ERIC Number: EJ1174695
Record Type: Journal
Publication Date: 2018
Pages: 8
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1939-1382
EISSN: N/A
Near Real-Time Comprehension Classification with Artificial Neural Networks: Decoding e-Learner Non-Verbal Behavior
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.
Descriptors: Comprehension, Classification, Artificial Intelligence, Networks, Electronic Learning, Nonverbal Communication, Intelligent Tutoring Systems, Scoring, College Students, Foreign Countries, Design, Program Evaluation
Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
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