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ERIC Number: EJ1174695
Record Type: Journal
Publication Date: 2018
Pages: 8
Abstractor: As Provided
ISSN: ISSN-1939-1382
Near Real-Time Comprehension Classification with Artificial Neural Networks: Decoding e-Learner Non-Verbal Behavior
Holmes, Mike; Latham, Annabel; Crockett, Keeley; O'Shea, James D.
IEEE Transactions on Learning Technologies, v11 n1 p5-12 Jan-Mar 2018
Comprehension is an important cognitive state for learning. Human tutors recognize comprehension and non-comprehension states by interpreting learner non-verbal behavior (NVB). Experienced tutors adapt pedagogy, materials, and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time assessment for e-learner comprehension of on-screen information could provide a powerful tool for both adaptation within intelligent e-learning platforms and appraisal of tutorial content for learning analytics. However, literature suggests that no existing method for automatic classification of learner comprehension by analysis of NVB can provide a practical solution in an e-learning, on-screen, context. This paper presents design, development, and evaluation of COMPASS, a novel near real-time comprehension classification system for use in detecting learner comprehension of on-screen information during e-learning activities. COMPASS uses a novel descriptive analysis of learner behavior, image processing techniques, and artificial neural networks to model and classify authentic comprehension indicative non-verbal behavior. This paper presents a study in which 44 undergraduate students answered on-screen multiple choice questions relating to computer programming. Using a front-facing USB web camera the behavior of the learner is recorded during reading and appraisal of on-screen information. The resultant dataset of non-verbal behavior and question-answer scores has been used to train artificial neural network (ANN) to classify comprehension and non-comprehension states in near real-time. The trained comprehension classifier achieved normalized classification accuracy of 75.8 percent.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United Kingdom (Manchester)
Grant or Contract Numbers: N/A