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Summary

This study introduces new measurement models for adaptive learning systems that incorporate on-demand hints. The best model rewards correct answers with hints less than those without, accounting for individual hint-use tendencies.

Keywords:
adaptive learning systemshintsitem response theoryitem response tree modelsmeasurementmeasurement theorymultidimensional nominal response model

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Area of Science:

  • Educational Technology
  • Psychometrics
  • Artificial Intelligence in Education

Background:

  • Adaptive learning systems monitor learner progress through item-solving aligned with learning goals.
  • Scaffolding and hints are crucial for effective learning, with on-demand hints offering a student-controlled approach.
  • Hint usage can reflect learner ability but may also be influenced by individual characteristics.

Purpose of the Study:

  • To develop and evaluate measurement models for adaptive learning systems incorporating on-demand hints.
  • To account for the dual nature of hint use: as an indicator of ability and as a behavior influenced by individual traits.
  • To compare different modeling strategies for analyzing data from hint-enabled adaptive learning environments.

Main Methods:

  • Developed two measurement models: one based on a scoring rule incorporating response accuracy and hint use, and another using Item Response Tree models to jointly model hint choice and accuracy.
  • Analyzed the properties and implications of each modeling strategy.
  • Applied the models to data from Duolingo, an adaptive language learning system.

Main Results:

  • The scoring-rule-based model proved most effective for the Duolingo data.
  • This model assigns full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for incorrect responses.
  • A second dimension in the model effectively captures individual differences in the propensity to use hints.

Conclusions:

  • The proposed scoring-rule-based model accurately measures ability in adaptive learning systems with on-demand hints.
  • Individual differences in hint-seeking behavior can be effectively modeled alongside response accuracy.
  • These models provide a more nuanced understanding of learner performance in technology-enhanced learning environments.