Potential Abstract:
This research article presents a novel approach to algorithmic scoring in education, adopting a Marxist perspective and utilizing the collaborative platform GitHub. Algorithmic scoring has gained prominence in educational settings as a means to efficiently analyze and assess large volumes of student work. However, concerns have been raised regarding the potential biases and inequities embedded in such systems. To address these concerns, we propose a Marxist approach to algorithmic scoring that focuses on empowering students and promoting critical engagement with the evaluation process. GitHub, a widely used platform for collaborative software development, offers a unique environment for implementing this approach.
Abstract:
This study explores the use of GitHub as a platform for algorithmic scoring in educational contexts from a Marxist perspective. The proposed approach aims to counteract the inherent biases and inequities of traditional algorithmic scoring systems by fostering student agency, critical thinking, and collaborative knowledge construction. By leveraging GitHub’s features for version control, transparency, and decentralized decision-making, students become active participants in the scoring process. This approach aligns with the principles of a Marxist educational framework, which emphasizes social justice, equality, and the development of critical consciousness.
Potential References:
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures
- Going from molecules to genomic variations to scientific discovery: Intelligent algorithms and architectures for intelligent genome analysis
- Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
- Applying software quality criteria to blockchain applications: A criteria catalog
- glyXtoolMS: An Open-Source Pipeline for Semiautomated Analysis of Glycopeptide Mass Spectrometry Data
