A Neural Network Modelling and Prediction of Students’ Progression in Learning: A Hybrid Pedagogic Method

Ethan Lau, Kok Chai, Gokop Goteng, Vindya Wijeratne

2021

Abstract

The COVID-19 pandemic has changed dramatically the way how universities ensure the continuous and sustainable way of educating students. This paper presents the neural network (NN) modelling and predicting students’ progression in learning through a hybrid pedagogic method. The hybrid pedagogic approach is based on the revised Bloom’s taxonomy in combination with the flipped classroom, asynchronous and cognitive learning approach. To evaluate the effectiveness of the hybrid pedagogic approach and the students’ progression in learning, educational data is collected that comprises of labs and class test scores, as well as students’ total engagement and attendance metrics for the programming module considered. Conventional statistical evaluations are performed to evaluate students’ progression in learning. The NN is further modelled with six input variables, two layers of hidden neurons, and one output layer. Levenberg-Marquardt algorithm is employed as the back propagation training rule. The performance of neural network model is evaluated through the error performance, regression and error histogram. The NN model has achieved a good prediction accuracy along with limitations. Overall, the NN model presents how the hybrid pedagogic method in this case has successfully quantified students’ progression in learning throughout the COVID-19 period.

Paper Citation

in Harvard Style

Lau E., Chai K., Goteng G. and Wijeratne V. (2021). A Neural Network Modelling and Prediction of Students’ Progression in Learning: A Hybrid Pedagogic Method. In Proceedings of the 13th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-502-9, pages 84-91. DOI: 10.5220/0010405600840091

in Bibtex Style

@conference{csedu21,
author={Ethan Lau and Kok Chai and Gokop Goteng and Vindya Wijeratne},
title={A Neural Network Modelling and Prediction of Students’ Progression in Learning: A Hybrid Pedagogic Method},
booktitle={Proceedings of the 13th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2021},
pages={84-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010405600840091},
isbn={978-989-758-502-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - A Neural Network Modelling and Prediction of Students’ Progression in Learning: A Hybrid Pedagogic Method
SN - 978-989-758-502-9
AU - Lau E.
AU - Chai K.
AU - Goteng G.
AU - Wijeratne V.
PY - 2021
SP - 84
EP - 91
DO - 10.5220/0010405600840091