Manathunga, K., Hernández-Leo, D., Sharples, M., (2017) A Social Learning Grid for MOOCs: Exploring a FutureLearn Case, Springer LNCS (vol. 10254) Proceedings of eMOOCs 2017, Madrid, Spain, 243-253.
https://repositori.upf.edu/handle/10230/28273
https://link.springer.com/chapter/10.1007/978-3-319-59044-8_29
A Social Learning Space Grid for MOOCs, EMOOCs2017
1. A Social Learning Space Grid
for MOOCs:
Exploring a FutureLearn Case
1The human side of technologies http://gti.upf.edu
Kalpani Manathunga, Davinia Hernández-Leo, Mike Sharples
22nd May, 2017
2. Outline
• Social and Collaborative Learning, at scale
• Challenges
• Social Learning Space Grid
• MOOC Case study
• Discussion
• Conclusions
2
3. Collaborative & Social Learning
in MOOCs
• Social Learning - continuous mutual interactions
influence humans to learn [Bandura, 1971]
• CSCL: “Mere” student grouping - fruitful learning ??
• Regulation or/and structured collaborative learning
[Dillenbourg, 2015; Hernández-Leo, et al., 2010]
• Forum discussions
• Widely used in large learning scenarios [Manathunga & Hernández-Leo,
2015]
• Challenges in MOOC
• Overwhelming amount of threaded discussions, hindering knowledge
building process, monitoring overload [Scardamalia & Bereiter,2006]
3
4. Requirements & Challenges
• Computer support for Collaborative Learning had been
mostly applied in small scale
• Scalability has not been considered in their design
• Difficulties:
• Diversity in learner motivations and expectations
• Differences in cultural expectations (e.g., how individuals should
behave in social spaces)
• …
• Resulting in diverse behaviors when taking MOOCs
4
5. Emerging opportunities
• Study groups - FutureLearn
• Local, private spaces for around 80 MOOC participants to discuss
and share knowledge
• Cohort-specific discussions - edX
• Private group discussions visible only for a specific cohort
• Meet-ups at Learning Hubs - Coursera
• Learners from nearby local get-together for discussions or project
based learning
• Social media like Facebook, Twitter, Google+ or
Hangout
• ...
5
6. Research objective
• Social Learning Space Grid
• Exploratory study (three spaces, FL MOOC)
6
Contributions
• To model social learning design opportunities for MOOCs
• To provide a framework guiding social learning research in
MOOCs
7. Social Learning Space Grid
Small Increasing size Whole cohort
Time and Task
Unconstraine
d
Groups exist throughout
the course. Participants
are free to interact at
any given moment, for
any given task.
Small groups can be
joined based on certain
criteria or behavior to
interact at any time, for
any given task.
An open space for all
course participants to
interact regarding any
topic at any time.
Task
Constrained
Small groups formed to
attend a given task.
Small groups are
combined based on
task completion to
attend another given
task.
All course participants
attend given task in a
common interaction
space.
Time
Constrained
Small groups formed to
work during a specific
time period.
Small groups are
combined based on
time expiration to work
together for another
specific time period.
All course participants
attend in a common
interaction space during
a specific time period.
7
9. MOOC Case Study
• “3D Graphics for Web Developers”
• 5 weeks, FL, Instructors: Alun Evans, Javier Agenjo
• Target crowd: Web developers
• Aim: Learn how to develop quality interactive 3D
applications to run natively on a browser
• 10500 enrolments (2 editions)
9
10. MOOC case study: social learning space I
10
Conversational
Flows
11. MOOC case study: social learning space II
11
Prompt-based
study groups
12. MOOC case study: social learning space III
12
PyramidApp
[Manathunga &
Hernández-Leo, 2016]
External tool,
not integrated in FL
Optional tasks
13. Observations in conversational flows
• Abundantly used
• FutureLearn social networking concepts (likes, following)
were used to filter lengthy conversational flows
• Experts were offering help to novices
• Shared programming code samples
• Late joiners’ queries and comments did not receive
much attention
13
14. • Mostly active upon receiving the educator’s prompts at the
beginning of each week
• 16 groups were formulated
• Groups deviated from the main task
14
Observations in study groups
15. 15
• Different participation patterns
• Length is proportionate to the number of days that group members
were actively participating
• Five out of 12 active groups engaged in activities for three
weeks from the day the group was formulated
16. 16
• Tasks:
• Proposing questions to be answered in deep by instructors
• Share learner artefacts to rate and critique
• Curious about questions and rating. Some attempted to provide
answers to the questions in the discussion thread
• Participants appreciated
artefacts and provided
suggestions for improvements
(e.g., use different 3D materials)
• Lower participation
• PyramidApp email
notifications helped learners
to know when subsequent
levels were ready
Observations in PyramidApp
17. Discussion
17
• Effect task design?
E.g., design of prompts - more structured and precise?
• E.g., “Does your first 3D scene look “realistic”, “artistic” and “imaginative”?
Vs. “discuss about the first 3D scene”
• (Quasi-)synchronous interaction mechanisms in a
MOOC can be futile
• With task and time constraining, PyramidApp tries to achieve a
level of synchronicity
• Facilitate rich interactions among learners at similar paces
• Orchestration challenges when learners dropping (regrouping,
simulated students)
• Monitoring efforts depend on type of space?
18. Conclusions
18
• Towards scalable active learning pedagogies
• Social Learning Space Grid, for design and research
• 3 spaces explored in a FL MOOC
• Study groups deviated from the intended tasks (help-seeking groups or to
get to know each other)
• In conversational flows, late-joiners were not receiving responses and help
as early-joiners
• PyramidApp, no many engaged, handle late-joiners since new pyramids are
created on-demand
• Future work
• Quasi-experimental studies, effects of spaces expressed in the Grid
• Effect of task design, monitoring / orchestration load by MOOC facilitators
• Intelligent orchestration (e.g., regrouping, agents)