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2010 Creating Compelling Learning Experiencesarningexp139


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2010 Creating Compelling Learning Experiencesarningexp139

  1. 1. The Science of Education: Creating a Compelling Learning Experience Dr. Angie McQuaig, Director of Data Innovation Product Strategy & Development, Apollo Group
  2. 2. 2 Our Goals Improve student learning outcomes & increase success in achievement of career goals Understand learners to individualize pathways Develop a model of “learning and instruction rooted in a firm empirical basis” Use evidence from data-informed insights for continuous improvement Build a learner-centric environment –great experience –killer content –personalized guidance  Inspired by Ann Brown, educational researcher, UC Berkeley
  3. 3. 3 Learner-Centric Approach Dorothy Frank
  4. 4. 4 Learner-Centric Approach Currently school secretary Seeking teaching degree Strong student overall Devours books Motivated and devotes adequate time to study Struggles with writing Demonstrates leadership in classDorothy
  5. 5. 5 Learner-Centric Approach Police officer Aspires to be a HS football coach Volunteers at local YMCA 2.5 GPA in HS, but motivated by football English is 2nd language Struggling to find time to complete assignments Frank
  6. 6. 6 Dorothy Frank Should Dorothy and Frank have the same learning experience?
  7. 7. 7 Challenges – What are our learners’ goals? – How do we ascertain prior knowledge and skills? – How do we model learners? – What are the possible sets of experiences (instructional strategies, content, assessment, interactions) available? – What kinds of personalized guidance should we provide? – How can we know that the guidance is effective? – How do we understand learners’ intervention needs and create specific guidance? – How do teachers and intelligent agents work together effectively to address the student needs? – How do we measure success? – How do we do this all cost-effectively?
  8. 8. 8 Individualized Learning Platform (ILP) Project An open, extensible system with user options Collaborative & social learning environment ‘Peda/andragogy-neutral’ system Content innovations –Get learner to ‘aha moment’ and the right Bloom’s level Adaptive Learning Engine (ALE) –Adaptive-Pathways construction using learner data + domain ontologies + machine learning –Learner Genome Project Data-informed guidance
  9. 9. 9 Core Principles We selected the following memes as a starter set … Learning is a personal journey –“What we share in common makes us human. How we differ makes us individuals.” ~Carol Ann Tomlinson Learning is collaborative & social –“Real-life learning inevitably takes place in a social context, one such setting being the classroom” ~Ann Brown  Use evidence-based guidance where possible –““There are many hypotheses in science which are wrong. That's perfectly all right; they're the apertures to finding out what's right. Science is a self-correcting process. To be accepted, new ideas must survive the most rigorous standards of evidence and scrutiny.” ~Dr. Carl Sagan
  10. 10. 10 Core Principles We selected the following memes as a starter set … Learning should be active & engaging –“If you hold a cat by the tail you learn things you cannot learn any other way.” ~Mark Twain Content should be contextual and relevant –“Learning in context is paying attention to the interaction and intersection among people, tools, and context within a learning situation.” ~Catherine Hansman
  11. 11. 11 Individualized Learning Environment Model Individualized Learning Platform Contributions from/to learning theories & instructional strategies Learner “DNA” Teacher “DNA” Classroom ethos Curriculum Intelligence Engine (Dissemination) Goal Completion Satisfaction Inputs Outputs Assessments (of the learning environment) Model derived from Ann Brown’s Design Experimentation – our goal is to put this into practice.
  12. 12. 12 Learning Behaviors Insights Learning Experiences Apollo ILP Modeling Learners Learner Genome Project Learner Perception Data Model Learners Search for Evidence Rules Engine Ontologies Machine Learning Peda-/Andragogy-Neutral Personalized Guidance Dynamic Interventions Choice and Ownership “Ed Cloud” BI/Hadoop Projects Academic Analytics Adaptive Learning Engine ILP Classroom Technology strategy Concepts Strategies Projects
  13. 13. 13 Learner Genome Project We aim to deeply know our learners –Demographics –Domain-specific knowledge and skills •Pre-requisite or composite knowledge and skills –Behaviors and interactions in the system –‘Cognitive DNA’ •Conative attributes – Motivation, resistance and anxiety •Cognitive attributes – Metacognition, working memory, reasoning ability, modality strengths, information literacy, etc. •Affective attributes – Openness, responses to interactions, emotional stability
  14. 14. 14 Platform Construction There is no ‘one-size’ fit all solution – so a technology platform must be: –Highly flexible: support a variety of experiences, content modalities, etc. –Highly open & extensible: extend easily using 3rd-party applications, etc. –Highly-engaging: supporting real and relevant experiences, content, etc. –Data-driven: measures all interactions, move the data into data marts as well as real-time data analytics systems, and creates usable signals out of data –Machine-driven: categorizes incoming students based on learner attributes across a number of dimensions (cognitive, affective, conative) and looks for patterns in data
  15. 15. 15 Platform Construction –Science-driven: looks for existence of causal models mapping learner attributes to known outcomes & measured signals –Evidence-driven: uses models, understands what variables can be manipulated to create the right outcome, & continues to evolve the system based on this knowledge becoming real –Human-driven: combines machine insights and teacher insights to build specific pathways for a student –Scalable: at costs that are not prohibitive
  16. 16. 16 Translation to Platform Features Principle Foundations Product/Platform Goals Learning is a personal journey  Modeling the learner  Understanding learner goals  Understanding learner attributes  Individualized Pathways  Direct & Inferred measurements  Diagnostics  Signals (from learner, faculty, system)  Assessments  Remediation Plan  Choices (content, learning tools, assessments, …) Learning is Collaborative and Social  Teams, Cohorts, Peers  Different types of networks  Interaction tools  Communication tools  Networks
  17. 17. 17 Translation to Platform Features Principle Foundations Product/Platform Goals Guidance should be Evidence-based  Empirical evidence from long-running and statistically-significant measurements  Signals collected from interaction between Individuals X Materials X Tools X Networks  Clustering, Classifications  Predictive Models  Recommendations  Insights Learning should be Active & Engaging  Pull & Push vs. Push only  Developing critical learning skills  Curriculum Construction  Assessment Construction (as a way of reinforcing critical learning skills)  Design of learning activities Content should be Contextual and Relevant  Understanding Student Goals  Design of curriculum content that can be mapped to student goals  Sourcing & curating content from a variety of source for freshness and relevance  Selection of content experiences that map to student goals
  18. 18. 18 Challenges Technology scale (for our size) –Open source, mainstream web 2.0 techniques for data handling & machine learning (ex. Hadoop) Cost –Infrastructure, content, … Resistance to change Human vs. machine perception Gatekeepers (Institutional inertia, HLC, …)
  19. 19. 19 What all of this means Resting on the core tenets: –Learning is a personal journey –Learning is collaborative and social –Seek evidence-based guidance –Learning should be active and engaging –Content must relevant and contextual Online learning is entering a new era –Learning is personalized for an optimal experience –Continuous improvement from examination of data –Flexible, powerful platforms adapt based on results
  20. 20. 20 Our Status today Exploring the literature Tapping into the precedents of other industries Building a flexible platform Collaborating with scholars and practitioners who are interested in this endeavor
  21. 21. 21 Questions?
  22. 22. Thank you