Student Work


(2021-22) CS IQP with Dr. Neil Heffernan: A Review of Educational Data Mining Features Public

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This article reviews publications related to the use of student data as features in educational systems. As education becomes more and more digital, systems are able to collect and analyze different types of student data. These features can be used to improve the teaching materials or students’ learning experiences. This review investigates methods of collecting, processing, and using student data. This review covers the knowledge tracing problem and method and its extensions and alternatives, as well as systems measuring students’ affect and behavior such as wheel spinning, gaming the system, and stopout. Topics relating to problem features and content are reviewed, as well as students’ written responses. This review also covers research about the quality and learning outcome of feedback types as well as demographics and socioeconomic status and their impact on students’ learning. In each section, this review describes methods from publications using each type of data or feature.

  • This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
  • E-project-012522-113544
  • 47041
  • 2022
UN Sustainable Development Goals
Date created
  • 2022-01-25
Resource type
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