SHOWCASE: Do students who spend more time in the course earn higher grades?

Courses in Learning Analytics

Instructor: Dr. Son T. H. Pham Nha Viet Institute, Massachusetts

Course Overview: This comprehensive course is designed for educators, administrators, policy makers, and education practitioners seeking to harness data-driven insights to improve educational outcomes. The curriculum covers foundational to advanced topics in learning analytics, emphasizing practical applications and ethical considerations in education.

Core Topics:

Data Wrangling & Exploratory Analysis: Introduction to data preparation, visualization, and basic modeling using tools such as R, Python, GitHub, APIs, and modern data-intensive research workflows.
Supervised Learning: Techniques including feature engineering, model tuning, and evaluation metrics for predictive modeling.
Unsupervised Methods: Clustering, factor analysis, and knowledge structure exploration to identify hidden patterns.
Relationship Mining: Methods for correlation analysis, association and sequential rule mining, and evaluation of association metrics.
Knowledge Tracing: Study of student learning models including Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA/LKT), and Deep Knowledge Tracing (DKT).
Social Network Analysis: Measurement of network properties, identification of key actors and groups, and network modeling approaches.
Text Mining: Techniques such as topic modeling, text classification, and epistemic network analysis for educational text data.
Emerging Technologies: Exploration of large language models (LLMs) and knowledge graphs in educational contexts.

Key Features:

Strong focus on reproducible research and open science principles.
Critical examination of ethical and legal issues, including student privacy, data ethics, and algorithmic bias.
Application-driven insights for STEM education, covering intelligent tutoring systems, adaptive learning environments, curriculum design, and early identification of at-risk students.
Preparation for collaborative work through research-practice partnerships with educational organizations at local, district, and state levels.

Whether you’re new to learning analytics or looking to deepen your expertise, our curriculum can be customized to align with your specific goals and context.

Contact us to access free self-learning materials or request a personalized class tailored to your organization’s unique needs.

Email: Dr. Son T. H. Pham at phamson[at]nhaviet.org