Dialogue as Data in Learning Analytics for Productive Educational Dialogue

Autor/inn/en	Knight, Simon; Littleton, Karen
Titel	Dialogue as Data in Learning Analytics for Productive Educational Dialogue
Quelle	In: Journal of Learning Analytics, 2 (2015) 3, S.111-143 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Dialogs (Language); Data Collection; Data Analysis; Artificial Intelligence; Natural Language Processing; Man Machine Systems; Accountability; Group Discussion; Computer Mediated Communication + Suchen Sie Ihr Suchwort? Dialog; Dialogs; Dialogue; Dialogues; Data capture; Datensammlung; Auswertung; Künstliche Intelligenz; Natürliche Sprache; Mensch-Maschine-System; Verantwortung; Gruppendiskussion; Computerkonferenz
Abstract	This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers. (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