The document discusses the evolution and challenges of MOOCs (Massive Open Online Courses), highlighting the distinction between behaviorist xMOOCs and connectivist cMOOCs. It emphasizes that while MOOCs were initially intended to promote social learning, they often fail in providing a community-based learning experience due to the large scale and focus on content delivery. The paper suggests the need for improved course design, community building, and the integration of learning analytics to enhance the social aspects of MOOCs.

1. MOOCs & Social Learning
Challenges and opportunities
Vitomir Kovanovic
School of Informatics
The University of Edinburgh
v.kovanovic@ed.ac.uk
ANC Workshop
2 June 2015

2. MOOCs: an overview
● Major hype in EdTech world since 2011
● Millions of $$$ raised
● 2012 “year of the MOOCs”
● Led by highly respected institutions
● Attracted interest of general public
○ “Tsunami in world of education”
○ “Disruptive change”
● Primarily content-focused
○ short video lectures
○ quizzes
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3. MOOCs: early starts
● MOOCs were envisioned as a platform for connectivist learning
● First MOOC: 2008 Connectivism and Connective Knowledge (CCK08)
by G. Siemens & S. Downes
● Little resemblance with today’s “Coursera-style” MOOCs
○ Novel “post-industrialist” form of learning (Anderson & Dron, 2010)
○ Learning is about building connections (with content, people, and organizations)
○ Self-directed, no “formal” assessment, no certificates
○ Focus on building communities and starting up interesting conversations
○ Student interests define topics, instructors are there to support
○ Every week a new lecture on a particular topic
○ Students write blogs, research the domain, start conversation in their own social media space
■ focus on self-reflection and social interactions
○ Every week there is an aggregation email with links to all produced content
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4. cMOOCs vs. xMOOCs
● Behaviorist MOOCs (xMOOCs) and Connectivist MOOCs (cMOOCs)
xMOOCs cMOOCs
Scalability of provision Massive Community and connections
Open access - Restricted license Open Open access & license
Individual learning in a single
platform
Online Networked learning across multiple
platforms and services
Acquire a curriculum of knowledge &
skills
Course Develop shared practices, knowledge and
understanding
Comparison of xMOOCs and cMOOCs by Yuan, Powell, & Olivier (2014)
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5. cMOOCs vs. xMOOCs
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6. MOOCs: Revolution or evolution?
Three generations of distance education pedagogies (Anderson & Dron, 2010):
Cognitive-Behaviorism -> Social Constructivism -> Connectivism
Modern educational psychology:
● Learners do not acquire knowledge, they construct knowledge
● Learners are agents making decisions about their own learning tactics and approaches
MOOCS are a current stage of progress in evolution of distance education
● MOOCs were envisioned as social-constructivism 2.0
● In some aspects, xMOOCs are even a step back in online learning
○ Step back to cognitive-behaviorist learning models
○ xMOOCs were a step back because of practical reasons
● We need to look what we already know from distance/online learning (Kovanović et al., 2015)
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7. Trends & challenges
● How we can use MOOCS for improving face-to-face courses and traditional online courses?
○ Move toward digital learning: a blend of face-to-face learning, online learning and MOOCs
(Siemens, Gašević, & Dawson, 2015)
● How to make MOOC experience more social?
○ Dual models (c+xMOOCs) (Dawson, Joksimović, Kovanović, Gašević, & Siemens, 2015)
● How assessment should look like?
○ What it means to “complete” the course? How about not having courses at all?
● Media coverage is rapidly decreasing (Kovanović, Joksimović, Gašević, Siemens, & Hatala, 2014)
○ MOOCs are not new anymore
○ Topics with a growing interest:
■ government regulations
■ adoption in different parts of the world
■ use of data & analytics
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8. Goal: More social MOOCs
● Goal to enable for an environment in which students are able to learn together at scale
○ Online discussions should be better
■ Currently work mostly as Q/A
■ More knowledge building in discussions
○ Currently, students are having solitary experiences in MOOCs at scale
● Build on the existing models of online learning
○ Community of Inquiry model (Garrison, Anderson, & Archer, 1999)
● Use Learning Analytics and Data Mining to achieve research goal
○ Discourse analysis and social network analysis
○ Build tools that can be used by instructors
○ Extend knowledge about social learning processes
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9. Community of Inquiry (CoI) model
Social
presence
Cognitive
presence
Teaching
presence
Educational
experience
1. Affectivity
2. Interactivity
3. Group cohesion
1. Triggering event: Problem
identification, sense of
puzzlement
2. Exploration: Brainstorming,
Idea exploration, divergence
3. Integration: Synthesis of
relevant information
4. Resolution: Problem resolution,
testing application
1. Design & Organization
2. Direct instruction
3. Facilitation
Garrison, Anderson, and Archer (1999) 9

