Machine Learning for Enhanced Classroom Homogeneity in Primary Education
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| Title: | Machine Learning for Enhanced Classroom Homogeneity in Primary Education |
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| Language: | English |
| Authors: | Faruk Bulut (ORCID |
| Source: | International Online Journal of Primary Education. 2024 13(1):33-52. |
| Availability: | International Online Journal of Primary Education. e-mail: editor.online.iojpe@gmail.com; Web site: http://www.iojpe.org/ |
| Peer Reviewed: | Y |
| Page Count: | 20 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Early Childhood Education Grade 1 Primary Education |
| Descriptors: | 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 |
| Geographic Terms: | Turkey |
| ISSN: | 1300-915X |
| 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. |
| Abstractor: | As Provided |
| Notes: | https://sites.google.com/site/bulutfaruk/study-of-clustering-on-education |
| Entry Date: | 2024 |
| Accession Number: | EJ1420351 |
| Database: | ERIC |
| 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. |
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| ISSN: | 1300-915X |
