Improving Thermodynamics Performance using a Multi-Attempt Feedback Mechanism in a Digital Assessment Platform

Abstract

Introduction: Science education in the Philippines continues to face challenges, particularly in mastering complex subjects like thermodynamics. Traditional assessment methods often fail to foster deep understanding and problem-solving skills. To address this, innovative feedback mechanisms are needed to enhance student learning outcomes in STEM courses.

Objectives: This study aims to determine the effectiveness of a digital multi-attempt feedback system in improving the academic performance, motivation, and conceptual understanding of students enrolled in a Thermodynamics course.

Methods: A convergent parallel mixed-methods design was used. Quantitative data were collected via pretest and posttest scores of 177 students and analyzed using paired t-tests and Hake’s factor. Qualitative data from 170 student responses were analyzed thematically to assess perceptions of the feedback system's impact on learning and motivation.

Results: Statistical analysis showed a significant improvement in posttest scores (p < 0.001) with medium to high gains (Hake factor: 0.62–0.89). Thematic findings revealed that the feedback system supported three core processes: executing problem-solving procedures, grasping thermodynamic concepts, and visualizing word problems. Students also reported increased engagement, motivation, and ability to connect concepts to real-world applications.

Conclusions: The multi-attempt feedback mechanism significantly improved student performance and learning engagement in Thermodynamics. Its integration into digital assessment platforms offers a promising approach to formative assessment, aligning with SDG 4’s goal of quality education through innovative, student-centered strategies.