ITS 플랫폼의 피드백 유형 분석: CTAT를 중심으로

초록

The purpose of this study is to identify the functions and types of feedback provided by intelligent tutoring systems (ITSs), and to derive improvement directions for educational effectiveness and implications for future development. This study selected four ITS platforms (Mathtutor, Stoichiometry Tutor, AdaptErrex, and Chem Tutor) developed with Cognitive Tutor Authoring Tools (CTAT) built by Carnegie Mellon University (CMU) and analyzed the timing, purpose, content, and subject of feedback provided by each platform. As a result of the analysis, we found that there was no feedback for the purpose of self-regulation, and that feedback with definitional content was provided at an insufficient level, confirming the need for improvement in relevant variables, especially in terms of supporting motivation. We also suggested adding a function that allows AI to generate individualized feedback based on learner characteristics, as there is no generative feedback function.

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