Learning motivation of college students in multimedia environment with machine learning models

Abstract

As the main force of higher education, ensuring the learning status and quality of college students is undoubtedly an important task in the education industry. Analyzing their learning motivation can provide a good understanding of their learning status. Especially in the new educational environment supported by multimedia technology, efficient and convenient learning channels can eliminate students' concerns about educational facilities and instead strengthen the analysis of learning motivation in other aspects. As part of our comprehensive study of learning motivation, we draw on established learning theories, such as reinforcement theory, associative learning, and self-determination theory. Applying such learning theories encourages positive reinforcement, establishes constructive relationships with learning, and nurtures competence and autonomy. This article believed that using machine learning models to predict students' grades or behaviours and analyze their learning motivation is a good approach. Moreover, this article also tested the prediction accuracy by setting different improved random forest model runs, and concluded that the more runs, the higher the accuracy. Especially when the runs reached 100, the accuracy reached 99.98 %.