Data-Mining Textual Responses to Uncover Misconception Patterns [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).

Autor/inn/en	Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G.
Titel	Data-Mining Textual Responses to Uncover Misconception Patterns [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).
Quelle	(2017), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Data Analysis; Misconceptions; Student Attitudes; Feedback (Response); Teaching Methods; Educational Quality; Educational Improvement; Guidelines; Classification; Questionnaires; Natural Language Processing; Markov Processes; Monte Carlo Methods; Biology; Science Instruction; Intelligent Tutoring Systems; Advanced Placement; High School Students + Suchen Sie Ihr Suchwort? Auswertung; Missverständnis; Schülerverhalten; Teaching method; Lehrmethode; Unterrichtsmethode; Quality of education; Bildungsqualität; Teaching improvement; Unterrichtsentwicklung; Richtlinien; Classification system; Klassifikation; Klassifikationssystem; Fragebogen; Natürliche Sprache; Markowscher Prozess; Monte-Carlo-Methode; Biologie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Intelligentes Tutorsystem; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin
Abstract	An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of instruction. In this paper, we propose a new natural language processing-based framework to detect the common misconceptions among students' textual responses to short-answer questions. We propose a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Experimental results show that our proposed framework excels at classifying whether a response exhibits one or more misconceptions. More importantly, it can also automatically detect the common misconceptions exhibited across responses from multiple students to multiple questions; this property is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit. [For the full proceedings, see ED596512.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01