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Adaptive Learning – The Next Generation June 8, 2011

Adaptive Learning – The Next Generation June 8, 2011. Bror Saxberg Chief Learning Officer, Kaplan, Inc.

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Adaptive Learning – The Next Generation June 8, 2011

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  1. Adaptive Learning – The Next Generation June 8, 2011

  2. Bror SaxbergChief Learning Officer, Kaplan, Inc. • Integrating the design, building, monitoring, and improvement of learning environments; individualize learning experiences using our scale; and, ultimately, drive greater student career success. • Former CLO for K12, Inc. – structured use of technology, cognitive science, on-line and off-line materials for 1,700 teachers, 55k students • Former Publisher and General Manager for DK Multimedia, Inc. • Management consultant with McKinsey & Company • Education: • Ph.D. in Electrical Engineering and Computer Science from MIT • M.D. from Harvard Medical School • M.A. in Electrical Engineering and Computer Science from MIT • M.A. in Mathematics from Oxford University • B.S. in Electrical Engineering and B.S. with Honors in Mathematics from the University of Washington

  3. Kaplan education spans domains and geography • KTPA • Kaplan Tutoring • Kaplan Bar Review • Kaplan Publishing • Kaplan Continuing Education • Kaplan University • Kaplan Higher Ed Campuses • Kaplan Legal Education • Kaplan Professional Education • Nursing (U.S.) (U.S.) • Kaplan Higher Ed – Europe • Kaplan Professional – Europe • Kaplan Int’l Colleges • Global Pathways/English Language • Kaplan Compliance Solutions • Kaplan EduNeering • Kaplan IT Learning • Kaplan Latin America • Education Connection • Kidum • Colloquy • Kaplan Higher Ed – Asia • Kaplan Professional – Asia • Kaplan Higher Ed – Australia • Kaplan Professional – Australia • In Country Pathways – China

  4. Student’s wants and needs for learning align Student’s view of what the best educator would be like: Student First Personalized Effective & Efficient Innovative Lifelong Learning An expert view: “Your fastest path to goals that matter” • Targeted • Diagnostic • Adaptive • Flexible • Career-long • Targeted • Efficient • Diagnostic • Adaptive • Engaging • Flexible • Flexible • Career-long

  5. Today… Today, students are treated as if they are the same. The programs focus on graduation criteria. To be successful, students’ fluencies should match evolving expert work There is a disconnect that needs to be addressed.

  6. 2020… Cognitive Task Analysis Cognitive task analysis can objectively identify the evolving skills students need for their next stage Designand Deliver Measureand Evaluate The Kaplan Way to Design and Deliver, then Measure and Evaluate will drive better outcomes

  7. 2020… Cognitive Task Analysis We can then focus on unique strengths and challenges for each student to reach success. Adaptive learning lets us personalize the educational experience, matching pace, progress and motivation

  8. We have to do more than “normal” development “Normal” course development process • Look at existing materials for inspiration • Ask individual faculty to make “best guess” changes • Possibly pilot for user acceptance • Build a new version with incremental changes • Distribute immediately Augments to drive excellence • Explicitly connect with learning science to drive what to do • Tie explicitly to research on what defines experts’ success • Use history of previous learners to alter current instruction • Personalize instruction based on student skills and needs • Collect data on what works by testing improvements

  9. Instructional design drives effectiveness Instructional Design is the design of external conditions to support the internal conditions necessary for learning. Robert Gagne, 1965 Instructional Events (in the learning environment) Learning Events (hidden - inside students’ minds) Student Performance(observable -indicates knowledge) Koedinger, K.R., Corbett, A.T., and Perfetti, C. (2010). The Knowledge-Learning-Instruction (KLI) Framework: Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning (Draft manuscript from the Pittsburgh Science of Learning Center)

  10. Evidence-Driven Instructional Design The evidence about learning points to a sequence of activities that optimizes learning. Design goes one way. . .. Design Delivery Guidance (for motivation and metacognition) . . .delivery the other. 10

  11. Best education will require investments up front Examples: Cognitive Task analysis (CTA) CTA for program design: $20,000 (internal resources) CTA for course design: $5,000 [NOT necessarily for every program, every course!] Interactive Media Java and Flash-rich training: $5,000+ per hour of rich instruction Complex simulations: $25,000+ per hour of simulation [NOT necessarily for every hour of instruction!] Platforms Adaptive E-Learning Platform development: $10-20M (5 years) Content rebuilds: Could be multiples of that!

