Prediciton of Student Academic Performance Using an ANFIS Approach

應用ANFIS方法於預測學生的學術表現

10.6186/IJIMS.2014.25.4.6

陳正芳(Jeng-Fung Chen)；Quang Hung Do

適應性類神經模糊推論系統 ； 預測 ； 學生入學 ； 高等教育 ； Adaptive Neuro-Fuzzy Inference System ； prediction ； student admission ； higher education

International Journal of Information and Management Sciences

25卷4期（2014 / 12 / 01）

371 - 389

英文

入學管理是大學治校的重要課題。入學管理程序中的一個重要議題是判斷入學申請者是否適合就讀所申請的學系。為處理這個議題，我們需要一個能準確預測學生未來學術表現的模型。在本研究中，我們提出一個以適應性類神經模糊推論系統(ANFIS)為基的方法來預測學生未來的學術表現。我們以學生先前的不同測驗成績為輸入變數，而由於輸入變數太多，我們先辨識出較有影響的變數，然後ANFIS再以這些較有影響的變數來預測學生未來的學術表現。我們將本研究所提的方法和多元線性迴歸以及人工神經網路方法作比較，比較結果顯示，本研究所提的方法優於多元線性迴歸以及人工神經網路方法，並且可獲得相當穩定的結果。我們期望本研究的模型可提供一個有效的工具來支援入學管理程序。

Admission is one of the key administrative branches in a university. Regarding the admission process, the issue of whether a candidate is suitable for an academic program is of importance. This raises the need to propose a model that predicts the student’s future academic performance. This study presents an approach to the prediction of student academic performance based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). We have used previous exam results as input variables, and then predicted the students’ expected performances. Due to a large number of input variables, only the most influential ones affecting student academic performance were selected. We also identified the most influential input variables by analyzing their influence on expected academic performance. The ANFIS model was then parameterized using these input variables to predict student performance. The results showed that the proposed model achieved a high reliability. These results were also compared with those obtained from the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) approaches. The comparative analysis indicated that the proposed approach performed better than the others. It is expected that this work may be used as a tool to support student admission procedures.

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