Potential Abstract:
This research article investigates the use of cloud operations (cloud ops) and directed acyclic graphs (DAGs) to explore and analyze online learners’ narratives. As more educational institutions move towards online learning platforms, it becomes crucial to understand the narratives that emerge in these digital spaces. Narratives provide insights into learners’ experiences, challenges, and successes, thus facilitating personalized and targeted support.
Traditional methods of analyzing narratives often rely on manual coding and analysis, which can be time-consuming and subjective. Leveraging the power of cloud ops, this study explores an automated approach to acquiring and analyzing narratives from online learning platforms. By utilizing the scalability and flexibility of cloud computing, researchers can efficiently gather and process vast amounts of data, providing a comprehensive understanding of learners’ narratives.
Furthermore, this study employs directed acyclic graphs (DAGs) as a visual representation of the narrative patterns within the collected data. DAGs enable researchers to identify and analyze the relationships between different narrative elements, such as learner goals, challenges faced, and support received. This approach allows for a holistic and interconnected understanding of learners’ narratives, shedding light on the complex dynamics at play in online learning environments.
The research methodology involves collecting data from an online learning platform through an application programming interface (API). The collected data includes learner profiles, course materials, discussion forum posts, and assessment results. Using cloud ops, the data is processed and analyzed, and relevant narrative patterns are identified. DAGs are then constructed to visualize the relationships between different narrative elements, facilitating a comprehensive exploration of learners’ experiences.
By uncovering and analyzing online learners’ narratives, this research aims to provide insights into the challenges they face, the support they require, and the strategies they employ to navigate online learning environments. The findings will inform the development of targeted interventions and personalized support systems to enhance learners’ experiences and outcomes in online education.
Potential References:
- Tutorial on directed acyclic graphs
- Telling stories: Enumerating maximal directed acyclic graphs with a constrained set of sources and targets
- Causal directed acyclic graphs
- Multiclass SVM active learning algorithm based on decision directed acyclic graph and one versus one
- Using directed acyclic graphs in epidemiological research in psychosis: an analysis of the role of bullying in psychosis
