Are You Talking to Me? Multi-Dimensional Language Analysis of Explanations during Reading [Konferenzbericht] Paper presented at the International Learning Analytics & Knowledge Conference (9th, Tempe, AZ, Mar 2019).

Autor/inn/en	Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S.
Titel	Are You Talking to Me? Multi-Dimensional Language Analysis of Explanations during Reading [Konferenzbericht] Paper presented at the International Learning Analytics & Knowledge Conference (9th, Tempe, AZ, Mar 2019).
Quelle	(2019), (7 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Language Processing; Science Instruction; Computational Linguistics; Teaching Methods; Reading Processes; Prediction; Discourse Analysis; Natural Language Processing; Intelligent Tutoring Systems; Classification; Accuracy; College Freshmen; Laboratory Experiments; Form Classes (Languages); Language Usage + Suchen Sie Ihr Suchwort? Sprachverarbeitung; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Linguistics; Computerlinguistik; Teaching method; Lehrmethode; Unterrichtsmethode; Leseprozess; Vorhersage; Diskursanalyse; Natürliche Sprache; Intelligentes Tutorsystem; Classification system; Klassifikation; Klassifikationssystem; Studienanfänger; Laboratory work; Laborarbeit; Analytischer Sprachbau; Sprachgebrauch
Abstract	This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146) typed either self-explanations or other-explanations while reading a science text. The linguistic properties of these explanations were calculated using three automated text analysis tools. Machine learning classifiers in combination with the features were used to predict instruction condition (i.e., self- or other explanation). The best machine learning model performed at rates above chance (kappa = 0.247; accuracy = 63%). Follow-up analyses indicated that students in the self-explanation condition generated explanations that were more cohesive and that contained words that were more related to social order (e.g., ethics). Overall, the results suggest that natural language processing techniques can be used to detect subtle differences in students' processing of complex texts. [This paper was published in: "Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK19)" (Article 4 p116-120). New York, NY: ACM.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01