Near Real-Time Comprehension Classification with Artificial Neural Networks: Decoding e-Learner Non-Verbal Behavior

Autor/inn/en	Holmes, Mike; Latham, Annabel; Crockett, Keeley; O'Shea, James D.
Titel	Near Real-Time Comprehension Classification with Artificial Neural Networks: Decoding e-Learner Non-Verbal Behavior
Quelle	In: IEEE Transactions on Learning Technologies, 11 (2018) 1, S.5-12 (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Holmes, Mike) ORCID (Latham, Annabel) ORCID (Crockett, Keeley)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2017.2754497
Schlagwörter	Comprehension; Classification; Artificial Intelligence; Networks; Electronic Learning; Nonverbal Communication; Intelligent Tutoring Systems; Scoring; College Students; Foreign Countries; Design; Program Evaluation; United Kingdom (Manchester) + Suchen Sie Ihr Suchwort? Verstehen; Verständnis; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz; Non-verbal communication; Nonverbale Kommunikation; Intelligentes Tutorsystem; Bewertung; Collegestudent; Ausland; Programme evaluation; Programmevaluation
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01