Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

Autor/inn/en	Blikstein, Paulo; Worsley, Marcelo
Titel	Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks
Quelle	In: Journal of Learning Analytics, 3 (2016) 2, S.220-238 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Data Analysis; Data Collection; Educational Research; Constructivism (Learning); Educational Environment; Computation; Educational Technology; Measurement; Discourse Analysis; Natural Language Processing; Speech; Content Analysis; Handwriting; Recognition (Psychology); Freehand Drawing; Bayesian Statistics; Nonverbal Communication; Affective Measures; Physiology; Eye Movements; Learning Modalities; Multimedia Materials; Literature Reviews + Suchen Sie Ihr Suchwort? Auswertung; Data capture; Datensammlung; Bildungsforschung; Pädagogische Forschung; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Unterrichtsmedien; Messverfahren; Diskursanalyse; Natürliche Sprache; Speaking; Sprechen; Inhaltsanalyse; Handschrift; Recognition; Wiedererkennen; Drawing; Zeichnen; Non-verbal communication; Nonverbale Kommunikation; Physiologie; Augenbewegung
Abstract	New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into student learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational applications. (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