• Length:
    8 Weeks
  • Effort:
    6–12 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Advanced
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.

About this course

Skip About this course

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.

What you'll learn

Skip What you'll learn
  • Key methods for educational data mining
  • How to apply methods using Python's built-in machine learning library, scikit-learn
  • How to apply methods using standard tools such as RapidMiner
  • How to use methods to answer practical educational questions
Week 1: Prediction Modeling
·       Regressors
·       Classifiers

Week 2: Model Goodness and Validation
·       Detector Confidence
·       Diagnostic Metrics
·       Cross-Validation and Over-Fitting

Week 3: Behavior Detection and Feature Engineering
·       Ground Truth for Behavior Detection
·       Data Synchronization and Grain Size
·       Feature Engineering
·       Knowledge Engineering

Week 4: Knowledge Inference
·       Knowledge Inference
·       Bayesian Knowledge Tracing (BKT)
·       Performance Factor Analysis
·       Item Response Theory

Week 5: Relationship Mining
·       Correlation Mining
·       Causal Mining
·       Association Rule Mining
·       Sequential Pattern Mining
·       Network Analysis

Week 6: Visualization
·       Learning Curves
·       Moment by Moment Learning Graphs
·       Scatter Plots
·       State Space Diagrams
·       Other Awesome EDM Visualizations

Week 7: Structure Discovery
·       Clustering
·       Validation and Selection
·       Factor Analysis
·       Knowledge Inference Structures

Week 8: Discovery with Models
·       Discovery with Models
·       Text Mining
·       Hidden Markov Models

Meet your instructors

Ryan Baker
Associate Professor
University of Pennsylvania

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