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A Machine Learning framework to Detect Student’s Online Engagement

Bharadva, Komal Riddhish (2021) A Machine Learning framework to Detect Student’s Online Engagement. Masters thesis, Dublin, National College of Ireland.

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Detecting student’s engagement in online lectures involves monitoring eye movement as they learn concepts or complete tutorials. The challenge is to detect if a student is engaged or distracted. This research proposes a machine learning framework to identify if the students are engaged in learning. The framework combines a machine learning model and an Eye-tracker device. Students must wear an eye tracker device and are then shown a video lecture of approximately 25 minutes. This is followed by a questionnaire that assesses the student’s cognitive processes, transfer, and retention learning. The video from the eye tracker device is analysed and then this data is processed by machine learning models. The results show that we were able to detect student’s online engagement using Random Forest Classifier which outperformed other models with 88.9% accuracy. This research can benefit the teaching industry to understand student’s engagement in an online learning environment as this directly impacts their learning outcomes.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Eye-tracker device; Machine learning; Engagement detection; Online learning; Cognitive processes
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
L Education > LC Special aspects / Types of education > E-Learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 15 Nov 2021 11:07
Last Modified: 15 Nov 2021 11:07

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