This Course aims to facilitate acquisition of knowledge and skills necessary to effectively collect, analyze, and interpret educational data, and derive actionable insights for informed decision-making in learning environments.

CLO1. Explain the key concepts and terminology related to learning analytics and data visualization in the context of AI-driven instruction.

CLO2. Discuss how learning analytics and data visualization can be used to identify patterns, trends, and insights from datasets generated by AI-driven instructional platforms.

CLO3. Apply learning analytics and data visualization techniques to optimize the design and delivery of AI-driven instruction, based on the insights gained from the data.

CLO4. Develop a reflective and critical attitude to evaluate AI-driven instruction using learning analytics and data visualization, proposing improvements to enhance learning outcomes.
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Introduction to Learning Analytics: Definition and overview of learning analytics, Importance of data-driven decision-making in education, Ethical considerations and privacy concerns (Understanding). 

Data Sources and Types in Educational Settings: Student information systems and learning management systems, Educational technology platforms and digital learning environments, Multimodal data sources (e.g., sensors, biometrics) (Analyzing).  Data Preprocessing and Cleaning: Data quality assessment and cleaning techniques, Handling missing data and outliers, Feature engineering and selection (Applying). 

Descriptive and Inferential Statistics for Educational Data: Measures of central tendency and dispersion, Hypothesis testing and statistical significance, Correlation and regression analysis (Analyzing). Data Mining and Machine Learning Techniques: Supervised learning algorithms (e.g., classification, regression), unsupervised learning algorithms (e.g., clustering, dimensionality reduction), Model evaluation and validation (Applying). 

Learning Analytics for Personalized Instruction: Adaptive learning systems and intelligent tutoring, Predictive modeling for student performance and retention, Curriculum and content optimization (Evaluating). 

Data Visualization Principles and Tools: Principles of effective data visualization, Visualization tools and libraries (e.g., Tableau, D3.js, Python libraries), Dashboard design and storytelling with data (Creating).  Visualizing Educational Data: Student performance and progress dashboards, Learning behavior and engagement visualizations, Stakeholder-specific dashboards (e.g., instructors, administrators) (Creating). 

Ethical and Privacy Considerations in Learning Analytics: Data privacy and security best practices, Mitigating bias and ensuring fairness in AI systems, Transparency and accountability in data-driven decisions (Evaluating). 

Course Project and Presentations: Developing a learning analytics solution or data visualization dashboard, Presenting and defending the project, Course wrap-up and future directions.