Incorporating an Intelligent Tutoring System into the DiscoverOChem Learning Platform

Abstract

The current work develops intelligent tutoring aspects for the DiscoverOChem learning platform. Intelligent tutoring systems are technology-based learning systems that can adapt the learning experience to better serve individual users. DiscoverOChem (www.discoverochem.com) is a free Internet-based platform for learning undergraduate-level organic chemistry. Data from previous years of students were used to analyze how well individual students performed on various pages of the platform. Correlations between pairs of pages were analyzed. Predictive models, which use a user's results on previous pages to predict that user's likely performance on upcoming pages, were developed and evaluated. The most successful set of models, which utilizes random forests of one-branch decision trees, was incorporated into the DiscoverOChem platform as a recommender system. This system helps individual users to identify pages that are likely to challenge them and provides targeted recommendations about which previous pages to review in order to help them become better prepared to succeed on the upcoming page. We anticipate that learners will benefit from this new individualization of their learning experiences. We also anticipate that the general 6-step framework that was used to develop this system will be broadly useful for creating intelligent learning platforms for other subjects as well.

An Organic Chemistry Activity to Improve Student Understanding of Molecules as 3-Dimensional Objects

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