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New Directions in Personalized Learning: Open, Informal, Social

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Keynote presentation at ICCE 2017 conference, Christchurch, New Zealand.

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New Directions in Personalized Learning: Open, Informal, Social

  1. 1. Open, informal, social Mike Sharples Institute of Educational Technology The Open University New directions in personalized learning @sharplm
  2. 2. Personalized learning
  3. 3. Personalized learning
  4. 4. Personalized learning on Khan Academy
  5. 5. Personalized learning is not new It re-appears in a different form every 20 years
  6. 6. 05/12/2017 1930s: Pressey’s Self-testing machine Image copyright OSU photo archives “There must be an ‘industrial revolution’ in education, in which educational science and the ingenuity of educational technology combine to modernize the grossly inefficient and clumsy procedures of conventional education. Work in the schools of the future will be marvelously though simply organized, so as to adjust almost automatically to individual differences and the characteristics of the learning process. There will be many laborsaving schemes and devices, and even machines – not at all for the mechanizing of education, but for the freeing of teacher and pupil from educational drudgery and incompetence.” Sidney Pressey (1933) Psychology and the New Education
  7. 7. Based on theories from cybernetics (adaptive systems, feedback control) Student is presented with multiple choice response Feedback depends on the student’s response 1950s: Adaptive teaching machines Adaptive teaching machine
  8. 8. Tutor: Do you think the Amazon jungle has a heavy rainfall or a light rainfall? Student: Heavy rainfall Tutor: Why does the Amazon have a heavy rainfall? Student: Because it’s near the Atlantic Tutor: Yes, the Amazon jungle is near the equatorial Atlantic. How does that affect the rainfall in the Amazon jungle? Student: The water in the current is warm Joseph Psotka, Sharon A. Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. Structured teaching content Student model Teaching administrator Natural language interface O'Shea, T, Bornat, R, du Boulay, B, Eisenstadt, M and Page, I, 1984, "Tools for creating intelligent computer tutors" In: Elithorn, A and Banerji, R, eds., Artificial and Human Intelligence, Elsevier Science Publishers. 1970s: Intelligent Tutoring Systems
  9. 9. Structured sequence of curriculum materials Present students with small chunks of instruction Test at each step Provide immediate feedback Adjust content based on student performance Can include mastery learning (re- teach until the student demonstrates understanding) 1990s: Integrated Learning Systems
  10. 10. Learner profiles Personal learning paths Competency-based progression Flexible learning environments Career readiness 2010s: Personalized Learning
  11. 11. 1930s Self-teaching machines Too early 1950s Adaptive teaching machines Too expensive 1970s Intelligent tutors Too difficult to author 1990s Integrated learning systems Not integrated 2010s Personalized learning ???
  12. 12. Will personalized learning work? Should we develop it further?
  13. 13. Mastery learning: requires a student to demonstrate understanding of educational content before progressing Adaptive teaching: matches educational content to the learner’s previous actions or inferred knowledge Personalized learning: provides students with learning experiences that meet their personal needs and interests Definitions
  14. 14. Mastery learning works Kulik et al. meta-analysis, effect size 0.52 Hattie (2015) effect size 0.58 Feedback works Hattie & Timperley (2007) meta-analysis of computer-assisted feedback, effect size 0.52 Computer-assisted instruction works a little Tamin et al. study of 25 meta-analyses, effect size 0.26 Intelligent tutoring works better Kulik & Fletcher meta-analysis of intelligent tutoring systems, effect size 0.66 Studies of Effectiveness
  15. 15. Wood et al. (1999). ‘Integrated Learning Systems in the classroom’: • Study 1, significant positive results • Study 2, positive and negative results • Study 3, negative results for achievement RAND (2013) study of Algebra Cognitive Tutor® in 73 high school and 74 middle schools: • High schools: No effect in first year. Small effect (0.2) in second year • Middle schools: No significant effect What Works Clearinghouse (2016) systematic review of Cognitive Tutor® in secondary schools: • Algebra1: mixed effects on algebra and no discernible effects on general mathematics for secondary students • Geometry: potentially negative effects for secondary students Effectiveness in classrooms
  16. 16. • Employed more rigorous evaluations – studies with appropriate control and measures • Need for teacher development • Finding suitable educational content • External pressure to move students on, whether they had mastered the material or not • Integration with other forms of learning Why lower effectiveness with large-scale classroom evaluations?
  17. 17. “Three personalized learning elements — Student grouping, Learning space supports personalized learning, and Students discuss data — had the greatest ability to isolate the success cases from the other schools. All of these elements were being implemented in the most successful schools.” RAND (2015). Continued Progress: Promising Evidence on Personalized Learning. http://k12education.gatesfoundation.org/resource/continued-progress-promising-evidence-on-personalized-learning-2/ Successful personalized learning
