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Data Con LA 2019 Keynote Elizabeth Owen, PhD

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Data Con LA
Data Con LA

Data Con LA 2019 Keynote Elizabeth Owen, PhD

Technology
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Data-driven Design for User Engagement and Personalization Elizabeth Owen, PhD Founder / Chief Data Scientist @ Learning Data Discovery Board Member / Data Science Advisor @ Age of Learning
Data-driven Design: How & Why OUTCOMES: • Higher engagement • Better revenue/retention • Effective target behavior Evidence & data considered from design inception Personalization built into the system ML / AI analysis: core design iteration, behavior detection & system intelligence
Phonemic Awareness DATA / EVIDENCE USER ACTION FOCAL SKILL/BEHAVIOR 2. Build academic words 1. Letter selection 3. Earn points Vocabulary Recall Contextual Word Use
Clearly aligned with design Comprehensive Consistent across the system Event-stream Data Structure & Integrity
(e.g. Baker & Siemens, 2014; Owen & Baker, 2019) Machine Learning / Educational Data Mining
Markov Modeling / Sequence Mining (Owen et al., 2014) Behavior Detection / Prediction (Owen et al., 2018) Bayesian Knowledge Tracing (Pardos & Heffernan, 2011) Heat Mapping / Visualization (Ramirez, 2016)
Data-driven Design: Engagement & Target Behavior v.elizabeth.owen@gmail.com Visualization Relationship Mining Prediction Evidence-driven Initial Design Rich Data Stream & Embedded Personalization Ongoing Machine Learning Analysis

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Data Con LA 2019 Keynote Elizabeth Owen, PhD

  • 1. Data-driven Design for User Engagement and Personalization Elizabeth Owen, PhD Founder / Chief Data Scientist @ Learning Data Discovery Board Member / Data Science Advisor @ Age of Learning
  • 2. Data-driven Design: How & Why OUTCOMES: • Higher engagement • Better revenue/retention • Effective target behavior Evidence & data considered from design inception Personalization built into the system ML / AI analysis: core design iteration, behavior detection & system intelligence
  • 3. Phonemic Awareness DATA / EVIDENCE USER ACTION FOCAL SKILL/BEHAVIOR 2. Build academic words 1. Letter selection 3. Earn points Vocabulary Recall Contextual Word Use
  • 4. Clearly aligned with design Comprehensive Consistent across the system Event-stream Data Structure & Integrity
  • 5. (e.g. Baker & Siemens, 2014; Owen & Baker, 2019) Machine Learning / Educational Data Mining
  • 6. Markov Modeling / Sequence Mining (Owen et al., 2014) Behavior Detection / Prediction (Owen et al., 2018) Bayesian Knowledge Tracing (Pardos & Heffernan, 2011) Heat Mapping / Visualization (Ramirez, 2016)
  • 7. Data-driven Design: Engagement & Target Behavior v.elizabeth.owen@gmail.com Visualization Relationship Mining Prediction Evidence-driven Initial Design Rich Data Stream & Embedded Personalization Ongoing Machine Learning Analysis