Potential Abstract:
Recent advancements in technology have led to the emergence of massive open online courses (MOOCs) as a viable platform for educational delivery. In MOOCs, learners engage with course content and interact with peers through online platforms, allowing for the creation of large-scale behavioral networks. These networks capture valuable data about learners’ behaviors, interactions, and learning patterns, which can be leveraged to personalize and optimize the learning experience. However, the potential of these behavioral networks in microlearning environments remains largely unexplored.
This study aims to investigate the role of behavioral networks in microlearning environments and explore how artificially intelligent technologies can be leveraged to enhance learners’ experiences in MOOCs. By analyzing behavioral data from MOOCs, we seek to identify patterns and relationships among learners’ behaviors, social interactions, and learning outcomes. We will employ techniques from social network analysis and machine learning to model the complex interactions within the behavioral networks, and subsequently develop predictive models that can inform personalized interventions and recommendations for learners.
The research will adopt a mixed-methods approach, combining quantitative analyses of large-scale behavioral data with qualitative interviews and observations. Through these methods, we aim to gain a comprehensive understanding of the factors influencing learning in microlearning environments. Moreover, the study will explore the impact of various artificially intelligent interventions, such as adaptive content recommendations and personalized feedback, on learners’ engagement and learning outcomes.
The findings of this research will contribute to the growing body of knowledge on the intersection of artificial intelligence and education. By shedding light on the role of behavioral networks in microlearning environments, the study will provide valuable insights for educational practitioners, policymakers, and technology developers. Ultimately, the research aims to inform the design and implementation of MOOCs that effectively leverage artificially intelligent technologies to support personalized learning experiences.
Potential References:
- Microlearning in the Workplace of the Future
- An Approach to Adaptive Microlearning in Higher Education
- Towards a Method to Predict the Evaluation Result in a Microlearning Context
- Computational Intelligence for the Micro Learning
- Proposal of Artificial Intelligence Educational Model Using Active Learning in a Virtual Learning Environment
