Research on Optimization of Online Learning Process Based on Personalized Recommendation

  • Ziyu Liu, Liye Dong, Wei Song*

Abstract

Online learning is a new way of study. Because of its rich resources, convenient access, and
economical application, it is becoming more and more popular among learners. However, most
online learning platforms can only provide simple keyword search and popular recommendation
functions, which can not completely solve the problems of information overload and
information loss caused by the accumulation of massive learning resources. Aiming at the
current situation of online learning, this paper proposes an online learning process optimization
method based on cognitive diagnosis and personalized recommendation. This method is based
on the theoretical knowledge such as cognitive diagnosis and personalized recommendation,
with the goal of optimizing the learning process, and the course of "Database Application" as
the empirical background. Firstly, this paper diagnoses learners' mastery of cognitive attributes
according to the steps of cognitive diagnosis. Secondly, the learner model, the learning resource
model and the knowledge structure diagram will be constructed, and the collaborative filtering
algorithm will be selected to recommend suitable learning resources for the learners. Finally,
through experiments, the method in this paper can effectively solve the problems of students in
the process of online learning from the four aspects which was recommendation accuracy, study
time, test scores, and learner satisfaction

Published
2020-06-30
How to Cite
Ziyu Liu, Liye Dong, Wei Song*. (2020). Research on Optimization of Online Learning Process Based on Personalized Recommendation. Design Engineering, 489 - 521. https://doi.org/10.17762/de.vi.549
Section
Articles