Educational assessment requires understanding student problem-solving processes, not just final answers. Current AI-driven analytics focus on static outcomes, missing valuable insights from temporal dynamics. We present Explain-from-Stroke, a practical framework that captures invisible learning processes by integrating handwriting dynamics with vision-language models. Our approach extracts temporal features—writing speed, pauses, and revisions—providing supplementary context for generating meaningful insights into hidden aspects of student reasoning. Deployed with real classroom data from a Japanese secondary school, our system demonstrates 18.2\% improvement in cognitive depth analysis over static approaches. This work provides educators with accessible process-oriented analysis that reveals invisible learning processes using standard tablet technology.