Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1076
Title: E-Learn Detector: Smart Behaviour MonitoringSystem to Analyze Student Behaviours DuringOnline Educational Activities
Authors: Bamunuge, H.K.T.
Perera, H.M.
Kumarage, S.
Savindri, P.A.P.
Kasthurirathna, D.
Kugathasan, A.
Keywords: E-Learning
User-Verification
Engagement-Detection
Suspicious-Behavior
Multiuser
Issue Date: 9-Dec-2021
Publisher: 2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT
Abstract: With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. “E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. “E-Learn Detector” is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.
URI: http://rda.sliit.lk/handle/123456789/1076
ISSN: 978-1-6654-0862-2/21
Appears in Collections:3rd International Conference on Advancements in Computing (ICAC) | 2021
Department of Computer Science and Software Engineering-Scopes



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