Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons

Autor/inn/en	Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori
Titel	Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons
Quelle	In: Educational Technology & Society, 20 (2017) 1, S.73-86 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Data Analysis; Information Retrieval; Academic Achievement; Student Attitudes; Predictor Variables; Learning Processes; Learning Activities; Semantics; Statistical Analysis; Progress Monitoring; Grades (Scholastic); Models; Evaluation Criteria; Definitions; Foreign Countries; College Students; Japan + Suchen Sie Ihr Suchwort? Auswertung; Schulleistung; Schülerverhalten; Prädiktor; Learning process; Lernprozess; Lernaktivität; Semantik; Statistische Analyse; Notenspiegel; Analogiemodell; Begriffsbestimmung; Ausland; Collegestudent
Abstract	The purpose of this study is to examine different formats of comment data to predict student performance. Having students write comment data after every lesson can reflect students' learning attitudes, tendencies and learning activities involved with the lesson. In this research, Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are employed to predict student grades in each lesson. In order to obtain further improvement of prediction results, a majority vote method is applied to the predicted results obtained in consecutive lessons. The research findings show that our proposed method continuously tracked student learning situations and improved prediction performance of final student grades. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01