At this stage, a new online course teaching mode has emerged in higher education, which has promoted the development of education and teaching to a certain extent. However, the existing online course teaching mode still has many problems. For example, the function of online course system is not perfect. Students’ online course learning lacks a sense of on-site learning atmosphere. The students’ self-discipline is not strong, which leads to the low completion rate of courses. The emergence of digital educational resources has provided teachers and students with rich educational resources, and has gradually become an important part of education and teaching. In view of the deficiency of online course teaching mode, this paper proposed that artificial intelligence (AI) technology was applied to online courses. Combined with feature weighted support vector machine algorithm, the online course learning behavior was analyzed experimentally. The experimental results showed that the average prediction accuracy of the four learning behaviors was 93.38%, and the average prediction error was 1.20. From the above data, it can seen that this algorithm could play a good optimization effect on the prediction of online course learning behavior. This paper also conducted a questionnaire survey on the use frequency of digital education resources in various disciplines. The results showed that English was the most frequently used discipline, accounting for 24.8%. Digital education resources have certain applications in various disciplines.

Yunbo Li. Effect Evaluation and Trend of Higher Education Online Courses Based on Digital Artificial Intelligence Technology. International Journal of Educational Innovation and Science (2023), Vol. 4, Issue 1: 14-26. https://doi.org/10.38007/IJEIS.2023.040102.

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