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Student Performance Prediction Model for Predicting Academic Achievement of High School Students

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Title: Student Performance Prediction Model for Predicting Academic Achievement of High School Students
Language: English
Authors: Nuankaew, Pratya (ORCID 0000-0002-3297-4198), Nuankaew, Wongpanya Sararat (ORCID 0000-0003-3805-9529)
Source: European Journal of Educational Research. 2022 11(2):949-963.
Availability: Eurasian Society of Educational Research. 7321 Parkway Drive South, Hanover, MD 21076. e-mail: publisher@eu-jer.com; Web site: https://www.eu-jer.com/
Peer Reviewed: Y
Page Count: 15
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Grade Prediction, Academic Achievement, High School Students, Rural Schools, Foreign Countries, Learning Analytics, Models, Lifelong Learning, Data Analysis
Geographic Terms: Thailand
ISSN: 2165-8714
Abstract: Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education management, to study the relationship of continuing education at the upper secondary level, and to construct an appropriate education program prediction model for high school students in a rural school. The data for research is the academic achievement data of 1,859 students from Manchasuksa School at Mancha Khiri District, Khon Kaen Province, Thailand, during the academic year 2015-2020. Research tools are separated into 2 sections. The first section is a basic statistical analysis step, it composes of frequency analysis, percentage analysis, mean analysis, and standard deviation analysis. Another section is the data mining analysis phase, which consists of discretization technique, XGBoost classification technique (Decision Tree, Gradient Boosted Trees, and Random Forest), confusion matrix performance analysis, and cross-validation performance analysis. At the end, the research results found that the reasonable distribution level of student achievement consisted of four clusters classified by academic achievement. All four clusters were modeled on predicting academic achievement for the next generation of students. In addition, there are four success models in this research. For future research, the researcher aims to develop an application to facilitate instruction for learners by integrating prediction models into the mobile application to promote the utilization of modern technology.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1341746
Database: ERIC
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  Data: Student Performance Prediction Model for Predicting Academic Achievement of High School Students
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  Data: <searchLink fieldCode="AR" term="%22Nuankaew%2C+Pratya%22">Nuankaew, Pratya</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3297-4198">0000-0002-3297-4198</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nuankaew%2C+Wongpanya+Sararat%22">Nuankaew, Wongpanya Sararat</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3805-9529">0000-0003-3805-9529</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22European+Journal+of+Educational+Research%22"><i>European Journal of Educational Research</i></searchLink>. 2022 11(2):949-963.
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  Data: Eurasian Society of Educational Research. 7321 Parkway Drive South, Hanover, MD 21076. e-mail: publisher@eu-jer.com; Web site: https://www.eu-jer.com/
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  Data: 15
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+Schools%22">Rural Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Lifelong+Learning%22">Lifelong Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Thailand%22">Thailand</searchLink>
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  Data: 2165-8714
– Name: Abstract
  Label: Abstract
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  Data: Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education management, to study the relationship of continuing education at the upper secondary level, and to construct an appropriate education program prediction model for high school students in a rural school. The data for research is the academic achievement data of 1,859 students from Manchasuksa School at Mancha Khiri District, Khon Kaen Province, Thailand, during the academic year 2015-2020. Research tools are separated into 2 sections. The first section is a basic statistical analysis step, it composes of frequency analysis, percentage analysis, mean analysis, and standard deviation analysis. Another section is the data mining analysis phase, which consists of discretization technique, XGBoost classification technique (Decision Tree, Gradient Boosted Trees, and Random Forest), confusion matrix performance analysis, and cross-validation performance analysis. At the end, the research results found that the reasonable distribution level of student achievement consisted of four clusters classified by academic achievement. All four clusters were modeled on predicting academic achievement for the next generation of students. In addition, there are four success models in this research. For future research, the researcher aims to develop an application to facilitate instruction for learners by integrating prediction models into the mobile application to promote the utilization of modern technology.
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      – Text: English
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        PageCount: 15
        StartPage: 949
    Subjects:
      – SubjectFull: Grade Prediction
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: High School Students
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      – SubjectFull: Rural Schools
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Learning Analytics
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      – SubjectFull: Models
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      – SubjectFull: Lifelong Learning
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      – SubjectFull: Data Analysis
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      – SubjectFull: Thailand
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