文章基本信息

标题：Data mining techniques for identifying students at risk of failing a computer proficiency test required for graduation
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作者：Chih-Fong Tsai ; Ching-Tzu Tsai ; Chia-Sheng Hung 等
期刊名称：Australasian Journal of Educational Technology
印刷版ISSN：1449-3098
电子版ISSN：1449-5554
出版年度：2011
卷号：27
期号：3
DOI：10.14742/ajet.956
语种：English
出版社：Australasian Society for Computers in Learning in Tertiary Education
摘要：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.