Analyzing and Predicting Student Academic Achievement Using Data Mining Techniques

Analyzing and Predicting Student Academic Achievement Using Data Mining Techniques

Eric P. Jiang (University of San Diego, USA)
DOI: 10.4018/978-1-4666-5888-2.ch238
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Background

Over the years there have been a considerable amount of research efforts addressing various educational issues. Among them, however, there are only a few published research papers in predicting student academic success (Huebner, 2013). Some of the papers in this area studied student academic performance for only a specific group of students. For instance, Kovacic (2010) and Cohn et al. (2004) focused on success prediction for students enrolled in a course in Information Systems, and in a course in Principles of Economics, respectively, while Garton et al. (2002) examined the factors associated with academic performance and retention for freshmen in a college of Agriculture. Other papers provided some specific perspectives on effects related to student success. For instance, Minnaert and Janssen (1999) conducted an analysis on cognitive test results of freshmen and their effects on student academic performance.

Key Terms in this Chapter

College Student Retention: Student retention refers to the percentage of students from particular cohorts and years who return when they are expected to do so.

College Enrollment Management: Enrollment management refers to well-planned strategies and tactics in higher education to shape the enrollment of an institution and meet established goals. Such strategies and tactics are often informed by collection, analysis, and use of data to project successful outcomes.

College Student Graduation Time Prediction: Student graduation time prediction is one of the applications of educational data mining. It is often involved in student data collection and analysis, and applications of various data mining methods to predict the time a student will likely graduate from an institution of higher education.

Data Mining: Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data using methods at the intersection of artificial intelligence, machine learning, statistics and database management systems.

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