PERSONAL LEARNING GRAPHS (PLeG)

George SiemensDragan Gasevic

Ryan BakerPresented to:

International Educational Data Mining ConferenceMadrid

June 27, 2015

The so-called “holy grail” of education: personalized and adaptive learning

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)

Elements

CognitiveProcess & strategy (meta-cognitive)Affective/EngagementSocial

Why PLeG?

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

McKinsey Quarterly, 2012

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

ExplorationLearning is the exploration of the unknown…

… not just mastery of what is already known.

Compelling QuestionsHabitable Worlds:

Are We Alone?Contagion:Can We Survive?

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The questions we care about don’t fit in silos

Transdisciplinary

Smart Courses

What will PLeG enable?

Career transitionsFull spectrum of learning (hobby, work, formal, personal)Integrated & immersive learningFoundation for personalized/adaptive learning

Multipartite graphs

Process & strategy

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Cognition & Affect/EngageProcess & strategy

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SocialProcess & strategy Cognition & Affect/Engage

High computational complexity, but suitable representation

Coding of nodes (necessary) to describe PLeG

Information, learning processes, affective states, social functions, etc.

Use of process/strategy graphs

Understanding of self-regulated learning

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.

Use of process/strategy graphs

Measurement of metacognitive monitoring

Learning strategy-transition graphs-

Student A (course 2 – graded)

Student B(course 4 – non-graded)

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).

Coding learning processes from unstructured sources

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

Currently missing!

Integration of LA/EDM & assessment

to guide learning progression

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 in the PLeG

• Behavioral Engagement• Affective Engagement

Engagement predicts learning

Engagement predicts long-term participation

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

Self-Regulated Learning

Similar challenges

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