Supporting Orthographic Recall of Chinese Characters through Self-Generated Compositional Mnemonic Stories: A Mixed-Methods Pilot Study
Aaron Friedrich Kurz
Abstract
Mnemonic studies on Chinese characters have mostly tested provided mnemonics against writing outcomes and self-generated mnemonics against meaning recall; whether writing one's own component-based story helps learners write the character has received little attention. I present Nuome, a tool that shows a character's component decomposition next to an editor in which learners write their own story, with live checks on whether the story covers every component and the character's meaning. In a within-subjects pilot, eight L2 learners each learned four self-chosen characters with the story builder and four with a read-only panel showing the same decomposition, with identical writing practice. Descriptively, partial-credit written-recall scores favoured the story builder by 18.8 percentage points immediately (N = 8) and 17.9 points after two days (N = 7); mean study time per character was 1.69 times as long (n = 6); usability was high (SUS 88.1). Interviews yielded three themes: participants felt that composing a story made them attend to components; knowing the components is not the same as being able to write them; and characters differ in how well they suit a story.
Create a lesson
Related papers
SEAM: Situated Executable Associative Memory for a Co-located Community
Keru Wang, Michael Xu, Ken Perlin et al.
OmniScribble: Mixed-Initiative Authoring of Animated Sketches through Inspectable and Repairable AI Decisions
Hien Trinh, Tan-Hiep To, Trung-Nghia Le
Agentic Path Maps: Exposing an Agent's Traversal Through the Garden of Forking Paths
Madeleine Grunde-McLaughlin, Hafsah Usman, Daniel Mitroshkov et al.
Must AI Spell It Out? Pragmatic Comprehension of Alerts in a Time-Critical Task
Anwesha Das, Vera Demberg, Anna Maria Feit
From Benchmarks to Behaviors: A Framework for Auditing AI Agents
Nick Hagar
Proceedings of the CSCW 2026 Workshop on Women's Health as Cooperative Work: Participatory AI Across Care, Community, and Policy
Sherilyn Francis