Remediation of Mathematics Knowledge in Engineering Students Through an AI-Based Selfstudy Educational Intervention Academic Article in Scopus uri icon

abstract

  • This study addresses the remediation of mathematical knowledge in engineering education, a critical issue as many students lack foundational skills upon entering essential courses. We conducted a quasi-experimental study that compared traditional instruction methods with a self-study strategy across three phases: diagnosing initial knowledge, providing tailored self-study resources to an experimental group, and assessing the effectiveness of the intervention. During the first period of the Fall 2024 semester, an initial diagnostic test on key mathematical topics was administered via CANVAS in a first-year Engineering and Sciences course. The experimental group received CANVAS instructional videos, supplementary documents, and access to the AI tool Jungle for optional self-study. In contrast, the control group had access to voluntary tutoring sessions. Post-intervention, both groups completed a final exam to measure improvements in problemsolving abilities. Additionally, a follow-up survey gathered feedback on self-study materials and Jungle. The analysis shows a positive correlation between Jungle usage and academic improvement. Jungle users initially scored slightly lower, saw greater improvements, and achieved higher post-test and final scores. The experimental group of Jungle users showed a statistically significant score improvement over the control group (p=0.05). Qualitative feedback highlights Jungle's effectiveness, feedback, and ease of use, though students suggested enhancing question variety. Results demonstrate the potential of structured self-study interventions to improve mathematical understanding among engineering students. The theoretical implications of this work are that by emphasizing tailored resources, this study aims to contribute effective strategies for bridging knowledge gaps in STEM education, enhancing student retention, and supporting academic success in engineering programs. © 2025 IEEE.

publication date

  • January 1, 2025