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A Study on Student Performance Prediction and Intervention Mechanisms in MOOC

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Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (SoCPaR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 648))

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Abstract

The open online learning platform’s popularity has developed in the last few years as the Internet’s development has empowered. This has resulted in the creation of Massive Open Online Course (MOOCs), which has enrolled an enormous number of individuals worldwide. MOOCs are one of the major significant educational advancements, bringing new chances for higher along with vocational education. But one of the exigent threats faced by any MOOC is its low completion rate and student retention. To tackle these issues, it is inevitable to make early student success predictions and timely interventions for at-risk students. This paper inspects diverse methodologies associated with students’ performance prediction in MOOC. The intervention mechanism that aids in extending the Learning Process (LP) in MOOC is also carefully examined in this study.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Noorul Islam Center for Higher Education, Kumarakovil, India

    S. Lakshmi

  2. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    C. P. Maheswaran

Authors
  1. S. Lakshmi
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  2. C. P. Maheswaran
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Corresponding author

Correspondence to S. Lakshmi .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

    Thomas Hanne

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, AL, USA

    Niketa Gandhi

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Mala, Kerala, India

    Pooja Manghirmalani Mishra

  5. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  6. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Lakshmi, S., Maheswaran, C.P. (2023). A Study on Student Performance Prediction and Intervention Mechanisms in MOOC. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_23

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