Affiliation:
1. KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECNOLOGY
2. University for Development Studies
3. Kwame Nkrumah University of Science and Technology
Abstract
Abstract
The application of Data Mining (DM) in education is helping educational leadership to make informed decisions. This review seeks to identify the pattern of DM research by looking at the levels and aspects of education. As the core of schooling is Basic Education (BE), the research seeks to find out the degree of application of DM at this level to identify the challenges and prepare learners for higher education. The databases of nine (9) top-ranked publishers namely; Elsevier, Springer, Hindawi, Sage, SAI, IEEE, MDPI, Emerald and Wiley are used to identify the most recent and relevant papers in Educational DM (EDM) published from the year 2017 to 2022 specifically applied to a specific level of education. After careful filtering, only ninety-four (94) articles and conference papers were fit for the specification. The investigations revealed that only 7.45% of the published research works in EDM for basic education, 11.70% for pre-tertiary education and an overwhelming 80.85% for the tertiary level. Lower levels of education are marginalized. Also, the available literature on educational DM concentrates on student performance using attributes such as demographic factors, family socio-economic life, school environment, learner behaviour and psychological factors among others neglecting the availability of resources to facilitate quality tuition. The use of pedagogical tools is necessary for learning to improve quality. The research has revealed both a population gag and a knowledge gap.
Publisher
Research Square Platform LLC
Reference85 articles.
1. Predicting student academic performance using multi-model heterogeneous ensemble approach;Adejo OW;Journal of Applied Research in Higher Education,2018
2. Data mining approach to predicting the performance of first year student in a university using the admission requirements;Adekitan AI;Education and Information Technologies,2019
3. The impact of engineering students’ performance in the first three years on their graduation result using educational data mining;Adekitan AI;Heliyon,2019
4. al Mazidi, A., & Abusham, E. (2018). Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches. International Journal of Engineering Business Management, 10. https://doi.org/10.1177/1847979018807020/ASSET/IMAGES/LARGE/10.1177_1847979018807020-FIG1.JPEG
5. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. In Telematics and Informatics (Vol. 37, pp. 13–49). Elsevier Ltd. https://doi.org/10.1016/j.tele.2019.01.007