Educational improvement through machine learning: Strategic models for better PISA scores.
In this study, in addition to traditional variables such as economic wealth or the number of books read, on which many studies have already been conducted, variables that are thought to influence student achievement and better predict success are identified. Random Forest algorithm was used to ident...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326121 |
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| _version_ | 1849427366295633920 |
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| author | Bilal Baris Alkan Serafettin Kuzucuk Şevki Yetkin Odabasi Leyla Karakuş |
| author_facet | Bilal Baris Alkan Serafettin Kuzucuk Şevki Yetkin Odabasi Leyla Karakuş |
| author_sort | Bilal Baris Alkan |
| collection | DOAJ |
| description | In this study, in addition to traditional variables such as economic wealth or the number of books read, on which many studies have already been conducted, variables that are thought to influence student achievement and better predict success are identified. Random Forest algorithm was used to identify important variables based on the PISA 2018 data, covering all three domains of science, mathematics and reading. The study found that the main factors influencing the success of students in countries that perform well in the PISA exam are essentially access to information technology, weekly hours of instruction in the subject, economic-social and cultural status, parents' occupation, level of metacognition, awareness of PISA, sense of competition and attitudes towards reading. New prediction models based on these variables were proposed. The proposed models will give a significant advantage to policy makers who want to improve their country's PISA score and implement appropriate education policies. |
| format | Article |
| id | doaj-art-7b02cdc44d054eac93e66fa02ccf3dfa |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7b02cdc44d054eac93e66fa02ccf3dfa2025-08-20T03:29:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032612110.1371/journal.pone.0326121Educational improvement through machine learning: Strategic models for better PISA scores.Bilal Baris AlkanSerafettin KuzucukŞevki Yetkin OdabasiLeyla KarakuşIn this study, in addition to traditional variables such as economic wealth or the number of books read, on which many studies have already been conducted, variables that are thought to influence student achievement and better predict success are identified. Random Forest algorithm was used to identify important variables based on the PISA 2018 data, covering all three domains of science, mathematics and reading. The study found that the main factors influencing the success of students in countries that perform well in the PISA exam are essentially access to information technology, weekly hours of instruction in the subject, economic-social and cultural status, parents' occupation, level of metacognition, awareness of PISA, sense of competition and attitudes towards reading. New prediction models based on these variables were proposed. The proposed models will give a significant advantage to policy makers who want to improve their country's PISA score and implement appropriate education policies.https://doi.org/10.1371/journal.pone.0326121 |
| spellingShingle | Bilal Baris Alkan Serafettin Kuzucuk Şevki Yetkin Odabasi Leyla Karakuş Educational improvement through machine learning: Strategic models for better PISA scores. PLoS ONE |
| title | Educational improvement through machine learning: Strategic models for better PISA scores. |
| title_full | Educational improvement through machine learning: Strategic models for better PISA scores. |
| title_fullStr | Educational improvement through machine learning: Strategic models for better PISA scores. |
| title_full_unstemmed | Educational improvement through machine learning: Strategic models for better PISA scores. |
| title_short | Educational improvement through machine learning: Strategic models for better PISA scores. |
| title_sort | educational improvement through machine learning strategic models for better pisa scores |
| url | https://doi.org/10.1371/journal.pone.0326121 |
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