Implementation of a soft grading system for chemistry in a Moodle plugin

Area of Science:

• Chemistry
• Education Technology
• Cheminformatics

Background:

• Remote teaching requires effective assessment tools for complex subjects like chemistry.
• Existing online platforms often use binary grading, which is insufficient for evaluating chemical structures.
• Nuanced evaluation is crucial for disciplines where precise answers allow for some variation.

Purpose of the Study:

• To develop a novel approach for grading chemical structure drawings in remote teaching environments.
• To overcome the limitations of binary grading systems in evaluating chemical structures.
• To create a flexible and adaptable Moodle plugin for chemical structure assessment.

Main Methods:

• Developed a Moodle plugin integrating a novel grading workflow.
• Utilized the Chemdoodle engine for chemical structure drawing and manipulation.
• Implemented a REST server to compute pairwise similarity scores using molecular descriptors.

Main Results:

• A functional Moodle plugin for grading chemical structures based on similarity scores was created.
• The system allows for nuanced evaluation beyond binary true/false grading.
• The plugin is configurable for different academic needs and similarity measures.

Conclusions:

• The developed Moodle plugin offers an improved method for assessing chemical structures in remote education.
• This approach provides a more accurate and flexible grading system compared to traditional binary methods.
• The tool is adaptable for various academic users and customizable for specific grading requirements.