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Implementation of a soft grading system for chemistry in a Moodle plugin
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2022-10-26 , DOI: 10.1186/s13321-022-00645-0
Louis Plyer 1 , Gilles Marcou 2 , Céline Perves 3 , Rachel Schurhammer 1 , Alexandre Varnek 2
Affiliation  

We report a novel approach for grading chemical structure drawings for remote teaching, integrated into the Moodle platform. Typically, existing online platforms use a binary grading system, which often fails to give a nuanced evaluation of the answers given by the students. Therefore, such platforms are unevenly adapted to different disciplines. This is particularly true in the case of chemical structures, where most questions simply cannot be evaluated on a true/false basis. Specifically, a strict comparison of candidate and expected chemical structures is not sufficient when some tolerance is deemed acceptable. To overcome this limitation, we have developed a grading workflow based on the pairwise similarity score of two considered chemical structures. This workflow is implemented as a Moodle plugin, using the Chemdoodle engine for drawing structures and communicating with a REST server to compute the similarity score using molecular descriptors. The plugin ( https://github.com/Laboratoire-de-Chemoinformatique/moodle-qtype_molsimilarity ) is easily adaptable to any academic user; both embedding and similarity measures can be configured.

中文翻译:

在 Moodle 插件中实现化学软分级系统

我们报告了一种用于远程教学的化学结构图分级的新方法,该方法集成到 Moodle 平台中。通常,现有的在线平台使用二进制评分系统,通常无法对学生给出的答案进行细致入微的评估。因此,这样的平台不均衡地适应不同的学科。在化学结构的情况下尤其如此,因为大多数问题根本无法在真/假的基础上进行评估。具体来说,当某些公差被认为是可接受的时,对候选化学结构和预期化学结构进行严格比较是不够的。为了克服这一限制,我们开发了一种基于两个考虑的化学结构的成对相似度得分的分级工作流程。这个工作流程是作为一个 Moodle 插件实现的,使用 Chemdoodle 引擎绘制结构并与 REST 服务器通信以使用分子描述符计算相似度分数。该插件(https://github.com/Laboratoire-de-Chemoinformatique/moodle-qtype_molsimilarity)很容易适应任何学术用户;嵌入和相似性度量都可以配置。
更新日期:2022-10-26
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