Assessing Question Quality Using NLP [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (18th, 2017).

Autor/inn/en	Kopp, Kristopher J.; Johnson, Amy M.; Crossley, Scott A.; McNamara, Danielle S.
Titel	Assessing Question Quality Using NLP [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (18th, 2017).
Quelle	(2017), (5 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Reading Comprehension; Reading Instruction; Intelligent Tutoring Systems; Reading Strategies; Natural Language Processing; Questioning Techniques; Mathematics; Feedback (Response); Taxonomy; Artificial Intelligence; Prediction; Accuracy; Educational Technology; Multivariate Analysis + Suchen Sie Ihr Suchwort? Leseverstehen; Leseunterricht; Intelligentes Tutorsystem; Reading strategy; Leselernstufe; Lesetechnik; Natürliche Sprache; Befragungstechnik; Fragetechnik; Mathematik; Taxonomie; Künstliche Intelligenz; Vorhersage; Unterrichtsmedien; Multivariate Analyse
Abstract	An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict question quality. NLP indices related to lexical sophistication modestly predicted question type. Accuracies improved when predicting two levels (shallow versus deep). [This paper was published in: E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, & B. du Boulay (Eds.), "Proceedings of the 18th International Conference on Artificial Intelligence in Education" (pp. 523-527). Wuhan, China: Springer.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01