Open AccessUsing Heart Rate to Predict Students’ Academic Performance
Author(s) -
Mu Lin Wong*,
S. Senthil,
L. Robert
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4740.098319
Subject(s) - remedial education , recall , predictive modelling , computer science , statistics , machine learning , mathematics education , psychology , mathematics , cognitive psychology
Timeliness was a missing factor in many studies on Academic Performance Prediction to identify at-risk students. This study embarked on a search to evaluate the feasibility of predicting students’ performance based on heart rate data collected during classes. This dimension of data was collected in the first four weeks after semester commencement to validate accurate prediction that will enable educationists to introduce remedial intervention to at-risk students. Another aim of this study is to determine the best threshold values for the different types of heart rate fluctuations that can be used in predicting academic achievements. The threshold values were tested further to verify whether the prediction model for individual course or combined courses was more accurate. Results revealed that heart rate data alone can achieve a maximum prediction accuracy of 88% and recall of 100%. Threshold values calculated in derived heart rate fluctuation types produces the best results. Prediction models for individual courses outperform the model using average threshold values of all courses.