Multimodal Learning Analytics to Inform Learning Design: Lessons Learned from Computing Education

Autor/inn/en	Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michalis
Titel	Multimodal Learning Analytics to Inform Learning Design: Lessons Learned from Computing Education
Quelle	In: Journal of Learning Analytics, 7 (2020) 3, S.79-97 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Mangaroska, Katerina) ORCID (Sharma, Kshitij) ORCID (Gaševic, Dragan) ORCID (Giannakos, Michalis)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Learning Analytics; Data Collection; Instructional Design; Learning Modalities; Computer Science Education; Programming; Troubleshooting; Cognitive Processes; Difficulty Level; Eye Movements; Physiology; Metabolism; Arousal Patterns; Performance Factors; Nonverbal Communication; College Students; Foreign Countries; Norway + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Lesson concept; Lessonplan; Unterrichtsentwurf; Lernumgebung; Computer science lessons; Informatikunterricht; Programmierung; Störfall; Cognitive process; Kognitiver Prozess; Schwierigkeitsgrad; Augenbewegung; Physiologie; Stoffwechsel; Leistungsindikator; Non-verbal communication; Nonverbale Kommunikation; Collegestudent; Ausland; Norwegen
Abstract	Programming is a complex learning activity that involves coordination of cognitive processes and affective states. These aspects are often considered individually in computing education research, demonstrating limited understanding of how and when students learn best. This issue confines researchers to contextualize evidence-driven outcomes when learning behaviour deviates from pedagogical intentions. Multimodal learning analytics (MMLA) captures data essential for measuring constructs (e.g., cognitive load, confusion) that are posited in the learning sciences as important for learning, and cannot effectively be measured solely with the use of programming process data (IDE-log data). Thus, we augmented IDE-log data with physiological data (e.g., gaze data) and participants' facial expressions, collected during a debugging learning activity. The findings emphasize the need for learning analytics that are consequential for learning, rather than easy and convenient to collect. In that regard, our paper aims to provoke productive reflections and conversations about the potential of MMLA to expand and advance the synergy of learning analytics and learning design among the community of educators from a post-evaluation design-aware process to a permanent monitoring process of adaptation. (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: https://learning-analytics.info/index.php/JLA/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01