Description

The Parental Monitoring Students for Peace (PMSP) dataset is designed to assess the influence of parental monitoring on students’ behaviors and attitudes, particularly in relation to promoting peaceful and non-violent behaviors. This dataset could include variables such as parental involvement in students’ education, communication frequency, disciplinary approaches, and the students’ response in terms of academic performance, emotional well-being, and social interactions.

Analysis and Use of PMSP Data

The analysis of the PMSP data can be used to:

Assess the impact of parental monitoring on students’ behavior. By examining various indicators of parental involvement (e.g., frequency of communication, rules enforcement), we can identify patterns associated with positive or negative student outcomes.

Understand correlations between parental engagement and peaceful student interactions. The data may reveal how consistent monitoring and guidance promote conflict resolution, reduce aggression, or improve cooperation among peers.

Predict student success based on different levels of parental monitoring. Machine learning models could be built to predict academic or behavioral outcomes.

Develop interventions that encourage effective parental engagement to support students’ peace-building capacities and overall well-being.

Goal: The main goal of working with the PMSP dataset is calibration or refining models to predict students’ peaceful and academic outcomes based on the intensity and quality of parental monitoring. The results of the analysis would help educators and policymakers develop strategies that foster healthy parent-student relationships, which in turn could support peaceful student behaviors and improved school performance.

Calibration

Calibration involves fine-tuning models developed to predict students’ outcomes based on parental monitoring data. For example, models can be adjusted to improve the accuracy of predictions on how parental engagement affects student behavior, with a focus on minimizing errors and ensuring the model performs well across different student groups.

This process typically involves:

Identifying key variables within the dataset.

Testing different models to see which ones offer the best predictions.

Adjusting model parameters (e.g., using techniques such as cross-validation) to enhance the accuracy of these predictions.

Bibliography

Parental Monitoring and Student Outcomes: Studies show that active parental monitoring, including communication and involvement in education, leads to better academic and behavioral outcomes (e.g., Steinberg, L., et al. “Parental Monitoring and Adolescent Adjustment.” Journal of Youth and Adolescence, 1992).

Peace Education in Schools: Research suggests that peace education initiatives can be enhanced when there is alignment between school and parental practices, promoting peaceful interactions (e.g., Harris, I.M. “Peace Education Theory.” Journal of Peace Education, 2004).

Impact of Parenting Styles: Examines the influence of different parenting approaches on children’s social behaviors (e.g., Baumrind, D. “Parenting Styles and Adolescent Development.” Encyclopedia of Adolescence, 1991).

Parental Involvement in Schooling: Studies on how parental involvement correlates with academic success and reduced behavior problems (e.g., Epstein, J. L., & Sanders, M. G. “Family, School, and Community Partnerships: Your Handbook for Action.” Corwin Press, 2002).