Using learning analytics to assess students' behavior in open-ended programming tasks

  • Authors:

  • Paulo Blikstein

  • Affiliations:

  • Stanford University School of Education, Galvez Mall, CERAS, Stanford, CA

  • Venue:
  • Proceedings of the 1st International Conference on Learning Analytics and Knowledge
  • Year:
  • 2011

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Abstract

There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students