Building Pipelines for Educational Data Using AI and Multimodal Analytics: A "Grey-Box" Approach

Autor/inn/en	Sharma, Kshitij; Papamitsiou, Zacharoula; Giannakos, Michail
Titel	Building Pipelines for Educational Data Using AI and Multimodal Analytics: A "Grey-Box" Approach
Quelle	In: British Journal of Educational Technology, 50 (2019) 6, S.3004-3031 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sharma, Kshitij)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12854
Schlagwörter	Learning Activities; Learning Analytics; Man Machine Systems; Artificial Intelligence; Computation; Inferences; Interdisciplinary Approach; Technology Uses in Education; Innovation; Eye Movements; Nonverbal Communication; Arousal Patterns; Predictor Variables; Physiology + Suchen Sie Ihr Suchwort? Lernaktivität; Mensch-Maschine-System; Künstliche Intelligenz; Inference; Inferenz; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Augenbewegung; Non-verbal communication; Nonverbale Kommunikation; Prädiktor; Physiologie
Abstract	Students' on-task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference-making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a "black-box" approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the "black-box" approach to a "grey-box" approach that bridges the hypothesis/literature-driven (feature extraction) "white-box" approach with the computation/data-driven (feature fusion) "black-box" approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self-assessment case study taking advantage of new, cutting-edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01