Predicting Learning Behavior Using Log Data in Blended Teaching

Abstract

Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students’ learning behavior analysis and student performance prediction based on the data about students’ behavior logs in three consecutive years of blended teaching in a college’s “Java Language Programming” course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algorithm (GA) and the error correcting output codes (ECOC) method, is developed to predict the grade levels of students. In the framework, GA is designed to realize both the feature selection and binary classifier selection to fit the ECOC models. Finally, key factors affecting grades are identified in line with the optimal subset of features selected by GA, which can be analyzed for teaching significance. The results show that the multiclass classification algorithm designed in this article can effectively predict grades compared with other algorithms. In addition, the selected subset of features corresponding to learning behaviors is pedagogically instructive.

A Study on Detection Methods of Multi-Featured Fusion Teaching Factors in Blended College English Teaching

“…Dale Wang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]…”

“…Fragmented mobile learning has been a hotspot in teaching research in recent years, and the original intention of its conception is to enable learners to transcend the limitations of time and space through the use of mobile tools. Effective selective separation of learning content allows learners to freely choose and control the content and time of learning and to obtain meaningful pieces of knowledge from huge amounts of information [10][11][12]. Fragmented learning is a learning mode adapted to the fast-paced modern life, which gives great freedom and independence to learning and deeply fits modern people's need for free, efficient, and fast learning [13][14][15].…”

Exploring the Influence of Students’ Modes of Behavioral Engagement in an Online Programming Course Using the Partial Least Squares Structural Equation Modeling Approach

“…Furthermore, engagement in self-assessment and self-reflection activities exerts a significant direct effect on engagement in online classes and social interaction. Xie et al (2021) conducted learning behavior analysis and student performance prediction based on data from students' behavior logs from three consecutive years of a college-level Java Language Programming course conducted in a blended format. Researchers revealed that, according to the finegrained results of feature selection and correlation analysis, the learning features that have a more significant impact on the students' final grades are selected from their learning behaviors for analysis, which is conducive to teachers' individualized teaching.…”

Analyzing the efficacy of Higher Vocational Online-Offline Blended Teaching using Koch’s Model

“…How to integrate traditional face-to-face classroom teaching and network online teaching reasonably and organically, make a good teaching plan in advance, and maximize the value of teaching, blended teaching came into being. Literature [13] to a university JAVA course hybrid teaching student behavior log data as the basis to explore the impact of blended teaching on student teaching behavior and performance, combined with genetic algorithms and error-correcting output code, proposed a multi-class classification framework, the feasibility of the method is confirmed by the analysis of the example, the analysis results show that blended teaching promotes classroom interaction, ensures the learning efficiency of the students, and is conducive to the teacher to master the students' status and design personalized teaching. Literature [14] studies the evaluation of blended oral English teaching integrating SPOC and deep learning hybrid mode through the teaching survey of two English major classes, using analysis of covariance to study the impact of blended mode teaching on students' oral English, the experiment points out that this mode of teaching not only stimulates interest and improves the quality of learning, but also has a positive impact on the improvement of students' critical thinking, communication and collaboration skills.…”