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CalliSense: An Interactive Educational Tool for Process-based Learning in Chinese Calligraphy

Xinya Gong, Wenhui Tao, Yuxin Ma

TL;DR

CalliSense presents a low-cost, process-based learning tool for Chinese calligraphy that captures brush posture, finger pressure, and writing dynamics with smartphones and sensors, aligning data to strokes and visualizing results through glyph, rhythm, and stroke analyses. A formative study identifies barriers in brushwork learning, guiding five design considerations, which drive a data-collection and visualization pipeline implemented in CalliSense. A user study with 4 teachers and 12 students demonstrates improved understanding and teaching efficiency, evidenced by higher knowledge mastery and usability scores (SUS ≈ 78) and favorable NASA TLX ratings. The work advances ICH preservation by enabling detailed process visualization, supports both classroom and potential remote/self-guided learning, and lays groundwork for crowdsourced data collection and cross-disciplinary applications, with future work on real-time feedback, larger-character capture, and integration of LLM-based interpretation.

Abstract

Process-based learning is crucial for the transmission of intangible cultural heritage, especially in complex arts like Chinese calligraphy, where mastering techniques cannot be achieved by merely observing the final work. To explore the challenges faced in calligraphy heritage transmission, we conducted semi-structured interviews (N=8) as a formative study. Our findings indicate that the lack of calligraphy instructors and tools makes it difficult for students to master brush techniques, and teachers struggle to convey the intricate details and rhythm of brushwork. To address this, we collaborated with calligraphy instructors to develop an educational tool that integrates writing process capture and visualization, showcasing the writing rhythm, hand force, and brush posture. Through empirical studies conducted in multiple teaching workshops, we evaluated the system's effectiveness with teachers (N=4) and students (N=12). The results show that the tool significantly enhances teaching efficiency and aids learners in better understanding brush techniques.

CalliSense: An Interactive Educational Tool for Process-based Learning in Chinese Calligraphy

TL;DR

CalliSense presents a low-cost, process-based learning tool for Chinese calligraphy that captures brush posture, finger pressure, and writing dynamics with smartphones and sensors, aligning data to strokes and visualizing results through glyph, rhythm, and stroke analyses. A formative study identifies barriers in brushwork learning, guiding five design considerations, which drive a data-collection and visualization pipeline implemented in CalliSense. A user study with 4 teachers and 12 students demonstrates improved understanding and teaching efficiency, evidenced by higher knowledge mastery and usability scores (SUS ≈ 78) and favorable NASA TLX ratings. The work advances ICH preservation by enabling detailed process visualization, supports both classroom and potential remote/self-guided learning, and lays groundwork for crowdsourced data collection and cross-disciplinary applications, with future work on real-time feedback, larger-character capture, and integration of LLM-based interpretation.

Abstract

Process-based learning is crucial for the transmission of intangible cultural heritage, especially in complex arts like Chinese calligraphy, where mastering techniques cannot be achieved by merely observing the final work. To explore the challenges faced in calligraphy heritage transmission, we conducted semi-structured interviews (N=8) as a formative study. Our findings indicate that the lack of calligraphy instructors and tools makes it difficult for students to master brush techniques, and teachers struggle to convey the intricate details and rhythm of brushwork. To address this, we collaborated with calligraphy instructors to develop an educational tool that integrates writing process capture and visualization, showcasing the writing rhythm, hand force, and brush posture. Through empirical studies conducted in multiple teaching workshops, we evaluated the system's effectiveness with teachers (N=4) and students (N=12). The results show that the tool significantly enhances teaching efficiency and aids learners in better understanding brush techniques.

Paper Structure

This paper contains 45 sections, 28 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motion Capture and Analysis in Intangible Cultural Heritage
  4. Visual Feedback Through Motion Analysis in Kinesthetic Learning
  5. Formative Study
  6. Semi-structured Interviews
  7. Analysis and Results
  8. The Lack of Awareness in Brushwork
  9. Difficult-to-Detect Brushwork Techniques
  10. Forgotten Writing Process:
  11. Design Consideration
  12. Data Collection and Processing
  13. Image Data Collection
  14. Equipment Overview
  15. Image Preprocessing
  16. ...and 30 more sections

Figures (28)

  • Figure 1: Reproduction of the famous Chinese stele inscription Lantingji Xu from Wikipedia
  • Figure 2: Overview of the work: The entire process from data collection and processing to visualization
  • Figure 3: The data collection setup includes pressure sensors, inertial sensors, and cameras. The sensors are connected via Arduino to capture data, which is then synchronized and processed on the computer.
  • Figure 4: Capture the brushstoke process using two smartphones from different angles: Device A and Device B
  • Figure 5: Stroke Images Before and After Correction
  • ...and 23 more figures