Integrating Ethnomathematics Cases with EdCafe AI to Enhance Students’ Mathematical Connection Ability

Source abstract

Mathematics is often perceived as abstract due to limited integration of cultural and real-world contexts, which restricts students’ opportunities to develop meaningful mathematical connections. This study investigates the impact of integrating ethnomathematics-based case studies with the EdCafe AI platform in a Graph Theory course. A quantitative pre-experimental one-group pretest–posttest design was applied to 18 mathematics education students over six sessions (100 minutes each). The intervention combined local ethnomathematical contexts with EdCafe AI’s interactive features and automated feedback. Students’ mathematical connection ability was assessed using an essay-based test scored on a four-point scale. Results showed a significant improvement: the mean score increased from 2.100 in the pretest to 3.483 in the posttest, with a mean gain of 1.383 points. The Wilcoxon signed-rank test confirmed statistical significance (W = 0.000, Z = ?4.001, p < .001), with all students achieving higher posttest scores. The effect size was large (r = 0.943), and the normalized gain (N-gain) of 0.728 indicated a high-gain category. These findings indicate substantial improvement following the intervention, although the absence of a comparison group limits causal inference. Practically, the results highlight the potential of combining ethnomathematics with AI-assisted learning to strengthen conceptual mastery, contextual understanding, and digital literacy in mathematics education.