Educational Data Mining Tools Research Articles

Clustering, Classification, and Association Rule Mining for Educational Datasets

Understanding students/pupils within the context of school is the focus of the expanding research topic known as educational data mining. Before the adoption of data mining tools in student analysis, it was challenging to identify students who were at risk of failure. However, using educational data mining tools has made it straightforward to examine student performance on previous exams. It helps teachers understand the kind of children they are working with. This knowledge can assist teachers to tailor their lecture notes to each student's needs and help difficult students focus more throughout the class. In this study, 1428 newly admitted student records from a tertiary institution in Nigeria were examined. Decision trees, association rules, and K-means clustering techniques were used to analyze the data. The results showed that arts courses were primarily responsible for lows cores, social science courses for average scores, and the high JAMB scores came from science courses. Additionally, students from the geopolitical zones of the South-East, South-South, and North-East performed better than those from the geopolitical zones of the North-West, North Central, and South-West. The students with the highest JAMB marks were those who offered Physics and had an English score of at least 70. Most males offered Physics, Chemistry, Mathematics, and Biology while most females offered Economics, Government, CRS, and Literature in English. We highly recommend that Students who did not perform well should be given more attention and extra lessons/classes should be held for them to improve their grades in the various departments they were admitted.

A Comparison of EDM Tools and Techniques

Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. Such a practice ensures not only better academic outcomes but also helps the institutions to reorient their curriculums and teaching pedagogies so as to add to the students’ learning curve. Educational Data Mining (EDM) has risen as a useful technology in this league. EDM techniques are now being used for predicting the enrolment of students in a specific course, detection of any irregular grades, prediction about students’ performance, analyzing and visualizing of data, and providing feedback for overall improvement in the academic spheres. This paper reviews the studies related to EDM, including the approaches, data sets, tools, and techniques that have been used in those studies, and points out the most efficient techniques. This review paper uses true prediction accuracy as a standard for the comparison of different techniques for each EDM applications of the surveyed literature. The results show that the J48 and K-means are the most effective techniques for predicting the students’ performance. Furthermore, the results also cite that Bayesian and Decision Tree Classifiers are the most widely used techniques for predicting the students’ performance. In addition, this paper highlights that the most widely used tool was WEKA, with approximately 75% frequency. The present study’s empirical assessments would be a significant contribution in the domain of EDM. The comparison of different tools and techniques presented in this study are corroborative and conclusive, thus the results will prove to be an effective reference point for the practitioners in this field. As a much needed technological asset in the present day educational context, the study also suggests that additional surveys are recommended to be driven for each of the EDM applications by taking into account more standards to set the best techniques more accurately.

Data mining in educational technology classroom research: Can it make a contribution?

The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research. Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. Both of these studies examine student learning, behaviors, and experiences within computer-supported classroom activities. In the first study, the technique of association rules mining was used to understand better how learners with different cognitive types interacted with a simulation to solve a problem. Association rules mining was found to be a useful method for obtaining reliable data about learners' use of the simulation and their performance with it. The study illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology. In the second study, the technique of fuzzy representations was employed to inductively explore questionnaire data. The study provides a good example of how educational technologists can use data mining for guiding and monitoring school-based technology integration efforts. Based on the outcomes, the implications of the study are discussed in terms of the need to develop educational data mining tools that can display results, information, explanations, comments, and recommendations in meaningful ways to non-expert users in data mining. Lastly, issues related to data privacy are addressed.