Potential Abstract:
Abstract: This research study explores the potential of leveraging social networks and open data to generate synthetic standardized testing data for educational research purposes. With the increasing emphasis on data-driven decision making in education, the availability of reliable and comprehensive testing data is crucial. However, access to standardized testing data is often limited due to privacy concerns and data access restrictions. To address this challenge, this study proposes a novel approach that utilizes social networks and open data sources to create synthetic standardized testing data that closely mimics the characteristics of real testing data. By combining information from various social network profiles, such as demographics, academic history, and interests, with publicly available data on educational outcomes and performance, synthetic standardized testing data can be generated for research purposes. This approach not only provides researchers with access to a larger pool of testing data but also ensures the privacy and anonymity of individuals. The study will employ machine learning algorithms to validate the accuracy and validity of the synthetic data generated. The findings of this research have the potential to revolutionize educational research by providing researchers with a new tool to access and analyze standardized testing data in a more ethical and privacy-preserving manner.
Potential References:
- A synthetic data generator for online social network graphs
- Creating and Using Synthetic Data for Neural Network Training, Using the Creation of a Neural Network Classifier of Online Social Network User Roles as an Example
- On the utility of synthetic data: An empirical evaluation on machine learning tasks
- Perspectives on social media in and as research: a synthetic review
- Machine learning and the politics of synthetic data
