Learning Analytics and Educational Data Mining

Foundations of Learning Analytics and EDM: Provide an overview of the foundational concepts, theories, and methods in learning analytics and educational data mining (EDM), including data collection, preprocessing, analysis, and interpretation techniques. Data-driven Decision Making: Explore how learning analytics and EDM can inform data-driven decision-making processes in educational settings, including student assessment, curriculum design, instructional strategies, and personalized learning interventions. Predictive Modeling and Student Success: Discuss predictive modeling techniques used in learning analytics and EDM to identify patterns and trends in educational data, predict student outcomes (e.g., performance, retention), and provide early interventions to support student success. Ethical and Privacy Considerations: Address ethical and privacy considerations in the collection, analysis, and use of educational data for learning analytics and EDM, including issues related to data privacy, informed consent, data ownership, bias, and fairness. Case Studies and Applications: Illustrate key concepts and methods with real-world case studies and examples of learning analytics and EDM applications in diverse educational contexts, such as K-12 education, higher education, corporate training, and online learning environments.