Generation of reusable learning objects from digital medical collections: An analysis based on the MASMDOA framework

TL;DR

This paper analyzes the process of creating reusable learning objects from digital medical collections using the Clavy tool, emphasizing its ability to adapt, standardize, and export educational content for diverse healthcare learning scenarios.

Contribution

It introduces a qualitative analysis of Clavy's effectiveness in generating adaptable, standardized, and reusable medical learning objects from digital collections.

Findings

Clavy effectively retrieves and reconfigures medical data into multimedia learning objects.

Generated learning objects are adaptable to various healthcare educational scenarios.

Clavy supports exporting learning objects according to educational standards.

Abstract

Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing from a qualitative point of view the process of generating reusable learning objects (RLOs) followed by Clavy, a tool that can be used to retrieve data from multiple medical knowledge sources and reconfigure such sources in diverse multimedia-based structures and organizations. From these organizations, Clavy is able to generate learning objects which can be adapted to various instructional healthcare scenarios with several types of user profiles and distinct learning requirements. Moreover, Clavy provides the capability of exporting these learning objects through educational standard specifications, which improves their reusability features. The analysis insights highlight the importance of having a tool able to transfer knowledge from the available digital medical collections to learning objects that can be easily accessed by medical students and healthcare practitioners through the most popular e-learning platforms.