Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2018/03/15
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Indian UGC (journal)
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Refrences21
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Citations101
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Wanli Xing Department of Educational Psychology & Leadership, Texas Tech University, TX, USA
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Dongping Du Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA
Abstract
Cite
Xing, Wanli, and Dongping Du. “Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention”. Journal of Educational Computing Research, vol. 57, no. 3, 2018, pp. 547-70, https://doi.org/10.1177/0735633118757015.
Xing, W., & Du, D. (2018). Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research, 57(3), 547-570. https://doi.org/10.1177/0735633118757015
Xing, Wanli, and Dongping Du. “Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention”. Journal of Educational Computing Research 57, no. 3 (2018): 547-70. https://doi.org/10.1177/0735633118757015.
Xing W, Du D. Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research. 2018;57(3):547-70.
Journal Categories
Education
Education
Theory and practice of education
Social Sciences
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
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| Title | 2014 | |||
| Title | 2011 | |||
| Title | 2014 | |||
| C4. 5: programs for machine learning | 2014 | |||
| Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge | 2014 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Online learners’ self-regulated learning skills regarding LMS interactions: a profiling study | Journal of Computing in Higher Education |
| 1 | 2024 |
| Machine learning approaches to identify lithium concentration in petroleum produced waters | Mineral Economics |
| 2024 | |
Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM | International Journal of Computational Intelligence Systems |
| 2024 | |
| Quantification and prediction of engagement: Applied to personalized course recommendation to reduce dropout in MOOCs | Information Processing & Management |
| 2024 | |
| Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review | IEEE Access |
| 2024 |
Citations Analysis
The category
Education: Theory and practice of education 30
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development and was published in 2018. The most recent citation comes from a 2024 study titled A tri-phenomenon perspective to mitigate MOOCs' high dropout rates: the role of technical, pedagogical, and contextual factors on language learners' L2 motivational selves, and learning approaches to MOOC. This article reached its peak citation in 2022, with 28 citations. It has been cited in 58 different journals, 29% of which are open access. Among related journals, the IEEE Access cited this research the most, with 7 citations. The chart below illustrates the annual citation trends for this article.