Exploring Student Attitude and Performance in Mathematics through Game- Based Learning (GBL) Using Deepseek Codes

Abstract

This study examined the effectiveness of Game-Based Learning (GBL) using DeepSeek Codes in improving Grade 8 students’ mathematics performance and attitudes. It was conducted at Dagatkidavao Integrated School using two intact classes. One group was exposed to Game-Based Learning (GBL) with 29 participants, while the other group received traditional instruction (NGBL) with 30 participants. A quasi-experimental pretest–posttest research design was employed. Students’ performance was measured using a standardized test, while attitudes were assessed through a modified Auzmendi’s Scale. The scale evaluated value, anxiety, enjoyment, motivation, and confidence using a five-point Likert scale. The procedure included pretesting, implementation of the intervention, and posttesting. Data were analyzed using Analysis of Covariance (ANCOVA) to control for pretest differences. Results showed that the GBL group achieved significantly higher performance than the NGBL group. In terms of attitudes, the GBL group also demonstrated more positive attitudes toward mathematics compared to the NGBL group, indicating an improvement in learners’ affective responses alongside their academic performance. Overall, the study concludes that Game-Based Learning is an effective approach for enhancing both mathematics achievement and students’ attitudes.