Discovering Rules for Nursery Students using Apriori Algorithm

Mohammad Marufuzzaman, Dipta Gomes, Aneem Al Ahsan Rupai, Lariyah Mohd Sidek

Abstract


Over recent years, there has been a rise in the number of students completing nursery education in Bangladesh. However, in order to achieve a sustainable education goal, the dropout rate in education needs to be reduced. Therefore, this research worked on providing insights that would help to understand the possible causes of dropout from education. Since primary education is the starting point for every student, this research has been conducted on this part of education. The research used data obtained from a European country, Slovenia to use the insights of a developed country. The study was conducted using association rule mining where several mining rules were generated using the Apriori algorithm. The rules obtained had the confidence of 0.95 and support of 0.04. The results showed three major rules of dropping out children in nursery education and evetually helps to ensure higher education for all children.

Keywords


Apriori algorithm, nursery education, association rules, IT, data analysis

References


Information on https://www.undp.org/content/undp/en/home/sustainable-development-goals/goal-4-quality-education.html

Hossain Z. Status of secondary school libraries and librarians in Bangladesh, IFLA Journal, 2019 Apr 29:0340035219842317.

Information on http://www.newagebd.net/article/32556/188pc-dropout-in-primary-education

Shilpi M, Hasnayen S, Ilahi T, Parvin M, Sultana K. Education Scenario in Bangladesh: Gender perspective. Bangladesh Bureau of Statistics, UCEP and Diakonia Bangladesh 2017.

Ajayi BA, Hussin H. Conceptualizing Information Technology Governance Model for Higher Education: An Absorptive Capacity Approach. Bulletin of Electrical Engineering and Informatics. 2018. 7(1):117-24.

Marufuzzaman M, Reaz MBI, Ali MAM, Rahman LF. A time series based sequence prediction algorithm to detect activities of daily living in smart home. Methods of information in medicine. 2015. 54(03):262-270.

Marufuzzaman M, Reaz MBI, Rahman LF, Farayez A. A Location Based Sequence Prediction Algorithm for Determining Next Activity in Smart Home. Journal of Engineering Science & Technology Review. 2017. 10(2):161-165.

Matazi I, Messoussi R, Bellmallem SE, Oumaira I, Bennane A, Touahni R. Development of Intelligent Multi-agents System for Collaborative e-learning Support. Bulletin of Electrical Engineering and Informatics. 2018 Jun 1;7(2):294-305.

GarcĂ­a E, Romero C, Ventura S, De Castro C. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction. 2009. 19(1-2):99-132.

Zanker M, Jessenitschnig M. Case-studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction. 2009.19(1-2):133-66.

Ahmmed M, Sharma U, Deppeler J. Variables affecting teachers' attitudes towards inclusive education in Bangladesh. Journal of Research in Special Educational Needs. 2012 Jul;12(3):132-40.

Tissera WM, Athauda RI, Fernando HC. Discovery of strongly related subjects in the undergraduate syllabi using data mining. In IEEE International Conference on Information and Automation. 2006 Dec 15 (pp. 57-62).

Romero C, Ventura S. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2010 Nov;40(6):601-18.

Sun H. Research on student learning result system based on data mining. IJCSNS. 2010 Apr;10(4):203.

Olave M, Rajkovic V, Bohanec M. An application for admission in public school systems. Expert Systems in Public Administration. 1989;1:145-60.



Refbacks

  • There are currently no refbacks.


Bulletin of EEI Stats