MACHINE LEARNING FOR ENHANCED CLASSROOM HOMOGENEITY IN PRIMARY EDUCATION

Year 2024, Volume: 13 Issue: 1, 33 - 52, 31.03.2024

Faruk Bulut , İlknur Dönmez , İbrahim Furkan İnce , Pavel Petrov

https://doi.org/10.55020/iojpe.1390421

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.

Keywords

Unsupervised and semi-supervised methods, class distribution, classroom homogeneity, ability grouping, similar academic performance.

Ethical Statement

The survey studies were carried out with the official authorization under document number 15585669-604.01.02-E.3979850, dated April 08, 2016, acquired from the Çiğli National Education Directorate. The data collection involved ten first-grade students, proceeding with explicit consent from their parents and under the meticulous supervision and support of their classroom teacher. Since the official permissions and data used in the article study were obtained before the year 2020, there was no obligation to obtain an official permission from the Ethics Committee. The authors declare that they have no conflict of interest.

Supporting Institution

Governorship of Izmir (İzmir Valiliği) and Çiğli National Education Directorate (Çiğli İlçe Milli Eğitim Müdürlüğü)

Thanks

We would like to express our endless thanks and gratitude to the Governorship of Izmir (İzmir Valiliği) and the Çiğli National Education Directorate (Çiğli İlçe Milli Eğitim Müdürlüğü) for allowing this study, and to the administrators, teachers, and students of TOKİ Karşıyaka Municipality Primary School (TOKİ Karşıyaka Belediyesi İlkokulu) who helped us in collecting data and carrying out the necessary experimental studies.

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