Abstract
Students’ pedagogical progress plays a pivotal role in any educational institute in order to pursue imperative education. Educational institutes, Universities, Colleges implement various performance measures in order to keep analyzing and tracking progress of students to cultivate benefits of education in a better way. There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions. This study aims to analyze and track engineering under graduate student’s records to judge quality education, student motivation towards learning, and student pedagogical progress to maintain education at high quality level and predicting engineering student’s forthcoming progress. Different engineering discipline students’ (of three different cohorts) data have been analyzed for tracing current as well as future pedagogical progress based on their sessional (pre-examination) marks. In this research, the classification techniques by k-nearest neighbor, Naive Bayes and decision trees are applied to evaluate different engineering technologies student’s performance and also there are different methodologies that can be used for data classification.
Highlights
Following higher education is a challenging stage for students as well as educational institutes to deal with huge amount of data
The present study aims the significance, scope and techniques of data mining in the domain of education is addressed in a multiple education disciplines and technologies at higher education level interestedly
There are many classification algorithms but three mostly used classification algorithms have been used in this study
Summary
Following higher education is a challenging stage for students as well as educational institutes to deal with huge amount of data. Educational institutes with variety of educational data like student attendance records, examination records, fees records, personal information, etc., entails to be managed and tracked time to time. Data mining techniques have been used to discern and extract certain patterns that are potentially expedient in the domain of education at any level. Educational data mining can be regard as an interdisciplinary field that assists methods of extracting useful information from enormous sets of data [2]. The advancements in the field of educational data mining have made it possible to perform academic analysis in an innovative ways to focus on educational institutes‟ efficacy and to reduce student retention [3]
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