Academic Educational Data Mining predictive model for early detection of students at academic risk | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 2, Volume 9, Issue 1, 2018, Page 21-42 PDF (869.81 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2018.158379 | ||||
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| Abstract | ||||
| In this research, we aimed at increasing college student retention by performing early detection of academic risk using data mining methods. Predicting students’ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students’ performance during their period of studies in the institutions. Data mining technologies can be used to monitor students and simultaneously analyzing their academic behavior, thus providing a basis for implementing necessary intervention procedures, if required. The paper describes and lays out a methodological framework to develop model that can be used to perform inferential queries on student performance using student academic records. Preliminary results on Academic Educational Data Mining (AEDM) model development using Decision Tree data mining algorithms for classification are presented to classify students as early as possible, into three groups: ‘low‐ risk’ students who have a high probability of success; ‘medium‐ risk’ students who may succeed thanks to the measures taken by the university; and the ‘high‐ risk’ students who have a high probability of failing where several classification rules were generated. | ||||
| Keywords | ||||
| Learning Analytics; Data Mining; SAS Enterprise Miner 12.3; decision tree; Educational data mining; knowledge discovery in databases (KDD) | ||||
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