Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

Autor/inn/en	Douglas, Kerrie A.; Bermel, Peter; Alam, Md Monzurul; Madhavan, Krishna
Titel	Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course
Quelle	In: Journal of Learning Analytics, 3 (2016) 3, S.170-192 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Online Courses; Large Group Instruction; Distance Education; Technology Uses in Education; Educational Technology; Student Behavior; Learner Engagement; Statistical Analysis; Tests; Student Evaluation; Scores; Information Utilization; Educational Research; Data Collection; Data Analysis + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Distance study; Distance learning; Fernunterricht; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtsmedien; Student behaviour; Schülerverhalten; Statistische Analyse; Examination; Prüfung; Examen; Schulnote; Studentische Bewertung; Informationsnutzung; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung
Abstract	MOOCs attract a large number of learners with largely unknown diversity in terms of motivation, ability, and goals. To understand more about learners in highly technical engineering MOOCs, this study investigates patterns of learners' (n = 337) behaviour and performance in the Nanophotonic Modelling MOOC, offered through nanoHUB-U. The authors explored clusters of learner click-stream patterns using the k-means++ algorithm and found five clusters of learner behaviour, labelled according to learners' use of materials: "Fully Engaged," "Consistent Viewers," "One-Week Engaged," "Two-Week Engaged," and "Sporadic learners." The Kruskal-Wallis non-parametric statistical test yielded a significant difference (p<0.01) between learners' access of course materials in each cluster. The researchers then examined the participation and mean scores on course quizzes and exams for each learner group. One-Week Engaged learners, on average, scored significantly lower on the first week's assessment. Two-Week Engaged learners, on average, scored significantly lower on the second week's assessments. Other differences found in learners' participation and performance on quizzes and tests based on the five clusters are discussed. These findings suggest that some of the high dropout numbers in advanced MOOCs may be related to learners' performance on course assessments. In addition, integration of learner access to course material with course assessment scores provides a much richer understanding of learners in a MOOC. (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