Enhancing Chinese character achievement in primary education through multimedia-assisted deep learning module - Yulu Jin; Nik Muhammad Hanis Nek Rakami; Md. Nasir Masran (Institute of Advanced Engineering and Science, 2026)
Chinese character literacy is essential for developing literacy competence in primary education; however, traditional instructional methods often rely on rote memorization, limiting student engagement, and deep learning. This study examined the effectiveness of a multimedia-assisted deep learning (MADL) module, designed based on the cognitive theory of multimedia learning (CTML) and cognitive semantic theory, in enhancing primary students’ academic achievement in Chinese character learning. A quasi-experimental design was adopted with 222 second-grade students from three schools, with an experimental group (MADL, n=110) and a control group receiving traditional instruction (n=112). Academic achievement was assessed using pre-test and post-test, measuring overall scores, reading, writing, and understanding. Non-parametric Mann–Whitney U tests and Wilcoxon signed-rank tests revealed that the MADL group significantly outperformed the control group in overall post-test scores (p<.05) as well as in reading, writing, and understanding subtests. Within-group analyses further showed greater improvements in the MADL group. These findings indicate that the MADL module is an effective and pedagogically grounded tool for enhancing Chinese character learning in primary education. The study contributes empirical evidence supporting the integration of MADL strategies into early literacy instruction.