Principles and fundamental concepts of learning analytics; types of and research questions for learning analytics; data collection, management, and analysis including descriptive, visualization, and predictive techniques.
Related topics, including educational data mining, academic analytics, curriculum analytics, school analytics, teacher analytics, and assessment analytics; applications of learning analytics for designing evidence-based learning; ethics and data privacy concerns; trends and issues in learning analytic.
This course will introduce you to the complexity of classroom assessment as a means of supporting and measuring student learning. As such, this course is designed to facilitate your growth as an assessor, evaluator, and communicator of student learning.
The concept of educational data-driven decision making; data-driven organizations; types of educational data and indicators; data governance and ethics for educational institutions; diagnostic data analytic techniques such as drill-down analysis, cluster analysis, correlation analysis, trend analysis, and network analysis; predictive data analytics such as forecasting, and predictive modelling.
Risk assessment and decision making under uncertainty such as Bayesian decision making and simulation-based techniques; evaluation and optimization techniques; emphasizing on applying the analytical techniques to generate and communicate insights that lead to practical implementation and effective results.
This special seminar covers the purpose, importance, and major formats of literature review, as well as its significance in identifying research topics. The lecture also provides tutorials of tools and digital resources to facilitate the literature review process such as reference management software (e.g., Zotero, Endnote), bibliometric analysis tools (e.g., VOSviewer), and artificial intelligence-powered platforms (e.g., Scispace).
This lecture introduces the fundamentals of mixed methods research (MMR), distinguishing it from multi-method research by emphasizing the key feature of integration. We will explore both core and complex designs of MMR, with a particular focus on qualitative-oriented approaches. We also explore the capability of MMR to address research questions in non-linear environments such as the Covid-19 pandemic, which often resist simplistic cause-and-effect analyses and require adaptability to contextual changes.
This series of workshop introduces the measurement framework of classical test theory and item response theory to non-technical audiences. The workshop starts from discussing the fundamental concepts of item property such as item difficulty and discrimination. Test-level property such as reliability or test information is also covered. Finally, this workshop ends on practical notes of using the discussed information for item revision and test design.
This seminar introduces free and open-source tools to facilitate the process of research. The discussed tools help with data collection and management, data analysis, data visualization, reference management, bibliometric analysis and literature mapping, and lastly, artificial intelligence-assisted literature review.