Using text replay tagging to produce detectors of systematic experimentation behavior patterns

Michael A. Sao Pedro, Ryan S.J.D. Baker, Orlando Montalvo, Adam Nakama, Janice D. Gobert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

We present machine-learned models that detect two forms of middle school students' systematic data collection behavior, designing controlled experiments and testing the stated hypothesis, within a virtual phase change inquiry learning environment. To generate these models, we manually coded a proportion of the student activity sequence clips using "text replay tagging" of log files, an extension of the text replay method presented in Baker, Corbett & Wagner (2006). We found that feature sets based on cumulative attributes, attributes computed over all predecessor clips, yielded better detectors of CVScompliant and hypothesis-testing behavior than more local representations of student behavior. Furthermore, our detectors classify behaviors well enough to use them in our learning environment to determine which students require scaffolding on these skills.

Original languageEnglish (US)
Title of host publicationEducational Data Mining 2010 - 3rd International Conference on Educational Data Mining
Pages181-190
Number of pages10
StatePublished - 2010
Externally publishedYes
Event3rd International Conference on Educational Data Mining, EDM 2010 - Pittsburgh, PA, United States
Duration: Jun 11 2010Jun 13 2010

Publication series

NameEducational Data Mining 2010 - 3rd International Conference on Educational Data Mining

Conference

Conference3rd International Conference on Educational Data Mining, EDM 2010
Country/TerritoryUnited States
CityPittsburgh, PA
Period6/11/106/13/10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

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