Abstract
It is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not leave an evaluation record in the learning platform, the association rule algorithm in data mining is used to find out the videos that students are interested in and recommend them. For the students who have evaluation records in the platform, we use the collaborative filtering algorithm based on items in machine learning, and use the Pearson correlation coefficient method to find highly similar video materials, and then recommend the learning materials they are interested in. The two methods are used in different situations, and all students in the learning platform can get recommendation. Through the application, our methods can reduce the data search time, improve the stickiness of the platform, solve the problem of information overload, and meet the personalized needs of the learners.
Highlights
Research on PersonalizedE-Learning breaks through the limitation of time and space, so that learners who want to learn knowledge can get learning materials, watch learning videos, and study as if they are in a school classroom
The related algorithms of artificial intelligence are widely used in all professions and trades
For the students who have evaluated the system, the collaborative filtering algorithm of machine learning is used, and the Pearson correlation coefficient method is used to find out the similarity between the video materials, and recommend it to the interested students
Summary
E-Learning (electronic learning) breaks through the limitation of time and space, so that learners who want to learn knowledge can get learning materials, watch learning videos, and study as if they are in a school classroom. Online learning is even better than traditional teaching. In the field of medicine, students can watch some videos of the operation process repeatedly, which is better than the teacher’s live demonstration. Thanks to its ease of use and unlimited access, e-learning improved students’ clinical learning process by virtual visual support [1]. As a supplement to traditional teaching, video teaching has its advantages [2].
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