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This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining. It will cover the concepts and techniques that underlie current educational “student success” and “early warning” systems, giving you insight into how learners are categorized as at-risk through automated processes.
You will gain hands-on experience building these kinds of predictive models using the popular (and free) Weka software package. Also, included in this course is a discussion of supervised machine learning techniques, feature selection, model fit, and evaluation of data based on student attributes. Throughout the course, the ethical and administrative considerations of educational predictive models will be addressed.
Overview
Syllabus
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Week 1: Prediction
- Predictive models vs. explanatory models
- The predictive modeling lifecycle
- Predictive models of student success
- Ethical considerations with predictive models
- Overview of the state of the practice in educational predictive models
Week 2: Supervised Learning
- Supervised machine learning techniques, including Decision Trees and Naive Bayes
Week 3: Model Evaluation
- Making predictions
- Model evaluation and comparison
- Practical considerations
