Prediction of Essay Scores from Writing Process and Product Features Using Data Mining Methods

Autor/inn/en	Sinharay, Sandip; Zhang, Mo; Deane, Paul
Titel	Prediction of Essay Scores from Writing Process and Product Features Using Data Mining Methods
Quelle	In: Applied Measurement in Education, 32 (2019) 2, S.116-137 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sinharay, Sandip) ORCID (Deane, Paul)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2019.1577245
Schlagwörter	Scores; Prediction; Writing Processes; Data Analysis; Keyboarding (Data Entry); Essay Tests; Classification; Regression (Statistics); Writing Tests + Suchen Sie Ihr Suchwort? Vorhersage; Auswertung; Texterfassung; Schriftlicher Sprachgebrauch; Classification system; Klassifikation; Klassifikationssystem; Regression; Regressionsanalyse; Writing test; Schreibtest
Abstract	Analysis of keystroke logging data is of increasing interest, as evident from a substantial amount of recent research on the topic. Some of the research on keystroke logging data has focused on the prediction of essay scores from keystroke logging features, but linear regression is the only prediction method that has been used in this research. Data mining methods such as boosting and random forests have been found to improve over traditional prediction methods such as linear regression in various scientific fields, but have not been used in the prediction of essay scores from keystroke logging features. This article first provides a review of boosting, which is a popular data mining method. The article then applies boosting to predict essay scores from a large number of keystroke logging features and other predictor variables from two real data sets. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01