Use of semantic technologies for the development of a dynamic trajectories generator in a Semantic Chemistry eLearning platform

TL;DR

This paper demonstrates how mapping ChemgaPedia's static chemistry eLearning content to Linked Data enables the creation of personalized, dynamic learning paths based on user semantic profiles, enhancing educational experience.

Contribution

It introduces a novel approach to link static chemistry content with semantic technologies for personalized eLearning path generation.

Findings

Linked Data mapping enables dynamic path generation.

Personalized learning paths improve user engagement.

Semantic profiles tailor content to individual learners.

Abstract

ChemgaPedia is a multimedia, webbased eLearning service platform that currently contains about 18.000 pages organized in 1.700 chapters covering the complete bachelor studies in chemistry and related topics of chemistry, pharmacy, and life sciences. The eLearning encyclopedia contains some 25.000 media objects and the eLearning platform provides services such as virtual and remote labs for experiments. With up to 350.000 users per month the platform is the most frequently used scientific educational service in the German spoken Internet. In this demo we show the benefit of mapping the static eLearning contents of ChemgaPedia to a Linked Data representation for Semantic Chemistry which allows for generating dynamic eLearning paths tailored to the semantic profiles of the users.