Diversified resource access paths in MOOCs: Insights from network analysis

Li, Shuang; Du, Junlei; Yu, Shurong

doi:10.1016/j.compedu.2023.104869

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…x ij − min x j max x j − min x j (11) where x ij is the original value of the jth evaluation criterion of the ith alternative, z ij is the normalized value, and min x j and max x j are the minimum and maximum values of the jth evaluation criterion, respectively.…”

Section: Generation Of the Priority Of Attributes Improvementmentioning

confidence: 99%

“…MOOCs are now at the forefront of education [9]. However, despite the booming development of MOOCs, the rapid surge in the number of courses has given rise to numerous challenges, including a high dropout rate [10][11][12] and inconsistent quality [13], thereby hampering the sustainable progress of MOOCs. Since learner satisfaction plays a pivotal role in extending the duration of product usage [14], it has become imperative to pinpoint the issues prevailing in MOOCs and furnish targeted recommendations to course designers, aiming to enhance learner satisfaction.…”

Section: Introductionmentioning

confidence: 99%

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Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Yang,

Liu,

Luo

et al. 2024

Applied Sciences

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As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality.

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Section: Generation Of the Priority Of Attributes Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Yang,

Liu,

Luo

et al. 2024

Applied Sciences

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Understanding Knowledge Convergence in a Cross-cultural Online Context: An Individual and Collective Approach

Xiang,

Zhang,

2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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A personal social knowledge network (PSKN) facilitates learners’ wayfinding and its differences in behavior patterns between high and low performers in connectivist learning

Duan,

Xie,

Zhao

2024

Int J Educ Technol High Educ

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Wayfinding, which is a part of learning in connectivist learning, involves consolidating a wide variety of resources and information and building connections among them. However, learners often encounter difficulties in wayfinding, and are lost without technological support in connectivist learning. This study examined the wayfinding processes occurring within a network of learners in a personal social knowledge network (PSKN), explored differences in behavior patterns between high and low performers in PSKN. The results reveal the diversity and complexity of wayfinding in a PSKN, including finding and connecting nodes, forming cognitive maps, finding and filtering information, and creating new nodes. Moreover, the characteristics of wayfinding in the PSKN differed across participants, and high- and low-performing participants demonstrated different and unique wayfinding behavioral patterns, which provided a basis for comprehensive analyses of wayfinding. These findings can be used to provide instructional support and network navigation in connectivist learning for learners at various performance levels. The proposed PSKN shows promise in facilitate wayfinding including finding nodes and connecting nodes, as well as relations between knowledge nodes and the course base demonstrated by PSKN, providing great convenience for learners to form cognitive maps based on the node sequence. Compared with current studies, this research focuses on diversified interaction data and resource behavior rather than teaching videos and quizzes or exercises as the main resources and considering that course and technological factors influence the ways in which learners access resources in connectivist learning.

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Diversified resource access paths in MOOCs: Insights from network analysis

Cited by 3 publications

References 55 publications

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Understanding Knowledge Convergence in a Cross-cultural Online Context: An Individual and Collective Approach

A personal social knowledge network (PSKN) facilitates learners’ wayfinding and its differences in behavior patterns between high and low performers in connectivist learning

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