  12. Spending at scale: other industries are used to it Avg. = 4.2% Education organizations typically do not even report R&D Source: Booze Allen Hamilton Global Innovation 1000

  13. Concept Course Project Goals • Develop a “concept course”, a prototype to showcase elements of Kaplan’s next generation learning environments • Personalization • Evidence-based instructional and multimedia design • Open-source platform • Mobile delivery (iPad) 13

  14. Personalization: triple loop adaptation Student’s Field of Study Student’s Motivational State 3. Context forExamples, Practice,Tests 2. Student’s Performance MotivationGuidance 1. Amount and Type of Examples, Practice, Feedback • Personalizing content based on diagnosis of knowledge gaps improves learning outcomes and reduces the time to learn. • We can double the impact of adaptive learning by adding motivational guidance for students who need it. • This is the first scalable system that adapts to both student knowledge and motivation. 14

  15. Dynamically constructed learning objects and guidance For each learning outcome Motivational Guidance Preview Prepare Practice Test Information Example B Activity A Feedback C Assessment Rules Engine Current and pastPerformance & Motivationdata StudentModel Content CROSS-OBJECT Motivational Guidance Example A Example B Example C Activity A Activity B Activity C Feedback A Feedback B Feedback C 15

  16. Learner interface, guidance, rules

  17. Motivation theory: Beliefs drive behavior and performance Effort Low Moderate High Self-Efficacy Sources: Bandura; Eccles & Wigfield; Pintrich & Schunk; Clark; Dweck 17

  18. Motivation data Course Overview • Value • Intrinsic • Utility • Strengths • Self-Efficacy • Success • Distractions • Difficulty Prepare: After first example Confidence Practice: After low performance • Attribution • Controllable • Uncontrollable Time spent on each learning outcome Indicator of mental effort 18

  19. Rules and guidance for different patterns of skill and will Course overview Practice Test 19

  20. Content: Why Creativity? IBM poll of 1,500 CEOs identified creativity as the number one leadership competency needed to fuel business growth. On a global scale, creativity holds the potential for solving society’s largest problems. We need faster ways to develop this capability in the workforce. Video footage from leading innovators of our time from Techonomy conference 20

  21. Content: Differentiators of individual and team creativity Organizational Structure promotes networking Team members with diverse domain expertise Tea m Individual Domain Expertise Motivation • Cognitive Skills • Problem Definition • Divergence • Convergence • Execution Creativity Fluid Intelligence* Processes and Culture that foster creativity (e.g., doesn’t punish risk-taking) Autonomy in decision making, yet goal oriented 21

  22. Content: 7 Units - procedural learning outcomes Green = Prototype 22

  23. Demo 23

  24. Useful References on Learning and ID: • Why Students Don’t Like School, Daniel Willingham – highly readable! ;-) • Talent is Overrated, Geoffrey Colvin – highly readable! ;-) • E-Learning and the Science of Instruction, Clark and Mayer, 2nd ed. • “First Principles of Learning,” Merrill, D., in Reigeluth, C. M. & Carr, A. (Eds.), Instructional Design Theories and Models III, 2009. • How People Learn, John Bransford et al, eds. • “Design factors for educationally effective animations and simulations,” Plass, J.L., Homer, B.D., Hayward, E.O., J Comput High Educ (2009) 21:31–61 • “The Implications of Research on Expertise for Curriculum and Pedagogy”, David Feldon, Education Psychology Review (2007) 19:91–110 • “Cognitive Task Analysis,” Clark, R.E., Feldon, D., van Merrienboer, J., Yates, K., and Early, S.. in Spector, J.M., Merrill, M.D., van Merrienboer, J. J. G., & Driscoll, M. P. (Eds.), Handbook of research on educational communciatinos and technology (3rd ed., 2007) Lawrence Erlbaum Associates

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