Automating the Detection of Reflection-on-Action

Autor/inn/en	Saucerman, Jenny; Ruis, A. R.; Shaffer, David Williamson
Titel	Automating the Detection of Reflection-on-Action
Quelle	In: Journal of Learning Analytics, 4 (2017) 2, S.212-239 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Reflection; Problem Solving; Automation; Skill Development; Discourse Analysis; Cooperative Learning; Identification; Computer Simulation; Simulated Environment; Models; Prior Learning; Epistemology; Internship Programs; Urban Planning; High School Students; College Students; Coding + Suchen Sie Ihr Suchwort? Problemlösen; Kompetenzentwicklung; Qualifikationsentwicklung; Diskursanalyse; Kooperatives Lernen; Identifikation; Identifizierung; Computergrafik; Computersimulation; Künstliche Umwelt; Analogiemodell; Vorkenntnisse; Erkenntnistheorie; Berufspraktische Ausbildung; Stadtplanung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Collegestudent; Codierung; Programmierung
Abstract	Learning to solve "complex problems"--problems whose solutions require the application of more than basic facts and skills--is critical to meaningful participation in the economic, social, and cultural life of the digital age. In this paper, we use a theoretical understanding of how professionals use reflection-in-action to solve complex problems to investigate how students learn this critical 21st-century skill and how we can develop and automate learning analytic techniques to assess that learning. We present a preliminary study examining the automated detection of reflective discourse during collaborative, complex problem solving. We analyze student reflection-on-action in a virtual learning environment, focusing on both reflection in individual discourse and collaborative reflection among students. Our results suggest that it is possible to detect student reflection on complex problems in virtual learning environments, but that different models may be appropriate depending on students' prior domain experience. (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