Potential Abstract:
This research article explores the potential of a minimalist approach to ungrading in education by integrating principles of data science and Heideggerian regimes. Ungrading is a pedagogical movement that challenges traditional grading practices and emphasizes student autonomy and intrinsic motivation. While ungrading has gained attention in recent years, there is a need to further investigate its effects and implementation strategies. This study aims to fill this gap by examining how a minimalist perspective, rooted in the principles of data science and Heideggerian philosophy, can enhance the ungrading movement.
Drawing on the insights of data science, we propose that a minimalist approach to ungrading can provide educators with valuable insights into student learning and progress. By leveraging quantitative and qualitative data, educators can gain a deeper understanding of students’ strengths, challenges, and individual learning trajectories. This data-driven feedback can inform personalized instruction and support, fostering student engagement and growth within the ungrading framework.
Additionally, we integrate Heideggerian philosophy to explore the notion of regimes within educational settings. Heideggerian regimes refer to the institutionalized structures, practices, and discourses that shape educational experiences. Through a critical examination of existing grading systems and their impact on student experiences, we highlight the potential of ungrading as a transformative practice that challenges traditional regimes and fosters authentic engagement with learning.
This article presents a theoretical framework that combines the principles of ungrading, data science, and Heideggerian philosophy. By adopting a minimalist perspective, educators can streamline assessment practices, reduce administrative burden, and create a more student-centered learning environment. The proposed framework supports the cultivation of intrinsic motivation, self-reflection, and meaningful learning experiences for students, while providing educators with actionable insights to guide instructional decisions.
Potential References:
- AI ethics
- Recovering the divide: A review of the big data analytics—strategy relationship
- Displacement, space and dwelling: Placing gentrification debate
- Philosophical approaches to the nursing informatics data-information-knowledge-wisdom framework
- Hyperobjects: Philosophy and Ecology after the End of the World
