Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.
The study aims to assess the machine learning techniques in predicting students' associated factors that affect their academic performance. The study sample consisted of 5084 middle and high school students between the ages of 10 and 17, attending public and UNRWA schools in the West Bank. The 'Health Behaviors School Children' questionnaire for the 2013-2014 academic year was used for data collection, and was then analyzed through machine learning techniques in order to evaluate their relationship with student academic outcomes. Six machine learning techniques (Random Forest, Neural Network, Support Vector Machine, Decision Tree, Naïve Bayes, and Logistic Regression) were used for prediction. The results indicated that the logistic regression and Naïve Bayes models had the highest accuracy levels (94.3%, 94%) respectively, followed by a decision tree, Neural Network, Random Forest, and Support Vector Machine (93.3%,91.9%,91.7%, and 80.2%) respectively. Thus, the Logistic Regression and Naïve Bayes had the best performance in classifying and predicting student academic performance with the associated factors. Furthermore, Decision Tree, Random Forest, and Neural Network had better predictive performance than Support Vector Machine. The results indicated that perception, Smoking, Depression, PTSD, Healthy Food Consumption, Age, gender, Grade Level, and Family income are the most important and significant factors that influence student academic performance. Overall, machine learning techniques prove efficient tools for identifying and predicting the features that influence student academic performance. The deployment of machine learning techniques within schools' information systems will facilitate the development of health prevention and intervention programs that will enhance students' academic performance.
Serious games (SGs) are interactive and entertaining digital games with a special educational purpose. Studies have shown that SGs are effective in enhancing educational skills. Cognitive skills training through serious games have been used in improving students learning outcomes. In this article, we introduce the 'plants kingdom' serious game for improving adolescents' cognitive skills, mainly attention (Focus, selection, and sustained attention) and understanding skills. The game used the grade 8 Science book in designing the game content. The plant kingdom lesson was used for developing the game story and objects, its methods and tools were designed for the purpose of attention and understanding skills improvement. The game was evaluated on 43 students from public schools between the ages of 13-15 years, the study selected data from the students who had completed 5 playing sessions. The attention and understanding skills were assessed using the automatic recording and analysis of the game player's data. The variables utilized from the players' data included player ID, session number, gender, number of trials, level, drag and drop time, distance, reason for failure, position, speed, status, time, and playing tool. Results showed that the game improved the attention and understanding skills of students by 27% and 25 % respectively. The study showed the significant effect of serious games in enhancing students' cognition; thus, integrating serious games into the education system can potentially improve learning objectives and outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.