Balancing Standardization and Personalization in Education Keynote at “Framing the Future of Higher Education” Symposium 11 July 2014 Austin, Texas Norma Ming Co-Founder & Director of Learning Design @mindmannered Standardization & Personalization

COST VALUE @mindmannered

What should students learn? How should we facilitate that learning? How should we assess that learning? @mindmannered

Standardization: What should stay constant? Knowledge Entrance and exit standards Articulation of prerequisites Definitions of mastery @mindmannered

Define learning by knowledge, not time. Measure knowledge and learning, not time. Competency-based learning, badges. Blurring the lines between formal and informal learning. With OER and learning resources everywhere, need mechanisms for integrating easily across them. High standards and consistent standards. Not necessarily universal, uniform standards. Standardization & Personalization

Standardization: What should stay constant? Knowledge Entrance and exit standards Articulation of prerequisites Definitions of mastery Data For sharing and comparing information Across students Across institutions For better analytics to assess, evaluate, and improve @mindmannered

Which data, and how? Collect everything. Not just inputs and outputs, but also: Formative assessment Data on instructional processes Shared conventions and formats. Metrics of success Common Education Data Standards @mindmannered

Standardization: What should stay constant? Knowledge Entrance and exit standards Articulation of prerequisites Definitions of mastery Data For sharing and comparing information Across students Across institutions For better analytics to assess, evaluate, and improve Practices Operational: For consistency, efficiency, economy Instructional: For quality @mindmannered

Successful instructional practices Pellegrino, Chudowsky, & Glaser (2001) Bransford, Brown, & Cocking (2000) Bain (2004) 1. Prior Knowledge Affects Learning 2. How the Way Students Organize Knowledge Affects Learning 3. What Motivates Students to Learn? 4. How Students Develop Mastery 5. Practice and Feedback 6. Climate 7. Self-Directed Learners Ambrose, Bridges, DiPietro, Lovett, & Norman (2010) @mindmannered Standardization & Personalization

Why personalize? Equity Economy Meaningful learning Two interpretations of “economy”: Labor market and economics (need more specialization) Economical use of resources (consider opportunity cost) Inert knowledge won’t transfer and can’t be applied; need to make personal sense of it. @mindmannered Standardization & Personalization

Personalization: What should vary? Knowledge taught / expected Goals Entry and exit points @mindmannered

Past, present, & future knowledge vary. Graphic: map of hiking trail or mountain to climb Flexible entry points accommodate different prior knowledge. Flexible exit points accommodate different goals. Flexible paths accommodate different learning rates. Multiple routes to success Modular experiences @mindmannered Standardization & Personalization

Personalization: What should vary? Knowledge taught / expected Goals Entry and exit points Assessment What When How @mindmannered

Assessment: Beyond standardized testing “Collect and analyze everything.” Naturalistic, unstructured assessment Different resources, contexts, audiences, products @mindmannered

Predictive analytics to learning analytics Examine student behaviors, past backgrounds, test scores, grades, interests, income, employment, resource utilization, etc. Learning analytics: Provide real-time feedback to students on learning Provide feedback to instructors How is the whole class performing? What is the knowledge and learning of an individual student? @mindmannered Standardization & Personalization

Assessing knowledge in discussions 3-D projection Each point = 1 thread Discussion content converged: over time (ROYGBIV) across classes Economics course ~1000 students ~70,000 posts 3-D topic-space projection Each point = one thread ROYGBIV progression of discussion threads over time @mindmannered Standardization & Personalization

Unstructured assessment maps to grades. @mindmannered

Personalization: What should vary? Knowledge taught / expected Goals Entry and exit points Assessment What When How Instruction Needs, strengths, preferences Constraints, resources Support networks @mindmannered Standardization & Personalization

Adaptive learning Adapt, but don’t pander. Present: Past: Future: Learning styles? Student-as-consumer? Just-in-time learning? Present: Past: Future: Prior knowledge Extent / nature of scaffolding Motivation for learning Patterns of errors Response to feedback Self-regulation support Real-life constraints @mindmannered Standardization & Personalization

Personalized instruction Adaptive (machine) + Personalized (human) intelligence Personalize, don’t individualize. People learn from other people, because they are different. Create common ground. Build upon cohorts and communities. Incorporate instructors’ expertise. Decouple roles, learning activities, time: Different activities at same time Same activities at different time Different roles in same activity at same time @mindmannered Standardization & Personalization

Personalization demands self-directed learning. Autonomy Purpose Mastery Passive consumers of information get lost and left behind. Greater need for assessment feedback and self-regulation. This is what employers are looking for. @mindmannered Standardization & Personalization

Personalize instruction of self-directed learning. How do you scaffold a growth mindset? “Your hard work paid off!” “What could you do differently?” “Just keep swimming…” Mismatched and generic feedback won’t work. @mindmannered Standardization & Personalization

Standardization & Personalization @mindmannered Standardization & Personalization

Meta-questions: Do we need standards for meta-learning? How should we assess meta-learning? Which brings me back full circle— Not typically what people mean by “closing the loop”… Is the main reason why employers seek meta-learning because they can’t trust the measures of content learning? Or is meta-learning really more important, especially given the fast-changing nature of today’s information age / needs of labor market? @mindmannered Standardization & Personalization

Discuss. norma@socos.me