Online Small Sample Learner Modeling and Curriculum Recommendation with Healthy Emotional Factors of College Students.

Xia Li,Ruibo Di,Jun Cai

doi:10.1155/2022/8247203

Abstract

Online education is a popular way for college students at present, and it is also a good compensation way to meet the special situation that traditional offline teaching cannot complete the teaching task. Traditional classroom teaching methods have been difficult to meet the learning requirements of contemporary college students, while online classroom has made up for the shortcomings of traditional classroom teaching to some extent because of its short class hours, prominent focus, and online mobile learning. First, this paper proposes an online SSL (small sample learner) model for college students to integrate healthy emotional factors. The characteristics of learners are divided into three categories: basic characteristics of learners, characteristics of behavioral factors, and characteristics of emotional factors, and the problem of solving mapping functions is transformed into the problem of solving kernel functions. Second, a novel curriculum recommendation model integrating healthy emotional factors is proposed, which fully considers the influence of user comments on similarity and transforms the similarity of users in the overall score of the project into the similarity of users in the emotional tendency of the special attributes of the project. Through the experimental evaluation, the accuracy and stability of the recommendation are greatly improved.

Highlights

With the continuous deep integration of big data, the Internet of ings, cloud computing, and other information technologies and education fields, the intelligent transformation of education has been promoted, and the advanced educational form in the era of educational informationization 2.0 represented by intelligent education has been achieved [1, 2]
Based on a review of the literature on learner models, this paper focuses on the recognition of learners’ facial expressions and methods for obtaining emotional data and builds an online SSL learner model that incorporates healthy emotional factors in an online classroom setting
Emotional analysis refers to the process of gathering user feedback on a product, analyzing the content of the feedback, determining the polarity of the user’s emotional tendency at the feature level of the project, that is, the favorable or unfavorable attitude and opinion toward the course or project, and extracting more useful information

Summary

Introduction

With the continuous deep integration of big data, the Internet of ings, cloud computing, and other information technologies and education fields, the intelligent transformation of education has been promoted, and the advanced educational form in the era of educational informationization 2.0 represented by intelligent education has been achieved [1, 2]. Learning and communication through the online learning community provide people with a generalized learning method without the limitation of the number of participants and time and place [4]. Learning activities in such an environment can make everyone enjoy high-quality teaching resources and fair learning opportunities. A large amount of data has promoted the prosperity and development of artificial intelligence in recent years, especially the direction of DL (deep learning) [5, 6]. The rapid development of artificial intelligence in recent years largely depends on the development of data.

Methods

Results

Conclusion