Personal Learning Graph (PLeG)
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Transcript of Personal Learning Graph (PLeG)
PERSONAL LEARNING GRAPHS (PLeG)
George SiemensDragan Gasevic
Ryan BakerPresented to:
International Educational Data Mining ConferenceMadrid
June 27, 2015
Personalized learning models
Keller Plan (Personalized System of Instruction)Static learner profile (old school)Objective based (adaptivecourseware)Intelligent tutors (CMU OLI, cognitive tutor, ALEKS)Personalized (outer-loop, i.e. Knewton)Smart Sparrow (teacher at center)
Parallel developing partners
Platform PublisherKnewton PearsonSmart Sparrow McGraw-HillDesire2Learn adaptcoursewareLoudCloud CMU OLI
Introducing PLG
Learner ownedAPI-like interface to systems that need informationRelated to existing work:
eportfoliosPersonal learning networksExisting toolsets (Learning Locker)
Jobs: disappearing & new
Automation(Frey & Osborne, 2013)
Knowledge work (US Bureau of Economic Analysis,McKinsey & Co, 2012)
http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf http://www.bea.gov/industry/gdpbyind_data.htmhttp://www.mckinsey.com/insights/organization/preparing_for_a_new_era_of_work
Student profiles
Diversifying(OECD)
Less than 50% now full time(US Census Bureau)
http://www.oecd.org/edu/skills-beyond-school/EDIF%202013--N%C2%B015.pdf http://www.census.gov/prod/2013pubs/acsbr11-14.pdf
Complexification of higher education
Learning needs are complex, ongoing
Simple singular narrative won’t suffice going forward
The idea of the university (and learning) is expanding and diversifying
Granularization of learning
Competency-based degrees(Chronicle, 2014)
Prior learning assessment(Insider Higher Ed, 2012)
http://chronicle.com/article/Competency-Based-Degrees-/144769/ http://www.insidehighered.com/news/2012/05/07/prior-learning-assessment-catches-quietly
Granularization of assessment
Cracking the credit hour (New America Foundation)
Badges(Mozilla & others)
http://newamerica.net/publications/policy/cracking_the_credit_hour http://openbadges.org/
Something is needed that expands the idea of a “course” and moves control of learning experience/data from the institution to
the learner
Astr
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Ch
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Geolo
gy
Ph
ysic
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Bio
log
y
The questions we care about don’t fit in silos
Transdisciplinary
What will PLeG enable?
Career transitionsFull spectrum of learning (hobby, work, formal, personal)Integrated & immersive learningFoundation for personalized/adaptive learning
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Cognition & Affect/EngageProcess & strategy
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SocialProcess & strategy Cognition & Affect/Engage
Coding of nodes (necessary) to describe PLeG
Information, learning processes, affective states, social functions, etc.
Capturing traces of SRLMacro-Level SRL
Process Micro-Level SRL Process Description Example SRL Event
Planning
Task Analysis To become familiar with the learning context and the definition and requirements of a (learning) task at hand
Clicking on different competences under duties or projects related to the user
Goal Setting To explicitly set, define or update learning goals
Drag and dropping an available competence to a new or an existing learning goal
Making Personal Plans To create plans and select strategies for achieving a set learning goal
Choosing an available learning path as the path for a competence
Engagement
Working on the Task To consistently engage with a learning task and using tactics and strategies
Request collaboration for a competence, learning path or learning activity
Applying appropriate Strategy Changes
To revise learning strategies, or apply change in tactics
Adding a new activity to an existing learning path
Evaluation & Reflection
Evaluation Evaluating one’s learning process and comparing one’s work with the others
Rating a learning path, learning activity or knowledge asset
Reflection Reflecting on individual learning and sharing learning experiences
Adding a comment for a competence, learning path or learning activity
Siadaty, M., Gasevic, D., Hatala, M., Winne, P. H. (2015). Trace-based Micro-analytic Measurement of Self-Regulated Learning Processes. Submitted to the Journal of Learning Analytics.
Siadaty, M., Gasevic, D., Hatala, M., Winne, P. H. (2015). Trace-based Micro-analytic Measurement of Self-Regulated Learning Processes. Submitted to the Journal of Learning Analytics.
Siadaty, M., Gasevic, D., Hatala, M., Winne, P. H. (2015). Trace-based Micro-analytic Measurement of Self-Regulated Learning Processes. Submitted to the Journal of Learning Analytics.
