Effects of the anim4games self-learning module on the level of competency readiness among game design students through the addie approach and rasch analysis

The game design and animation industries complement one another with animation augmenting the visual and emotional aspects of the game world, while game design integrates technical, artistic, and interactive elements to create signifil.:antly meaningful, immersive, and innovative gaming experience. Such synergy has the potential to increase demand for highly skilled workforce within the creative industry. However, traditional animation predominantly focuses on cinematic approaches which frequently neglect the necessary adaptability in-game animation and consequently encumbers students' learning of critical skills. This study addresses this gap by designing and developing the Animation for Games (Anim4Games) Self-Learning Module, intended to measure the effects of the module on competency readiness among students from both creative and non-creative backgrounds entering game design. This study follows the ADDIE model, comprising three phases: Needs Analysis, Design and Development, and Evaluation. The study first employed the Fuzzy Delphi Method (FDM) for expert consensus, followed by a pilot study with 21 students to ensure the reliability of the instrument and module before full-scale implementation. This study then conducted a quasi-experimental pretest-posttest design with a non-equivalent control group (N=32) and an experimental group (N=32) to measure the module's effectiveness. The intervention results, analyzed using the Wright Map of Rasch analysis, visually depict changes in student performance. Findings indicate that students using the Anim4Games module demonstrated significant improvements compared to the control group, with higher gains in in-game animation knowledge (1.72 compared to 1.05), digital skillset creation (1.66 compared to 1.27), and competency readiness (1.84 compared to 0.69). This study contributes to game design education by introducing a validated self- learning module that is aligned with industry needs and has the potential for broader implementation in higher education curricula"