Enhancing Experiential Knowledge Acquisition in MOOCs using Distributed Hashing: A Feminist Perspective

Potential Abstract:
Experiential learning has long been recognized as a powerful pedagogical approach to foster deep understanding and practical skills development. Massive Open Online Courses (MOOCs) have grown exponentially in popularity due to their accessibility and potential for knowledge dissemination. However, MOOCs often struggle to provide effective experiential learning opportunities, as they typically rely on traditional didactic instructional methods. This research article proposes a novel approach to enhance experiential knowledge acquisition in MOOCs by leveraging distributed hashing techniques from artificial intelligence and adopting a feminist perspective.

This study introduces a distributed hashing framework that allows MOOC participants to collaboratively generate, share, and retrieve experiential knowledge resources. By employing distributed hashing, the system enables participants to store and access knowledge in a decentralized manner, minimizing the reliance on a centralized platform. This approach not only promotes experiential learning through active engagement and interaction but also facilitates the creation of a more inclusive learning environment where multiple perspectives and forms of knowledge are valued.

Drawing on feminist theories of education, this research article argues for the importance of challenging traditional hierarchies and power dynamics in knowledge production. By implementing a distributed hashing approach, MOOCs can empower learners to actively contribute to the creation and dissemination of knowledge, promoting a more democratic and egalitarian learning environment. This approach also provides opportunities for individuals who have been historically marginalized or underrepresented in academic spaces to share their experiential knowledge and contribute to the collective learning experience.

Potential References: