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Area of Science:

  • Technology-enhanced learning
  • Human-computer interaction
  • Educational technology

Background:

  • Sensors enable multimodal data collection for feedback in learning applications.
  • Handwriting learning can be challenging due to tediousness and overwhelming feedback.
  • A calligraphy trainer application uses expert models for learner feedback.

Purpose of the Study:

  • To evaluate the mental effort associated with different feedback types from a calligraphy trainer application.
  • To assess the impact of multimodal feedback on learner cognitive load.
  • To inform the design of feedback systems in educational technology.

Main Methods:

  • Pilot study with ten Ph.D. students in technology-enhanced learning.
  • Participants learned Devanagari script characters using the calligraphy trainer.
  • Comparison of mental effort between a control group and a treatment group receiving simultaneous feedback.

Main Results:

  • The treatment group experienced higher mental effort when all feedback types were presented simultaneously.
  • Mental effort levels were comparable to the control group when individual feedback types were used.
  • The calligraphy trainer application's feedback did not impose excessive mental load.

Conclusions:

  • Simultaneous multimodal feedback can increase cognitive load in handwriting learning.
  • Individual feedback types are manageable and do not significantly increase mental effort.
  • Design principles for multimodal feedback in educational applications can be derived from this study.