Potential Abstract: This research study explores the intersection of cognitive biases, machine learning, and open educational resources (OER) within the context of educational settings. Cognitive biases are inherent in human decision-making processes and can significantly impact how educators and learners interact with OER. By reframing the discussion around cognitive biases through the lens of machine learning, this study aims to uncover new insights into how biases manifest in the selection and utilization of OER, and how these biases can be mitigated to enhance the effectiveness of OER in educational settings. The context-laden nature of OER usage, where various contextual factors influence decision-making, further complicates the interaction between biases and resource selection.
Through the application of machine learning algorithms, this research seeks to develop tools and interventions that can help educators identify and address cognitive biases when selecting and using OER. By leveraging the power of machine learning to analyze large datasets of OER usage patterns and learner outcomes, this study aims to provide evidence-based recommendations for optimizing OER selection and utilization in diverse educational contexts. By reframing the discussion around cognitive biases and leveraging machine learning techniques, this research has the potential to transform the way educators approach the design and implementation of OER in their teaching practice.
Potential References:
- Quantifying cognitive bias in educational researchers
- Effect of cognitive biases on human understanding of rule-based machine learning models
- Machine learning for the educational sciences
- Assessing the impact of cognitive biases in AI project development
- Addressing bias in human cognition and computer systems: when the information age meets a global pandemic
