Applicability of Educational Data Mining in Afghanistan: Opportunities and Challenges

TL;DR

This paper explores the potential of educational data mining in Afghanistan to improve student guidance and reduce attrition by analyzing existing data and proposing new applications for educational process enhancement.

Contribution

It identifies opportunities and challenges for applying educational data mining in Afghanistan's education system and suggests practical approaches for its implementation.

Findings

Data mining can help predict suitable fields of study for students.

Current data practices limit analysis to basic facts and figures.

Educational data mining offers potential to improve student retention and guidance.

Abstract

The author's own experience as a student and later as a lecturer in Afghanistan has shown that the methods used in the educational system are not only flawed, but also do not provide the minimum guidance to students to select proper course of study before they enter the national university entrance (Kankor) exam. Thus, it often results in high attrition rates and poor performance in higher education. Based on the studies done in other countries, and by the author of this paper through online questionnaires distributed to university students in Afghanistan - it was found that proper procedures and specialized studies in high schools can help students in selecting their major field of study more systematically. Additionally, it has come to be known that there are large amounts of data available for mining purposes, but the methods that the Ministry of Education and Ministry of Higher Education use to store and produce their data, only enable them to achieve simple facts and figures. Furthermore, from the results it can be concluded that there are potential opportunities for educational data mining application in the domain of Afghanistan's education systems. Finally, this study will provide the readers with approaches for using Educational Data Mining to improve the educational business processes. For instance, predict proper field of study for high school graduates, or, identify first year university students who are at high risk of attrition.