Data Mining for Student Advising

Abstract

This paper illustrates how to use data mining techniques to help in advising students and predicting their academic performance. Data mining is used to get previously unknown, hidden and perhaps vital knowledge from a large amount of data. It combines domain knowledge, advanced analytical skills, and a vast knowledge base to reveal hidden patterns and trends that are applicable in virtually any sector ranging from engineering to medicine, to business. However, it is possible for educational institutes to use data mining to find useful information from their databases. This is usually called Educational Data Mining (EDM). Advancing the field of EDM with new data analysis techniques and new machine learning algorithms is vital. Classification and clustering techniques will be used in this project to study and analyse student performance. The key importance of this project is that it discusses different data mining techniques in the literature review to study student behaviour depending upon their performance. We tried to identify the most suitable algorithms from the existing research methods to predict the success of students. Various data mining approaches were discussed and their results were evaluated. In this paper, the J48 algorithm was applied to the data set, gathered from Umm Al-Qura University in Makkah.

Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

“…If the term performance is disaggregated from the phrase student academic performance, it embodies achievement in relation to assignments and courses, continuous progress in programmes, and a successful completion of programmes [2,18]. Moreover, it entails persistence, retention, progression, wastage) [37], and success or progress [38,39]. In this sense, student academic performance should be seen in the same way as student academic achievement [14].…”

“…In this regard, some of the factors (also known as attributes) employed to predict SAP are: academic factors (historical and current); student demographics; socio-economics factors; psychological factors; student e-learning activities; student environments; and extracurricular activities (18,24]. The superordinate factors listed in the preceding set are often utilised to predict SAP by most scholars [2,14,18,24,36,37,40,41]. These superordinate factors are further categorised into specific subordinate factors with the former serving as input variables or performance features, and with the latter serving as output variables or performance metrics [18].…”

“…Furthermore, there are algorithms that are employed to predict SAP. Among them are Artificial Neural Network (ANN), Bayesian Network (BN), Decision Tree (DT), K-Nearest Neighbour (K-NN), K-Means; Naïve Bayesian classifiers, Neural Network (NN), and Support Vector Machine (SVM) [4,7,24,37,40,41]. Other examples of such algorithms are Random Tree (RT); Random Forest (RF); REPTree; J48; LADTree; Sequential minimal optimisation (SMO) [4]; JRip; OneR; CART; C4.5; and Iterative Dichotomiser 3 (ID3) [2,4,38].…”

Educational Data Mining Applications and Techniques

“…In 2020, Alhakami et al in their study [23] used J48, Naive Bayes algorithms to predict students' academic performance and help in advising students using WEKA tool. They collected 38671 students' data of both male and female from Umm Al-Qura University for 5 years, with several attributes including Exams Marks, School, Sex, Age, Nationality, City and final grade.…”

A Comparison of EDM Tools and Techniques

“…In 2020, Authors in [29] used Naïve Bayes and J48 techniques for students' academic performance prediction and guided the students by using WEKA tool. They used over 3867 students' records upon of 5 years of Umm Al-Qura University.…”