Eighth Graders and a Math Intelligent Tutoring System: A Deep Neural Network Analysis

Kelun Lu, Lingxin Hao, I-Jeng Wang, Anqi Liu

Abstract: Intelligent tutoring systems (ITS) typically employ scaffolding to provide learning opportunities for needed students. To assess the ITS effectiveness, most past research relies on the end-of-year exam outcome. However, it is important to assess ITS’ immediate effectiveness after the scaffolding. To address this gap, this paper draws principles from theory on scaffolding and zone of proximal development to guide the analysis of micro-level, time-stamped student action data from the ASSISTments. Using a deep neural network to analyze the data, we reveal that the appropriate use of scaffolds in the ITS fosters modest growth of math problem-solving skills. Excessive use, however, is counterproductive when students are overly reliant on ITS. Our findings suggest that scaffolding in ITS or AI tutoring should encourage student active learning and prevent over-reliance on intelligent systems.