Study on the Evaluation Method of Blended Learning Effect Based on Multiple Linear Regression Analysis

Learner Characteristics

Learner characteristics are important factors that affect blended learning (Kiezebrink, 2021). G. Wang et al. (2021) used the path “demographic factors → cognitive factors → emotional factors → willpower factors → behavioral factors” to analyze the impact of multiple factors on the blended learning effect by using multiple linear regression methods. Lv (2022) analyzed learners’ characteristics based on the five dimensions of “basic characteristics—cognition—emotion—will—behavior,” classified learners, and analyzed the impact of learners’ characteristics on learning outcomes.

Learning Behavior

Jia et al. (2014) argued that online learning performance is related to indicators such as time spent online, number of times watching videos, number of times viewing web pages and browsing and downloading lectures,average test scores, forum participation, and learning start time. Zong et al. (2016), Jiang et al. (2015), He and Wu (2016), and others have also analyzed the factors influencing online learning effectiveness. Yang (2022) studied and analyzed the correlation between students’ online learning behavior and their final exam scores in terms of four aspects—namely, the number of times they entered the classroom, the number of times they participated in discussions, the duration of online learning, and the number of times they read the teaching materials of the course—to measure the relationship between online learning behavior and learning effectiveness. The above research mainly analyzed the impact of online learning behavior on learning outcomes without considering classroom teaching situations.

Other Factors

Zhang et al. (2021) conducted a questionnaire survey to investigate the effects of learner characteristics, curriculum characteristics, technical characteristics, cognitive usefulness, and cognitive ease of use on blended learning outcomes. Zhang (2022) utilized group experiments and empirical analysis to evaluate the impact of online learning groups, online homework, and their different combinations on teaching effectiveness among college students. Gao and Liu (2021) constructed a multiple linear regression model to analyze the impact of online teaching videos, online assignments, online tests, and offline classroom teaching on the teaching effectiveness of university mathematics courses.

Based on the above analyses, many factors affect teaching effectiveness in the blended online and offline teaching mode, and these must be considered comprehensively.