Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat
The technology of psycho-physiological measurement and Eye-tracking has opened up a wide range of possibilities for automating the prediction of human emotional state for a particular event. There is also growing interest in modeling machine learning and deep learning algorithms that can learn fr...
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Format: | Thesis |
Published: |
2022
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Online Access: | http://studentsrepo.um.edu.my/14617/2/Fatima_Jannat.pdf http://studentsrepo.um.edu.my/14617/1/Fatima_Jannat.pdf http://studentsrepo.um.edu.my/14617/ |
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Summary: | The technology of psycho-physiological measurement and Eye-tracking has opened up a
wide range of possibilities for automating the prediction of human emotional state for a
particular event. There is also growing interest in modeling machine learning and deep
learning algorithms that can learn from user’s data, understand and react to that individual’s
affective state. This research work has used novice programming learners’ eye-tracking
and Galvanic Skin Response (GSR) data in a novel approach. This work investigates
the suitability and effectiveness of machine learning algorithms such as Multinomial
Naive Bayes, KNN, Logistic Regression, Decision Tree for predicting levels of arousal
intensity among the programmers and LSTM deep learning algorithm to classify the
programmers according to their performance. Through experiments with the data-set, it
was found that Multinomial Naive Bayes outperformed other supervised machine learning
algorithms with 75.93% accuracy and 96.54% ROC while predicting levels of arousal
intensity. Hyper-parameter tuning has been used in all the algorithms using k-fold cross
validation to have the best accuracy and to avoid the over-fitting issue. The result implies a
good connection between how a novice programmer goes through a programming problem
and his/her emotional arousal at that moment. The Long Short-term Memory (LSTM)
deep learning model was chosen for classifying programming learners according to their
performance. LSTM model has the advantage of having internal memory suitable for
longer sequences like our Eye-tracking and GSR data sequence. The LSTM model resulted
in 65.71% test accuracy while classifying the students’ performance.
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