Using Learning Analytics to Characterize Student Experimentation Strategies in Engineering Design

Autor/inn/en	Vieira, Camilo; Goldstein, Molly Hathaway; Purzer, Senay; Magana, Alejandra J.
Titel	Using Learning Analytics to Characterize Student Experimentation Strategies in Engineering Design
Quelle	In: Journal of Learning Analytics, 3 (2016) 3, S.291-317 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Engineering; Design; Experiments; Student Behavior; Models; Identification; Middle School Students; Interaction; Computer Assisted Design; Comparative Analysis; Data Analysis; Pretests Posttests; Correlation; Learning Processes; Technology Uses in Education + Suchen Sie Ihr Suchwort? Maschinenbau; Erprobung; Student behaviour; Schülerverhalten; Analogiemodell; Identifikation; Identifizierung; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Interaktion; Auswertung; Korrelation; Learning process; Lernprozess; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	Engineering design is a complex process both for students to participate in and for instructors to assess. Informed designers use the key strategy of conducting experiments as they test ideas to inform next steps. Conversely, beginning designers experiment less, often with confounding variables. These behaviours are not easy to assess in educational settings because they occur throughout the design process. This paper reports on a two-fold study carried out to test the model for identifying student behaviours during design experimentation. The first phase uses the process data from 48 middle-school students designing an energy-plus house. The study utilized learner interaction data sets collected through automatic, unobtrusive logging of student actions in a CAD platform. The analysis of learner process data is compared to student performance on an open-ended post-test. The second phase correlates the number of experiments students conducted to the quality of student prototypes. The results suggest that the proposed model can be used to identify, characterize, and assess student strategies associated with conducting experiments. Implications of this work are relevant to engineering and design educators as well as researchers interested in the role of learning analytics in studying complex processes. (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