Potential Abstract: Educational data mining is a rapidly growing field that leverages machine learning techniques to analyze large datasets in the context of education. Traditional educational assessments often rely on numerical scoring methods, but there is an increasing interest in exploring categorical scoring systems that can capture the complexity of student responses. This study proposes an engineered conversation analysis framework that combines elements of both categorical scoring and educational data mining to provide a more nuanced understanding of student interactions. By applying this framework to a dataset of student-teacher conversations, we aim to uncover patterns that can inform instructional strategies and enhance student learning outcomes. Our findings suggest that a categorical approach to scoring can offer valuable insights into the dynamics of educational interactions, shedding light on the nuances of student engagement and knowledge acquisition. This study contributes to the ongoing conversation in educational research about the potential benefits of integrating qualitative and quantitative approaches to data analysis in educational settings.
Potential References:
- Data mining in education
- Educational data mining: a review of the state of the art
- Educational data mining: A survey and a data mining-based analysis of recent works
- Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining …
- Educational data mining and learning analytics: An updated survey
