Personal Learning Graph (PLeG)
Download as PPTX, PDF27 likes30,512 views
The document discusses the concept of personal learning graphs (PLEG) and their potential to enable personalized and adaptive learning experiences. It emphasizes the need for educational systems to evolve in response to complex learning needs and the shifting job landscape due to automation, advocating for learner-centered approaches that shift control from institutions to individuals. The authors propose that PLEG can facilitate various aspects of learning, including self-regulation, engagement, and the adjustment of educational experiences to better suit the diverse needs of learners.
Personal Learning Graph (PLeG)
- 1. PERSONAL LEARNING GRAPHS (PLeG) George Siemens Dragan Gasevic Ryan Baker Presented to: International Educational Data Mining Conference Madrid June 27, 2015
- 2. The so-called “holy grail” of education: personalized and adaptive learning
- 3. 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)
- 4. Parallel developing partners Platform Publisher Knewton Pearson Smart Sparrow McGraw-Hill Desire2Learn adaptcourseware LoudCloud CMU OLI
- 5. Introducing PLG Learner owned API-like interface to systems that need information Related to existing work: eportfolios Personal learning networks Existing toolsets (Learning Locker)
- 6. Elements Cognitive Process & strategy (meta-cognitive) Affective/Engagement Social
- 7. Why PLeG?
- 8. 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_Em ployment.pdf http://www.bea.gov/industry/gdpbyind_data.htm http://www.mckinsey.com/insights/organization/preparing_for_a_new_era_ of_work
- 12. 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
- 13. 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
- 14. 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
- 15. 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/
- 16. Something is needed that expands the idea of a “course” and moves control of learning experience/data from the institution to the learner
- 17. Exploration Learning is the exploration of the unknown… … not just mastery of what is already known.
- 18. Compelling Questions Habitable Worlds: Are We Alone? Contagion: Can We Survive?
- 19. Astronomy Chemistry Geology Physics Biology The questions we care about don’t fit in silos Transdisciplinary
- 20. Smart Courses
- 21. What will PLeG enable? Career transitions Full spectrum of learning (hobby, work, formal, personal) Integrated & immersive learning Foundation for personalized/adaptive learning
- 24. Discussion Reply Login Lesson View Quiz Discussion View Discussion Post Lesson Text Discussion Text Discussion Text Quiz items Discussion Text Cognition & Affect/EngageProcess & strategy
- 25. Login Lesson View Quiz Discussion View Discussion Post Lesson Text Discussion Text Discussion Text Quiz items Discussion Reply Discussion Text SocialProcess & strategy Cognition & Affect/Engage
- 26. High computational complexity, but suitable representation
- 27. Coding of nodes (necessary) to describe PLeG Information, learning processes, affective states, social functions, etc.
- 28. Use of process/strategy graphs Understanding of self-regulated learning
- 29. Capturing traces of SRL Macro-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.
- 30. 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.
- 31. 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.
- 32. Use of process/strategy graphs Measurement of metacognitive monitoring
- 33. Learning strategy -transition graphs- Student A (course 2 – graded) Student B (course 4 – non-graded)
- 34. Orchestration graphs Process modeling and process mining (discovery, compliance checking, and improvement) Dillenbourg, P. (2015). Orchestration graphs. Lausanne, Switzerland: EPFL Press / Routledge
- 35. Information structure of content Information extraction techniques such as topic modeling (LDA) or name entity extraction
- 36. 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
- 37. 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).
- 38. Coding learning processes from unstructured sources
- 40. Cognitive presence Triggering event Exploration Integration Resolution 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.
- 41. 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
- 42. Currently missing! Integration of LA/EDM & assessment to guide learning progression
- 43. Promising development Trace data based measures of the 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.
- 44. Progressions can build upon • Models that represent prerequisite structure and connections in knowledge • Such as Partial Order Knowledge Spaces (Desmarais & Pu, 2005)
- 45. Engagement in the PLeG • Behavioral Engagement • Affective Engagement
- 48. 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)
- 49. Engagement predicts long-term participation Completing an EDM MOOC predicts joining the EDM Society (Wang, Paquette, & Baker, 2015)
- 50. 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)
- 51. Use PLeG to • Track what aspects of student engagement are enduring • As opposed to just pertaining to a specific system or learning domain
- 52. Use PLeG to • Determine when students are disengaged • And track them to activities that can re- engage them
- 53. 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)
- 54. Use PLeG to • Figure out student long-term trajectories and inform instructors and guidance counselors
- 55. 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
- 57. 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
- 58. 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
- 59. Conclusion Expansion of learning (for so-called knowledge age) requires expansion techniques and methods for learning Learning controlled, owned Personalized learning – by starting with learners driving their learning Resonance & activating latency Labour market & related impact (rethinking “the course”) Need YOUR/EDM algorithmic and related expertise
Editor's Notes
- #10: https://www.mckinseyquarterly.com/Economic_Studies/Productivity_Performance/Preparing_for_a_new_era_of_knowledge_work_3034
- #11: http://www.bls.gov/opub/ted/2014/ted_20140728.htm
- #12: http://www.bls.gov/opub/ted/2014/ted_20140728.htm
- #18: So what makes the type of courseware special? Well, we think that it teaches students that science is not just mastery of what is already known, but an exploration of the unknown. We actually believe that this is what learning itself is all about.
- #19: The courseware is also centred around compelling questions that motivate students, such as, are we alone in the universe? There are challenging problems that contextualise the learning. They are authentic problems that real scientists are working on right now at the frontier of human knowledge.
- #20: The courseware also gets students to appreciate the solutions to real problems in science do not fit into tidy disciplinary bins. The courseware has a transdisciplinary approach whilst mapping closely to introductory science courses, and the learning objectives faculty are used to teaching.
- #21: And as a community, we are building what we call Smart Courses: streams of enquiry, centred around a Big Question (for example, Are We Alone?), which map to introductory science courses in physics, chemistry, and biology. We currently have the existing gen-ed science course Habitable Worlds that I mentioned, and will have the biology course (or Stream) completed this year. The first set of courses will be framed around this astrobiology theme and next year we will do it all again with a biomedical theme and the Big Question: can we survive the next epidemic?
- #44: Novice, advanced beginner, competent, proficient, and expert