Potential Abstract:
This research investigates the nuanced interpersonal dynamics that shape scoring practices in the context of economics education through the utilization of educational data mining techniques. Drawing on a phenomenological approach, we aim to uncover the subjective experiences and perspectives of educators involved in the assessment and grading processes. By analyzing large-scale educational data sets, we seek to identify patterns and insights that illuminate the intricate relationships between teachers, students, and assessments in the field of economics education.
Our study is guided by the following research questions: How do educators perceive and navigate the complexities of scoring in economics education? What factors influence their decision-making processes when assigning grades? How do these interpersonal dynamics impact student learning outcomes and academic achievement? To address these questions, we will employ qualitative data analysis methods to delve into the rich narratives and reflections shared by educators regarding their scoring practices.
Through a combination of qualitative inquiry and educational data mining techniques, this research aims to provide a deeper understanding of the multifaceted nature of scoring in economics education. By shedding light on the interpersonal interactions and subjective interpretations that underpin assessment practices, our study contributes to the ongoing discourse on effective evaluation methodologies and their implications for student success in the field of economics education.
Potential References:
- Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief.
- Educational data mining: a review of the state of the art
- Tools for educational data mining: A review
- Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld
- Educational data mining and learning analytics: A review of educational management in e-learning
