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Machine Learning for Enhanced Classroom Homogeneity in Primary Education

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Title: Machine Learning for Enhanced Classroom Homogeneity in Primary Education
Language: English
Authors: Faruk Bulut (ORCID 0000-0003-2960-8725), I?lknur Dönmez (ORCID 0000-0002-8344-1180), I?brahim Furkan I?nce (ORCID 0000-0003-1570-875X), Pavel Petrov (ORCID 0000-0002-1284-2606)
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
FullText Text:
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  Data: Machine Learning for Enhanced Classroom Homogeneity in Primary Education
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  Data: <searchLink fieldCode="AR" term="%22Faruk+Bulut%22">Faruk Bulut</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2960-8725">0000-0003-2960-8725</externalLink>)<br /><searchLink fieldCode="AR" term="%22I%3Flknur+Dönmez%22">I?lknur Dönmez</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8344-1180">0000-0002-8344-1180</externalLink>)<br /><searchLink fieldCode="AR" term="%22I%3Fbrahim+Furkan+I%3Fnce%22">I?brahim Furkan I?nce</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1570-875X">0000-0003-1570-875X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Pavel+Petrov%22">Pavel Petrov</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1284-2606">0000-0002-1284-2606</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Online+Journal+of+Primary+Education%22"><i>International Online Journal of Primary Education</i></searchLink>. 2024 13(1):33-52.
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  Data: International Online Journal of Primary Education. e-mail: editor.online.iojpe@gmail.com; Web site: http://www.iojpe.org/
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  Data: 20
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  Data: 2024
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+1%22">Grade 1</searchLink><br /><searchLink fieldCode="EL" term="%22Primary+Education%22">Primary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+1%22">Grade 1</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Supervision%22">Supervision</searchLink><br /><searchLink fieldCode="DE" term="%22Classes+%28Groups+of+Students%29%22">Classes (Groups of Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Classroom+Techniques%22">Classroom Techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Placement%22">Student Placement</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink>
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  Data: 1300-915X
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  Label: Abstract
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  Data: 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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  Data: As Provided
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  Data: https://sites.google.com/site/bulutfaruk/study-of-clustering-on-education
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  Data: 2024
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  Data: EJ1420351
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 33
    Subjects:
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Grade 1
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Supervision
        Type: general
      – SubjectFull: Classes (Groups of Students)
        Type: general
      – SubjectFull: Classroom Techniques
        Type: general
      – SubjectFull: Learning Strategies
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Student Placement
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Turkey
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      – TitleFull: Machine Learning for Enhanced Classroom Homogeneity in Primary Education
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