Can You Ink While You Blink? Assessing Mental Effort in a Sensor-Based Calligraphy Trainer

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.