MACHINE LEARNING FOR ENHANCED CLASSROOM HOMOGENEITY IN PRIMARY EDUCATION

Author:

Bulut Faruk1ORCID,Dönmez İlknur2ORCID,İnce İbrahim Furkan3ORCID,Petrov Pavel4ORCID

Affiliation:

1. ISTANBUL ESENYURT UNIVERSITY

2. Turkish Scientific and Technical Research Council, Gebze, Türkiye

3. NİŞANTAŞI ÜNİVERSİTESİ

4. University of Economics - Varna, Bulgaria

Abstract

A homogeneous distribution of students in a class is accepted as a key factor for overall success in primary education. A class of students with similar attributes normally increases academic success. It is also a fact that general academic success might be lower in some classes where students have different intelligence and academic levels. In this study, a class distribution model is proposed by using some data science algorithms over a small number of students’ dataset. With unsupervised and semi supervised learning methods in machine learning and data mining, a group of students is equally distributed to classes, taking into account some criteria. This model divides a group of students into clusters by the considering students’ different qualitative and quantitative characteristics. A draft study is carried out by predicting the effectiveness and efficiency of the presented approaches. In addition, some process elements such as quantitative and qualitative characteristics of a student, data acquisition style, digitalization of attributes, and creating a future prediction are also included in this study. Satisfactory and promising experimental results are received using a set of algorithms over collected datasets for classroom scenarios. As expected, a clear and concrete evaluation between balanced and unbalanced class distributions cannot be performed since these two scenarios for the class distributions cannot be applicable at the same time.

Funder

Governorship of Izmir (İzmir Valiliği) and Çiğli National Education Directorate

Publisher

International Online Journal of Primary Education (IOJPE)

Reference56 articles.

1. Adams-Byers, J., Whitsell, S. S., & Moon, S. M. (2004). Gifted students' perceptions of the academic and social/emotional effects of homogeneous and heterogeneous grouping. Gifted Child Quarterly, 48(1), 7-20.

2. Alpaydin, E. (2021). Introduction to machine learning. Adaptive Computation and Machine Learning series, MIT press.

3. Backer, E., & Jain, A. K. (1981). A clustering performance measure based on fuzzy set decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1), 66-75.

4. Basu, S., Banerjee, A., & Mooney, R. (2002). Semi-supervised clustering by seeding. In Proceedings of 19th International Conference on Machine Learning (ICML-2002).

5. Bellinger, G., Castro, D., & Mills, A. (2004). Data, information, knowledge, and wisdom. https://homepages.dcc.ufmg.br/~amendes/SistemasInformacaoTP/TextosBasicos/Data-Information-Knowledge.pdf

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3