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Prediction of Students’ Performance Based on Their Learning Experiences and Assessments: Statistical and Neural Network Approaches

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Computer Supported Education (CSEDU 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1624))

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Abstract

The education approaches in the higher education have been evolved due to the impact of covid-19 pandemic. The predicting of students’ final performance has become more crucial as various new learning approaches have been adopted in the teaching. This paper proposes a statistical and neural network model to predict students’ final performance based on their learning experiences and assessments as the predictor variables. Students’ learning experiences were obtained through educational data analytic platform on a module that delivered the mixed-mode education strategy using Flipped classroom, asynchronous and cognitive learning in combination with the revised Bloom’s taxonomy. Statistical evaluations including multiple regressions, ANOVA correlations are performed to evaluate the appropriateness of the input variables used for the later Neural Network output prediction. The Levenberg-Marquardt algorithm is employed as the training rule for the Neural Network model. The performance of neural network model is further verified to prevent the overfitting issue. The Neural Network model has achieved a high prediction accuracy justifying the students’ final performance through utilising the aforementioned pedagogical practises along with limitations.

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Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, 10 Godward Square, Mile End Road, London, E1 4FZ, UK

    Ethan Lau, Vindya Wijeratne & Kok Keong Chai

Authors
  1. Ethan Lau
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  2. Vindya Wijeratne
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  3. Kok Keong Chai
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Correspondence to Ethan Lau .

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Editors and Affiliations

  1. Institute of Education, University of Szeged, Szeged, Hungary

    Beno Csapó

  2. School of Engineering, University of Ulster, Newtownabbey, UK

    James Uhomoibhi

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Lau, E., Wijeratne, V., Chai, K.K. (2022). Prediction of Students’ Performance Based on Their Learning Experiences and Assessments: Statistical and Neural Network Approaches. In: Csapó, B., Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2021. Communications in Computer and Information Science, vol 1624. Springer, Cham. https://doi.org/10.1007/978-3-031-14756-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-14756-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14755-5

  • Online ISBN: 978-3-031-14756-2

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