Authors:
Ethan Lau
;
Kok Keong Chai
;
Gokop Longinus Goteng
and
Vindya Wijeratne
Affiliation:
School of Electronic Engineering and Computer Science, Queen Mary University of London, 10 Godward Square, Mile End Rd., Mile End, London E1 4FZ, U.K.
Keyword(s):
Pedagogic Approach, Blended Learning, Statistical Evaluations, Neural Network.
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 rul
e. 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.
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