Uploaded byJohn Rinderle
Data Driven Continuous Improvement
The session on data-driven continuous improvement focuses on utilizing data to enhance educational outcomes, emphasizing the importance of a learner-centered approach and effective course design. Participants will learn strategies to capture and analyze meaningful learning data, identify pitfalls, and communicate clear objectives. Key concepts include the design triangle, the necessity for actionable data, and different types of learning data that can inform teaching practices.
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Data Driven Continuous Improvement
- 1. Data Driven Continuous Improvement JohnRinderle @JohnRinderle Norman Bier @NormanBier
- 2. Outcomes for Today Bythe end of this session you will be able to… • Explain the value of a data driven approach. • Implement design strategies that facilitate meaningful data capture and use. • Avoid commonly pitfalls in the use of learning data. • Share strategies for data driven improvement with your project team. • Explain the data collected and analysis tools available to co-development and platform+.
- 3. Infinite Points ofLight
- 4. Infinite Points ofLight
- 5. Infinite Points ofLight
- 6. Infinite Points ofLight
- 7. Infinite Proliferation The 4R’s Reuse Redistribute Revise Remix
- 8. Infinite Proliferation The 4R’s NOT: Reuse Recreate Redistribute Revise Remix Add: Evaluate
- 9. The problems ofvariety • Quality is highly variable • Much duplication of effort • Difficult to choose appropriately • Hard to evaluate • Impossible to improve • Hard to scale success up
- 10. Effectiveness Can be hitor miss
- 11. Effectiveness Demonstrably support students in meeting articulated, measurable learning outcomes in a given set of contexts
- 12. How do wedesign for effectiveness?
- 13. The Course DesignTriangle Objectives Descriptions of what students should be able to do at the end of the course Assessments Tasks that provide feedback on students’ knowledge and skills Instructional Activities Contexts and activities that foster 13 students’ active engagement in learning
- 14. Why Focus onObjectives? 1. They communicate our intentions clearly to students and to colleagues. 2. They provide a framework for selecting and organizing course content. 3. They guide in decisions about assessment and evaluation methods. 4. They provide a framework for selecting appropriate teaching and learning activities. 5. They give students information for directing their learning efforts and monitoring their own progress. 14 Based on A.H. Miller (1987), Course Design for University Lecturers. New York: Nichols Publishing. Also see, C.I. Davidson & S. A. Ambrose (1994), The New Professor’s Handbook: A Guide to Teaching and Research in Engineering and Sciences. Bolton, MA: Anker Publishing Company Inc.
- 15. Why a “learner-centered” approach? Learning results from what the student does and thinks and only from what the student does and thinks. The teacher can advance learning only by influencing what the student does to learn (Herb Simon, 2001). It’s not teaching that causes learning. Attempts by the learner to perform cause learning, dependent upon the quality of feedback and opportunities to use it (Grant Wiggins, 1993).
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- 17. Let’s Analyze SomeExamples Checklist: Is the objective…? • Student centered (i.e., student should be able to…) • Broken down into component skills (grain size) • Phrased with an action verb • Measurable Here are some samples: • Understand the U.S. stock market • Recognize logical flaws in a written argument • Appreciate the historical context of the 1940’s 17 • Apply Newton’s Second Law appropriately
- 18. Scenario #1: EarlyEnrollment vs. Performance Success by Enrollment Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Days Before Semester Start
- 19. Data needs tobe actionable! 19
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- 22. Types of LearningData Program/Degree Demographic Contextual What gets captured Behavioral Interaction Semantic Actions Raw
- 23. Learning Curve Analysis DataShop:Pittsburgh Science of Learning Center
- 24. Other Learning Curves learnig DataShop:Pittsburgh Science of Learning Center
- 25. 1.2 1 0.8 0.6 0.4 Activites 1st Try Correct 0.2 Activities Eventually Correct Assessment Correct 0
- 26. Other Metrics Engaged Didn’t Engage College Pass Fail Pass Fail CFA 87% 13% 36% 64% CIT 98% 2% 81% 19% CMU 100% 0% 67% 33% HSS 98% 1% 58% 42% MCS 96% 4% 75% 25% SCS 99% 1% 83% 17% TSB 98% 2% 69% 31% Total 96% 4% 67% 33%
- 27. A Virtuous Cycle Educational Technology Data & Practice Theory
- 28. Share Alike andShare Data