments
Levels of cognition- how is it measured?
Levels of unconscious cognition- how is it measured?
Unconscious cognition vs. implicit learning?
Measurements of Engagement- digital tracking of likes and shares. Click
streams. Meaning behind the shares, likes:

12. Analytics
Oct 7:
214,271 shares
15,306 likes
13 K comments
#Belief of real
#Movie
recognition
First 4,000 shares/likes
analysed.
April 16:
142,487 shares
15,306 likes
11 K comments
# Belief of real
# Belief of unreal

13. Ethics of Leveraging
Data- Private vs. Public?
IRB- Non-human study
Informed consent
Anonymity
Risk of harm
Terms, conditions and ethical research
with social media
Privacy and risk
Re-use and republication
For educators of digital literacy: Ethics need to be taught in digital literacy.

14. References
Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human development, 49(4), 193-224
Bell, F. (May, 2010). Network theories for technology-enabled learning and social change: Connectivism and actor network theory. In Networked Learning Conference 2010: Seventh International Conference on Networked Learning.
Bell, Philip, et al. Theoretical Perspectives. Learning Science in Informal Environments: People, Places, and Pursuits.Washington, D.C. NAP. 2009. pp. 27-53
Bransford, John D., et al. Foundations and Opportunities for an Interdisciplinary Science of Learning. Cambridge Handbook of the Learning Sciences. Sawyer, K. ed. New York. Cambridge University Press. 2006. 0521607779. Ch. 2. pp.
19-34.
Center for Teaching. (2016). Retrieved May 26, 2016, from https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/
Cummins, M., Johnson, L., & Adams, S. (2012). The NMC horizon report: 2012 higher education edition. The New Media Consortium.
Culter, D. (May 1, 2015). Shift Happens: Karl Fisch On Education. Retrieved October26 , 2015, retrieved from http://www.spinedu.com/shift-happens-karl-fisch-education/ - .VkjtDVNViko
Driscoll, M. (2005). Introduction to Theories of Learning and Instruction. In Psychology of Learning for Instruction (pp. 29-68). Boston, MA: Pearson.
Ito, M., Gutierrez, K., Livingstone, S., Penuel, B., Rhodes, J., Salen, K., ... & Watkins, S. C. (2013). Connected learning: An agenda for research and design. Digital Media and Learning Research Hub.
Gee, James P. (2004) Affinity Spaces. Situated Language and Learning. New York. Routledge.0415317762. pp. 79-89.
Statistia., (2016). Leading global social networks 2016. Retrieved June 07, 2016, from http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

15. References - continued
Looi, C. K., Seow, P., Zhang, B., So, H. J., Chen, W., & Wong, L. H. (2010). Leveraging mobile technology for sustainable seamless learning: a research agenda. British Journal of Educational Technology, 41(2), 154-169.
Morris, D. Z. (March 27, 2016). Netflix says Geography, Age, and Gender are “Garbage” for Predicting Taste. Retrieved April 14, 2016, from http://fortune.com/2016/03/27/netflix-predicts-taste/
Office of Educational Technology, U.S. Department of Education, "Learning, " Future Ready Learning: Reimagining the Role of Technology in Education, 2016 National Education Technology Plan.
Pea, R. D., & Gomez, L. M. (1992). Distributed multimedia learning environments: Why and how?. Interactive learning environments, 2(2), 73-109.
Sawyer, R. K. (Ed.). (2014). The Cambridge Handbook of the Learning Sciences (2nd ed.). Cambridge University Press.
Siemens, G. (2014). Connectivism: A learning theory for the digital age.
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30.
Shulman, D. (March 7, 2016). Personalized Learning: Toward a Grand Unifying Theory. EDUCAUSE. Retrieved May 10, 2016, from http://er.educause.edu/articles/2016/3/personalized-learning-toward-a-grand-unifying-theory
21st Century Skills Definition. (2013). The glossary of education reform. Retrieved May 26, 2016, from http://edglossary.org/21st-century-skills/

16. References- continued
Case Study data source link: https://www.facebook.com/shares/view?id=1595894337124597
Snopes article: http://www.snopes.com/vietnam-veterans-tropic-thunder/
http://www.spinedu.com/shift-happens-karl-fisch-education/#.WAD70pMc6ko
http://infed.org/mobi/what-is-non-formal-education/
http://expandedramblings.com/index.php/by-the-numbers-a-gigantic-list-of-google-stats-and-facts/