Design and Implementation of a Computational Model for the Enhancement of College Students’ Independent Learning Ability Supported by Big Data

College students’ prediction of independent learning ability is an effective way to improve self-learning ability. This paper utilizes big data technology to collect the basic information of self-learning ability assessment, in combination with the hierarchical structure of college students’ self-learning indicators. A prediction model of college students’ independent learning ability based on Markov chains is designed using the collected data. The difference between the predicted results and the actual situation is analyzed using the mean relative error test. The prediction error of the college students’ independent learning ability prediction experiment fully meets the actual requirement of prediction error less than 20%, which proves the effectiveness of the prediction model of college students’ independent learning ability based on Markov chain. After using the prediction model to provide personalized guidance for students to improve their learning ability, there are significant differences in the before-and-after comparisons of students’ learning motivation and learning strategies, and the before-and-after comparisons of the indicators under the two dimensions also show significant differences. It is proven that the prediction model constructed in this paper can improve college students’ independent learning ability.