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Feature Engineering for Improving Learning Environments

Every model used to predict a future outcome depends upon the quality of features used. This course focuses on developing better features to create better models.
Feature Engineering for Improving Learning Environments
This course is archived
Future dates to be announced
Estimated 3 weeks
5–7 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

About this course

Skip About this course

How can data-intensive research methods be used to create more equitable and effective learning environments? In this course, you will learn how data from digital learning environments and administrative data systems can be used to help better understand relevant learning environments, identify students in need of support, and assess changes made to learning environments.

This course pays particular attention to the ways in which researchers and data scientists can transform raw data into features (i.e., variables or predictors) used in various machine learning algorithms. We will provide strategies for using prior research, knowledge from practice, and logic to create features, as well as build and evaluate machine learning models. The process of building features will be discussed within a broader data-intensive research workflow using R.

At a glance

  • Institution: UTArlingtonX
  • Subject: Data Analysis & Statistics
  • Level: Intermediate
  • Prerequisites:
    We highly recommend that you take the previous course in this series before beginning this course:
    Predictive Modeling in Learning Analytics

    This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.
  • Language: English
  • Video Transcript: English

What you'll learn

Skip What you'll learn
  • How to transform and visualize data using R
  • How to apply selected machine learning algorithms (e.g., logistic regression and decision trees) to regression and classification tasks in R
  • Strategies for applying data-intensive research workflows for feature engineering and model building

Week 1: Finding features
Introduction to setting up a feature engineering workflow, which includes identifying problems of practice, relevant research, and brainstorming potential features.

Week 2: Data wrangling and visualization
Introduction to data wrangling, data visualization techniques, and structure discovery algorithms. Integrating theory, knowledge from practice, logic, and contextual factors into feature engineering will also be discussed.

Week 3: Modeling features
Introduction to using features within selected machine learning algorithms (e.g. logistic regression and decision tree) and the tradeoffs between interpretability and prediction.

About the instructors

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