Ensemble models based on CNN and LSTM for dropout prediction in MOOC

Talebi, K Shah; Torabi, Zeinab; Daneshpour, Negin

doi:10.1016/j.eswa.2023.121187

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Utilizing Equations ( 6)- (10) and the preceding results, we constructed the decision matrices for both courses, as presented in Tables 4 and 5. Based on the results in Tables 4 and 5, we can use Formulas ( 11)-( 14) to calculate the criteria weights.…”

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%