Predicting students’ performance has emerged as an attractive task among researchers. They use supervised and unsupervised educational data mining (EDM) techniques to build an understandable and effective model. This helps decision makers enhance the performance of the students. The challenge of finding an optimal model leads to appearance of many techniques from both EDM techniques. Hence, we propose a unified framework to build a novel supervised cluster-based (CB) classifier model. The unified framework uses clustering technique to group historical records of students into a set of homogeneous clusters. Then, classifier model for each cluster is built and the final unified classifiers along with the centroids at each cluster are used as CB classifier model. The experimental results show that the CB model gains a high accuracy performance reached 96.25%. In addition, we use feature selection techniques for selecting the relevant features from a space of features. The model obtains a high accuracy performance using relevant features reached to 96.96% where the percentage of relevant features on average is 57.4% of overall features.

Clustering-Based EMT Model for Predicting Student Performance

Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educa-tional environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students’ performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

/pdf/educational-data-mining-applications-and-techniques-jos37kue2r.pdf

Educational Data Mining Applications and Techniques

Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.

/pdf/data-mining-applications-in-university-information-1sjtxnbr.pdf

Data mining applications in university information management system development

Purpose: This paper attempts to explore possible contributing factors of females' outperformance and males' underperformance in the higher education in Pakistan from teachers' perspective. The central question of the study is what are the key factors that affect female and male students' educational performance at the university level? Using Artificial Neural Network (ANN) as a framework, we attempted to predict differentials of the perceived “female outperformance” and “male underperformance” in higher education. We carried out the study by employing quantitative research methods. Design/methodology/approach: The data for the study come from 253 teachers from University of the Punjab-largest and oldest University in Pakistan. We used a structured questionnaire for data collection. The analysis was carried out with the help of ANN model. Statistical Package for Social Sciences (SPSS) was used to analyze the data. Findings: The testing results of ANN indicated 85.3% of teachers' perception was correctly predicted on various dimensions of performance differentials across female and male students in higher education. Research limitations/implications: The study banks on primary data collected from teachers of the University of University of the Punjab, Pakistan. Thus, the study's universe was limited to one university – University of Punjab. It is purely based on a quantitative approach employing ANN. Practical implications: The findings of this study have several significant implications, i.e. it makes a significant contribution to the existing body of scholarly texts on the issue of gender reverse change in academic performance in higher education. Originality/value: The findings of this research, derived from primary data in Pakistan context, qualify this research as an original one. We also claim that this study is one of the first studies on gender reverse change in academic performance among graduate students in a public sector university of Pakistan employing ANN.

Teachers' perspectives on factors of female students' outperformance and male students' underperformance in higher education

Investigation of factors affecting student performance evaluation using education materials data mining technique

Predicting students’ performance is a very important task in any educational system. Therefore, to predict the learner’s behavior towards studies many data mining techniques are used like clustering, classification, regression. In this paper, new student’s performance prediction model and new features are introduced that have a great influence on student’s academic achievement i.e. student absence days in class and parents’ involvement in the learning process. In this paper, considerable attention is on the punctuality of students and the effect of participation of parents in the learning process. This category of features is concerned with the learner’s interaction with the e–learning management system. Three different classifiers such as Naive Bayes, Decision Tree, and Artificial Neural Network are used to examine the effect of these features on students’ educational performance. The accuracy of the proposed model achieved up to 10% to 15% and is much improved as compared to the results when such features are removed.

/pdf/analyzing-students-academic-performance-through-educational-57uwzacgpv.pdf

Isma Farrah Siddiqui

Papers

Analyzing Students’ Academic Performance through Educational Data Mining