Home-Grown Automated Essay Scoring in the Literature Classroom: A Solution for Managing the Crowd?

Autor/in	Uzun, Kutay
Titel	Home-Grown Automated Essay Scoring in the Literature Classroom: A Solution for Managing the Crowd?
Quelle	In: Contemporary Educational Technology, 9 (2018) 4, S.423-436 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1309-517X
Schlagwörter	Computer Assisted Testing; Essays; Scoring; English Literature; Computer Software; Artificial Intelligence; Scores; Comparative Analysis; Evaluators; Classification; Mathematics + Suchen Sie Ihr Suchwort? Essay; Aufsatzunterricht; Bewertung; Englische literatur; Künstliche Intelligenz; Classification system; Klassifikation; Klassifikationssystem; Mathematik
Abstract	Managing crowded classes in terms of classroom assessment is a difficult task due to the amount of time which needs to be devoted to providing feedback to student products. In this respect, the present study aimed to develop an automated essay scoring environment as a potential means to overcome this problem. Secondarily, the study aimed to test if automatically-given scores would correlate with the scores given by a human rater. A quantitative research design employing a machine learning approach was preferred to meet the aims of the study. The data set to be used for machine learning consisted of 160 scored literary analysis essays written in an English Literature course, each essay analyzing a theme in a given literary work. To train the automated scoring model, LightSide software was used. First, textual features were extracted and filtered. Then, Logistic Regression, SMO, SVO, Logistic Tree and Naïve Bayes text classification algorithms were tested by using 10-Fold Cross-Validation to reach the most accurate model. To see if the scores given by the computer correlated with the scores given by the human rater, Spearman's Rank Order Correlation Coefficient was calculated. The results showed that none of the algorithms were sufficiently accurate in terms of the scores of the essays within the data set. It was also seen that the scores given by the computer were not significantly correlated with the scores given by the human rater. The findings implied that the size of the data collected in an authentic classroom environment was too small for classification algorithms in terms of automated essay scoring for classroom assessment. (As Provided).
Anmerkungen	Contemporary Educational Technology. Faculty of Communication Sciences, Anadolu University, Yunus Emre Campus, Eskisehir 26470, Turkey. e-mail: editor@cedtech.net; Web site: http://www.cedtech.net
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01