Potential Abstract:
Algorithmic decision-making processes are increasingly shaping various aspects of society, including education. GitHub repositories have become a treasure trove of algorithms, providing researchers with valuable insights into the development and deployment of these algorithms. This study employs a hermeneutic approach to uncover the underlying mechanisms and implications of algorithms in education by examining GitHub repositories. By leveraging a combination of computational analysis and qualitative interpretation, we investigate the intricate equation between algorithmic design and educational outcomes.
Our analysis reveals the multifaceted nature of algorithms in educational contexts, shedding light on their potential impacts on student learning, teacher practices, and institutional policies. Through a critical examination of algorithms stored in GitHub repositories, we aim to challenge prevailing assumptions and spark discussions on the ethical and pedagogical considerations surrounding algorithmic technologies in education. This research contributes to the emerging field of algorithmic education studies by offering a nuanced perspective that transcends mere technical functionalities to delve into the socio-cultural implications of algorithms in educational settings.
Potential References:
- Computational text analysis within the Humanities: How to combine working practices from the contributing fields?
- Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability
- Digital Culture and the Hermeneutic Tradition: Suspicion, Trust, and Dialogue
- Humanistic interpretation and machine learning
- Algorithms as culture: Some tactics for the ethnography of algorithmic systems
