Leveraging GitHub Data to Investigate Causal Relationships in Grading: Engineered Causal Models

Potential Abstract:
In the context of education, the practice of assigning grades is a critical aspect of the learning process. Despite its importance, the factors that influence grading decisions and outcomes are often complex and multifaceted. In recent years, the availability of digital tools and platforms, such as GitHub, has provided researchers with new opportunities to explore and analyze educational data in innovative ways. This study aims to leverage GitHub data to investigate causal relationships in grading practices using engineered causal models.

By analyzing student contributions and interactions within the GitHub platform, this research seeks to identify key factors that impact grading outcomes. The use of causal models allows for the exploration of causal relationships between various input variables, such as student engagement, participation, and performance, and the resulting grades assigned by instructors. By employing sophisticated data analysis techniques, including structural equation modeling and causal inference methods, this study aims to uncover the underlying mechanisms that drive grading decisions in educational settings.

The findings from this research have the potential to inform and enhance grading practices in educational institutions by providing insights into the complex relationships between student behaviors, instructor feedback, and final grades. Additionally, this study contributes to the growing body of literature on the application of causal modeling techniques in educational research, particularly in the context of analyzing digital learning environments. By bridging the gap between artificial intelligence and education, this research aims to advance our understanding of the factors that influence grading outcomes and contribute to the development of more effective assessment strategies.

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