Potential Abstract:
This research article explores the potential of leveraging educational data mining and artificial intelligence to uncover context-laden stories within the market of education. As technology continues to advance, the availability and accessibility of educational data have increased significantly. However, the true value of this data lies not in its mere collection, but in its interpretation and application to improve teaching and learning experiences. By employing innovative data mining techniques and artificial intelligence algorithms, educators and researchers can extract meaningful insights from vast amounts of educational data and utilize them to inform instructional practices and policies.
The conceptual framework of this study is grounded in the belief that context plays a critical role in understanding and addressing educational challenges. Context-laden stories encompass the multifaceted factors that shape educational experiences, such as sociocultural backgrounds, learning environments, and individual characteristics. By examining and analyzing these stories, educational stakeholders can gain a deeper understanding of the complex dynamics that influence student outcomes.
Educational data mining serves as the foundation for this research, allowing for the systematic extraction of patterns, relationships, and trends from educational data. Machine learning algorithms, in conjunction with natural language processing techniques, enable the identification and analysis of context-laden stories embedded within the data. This approach can uncover both quantitative and qualitative insights, facilitating a holistic understanding of the multifaceted educational landscape.
The application of artificial intelligence further enhances the analysis of context-laden stories. Through the integration of semantic analysis, sentiment analysis, and topic modeling, the underlying emotions, perspectives, and themes within educational data can be identified and interpreted. This advanced analytical framework empowers educators and policymakers to make data-driven decisions, design targeted interventions, and tailor educational strategies to meet the diverse needs of students.
Potential References:
- A COMPUTATIONAL ONTOLOGICAL MODEL FOR MACHINE-UNDERSTANDABLE DATA IN ARTIFICIAL INTELLIGENCE
- Artificial intelligence/machine learning in marketing
- MoodleREC: A recommendation system for creating courses using the moodle e-learning platform
- Harvesting a New Synergy: Exploring ‘Smart and Green’Hotels within the Context of the Irish Hospitality Industry
- Smart oceans governance: Reconfiguring capitalist, colonial, and environmental relations
