May 1, 2024
3 minute read
Educational Data Mining (EDM) delves into the analysis of educational data with the objective of uncovering useful patterns and actionable insights. It plays a crucial role in understanding the intricacies of teaching and learning processes, offering valuable information for improving educational practices.
Why Study Educational Data Mining?
Whether you're a learner, student, educator, or researcher, EDM can be an invaluable tool for deepening your understanding of education and its impact.
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Find a path to becoming a Educational Data Mining. Learn more at:
OpenCourser.com/topic/sbpkvo/educational
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Educational Data Mining.
This handbook provides a comprehensive overview of EDM, covering the theoretical foundations, methodologies, and applications of EDM. It valuable resource for researchers and practitioners in the field.
Provides a comprehensive overview of EDM and learning analytics, including the data mining process, educational data sources, and techniques for analyzing educational data. It includes case studies and examples to illustrate the application of EDM in practice.
Provides a comprehensive overview of EDM, including the data mining process, educational data sources, and techniques for analyzing educational data. It is written for researchers and practitioners with little or no background in data mining.
Provides a comprehensive introduction to data mining techniques specifically tailored for educational data. It covers both theoretical concepts and practical applications, making it suitable for both students and researchers in the field.
Explores the use of data mining techniques to improve information access in educational settings. It discusses methods for extracting insights from educational data and their applications in areas such as personalized learning and adaptive systems.
Provides a practical guide to using data analytics to improve educational outcomes. Although it does not delve deeply into EDM, it provides a good overview of the broader field of data analytics in education, which can be beneficial for understanding the context and applications of EDM.
This handbook provides a broad overview of data science in education, covering topics such as data collection, analysis, and visualization. Although it does not specifically focus on EDM, it provides valuable insights into data science methods that can be applied to educational data.
Explores the application of data mining techniques to security-related problems. Although it does not specifically focus on EDM, it provides valuable insights into data mining techniques that can be useful for analyzing educational data in the context of security applications, such as detecting plagiarism.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/sbpkvo/educational
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