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Knewton EdTech Raises $51M Helps 2000 ASU Students With Adaptive Learning
- 2. What do they do? • EdTech • Adaptive learning PaaS • Big Data (Identity, UI, Content, Student proficiency) • Enterprise API
- 3. Financials • 5 Funding Rounds • Series A: 2.5 M • Series B: 6 M • Series C: 12.5 M • Series D: 33 M • Series E: 51 M • Dozens of partners (publishers, schools, developers)
- 4. Timeline • Founded 2008 by Jose Ferreira • Former executive at Kaplan • differentiated vs adaptive learning • observable vs inferable data • solve the high overhead for adaptive learning problem
- 5. • Online Education • Personalized Curriculum that responds to you in real time • APIs • De-compartmentalized learning • NoSQL & Hadoop
- 6. Secret Sauce • Platform Architecture • Recommendations, Analytics, Proficiency, Student Profiles • Gamification
- 7. Knewton at ASU • 2000 students • Encourages group work in the classroom • Not every student is at the same place
- 8. Competition • Partnered with most companies using adaptive learning • Desire2Learn • more institution focused • Individual tools not enterprise focused
- 9. Future • Continuous growth • Proportional to online learning & courses • Personal adaptive learning tools to come • More partners, Bright Future • International presence
- 10. Questions?
Editor's Notes
- 5 types of big data in education: Identity data —> who are you, demographic info etc User Interaction data —> engagement metrics, click rate, page views etc Inferred content data —> how well does a piece of content perform across a group or subgroup. how well does a question assess what it intends to, student proficiency data System Wide data —> rosters, grades, attendance information, less important Inferred Student data —> exactly what content does a student know at exactly what percentile of proficiency? What was an incorrect answer due to? What’s the probability of the student’s success in tests and quizzes? Most important data, too costly to build for just one application, so it exists as a platform
- Data Collection: Adaptive Ontology —> maps relationships between individual concepts that make up learning content. Then integrates learning objectives, interactions, etc Model Computation Engine —> processes data from real time streams for use by personalization engine Inference: (generates insights from collected data and allows for personalization features in apps) Psychometrics Engine —> evaluates student proficiencies, content parameters, how effective instructional content is, etc Strategy Engine —> evaluates sensitivity of students to content presentation, assessments, teaching strategies and more Feedback Engine —> Unifies inference data and feeds results back into the adaptive ontology Personalization: (model driven recommendations and actionable insights) Recommendations Engine —> What student should do right now Predictive Analytics Engine —> provides detailed reports and metrics for students Unified Learning History —> Connects learning experiences across disparate apps and learning areas