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Predicting Java Computer Programming Task Difficulty Levels Using EEG for Educational Environments

Palaniappan, Ramaswamy, Duraisingam, Aruna, Chinnaiah, Nithyakalyani, Murugappan, Murugappan (2019) Predicting Java Computer Programming Task Difficulty Levels Using EEG for Educational Environments. In: Lecture Notes in Computer Science. Lecture Notes in Computer Science. . springer, Nethe ISBN 978-3-030-22418-9. (doi:10.1007/978-3-030-22419-6_32)

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http://dx.doi.org/10.1007/978-3-030-22419-6_32

Abstract

Understanding how difficult a learning task is for a person allows teaching material to be appropriately designed to suit the person, especially for programming material. A first step for this would be to predict on the task difficulty level. While this is possible through subjective questionnaire, it could lead to misleading outcome and it would be better to do this by tapping the actual thought process in the brain while the subject is performing the task, which can be done using electroencephalogram. We set out on this objective and show that it is possible to predict easy and difficult levels of mental tasks when subjects are attempting to solve Java programming problems. Using a proposed confidence threshold, we obtained a classification performance of 87.05% thereby showing that it is possible to use brain data to determine the teaching material difficulty level which will be useful in educational environments.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-22419-6_32
Uncontrolled keywords: Confidence threshold Education EEG Java Mental task level NASA TLX Programming task
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 18 Dec 2018 14:32 UTC
Last Modified: 09 Sep 2019 09:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71265 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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