A case-based reasoning approach to providing feedback to novice programmers

College

College of Computer Studies

Document Type

Conference Proceeding

Source Title

DLSU Research Congress 2015

Publication Date

3-2015

Abstract

Adaptive feedback contains information that individual users of a system will find helpful rather than cryptic. A case-based reasoning (CBR) approach to automatic feedback generation can provide feedback that is timely and adaptive; however, such an approach generally needs a sufficiently populated case base. In this paper, we describe a pedagogical programming tool called CBR-C that uses a CBR-based approach to give meaningful and adaptive feedback depending on the number of cases in its case base and the required remediation of the student, and is able to give feedback despite having insufficient cases in its case base. Experiments for evaluating the feedback generation capability of CBR-C were conducted with students learning to program in C for the first time. These students were assigned to a control group and an experimental group, and each student was instructed to submit solutions to a programming problem incrementally until the student finally get a correct answer, i.e., a C program that met all the given programming requirements. The improvement in code quality of each submission was then determined to see whether the feedback generated by the CBR-C had any effect on the code of the students. The improvement in code quality of the students who used CBR-C was greater, with mild statistical significance, than that of those who did not, indicating that receiving feedback from CBR-C regarding one's program is better than not receiving any feedback at all, at least as far as students learning C for the first time are concerned.

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Disciplines

Computer Sciences

Keywords

Case-based reasoning; Feedback control systems

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