Leveraging Deep Learning for Classifying Learner-Generated Course Evaluation Texts
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4442-8_24
Reference20 articles.
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2. Gregori, E.B., Zhang, J., Galván-Fernández, C., de Asís Fernández-Navarro, F.: Learner support in MOOCs: identifying variables linked to completion. Comput. Educ. 122, 153–168 (2018)
3. Adnan, M., et al.: Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access 9, 7519–7539 (2021)
4. Mubarak, A.A., Cao, H., Zhang, W., Zhang, W.: Visual analytics of video-clickstream data and prediction of learners’ performance using deep learning models in MOOCs’ courses. Comput. Appl. Eng. Educ. 29, 710–732 (2021)
5. Yürüm, O.R., Taşkaya-Temizel, T., Yıldırım, S.: The use of video clickstream data to predict university students’ test performance: a comprehensive educational data mining approach. Educ. Inf. Technol. 28, 5209–5240 (2023)
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