Enhancing Academic Performance in Motor Control: A Structured H5P-Based Multiple-Choice Intervention in Higher Education

Background: Interactive learning resources developed with the H5P platform have been progressively adopted to support autonomous learning and conceptual consolidation. However, empirical evidence regarding their impact on academic performance in theoretically demanding university courses remains limited. The primary aim of this study was to examine the effect of the structured integration of an interactive digital pedagogical resource developed with multiple-choice H5P on the academic performance of higher education students enrolled in a Motor Control course. Methods: A quasi-experimental study was conducted to compare two independent groups: a control group (CG; n = 90) and an intervention group (IG; n = 115), which had access throughout the semester to a multiple-choice interactive resource developed using the H5P platform. Academic performance was operationalized as the score obtained on a written summative assessment. Baseline equivalence between groups was assessed using an initial diagnostic test. Between-group comparisons were performed using robust non-parametric statistical procedures and further examined using a linear regression model adjusted for relevant covariates. Results: No statistically significant differences were found between groups in the baseline diagnostic test (p > 0.05), indicating comparable starting levels. At the end of the intervention period (≈2 months), the intervention group obtained significantly higher scores in the summative assessment (p < 0.001), with a large effect size (d = 0.87). Conclusions: The findings suggest that the structured integration of multiple-choice H5P resources may positively contribute to academic performance when used as a complementary tool alongside traditional teaching. These results reinforce the pedagogical potential of multiple-choice H5P to support autonomous learning and conceptual consolidation, while also highlighting the need for future research employing more rigorous experimental designs and process-based measures to better understand the underlying learning mechanisms.