Supporting Feedback and Assessment of Digital Ink Answers to In-Class Exercises

Kimberle Koile, Kevin Chevalier, Michel Rbeiz, Adam Rogal, David Singer, Jordan Sorensen, Amanda Smith, Kah Seng Tay, Kenneth Wu

Effective teaching involves treating the presentation of new material and the assessment of students' mastery of this material as part of a seamless and continuous feedback cycle. We have developed a computer system, called Classroom Learning Partner (CLP), that supports this methodology, and we have used it in teaching an introductory computer science course at MIT over the past year. Through evaluation of controlled classroom experiments, we have demonstrated that this approach reaches students who would have otherwise been left behind, and that it leads to greater attentiveness in class, greater student satisfaction, and better interactions between the instructor and student. The current CLP system consists of a network of Tablet PCs, and software for posing questions to students, interpreting their handwritten answers, and aggregating those answers into equivalence classes, each of which represents a particular level of understanding or misconception of the material. The current system supports a useful set of recognizers for specific types of answers, and employs AI techniques in the knowledge representation and reasoning necessary to support interpretation and aggregation of digital ink answers.


Submitted: Apr 3, 2007
