2026/06/15 by Xiao Li, Changyu Hu, Yue Su +2
Chemistry · #Cheminformatics #Chemistry and Stereochemistry Studies #Cornerstone #Download #Grading (engineering) #History and advancements in chemistry #Invariant (physics) #Parsing #Various Chemistry Research Topics
paper · pdf · doi:10.1021/acs.jchemed.6c00355
published in Journal of Chemical Education (American Chemical Society)
openalex publication_date 2026/06/15 · openalex created_date 2026/06/16 · openalex updated_date 2026/07/31
Abstract 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.