Multi-Perspective Fuzzy Reasoning and XGBoost-Based Analysis of Online Learning Behavior

Summary

This study uses a multi-perspective fuzzy reasoning model and an improved extreme gradient boosting (XGBoost) algorithm (optimized by an improved grey wolf optimization algorithm) to analyze online learning behavior and classify student comment emotions, providing support for personalized teaching and timely intervention.

Abstract

The booming development of online education has made online classrooms an important component of the education field. In-depth analysis of students' learning behavior in online teaching can help teachers optimize teaching strategies and provide personalized learning support for students. Therefore, to carry out an in-depth analysis of students' learning behavior, this study collects data from online teaching platforms and preprocesses it. Subsequently, this study constructs a multi-perspective fuzzy reasoning model covering three dimensions: curriculum, individual, and class, to comprehensively consider students' learning performance from different levels. This model processes uncertain information in learning behavior data through fuzzy sets and fuzzy rules, achieving a multidimensional evaluation of learning performance. An improved XGBoost algorithm is designed to classify students' comment emotions. This improved algorithm optimizes the hyperparameters of the XGBoost algorithm by improving the grey wolf optimization algorithm. The algorithm enhances the accuracy of emotion classification and further explores the emotional tendencies and attitude feedback behind their learning behavior. The results showed that from a curriculum perspective, the completion rate of course tasks 3 weeks before the exam was basically above 45%, which was much higher than the completion rate three weeks after the task was released (both lower than 18%). These results indicated that students were more inclined to complete tasks before the deadline and had obvious procrastination. The maximum accuracy of the improved classification algorithm was 98.78%, which was 8.57%, 7.55%, 6.38%, and 6.01% higher than the comparison model, and its average time consumption was 58 ms. The recall rates on negative, positive, and neutral emotions were 98.35%, 97.69%, and 98.02%. The research model can effectively analyze students' online learning behavior and enable early identification of at-risk students, facilitating personalized teaching and precise intervention in online education.

Introduction

The deep integration of the Internet and educational technology has made online education leap from a marginal supplement to a mainstream form. By the end of 2023, the number of registered online learners worldwide had exceeded 1.2 billion, and China had consistently ranked first in the world in both the number of Massive Open Online Courses (MOOCs) and learners^1,2. However, while online teaching brings convenience, it also exposes challenges such as the opaque learning process, the separation of teachers and students in time and space, and lagging regulatory feedback. There is a positive correlation between l....

Protocol

Design of the LBA method considering MPFR and emotional perspectives

A multi-perspective evaluation system is constructed for analyzing Students' Online Learning Behavior (SOLB), and an MPFR model is designed. To further analyze students' subjective feelings in learning, this study adopts the XGBoost algorithm and designs the IGWO algorithm for hyperparameter optimization to improve classification accuracy.

Construction of the MPFR model for LBA

To analyze SOLB, this study mainly starts from two perspectives to form a complete analysis plan. Firstly, i....

Results

LBA results considering multi-perspective and sentiment classification

To verify performance, the experimental environment is set up and the experimental dataset is described. In addition, this study selects the comparative algorithm IGWO-XGBoost and analyzes and verifies it using indicators such as accuracy, time consumption, and recall rate. In the results analysis, the study divides it into two clear sections: the results of LBA via MPFR and the performance validation of se.......

Discussion

For the analysis of SOLB, this study designed MPFR and IGWO-XGBoost models and used data from online teaching platforms. In the experiment, there was a high consistency between the rating of the inference model and the actual score, indicating the effectiveness of the inference model. About 25% of students had a study time of 1-10 h in the course. The proportion of students with study hours between 11-20, 21-30, 31-40 h, and over 41 h was 30%, 20%, 15%, and 10%. This indicated that the majority of students maintained mod.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BERT pre trained language model	Google research	https://github.com/google-research/bert
CNKI emotion dictionary	CNKI	https://www.cnki.net/
Chaoxing platform learns behavioral data	Chaoxing INFORMATION TECHNOLOGY DEVELOPMENT Co., Ltd	https://www.chaoxing.com/
Computer Memory			Universal hardware supplier (no specific model)
Harbin institute of technology's list of discontinued terms	Harbin Institute of Technology	https://github.com/goto456/stopwords/blob/master/hit_s
Intel Core i5-12600KF	Intel Corporation	BX8071512600KF
Jieba word segmentation tool (precision mode)	Third party open source community	https://github.com/fxsjy/jieba.
MOOC platform student review data	Love Course Network	https://www.icourse163.org/
NLTK library	NLTK development team	https://www.nltk.org/
Python programming language	Python Software Foundation	https://www.python.org/
Scikit-learn 1.2.2	Scikit learn team	https://scikit-learn.org/stable/
Smart Tree platform learns behavioral data	Smart Tree Network	https://www.zhihuishu.com/
SMOTE technology	Imbalanced Learn Development Team	Integrated into the Imbalanced Learn library, https://imbalanced-learn.org/stable/
Windows 10 operating system	Microsoft corporation	https://www.microsoft.com/zh-cn/windows/windows-10
XGBoost 1.7.5	XGBoost development team	https://xgboost.readthedocs.io/