research-article

Classifying students based on cognitive state in flipped learning pedagogy

Authors:

Rabi Shaw,

Bidyut Kr. PatraAuthors Info & Claims

Volume 126, Issue C

Pages 305 - 317

https://doi.org/10.1016/j.future.2021.08.018

Published: 01 January 2022 Publication History

Abstract

The flipped learning (FL) is found to be an effective teaching methodology which is accomplished in two stages. In the first stage, students take instructions and learn from pre-loaded lecture videos (out-of-class learning). In the second stage, students carry out various activities such as group discussion, think-pair-share, group quiz, etc. in presence of the instructor (in-class learning). Therefore, students get enough time to brainstorm on the topic learnt from the pre-loaded lecture. This new learning pedagogy offers quality learning for many students. However, this teaching methodology does not have provision to monitor students while taking lesson from pre-loaded lecture video unlike live classroom teaching. This may lead to severe learning incompetence for weak students.

In this study, we propose to develop a prototype (model) to monitor the student in flipped learning by capturing the brain wave of individual students passively while they are engaged with lecture video. The siamese neural network is exploited to analyze captured brain waves (EEG signal) in order to classify the students into three categories (Weak, Good, Outstanding) and two categories (Weak and Strong) based on their attention level, respectively. This will help the instructor to identify the weak students and treat them with special care. We validate the performance of the proposed classification task with the data obtained from the proposed prototype. The experimental result shows that the proposed siamese neural network outperforms other classification models.

Highlights

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A model is developed for capturing the brain wave signal for flipped learning classroom.

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Siamese Neural Network (SNN) is exploited to analyze the captured brain wave.

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A SNN model is efficiently utilized to classify the student based on the attention level.

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Using SNN classification, we identify the weak student in the flipped learning.

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Proposed model is validated with data acquired from the experimental set-up.

References

[1]

Aldowah H., Al-Samarraie H., Alzahrani A.I., Alalwan N., Factors affecting student dropout in MOOCs: a cause and effect decision-making model, J. Comput. Higher Educ. (2019) 1–26,.

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