Potential Abstract: Genetic algorithms have shown promise in various domains, including optimization problems and pattern recognition. However, their potential in supporting collective learning within online educational communities remains largely unexplored. This study aims to investigate the effectiveness of utilizing genetic algorithms in promoting collaborative learning on Slack channels and the role of distributed hashing in facilitating knowledge exchange. Drawing on a deconstructionist perspective, this research seeks to unravel the underlying mechanisms through which genetic algorithms can enhance collective learning and foster community engagement.
The research will employ a mixed-methods approach, combining quantitative data analysis and qualitative content analysis. To collect data, a controlled experiment will be conducted involving a sample of students from diverse educational backgrounds. The participants will be assigned to experimental and control groups, with the experimental group engaging in collaborative problem-solving activities facilitated by genetic algorithms and distributed hashing techniques on Slack channels. The control group will engage in similar activities, but without the integration of genetic algorithms. Pre- and post-assessments will be administered to measure individual learning outcomes, while engagement metrics (e.g., participation frequency, quality of contributions) and social network analysis will be employed to capture collective learning and community dynamics.
The analysis will involve comparing the learning outcomes and engagement metrics between the experimental and control groups. Additionally, the qualitative data collected from transcripts and content analysis will provide insights into the underlying mechanisms driving the observed effects. The findings will shed light on the potential benefits and challenges associated with incorporating genetic algorithms and distributed hashing techniques in educational environments, particularly on online platforms like Slack.
Potential References:
- Toward fast niching evolutionary algorithms: A locality sensitive hashing-based approach
- Vehicular blockchain-based collective learning for connected and autonomous vehicles
- DSGA: a distributed segment-based genetic algorithm for multi-objective outsourced database partitioning
- Evolutionary design of molecules based on deep learning and a genetic algorithm
- A genetic algorithm analysis towards optimization solutions
