Open AccessDesign of Cognitive MCQ test in Virtual Learning Systems to Determine Learner Affect
Author(s) -
Kavita Kelkar,
Donald A. Bakal
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6549.018520
Subject(s) - affect (linguistics) , confusion , grasp , computer science , test (biology) , virtual learning environment , usability , cognition , human–computer interaction , psychology , artificial intelligence , machine learning , cognitive psychology , multimedia , communication , paleontology , neuroscience , psychoanalysis , biology , programming language
Virtual learning systems are expected to be adaptive to the grasp exhibited by the learner. Learner affects like confusion and confidence are displayed by the learner through behavioural cues. Identifying affect in a non-intrusive, sensor-free and scalable setting is preferable. Using interaction based behavioural log features; methodology for determining learner affect is presented. The MCQ test questions in the system are based on Bloom’s Taxonomy Cognitive levels. The system records interactions of the learner. The regression analysis result on the dataset shows accuracy of confusion detection above 70%.