An e-Exam Platform Approach to Enhance University Academic Student’s Learning Performance
Nowadays it is common for higher education institutions to use computer-based exams, partly or integrally, in their evaluation processes. The fact that exams are undertaken in a computer allows for new features to be acquired that may provide more reliable insights into the behaviour and state of the student during the exam. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. In this paper, we outline a non-intrusive and non-invasive performance monitoring approach developed, as a stress detection system. It is based on guidance from psychological stress studies, as well as from the nature of stress detection during high-end exams, through real-time analysis of mouse movements and decision-making behavioural patterns during the execution of high-end exams, in order to enhance university academic students’ learning performance.
KeywordsPsychological stress classification Human-computer interaction Biometric analysis Performance assessment Machine Learning
This work is part-funded by ERDF–European Regional Development Fund and by National Funds through the FCT–Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. The work of Filipe Gonçalves is supported by a FCT grant with the reference ICVS-BI-2016-005.
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