Applied research on data analysis in creative multimedia courses in universities

Abstract

Creative multimedia has become a key component of innovation in today’s quickly changing digital world, blending technology and artistry to provide captivating, interactive, visual, and aural experiences. Universities worldwide are offering specialized courses in creative multimedia to equip students with skills for industries like entertainment, advertising, education, and digital communication. This course integrates graphic design, animation, video production, game development, and virtual reality, fostering a holistic knowledge atmosphere. The purpose of the research is to establish the application of data analysis in creative multimedia courses in universities to enhance student achievement evaluation and foster innovative and technical development in university-level graphic design courses focused on packaging design. The Efficient African Buffalo Tuned Adaptive Random Forest (EAB-ARF) is applied to assess student performance based on various criteria, including creativity, technical proficiency, and the effectiveness of packaging designs. Data collection includes student performance, design samples, teacher ratings, and packaging design. The data was preprocessed using data cleaning and normalization from the acquired data. EAB is used to select the features from data, and ARF is employed to assess student performance and enhance creativity. The recommended EAB-ARF outperforms all other models with the highest accuracy values of 95,8 %, (95,6 %) precision, (99,2 %) recall, and (97,6 %) F1-score. This illustrates how EAB-ARF performs well across all evaluation categories and has a superior ability to forecast student results.