Potential Abstract:
Abstract: In the era of digital transformation, algorithmic scoring has become increasingly prevalent in educational assessment, particularly in the realm of cloud-based operations (cloud ops). This study examines the disruptive implications of algorithmic scoring on traditional educational practices, pedagogies, and learning outcomes. Through a mixed-methods approach, we investigate how the implementation of algorithmic scoring systems in cloud ops environments impacts student assessment, teacher evaluation, and educational decision-making processes. The study also explores the ethical considerations and potential biases associated with automated scoring algorithms, shedding light on the need for transparency and accountability in algorithmic decision-making in education. Furthermore, this research elucidates how algorithmic scoring in cloud ops can shape educational policies, practices, and the broader landscape of teaching and learning.
Potential References:
- A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment
- Evaluating machine learning algorithms for anomaly detection in clouds
- Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks
- How the machine ‘thinks’: Understanding opacity in machine learning algorithms
- Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds
