Machine Learning for Enhanced Classroom Homogeneity in Primary Education

International Online Journal of Primary Education. e-mail: [email protected]; Web site: http://www.iojpe.org/

Y

20

Elementary School Students; Grade 1; Teaching Methods; Supervision; Classes (Groups of Students); Classroom Techniques; Learning Strategies; Artificial Intelligence; Technology Uses in Education; Student Placement; Foreign Countries

1300-915X

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.

As Provided

2024

EJ1420351