Orchestration graphs
Process modeling and process mining (discovery, compliance checking, and improvement)
Dillenbourg, P. (2015). Orchestration graphs. Lausanne, Switzerland: EPFL Press / Routledge
Information structure of content
Information extraction techniques such as topic modeling (LDA) or name entity extraction
Connectivism as a learning theory
Networked learning
Educational technology
- Connectivism,- Social media,- Emergence,- …
- E-learning,- Complex
adaptive system,
- edtech,- …
- Social network,- Networked
learning,- Social group,- …
Connectivism in practice- Collaboration,
- Knowledge,- Thought,- …
Joksimović, S., Kovanović, V., Jovanović, J., Zouaq, A., Gašević, D., Hatala, M. (2015). What do cMOOC participants talk about in Social Media? A Topic Analysis of Discourse in a cMOOC," In Proceedings of the 5th International Conference on Learning Analytics & Knowledge (LAK 2015), Poughkeepsie, NY, USA (pp. 156-165).
Topic extraction
Readings and Discourse Similarity
Joksimović, S., Kovanović, V., Jovanović, J., Zouaq, A., Gašević, D., Hatala, M. (2015). What do cMOOC participants talk about in Social Media? A Topic Analysis of Discourse in a cMOOC," In Proceedings of the 5th International Conference on Learning Analytics & Knowledge (LAK 2015), Poughkeepsie, NY, USA (pp. 156-165).
Cognitive presenceTriggering event
ExplorationIntegrationResolution
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical Thinking and Computer Conferencing: A Model and Tool to Assess Cognitive Presence. American Journal of Distance Education, 15(1), 7-23.
Cognitive presence classifier
SVM classifier with the RBF kernel 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
Cohen’s κ = 0.42. Unigram baseline model: Cohen’s κ =0.33
Promising development
Trace data based measures ofthe crowd-sourced learning skill
E.g., Dreyfus model of skill acquisition
Milligan, S. (2015). Crowd-sourced learning in MOOCs: learning analytics meets measurement theory. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 151-155). ACM.
Progressions can build upon
• Models that represent prerequisite structure and connections in knowledge
• Such as Partial Order Knowledge Spaces(Desmarais & Pu, 2005)
Engagement predicts long-term participation
Engagement during middle school math predicts– College attendance (San Pedro et al., 2013)– College selectivity (San Pedro et al., in
preparation)– College major (San Pedro et al., 2014, 2015)
Engagement predicts long-term participation
Completing an EDM MOOC predicts joining the EDM Society (Wang, Paquette, & Baker, 2015)
Community Factors Matter
Communities form during MOOCs like this one (Brown et al., 2015)
Future work – study how these communities persist into the future(early evidence from CCK08 MOOC)
Use PLeG to
• Track what aspects of student engagement are enduring
• As opposed to just pertaining to a specific system or learning domain
Use PLeG to• Determine when students are disengaged• And track them to activities that can re-
engage them
Use PLeG to
• Find what does motivate a student• And personalize less motivating content to
connect it to what motivates the student (cf. Walkington & Bernacki, 2014; Walkington et al., 2014)
Use PLeG to
• Figure out student long-term trajectories and inform instructors and guidance counselors
Challenges
• Linking engagement models from different learning systems to each other– Models of different constructs– Models with different reliabilities– More and less aggressive models
• Figuring out how to decay engagement data over time, and where it does and doesn’t apply
We know…
• Scientific inquiry skills transfer across domains (Sao Pedro et al., 2012)– Essential if we are dealing with complex and multi-disciplinary
problems• SRL skill that a student develops can be enduring across
a semester (Roll et al., 2011)
• These processes and strategies support the development of cognition– Can also support social skills, and affect and engagement
regulation skills
But…
• To what degree does SRL process skills in one learning environment transfer to other environments?
• Are the same strategies and processes positive across different learning environments?– What behaviors are beneficial across learning
environments?
• Are the same strategies and processes effective for different cultures and populations?– Soriano et al. (2013) has found evidence that this is not the
case
Conclusion
Expansion of learning (for so-called knowledge age) requires expansion techniques and methods for learningLearning controlled, ownedPersonalized learning – by starting with learners driving their learningResonance & activating latencyLabour market & related impact (rethinking “the course”)Need YOUR/EDM algorithmic and related expertise