10. CoI instruments
Quantitative coding schemes for each of the presences:
● Labour-intensive manual coding
● Requires experienced coders
34 likert items survey instrument
● 13 Teaching presence
● 9 Social presence
● 12 Cognitive presence
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11. MOOCs: Challenges
CoI (and other social-constructivist) models require a strong teacher’s presence
-> up to 30-40 student cohorts (Anderson & Dron, 2010)
MOOCs?
● In short, just too many students for strong teaching presence during course
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12. How different is MOOC context?
● We evaluated CoI survey
instrument
● EFA of existing CoI survey
instrument using data from 5
MOOCs
● Course design & organization
are of particular importance
● Less affective communication
● Less resolution
Resolution &
application
Affectivity
Course design
& organization
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13. How different is MOOC context?
● SEM model of relationships between
presences
● Main findings:
○ Strong direct effect of teaching
presence on cognitive presence
○ Social presence acts as a mediator
between teaching and cognitive
presences
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14. How different is MOOC context?
● SEM model of relationships between
presences
● Still in progress
○ Moderately good fit (RMSEA = 0.09)
○ Stronger direct effect of teaching on
cognitive presence
○ Lesser mediating effect of social
presence
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15. CoI content analysis
● Besides survey, CoI coding scheme for each of the presences
● For each of the presences, quantitative coding scheme
● Message unit of analysis
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16. CoI content analysis
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17. Challenges of content analysis
● Very labor intensive
● Crude coding scheme
● Requires experienced coders
● Can’t be used for real-time monitoring
● Not explaining reasons behind observed levels of presences
● Used for analysis of learning long after courses are over
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18. Research approach
● Use text analytics to address these problems.
● Develop automated content analysis system for message coding.
● Provide better operationalization of the CoI coding instrument.
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19. Dataset
● Six offerings of graduate level course in
software engineering.
● Total of 1747 messages, 81 students.
● Manually coded by two coders (agreement =
98.1%, Cohen’s κ = 0.974).
● Currently coding E-learning and Digital
Cultures UoE MOOC
ID Phase No. Messages (%)
0 Other 140 (8.01%)
1 Triggering Event 308 (17.63%)
2 Exploration 684 (39.17%)
3 Integration 508 (29.08%)
4 Resolution 107 (6.12%)
All phases 1747 (100%)
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20. Methodology
● SVM classifier with RBF kernel.
● Parameter tuning & accuracy evaluated using nested 5-fold cross-validation.
● Extracted features:
○ N-grams
○ Part-of-Speech N-grams
○ Back-Off N-grams
○ Dependency Triplets
○ Back-Off Dependency Triplets
○ Named Entities
○ Thread Position Features
○ LSA Features
○ LIWC Features
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21. Results
● We achieved Cohen’s κ of 0.42 for our classification problem
● Better than the existing Neural Network system (Cohen’s κ=0.31)
● Unigram baseline model achieved Cohen’s κ of 0.33
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(Kovanović, Joksimović, Gašević, & Hatala, 2014)

22. Challenges
● Disproportionate class sizes
● Effect of the code-up rule for coding
● Context is not taken into the account
● No explanatory value
Code-up coding rule
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23. In progress
• Discussions (and
students’ learning)
progresses from triggering
to resolutions.
• Content of a message
depends on the content of
the previous messages.
• Content of a message
depends on the learning
progress of a given
student.
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24. Summary
● Social-constructivist pedagogies do not work well in MOOC context
○ xMOOCs: focus on content-delivery
○ cMOOCs: focus on connecting, reflecting in a self-directed way
● MOOCs
○ put more emphasis on course organization and design
○ building community a challenge
○ focus more on remembering than on understanding and evaluating
○ need for a more social experience in MOOCs
● Community of Inquiry model
○ Widely used, hard to adopt
○ Automated coding of messages:
● An overview of student progress in development of cognitive presence
● Support instructional interventions
● Further understanding of CoI model, particularly in MOOC context
● More detailed operationalization of CoI coding scheme
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25. 25
Thank you

26. References
Anderson, T., & Dron, J. (2010). Three generations of distance education pedagogy. The International Review of Research in
Open and Distance Learning, 12(3), 80–97.
Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in
Higher Education. The Internet and Higher Education, 2(2–3), 87–105.
Kovanović, V., Joksimović, S., Gašević, D., Siemens, G., & Hatala, M. (2014). What public media reveals about MOOCs?
British Journal of Educational Technology 43(3), 510-527.
Dawson, S., Joksimović, S., Kovanović, V., Gašević, D., & Siemens, G. (2015). Recognising learner autonomy: Lessons and
reflections from a joint x/c MOOC. In Proceedings of 2015 HERDSA conference. Melbourne, AU.
Siemens, G., Gašević, D., & Dawson, S. (2015). Preparing for the digital university: a review of the history and current state of
distance, blended, and online learning. Athabasca University. Retrieved from http://linkresearchlab.
org/PreparingDigitalUniversity.pdf
Kovanović, V., Joksimović, S., Skrypnyk, O., Gašević, D., Dawson, S., & Siemens, G. (2015). The history and state of distance
education. Athabasca University.
Kovanović, V., Joksimović, S., Gašević, D., & Hatala, M. (2014). Automated Content Analysis of Online Discussion Transcripts.
In Proceedings of the Workshops at the LAK 2014 Conference co-located with 4th International Conference on Learning
Analytics and Knowledge (LAK 2014). Indianapolis, IN. Retrieved from http://ceur-ws.org/Vol-1137/
Yuan, L., Powell, S., & Olivier, B. (2014). Beyond MOOCs: Sustainable Online Learning in Institutions. CETIS: Centre for
Educational Technology, Interoperability and Standards.
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