Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model
School bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/14/1/73 |
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| author | Tian Qiu Sizhe Wang Di Hu Ningning Feng Lijuan Cui |
| author_facet | Tian Qiu Sizhe Wang Di Hu Ningning Feng Lijuan Cui |
| author_sort | Tian Qiu |
| collection | DOAJ |
| description | School bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machine learning approach (i.e., the gradient boosting decision tree model, GBDT), the present longitudinal study aims to systematically examine individual, family, and school environment factors that can predict the risk of bullying victimization among primary and secondary school students a year later. A total of 2767 participants (2065 secondary school students, 702 primary school students, 55.20% female students, mean age at T1 was 12.22) completed measures of 24 predictors at the first wave, including individual factors (e.g., self-control, gender, grade), family factors (family cohesion, parental control, parenting style), peer factor (peer relationship), and school factors (teacher–student relationship, learning capacity). A year later (i.e., T2), they completed the Olweus Bullying Questionnaire. The GBDT model predicted whether primary and secondary school students would be exposed to school bullying after one year by training a series of base learners and outputting the importance ranking of predictors. The GBDT model performed well. The GBDT model yielded the top 6 predictors: teacher–student relationship, peer relationship, family cohesion, negative affect, anxiety, and denying parenting style. The protective factors (i.e., teacher–student relationship, peer relationship, and family cohesion) and risk factors (i.e., negative affect, anxiety, and denying parenting style) associated with the risk of bullying victimization a year later among primary and secondary school students are identified by using a machine learning approach. The GBDT model can be used as a tool to predict the future risk of bullying victimization for children and adolescents and to help improve the effectiveness of school bullying interventions. |
| first_indexed | 2024-03-08T11:05:48Z |
| format | Article |
| id | doaj.art-e95355679dd14f02b4c32c6f707113f4 |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-03-08T11:05:48Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-e95355679dd14f02b4c32c6f707113f42024-01-26T15:06:01ZengMDPI AGBehavioral Sciences2076-328X2024-01-011417310.3390/bs14010073Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning ModelTian Qiu0Sizhe Wang1Di Hu2Ningning Feng3Lijuan Cui4Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, ChinaSchool of Statistics, East China Normal University, Shanghai 200062, ChinaSliver School of Social Work, New York University, New York, NY 10012, USAShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, ChinaShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, ChinaSchool bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machine learning approach (i.e., the gradient boosting decision tree model, GBDT), the present longitudinal study aims to systematically examine individual, family, and school environment factors that can predict the risk of bullying victimization among primary and secondary school students a year later. A total of 2767 participants (2065 secondary school students, 702 primary school students, 55.20% female students, mean age at T1 was 12.22) completed measures of 24 predictors at the first wave, including individual factors (e.g., self-control, gender, grade), family factors (family cohesion, parental control, parenting style), peer factor (peer relationship), and school factors (teacher–student relationship, learning capacity). A year later (i.e., T2), they completed the Olweus Bullying Questionnaire. The GBDT model predicted whether primary and secondary school students would be exposed to school bullying after one year by training a series of base learners and outputting the importance ranking of predictors. The GBDT model performed well. The GBDT model yielded the top 6 predictors: teacher–student relationship, peer relationship, family cohesion, negative affect, anxiety, and denying parenting style. The protective factors (i.e., teacher–student relationship, peer relationship, and family cohesion) and risk factors (i.e., negative affect, anxiety, and denying parenting style) associated with the risk of bullying victimization a year later among primary and secondary school students are identified by using a machine learning approach. The GBDT model can be used as a tool to predict the future risk of bullying victimization for children and adolescents and to help improve the effectiveness of school bullying interventions.https://www.mdpi.com/2076-328X/14/1/73bullying victimizationschool bullyecological factorslongitudinal studymachine learningGBDT |
| spellingShingle | Tian Qiu Sizhe Wang Di Hu Ningning Feng Lijuan Cui Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model Behavioral Sciences bullying victimization school bully ecological factors longitudinal study machine learning GBDT |
| title | Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model |
| title_full | Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model |
| title_fullStr | Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model |
| title_full_unstemmed | Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model |
| title_short | Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model |
| title_sort | predicting risk of bullying victimization among primary and secondary school students based on a machine learning model |
| topic | bullying victimization school bully ecological factors longitudinal study machine learning GBDT |
| url | https://www.mdpi.com/2076-328X/14/1/73 |
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