An Evaluation Model of MOOC Learning Engagement Based on Learning Behaviors and Social Network Analysis

Yi, Xitian; Zhan, Zehui; Ma, Shuyao

doi:10.1109/icet55642.2022.9944529

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Future research should use the three types of predictive models under different conditions to determine whether they are applicable to specific learner samples and courses. The choice of predictors could also focus more on learners’ social interaction behaviors and independent learning behaviors rather than simply on learners’ clicking on learning resources (Yi et al , 2022). Another limitation of this study is the simple use of learner performance indicators for the analysis of the study, whereas it is important to combine dispositional and behavioral performance indicators as characteristic indicators to analyze learner impact and prediction of learning outcomes.…”

Section: Discussionmentioning

confidence: 99%

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Tong

Zhan

2023

ITSE

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Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

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Section: Discussionmentioning

confidence: 99%

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Tong

Zhan

2023

ITSE

Self Cite

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“…With the emergence of various online learning platforms in China, student engagement in online learning has been of interest to Chinese researchers [16] [17] [18]. Although online learning differs from traditional classroom learning in terms of environments (virtual versus physical), the expectation for students' participation and dedication remains constant.…”

Section: Introductionmentioning

confidence: 99%

Danmaku-Based Automatic Analysis of Real-Time Online Learning Engagement

Zeng,

Tan,

et al. 2024

Int. J. Interact. Mob. Technol.

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In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students’ lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students’ learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics.

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The influence of university library environment on student interactions and college students’ learning engagement

Zheng,

Zeng,

Huang

et al. 2024

Humanit Soc Sci Commun

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Over the past decade, learning engagement has received increasing attention from researchers. As a critical factor in college students’ academic achievement, learning engagement is significant in students’ long-term future development and social creativity. Present studies show that the overall level of college students’ learning engagement is low, which is highly unfavourable to educational quality and long-term development. There is no doubt about the position of the university library in academic circles. However, the influence of the university library environment on college students’ learning engagement has not been fully explored. The data in this study came from a survey of 45 Chinese universities. This paper investigates the relationship between the university library environment, students’ interaction, and students’ learning engagement. The results showed differences in Chinese college students’ learning engagement in liberal arts and sciences majors. The learning engagement of liberal arts majors is much lower than that of science students. We found that the library environment has an important influence on college students’ learning engagement. And the library environment can affect college students’ learning engagement through the intermediary role of interactive participation among students. In addition, for liberal arts students, the direct effect of the library environment on their learning engagement is more significant. However, for science students, the influence of the library environment on their learning engagement is more strongly intermediated by the students’ interaction. Therefore, future construction and renovation of library environments should cater not only to the diverse needs of different academic disciplines, especially liberal arts students who need special attention but also guide more students to interact friendly through the spatial characteristics of the library so as to improve the overall learning engagement of college students.

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An Evaluation Model of MOOC Learning Engagement Based on Learning Behaviors and Social Network Analysis

Cited by 6 publications

References 11 publications

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Danmaku-Based Automatic Analysis of Real-Time Online Learning Engagement

The influence of university library environment on student interactions and college students’ learning engagement

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