  18. 18. New personalized learning 1990s 2010s Computer control Learner & teacher control Focus on content Focus on strategy Hidden paths Open paths Individual learning Group learning Computer lab Flexible learning space Lab research studies School studies Separated from other learning Blended with other learning is flexible, social and blended
  19. 19. Crowdsourced adaptive teaching Personal inquiry learning Personalized social learning New personalized learning
  20. 20. Karataev, E., & Zadorozhny, V. (2017). Adaptive social learning based on crowdsourcing. IEEE Transactions on Learning Technologies, 10(2), 128-139. Crowdsourced adaptive learning Each mini-lesson has: Name Explanation Example Test Correct answer Mini-lessons (‘lesslets’) contributed by students and teachers
  21. 21. OSMOSIS Explanation The process by which molecules pass through a semipermeable membrane from a less concentrated solution into a more concentrated one. This equalizes the concentrations on each side of the membrane. Example Cucumber slices in distilled water gain weight. Cucumber slices in salt water lose weight. Because cucumber cells are semi-permeable membranes, so cucumber in salt loses water to equalize concentration. Test What is osmosis? 1. When a fluid passes through a semi-permeable membrane into a more concentrated solution. 2. When a fluid passes through a semi-permeable membrane into a less concentrated solution. 3. When fluids have similar concentrations each side of a semi-permeable membrane. Answer: 1
  22. 22. Karataev, E., & Zadorozhny, V. (2017). Adaptive social learning based on crowdsourcing. IEEE Transactions on Learning Technologies, 10(2), 128-139. Crowdsourced adaptive learning Mini-lessons (‘lesslets’) contributed by students and teachers Create personal learning pathways Based on answers to tests Osmosis A Osmosis B
  23. 23. Karataev, E., & Zadorozhny, V. (2017). Adaptive social learning based on crowdsourcing. IEEE Transactions on Learning Technologies, 10(2), 128-139. Crowdsourced adaptive learning Mini-lessons (‘lesslets’) contributed by students and teachers Create personal learning pathways Based on answers to tests Osmosis A Osmosis B Isotonic A
  24. 24. Karataev, E., & Zadorozhny, V. (2017). Adaptive social learning based on crowdsourcing. IEEE Transactions on Learning Technologies, 10(2), 128-139. Crowdsourced adaptive learning Mini-lessons (‘lesslets’) contributed by students and teachers Create personal learning pathways Based on answers to tests Osmosis A Osmosis B Correct ✓ Wrong ✗ Isotonic A
  25. 25. Karataev, E., & Zadorozhny, V. (2017). Adaptive social learning based on crowdsourcing. IEEE Transactions on Learning Technologies, 10(2), 128-139. Crowdsourced adaptive learning Create personal learning pathways based on answers to tests Osmosis A Osmosis B Correct ✓ Correct ✓ Adapt to learners’ abilities Crowdsource new lessons to fill gaps Mini-lessons (‘lesslets’) contributed by students and teachers Isotonic A
  26. 26. Crowdsourced adaptive teaching Personal inquiry learning Personalized social learning New personalized learning
  27. 27. Personal inquiry learning Making learning through inquiry personal, meaningful, visible, sharable ‘Is my diet healthy?’ ‘How can I measure how fit I am?’ ‘Are birds scared away from cities by noise?’
  28. 28. 1. Teacher introduces topic Curriculum-based instruction 2. Students propose personal inquiry questions Personally meaningful, achievable 3. In classroom groups, students plan investigation Collaborative learning 4. Individually, students work outside the classroom to collect evidence Personal inquiry 5. In classroom groups, students analyze and share results Collaborative analysis Guided improvisation 6. In classroom groups, students respond to the inquiry question Collaborative reflective learning 7. In groups or individually, students present results Shared presentation 8. Individually, students reflect and report on their progress Personalized reflective learning
  29. 29. Healthy eating study: attainment 0 10 20 30 40 50 60 70 80 Personal Inquiry Control Pre-test Post-test Intervention n=14 Control n=13 Anastopoulou, A., Sharples, M., Ainsworth, S., Crook, C., O’Malley, C. & Wright, M. (2012) Creating personal meaning through technology-supported science learning across formal and informal settings. International Journal of Science Education, 34,2, 251–273.
  30. 30. Healthy eating study: enjoyment of science lessons 22 23 24 25 26 27 28 29 Pre-test Post-test PI Control Intervention n=21 Control n=15 F(1,34) = 6.06, p < 0.02 Anastopoulou, A., Sharples, M., Ainsworth, S., Crook, C., O’Malley, C. & Wright, M. (2012) Creating personal meaning through technology-supported science learning across formal and informal settings. International Journal of Science Education, 34,2, 251–273.
  31. 31. Crowdsourced adaptive teaching Personal inquiry learning Personalized social learning New personalized learning
  32. 32. www.futurelearn.com FutureLearn
  33. 33. Each learner experiences ancient Rome Learner explores an interactive digital model of the city Educators ask questions to prompt exploration Learners discuss their personal experiences
  34. 34. Bringing it all together New personalized learning is flexible, social, blended How can we: Combine individual study with group learning? Create new spaces (physical and online) that support personalized study? Support students to view and discuss their progress?
  35. 35. Bringing it all together New personalized learning is flexible, social, blended Crowdsourced adaptive teaching Personal inquiry learning Personalized social learning

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