Data Mining Techniques for Identifying Students at Risk of Failing a Computer Proficiency Test Required for Graduation

Autor/inn/en	Tsai, Chih-Fong; Tsai, Ching-Tzu; Hung, Chia-Sheng; Hwang, Po-Sen
Titel	Data Mining Techniques for Identifying Students at Risk of Failing a Computer Proficiency Test Required for Graduation
Quelle	In: Australasian Journal of Educational Technology, 27 (2011) 3, S.481-498 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1449-5554
Schlagwörter	Foreign Countries; Undergraduate Students; At Risk Students; Graduation Requirements; Achievement Tests; Computer Literacy; Data; High Stakes Tests; Data Analysis; Case Studies; Prediction; Predictive Measurement; Identification; Multivariate Analysis; Predictor Variables; Taiwan + Suchen Sie Ihr Suchwort? Ausland; Abschlussordnung; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Computerkenntnisse; Daten; Auswertung; Case study; Fallstudie; Case Study; Vorhersage; Identifikation; Identifizierung; Multivariate Analyse; Prädiktor
Abstract	Enabling undergraduate students to develop basic computing skills is an important issue in higher education. As a result, some universities have developed computer proficiency tests, which aim to assess students' computer literacy. Generally, students are required to pass such tests in order to prove that they have a certain level of computer literacy for successful graduation. This paper applies data mining techniques to make predictions about students who are going to take the computer proficiency test and fail. A national university in Taiwan is considered as the case study. Three different clustering techniques are used individually to cluster students into different groups, which are k-means, self-organising maps (SOM), and two-step clustering (i.e. BIRCH). After the best clustering result is found, the decision tree algorithm is used to extract useful rules from each of the identified clusters. These rules can be used to warn or counsel students who have higher probability of failing the test. The results can help the university identify a number of student groups who need to pay much more attention to preparing for the test, which is likely to help conserve resources. Furthermore, this study can be regarded as a guideline for future developments in assessing students' English literacy, as this is also an important graduation requirement in many universities. (Contains 3 figures and 12 tables.) (As Provided).
Anmerkungen	Australasian Society for Computers in Learning in Tertiary Education. Ascilite Secretariat, P.O. Box 44, Figtree, NSW, Australia. Tel: +61-8-9367-1133; e-mail: info@ascilite.org.au; Web site: http://www.ascilite.org.au/ajet
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10