Using Multimodal Learning Analytics to Model Student Behaviour: A Systematic Analysis of Behavioural Framing

Autor/inn/en	Andrade, Alejandro; Delandshere, Ginette; Danish, Joshua A.
Titel	Using Multimodal Learning Analytics to Model Student Behaviour: A Systematic Analysis of Behavioural Framing
Quelle	In: Journal of Learning Analytics, 3 (2016) 2, S.282-306 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Student Behavior; Data Analysis; Models; Video Technology; Data Interpretation; Identification; Epistemology; Coding; Markov Processes; Elementary School Students; Grade 1; Grade 2; Semi Structured Interviews; Nonverbal Communication; Logical Thinking + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Auswertung; Analogiemodell; Data evaluation; Datenauswertung; Identifikation; Identifizierung; Erkenntnistheorie; Codierung; Programmierung; Markowscher Prozess; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Non-verbal communication; Nonverbale Kommunikation
Abstract	One of the challenges many learning scientists face is the laborious task of coding large amounts of video data and consistently identifying social actions, which is time consuming and difficult to accomplish in a systematic and consistent manner. It is easier to catalog observable behaviours (e.g., body motions or gaze) without explicitly attempting to identify their social relevance for the participants. We explore the potential for using a multimodal learning analytic approach to identify whether clusters of observable behaviours can be used to identify and characterize behavioural frames in rich video data of student interviews. We argue that by conducting a systematic analysis of behavioural frames using computerized algorithms we can model student frames as a latent class variable. We explore whether those behavioural frames overlap in productive ways with epistemological frames, thus supporting our efforts to interpret rich video data. We believe that a positive feedback loop between methodological approaches and theory will emerge as we further our understanding of framing by developing analytical models leveraged by multimodal learning analytics. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01