1. The Classroom Learning Partner Promoting Meaningful Instructor-Student Interactions in Large Classes Kimberle Koile, Howard Shrobe Jessie Chen, Karin Iancu, Michel Rbeiz, Amanda Smith MIT CS and AI Lab

2. Overview Problem – Personal interaction impossible in large classes – How to increase interaction for two-way conversation Project Goal – Improve student experiences and learning in large classes by: * supporting the use of in-class exercises * without overwhelming the instructor * maintaining student anonymity

3. Two months into two year project – Tablet-PC-based system – corpus of in-class exercises for MIT introductory CS class (6.001) – design, implementation of system components Overview (cont’d) Planned Evaluation – 3 terms in 6.001 recitations (~25 students) * how to test in much smaller class than target? – 1 term in lecture (~150 fall, 300 spring) * how to test without Tablets for all students?

4. Faster learning for others – learn more – learn quickly: less study time => less stress => happier students Potential Benefits Improved learning for struggling students – help with keeping up – formative assessment useful – organizing notes, example problems

5. CLP combines two active learning approaches: Large classes: Personal Response System (PRS) – Wireless polling system; students use transmitter to submit answers to multiple-choice or T/F questions in class – Successful in large classrooms What Small classes: Wide variety of in-class exercises – Instructor only has to evaluate small number of answers 2  CLP will let instructors use wide variety of exercises without being overwhelmed  CLP will let instructors use non-multiple-choice exercises

6. How Start with existing Tablet-PC-based Classroom Presenter system Wireless presentation system, instructor slides displayed on large screen and students’ Tablet PCs [ R. Anderson, et. al. 2004] Student anonymous submission of digital ink answers to exercises [ B. Simon, et. al. 2004] [ R. Anderson, www.cs.washington.edu/education/dl/presenter/] [ B. Simon, et. al. 2004]

7. How (cont’d) Extend it to work in large classes Aggregate student answers into equivalence classes –interpret handwritten text (e.g. number), selected items (e.g. links in a graph) –lay groundwork for matching sketches (a la Magic Paper) Instructor’s machine Screen Students’ machines Student answers

8. Extend it to work in large classes Aggregate student answers into equivalence classes Display results to instructor (and students) How (cont’d) Instructor’s machine Screen Students’ machines Summary

9. System Components Three components Instructor authoring tool: create presentation with expected answer type, correct, incorrect answers (plus pointers to additional study material) Interpreter: student ink answers  interpreted answers Aggregator: interpreted answers  equivalence classes and summary of results Authoring tool Presentation repository Instructor’s machine Stud ent’s machine Ink interpreter Answer repository answersinterpreted answers Answer repository Instructor’s machine Aggregator answers summary

10. System Architecture

11. Screen Mock-ups (a la Classroom Presenter) InstructorStudent

12. Screen Mock-ups (a la Classroom Presenter) Instructor

13. Classroom Learning Partner