Generalizing and Extending a Predictive Model for Standardized Test Scores Based On Cognitive Tutor Interactions

نویسندگان

  • Ambarish Joshi
  • Stephen Fancsali
  • Steven Ritter
  • Tristan Nixon
  • Susan R. Berman
چکیده

Recent work demonstrates that process data from intelligent tutoring systems (ITSs) can be used to predict student outcomes on high-stakes, standardized tests. Such models are important if ITSs are to be used for formative assessment and as replacements for external assessments. Recent work used various measures of learning efficiency and performance from problem-level, aggregate data from Carnegie Learning’s Cognitive Tutor to predict standardized test scores on the state of Virginia’s Standards of Learning exam. We generalize this model to a different school district, state, and standardized test and examine extending the model using finer-grained data.

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تاریخ انتشار 2014