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Technologies to support self-directed learning through social interaction


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This talk will describe underlying principles, design, and experience gained with ProSolo, a platform that supports personalized, competency-based learning through social interaction. Traditional educational models are primarily focused on classroom education and training typically associated with the notion of credit hours as the (only) route towards formal credentials. This limits opportunities for creating personalized learning pathways in the changing educational context. ProSolo provide users with the ability to unbundle education programs, courses, and units into discrete yet inter-related competencies, allowing learners to construct their education pathway in a manner that better reflects their interests and future career motivations and requirements. ProSolo is developed with the intention of providing learners with opportunities to customize, modify, and personalize their self-directed learning journey. ProSolo supports the development of skills for self-directed learning by allowing learners to control the planning, learning, and presentation of outcomes associated with their learning. To support learners with different levels of prior knowledge, study skills, and cultural backgrounds, ProSolo offers features for supporting self-directed learning through three types of scaffolds, including instructional, social, and technological. Learning in ProSolo occurs within a socially rich environment that aggregates learners’ information created and shared in their existing online spaces. ProSolo makes use of learning analytics to empower learners and instructors in this new model of education. ProSolo was used in the Data, Learning, and Analytics MOOC and is currently being piloted at several university sites.

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Technologies to support self-directed learning through social interaction

  1. 1. Technologies to support self-directed learning through social interaction Dragan Gašević @dgasevic March 15, 2016 Digital Learning Week 2016 UniSA, Adelaide, SA, Australia Joint work with Zoran Jeremic, Nikola Milikic, George Siemens, and Shane Dawson
  2. 2. Educational landscape today “Non-traditional” students Long time to complete degrees Large classrooms Long waiting lists
  3. 3. Confirmation of different needs MOOCs
  4. 4. Learning does not i) happen only in school and ii) end with graduation
  5. 5. Departing from credit hours & performance focus
  6. 6. Mastery learning through competency focus
  7. 7.
  8. 8. Personalized learning
  9. 9. Learning Personalization The Learner… Barbara Bray & Kathleen McClaskey, Personalization v Differentiation v Individualization Chart (v3) (2013) ,
  10. 10. The Learner… Learning Personalization Barbara Bray & Kathleen McClaskey, Personalization v Differentiation v Individualization Chart (v3) (2013) ,
  11. 11. Social interaction in learning
  12. 12. Personal learning environments Scaffolding? Assessment? Credentialing?
  13. 13. How to tie all these different pressing concerns together?
  15. 15. Self-directed learning with
  16. 16. Self-directed learning with instructional, social, and technological scaffolding
  17. 17. Learners construct knowledge Learners are agents Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41(1), 5–17.
  18. 18. Feedback, especially process and formative, is a must Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.
  19. 19. Competency is a social object
  20. 20. Learners are unique and so are their profiles
  21. 21. Learning does not happen in a silo
  22. 22. Learning is credentialed based on evidence produced
  23. 23. Institutions learn too (as learning organizations)
  24. 24. Tenologies builds on Existing learning technologies (LTI), social media, learning analytics, machine learning, information retrieval, recommender systems,…
  25. 25.
  27. 27. Design-based approach iterative with interventions
  28. 28. Siadaty, M., Gašević, D., & Hatala, M. (2016). Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning. Computers in Human Behavior, 59, 469-482. Effects of technological interventions
  30. 30. Individual and systemic
  31. 31. Adjustment to the new approach
  32. 32. External locus of control
  33. 33. Change of discourse needed! From adaptive learning to adaptive learners
  34. 34. Challenge Metacognitive skills Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annual Review of Psychology, 64, 417-444. doi:10.1146/annurev-psych-113011-143823
  35. 35. Instructional design as the most active ingredient Clark, R. E. (2009). Translating research into new instructional technologies for higher education: The active ingredient process. Journal of Computing in Higher Education, 21(1), 4-18.
  36. 36. Central role of instructors empowered with technology Learning is not algorithmically driven!
  37. 37. Credentialing as a big opportunity (if done right!)
  38. 38. Policy development necessary
  39. 39. Restructuring exiting educational resources around competencies
  40. 40. Content frameworks across different disciplines
  41. 41. Thank you! Dragan Gašević @dgasevic