Demonstrates improved structure-drawing proficiency in undergraduate students, suggesting a scalable e-learning solution.
High Resolution Image Download MS PowerPoint Slide This report introduces StereoLearn, a web-based platform for developing representational competency. While the ability to fluently translate between various styles of 2D diagrams and 3D models for molecular structure is a cornerstone of organic chemistry, assessing student-drawn structures at scale poses a significant challenge due to the manual overhead of grading. StereoLearn ( https://en.stereolearn.chemcognitive.online/ ) integrates interactive e-learning modules with a robust, automated grading system for dash-wedge drawings, Fischer projections, and Haworth projections. By utilizing a hybrid grading approach that combines RDKit-based canonical SMILES with custom rule-based algorithms to parse Molfiles, the platform ensures rotationally invariant and convention-accurate grading. Implemented in an undergraduate Organic Chemistry course, the platform demonstrated efficacy. Statistical analysis indicated an improvement in students’ structure-drawing proficiency, highlighting StereoLearn as a scalable, evidence-based solution for enhancing representational competency while alleviating the administrative burden of manual grading in high-enrollment courses.
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Li et al. (2026) studied this question.
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