Generation of reusable learning objects from digital medical collections: An analysis based on the MASMDOA framework
This work addresses the need for tools to transfer knowledge from medical collections into accessible learning objects for medical students and practitioners, but it is incremental as it focuses on qualitative analysis of an existing framework.
The paper analyzes the process of generating reusable learning objects (RLOs) from digital medical collections using the Clavy tool, which retrieves and reconfigures data into multimedia structures adaptable for various healthcare instructional scenarios and exports them via educational standards to enhance reusability.
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.