MOOC-BERT: Automatically Identifying Learner Cognitive Presence From MOOC Discussion Data

Li, Zhi; Kong, Xi; Chen, Hao; Liu, Sanya; Yang, Zhen

doi:10.1109/tlt.2023.3240715

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…In order to improve the performance of sentiment analysis, Liu et al [34] combined BERT and part-of-speech information in MOOC reviews of sentiment. Liu et al [35] proposed an MOOC-BERT model to automatically identify learners' cognition from online reviews, which has been verified to be superior to representative deep learning models in recognition and cross-curriculum.…”

Section: Mooc Review Text Miningmentioning

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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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: Mooc Review Text Miningmentioning

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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“…A variety of automated detectors have been developed that use non-invasive methods to classify students' emotional states, engagement, and cognitive presence during their participation in on-line classes (e.g., Baker et al, 2010;Liu et al, 2019Liu et al, , 2023.…”

Section: Urgency Of the Problem Of Dai In Educational Decision Makingmentioning

confidence: 99%

Disembodied AI and the limits to machine understanding of students' embodied interactions

Nathan

2023

Front. Artif. Intell.

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The embodiment turn in the Learning Sciences has fueled growth of multimodal learning analytics to understand embodied interactions and make consequential educational decisions about students more rapidly, more accurately, and more personalized than ever before. Managing demands of complexity and speed is leading to growing reliance by education systems on disembodied artificial intelligence (dAI) programs, which, ironically, are inherently incapable of interpreting students' embodied interactions. This is fueling a potential crisis of complexity. Augmented intelligence systems offer promising avenues for managing this crisis by integrating the strengths of omnipresent dAI to detect complex patterns of student behavior from multimodal datastreams, with the strengths of humans to meaningfully interpret embodied interactions in service of consequential decision making to achieve a balance between complexity, interpretability, and accountability for allocating education resources to children.

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Diversified resource access paths in MOOCs: Insights from network analysis

2023

Computers & Education

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MOOC-BERT: Automatically Identifying Learner Cognitive Presence From MOOC Discussion Data

Cited by 8 publications

References 45 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

Disembodied AI and the limits to machine understanding of students' embodied interactions

Diversified resource access paths in MOOCs: Insights from network analysis

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