Predicting NDUM Student's Academic Performance Using Data Mining Techniques

Wook, Muslihah; Yahaya, Yuhanim Hani; Wahab, Norshahriah Abdul; Isa, Mohd Rizal Mohd; Awang, Nor Fatimah; Seong, Hoo Yann

doi:10.1109/iccee.2009.168

Cited by 46 publications

(23 citation statements)

References 9 publications

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“…As a result, the technique that provides accurate prediction and classification was chosen as the best model. Using the proposed model, the pattern that influences the student's academic performance was identified [16].…”

Section: Literature Review H Bahiah Hj Ahmad and Smentioning

confidence: 99%

A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

Petchboonmee¹,

Phonak²,

Tiantong³

2015

IJIET

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Section: Literature Review H Bahiah Hj Ahmad and Smentioning

confidence: 99%

A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

Petchboonmee¹,

Phonak²,

Tiantong³

2015

IJIET

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“…According to Wook (2009): [1] "data mining is the process of automatically extracting useful information and relationships from immense quantities of data". In this sense data mining is not about acquiring specific information; rather, it is about answering a question or approving theory.…”

Section: Introductionmentioning

confidence: 99%

Mining Educational Data to Predict Students’ Academic Performance

Alsaleem

Al-Kathiry

Al-Osimi

et al. 2015

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Data mining is the process of extracting useful information from a huge amount of data. One of the most common applications of data mining is the use of different algorithms and tools to estimate future events based on previous experiences. In this context, many researchers have been using data mining techniques to support and solve challenges in higher education. There are many challenges facing this level of education, one of which is helping students to choose the right course to improve their success rate. An early prediction of students' grades may help to solve this problem and improve students' performance, selection of courses, success rate and retention. In this paper we use different classification techniques in order to build a performance prediction model, which is based on previous students' academic records. The model can be easily integrated into a recommender system that can help students in their course selection, based on their and other graduated students' grades. Our model uses two of the most recognised decision tree classification algorithms: ID3 and J48. The advantages of such a system have been presented along with a comparison in performance between the two algorithms.

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“…In Malaysia, Wook et al, presented a paper which predicts student's academic performance in a computer science department at the National Defense University of Malaysia (NDUM) [6]. The authors use two different techniques: Artificial Neural Networks (ANN) and the hybrid of clustering and decision trees.…”

Section: Related Workmentioning

confidence: 99%

Data Mining in Education- An Experimental Study

AbdulazizAlHammadi¹,

Aksoy²

2013

IJCA

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Data mining first proved to be beneficial to business related fields such as marketing and consumer related service enhancements. Slowly it has made its way toward other fields such as medicine, science, engineering, and education. The focus of this paper is to review several applications of data mining in education and their benefits, present some classification techniques, test some sample data, and then evaluate them against some selected criteria.

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Predicting NDUM Student's Academic Performance Using Data Mining Techniques

Cited by 46 publications

References 9 publications

A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

Mining Educational Data to Predict Students’ Academic Performance

Data Mining in Education- An Experimental Study

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