LEARNING WITH SMARTPHONES: Case study of learning drug prescribing using the PharmaFrog app
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Abstract
Background: Smartphone-based electronic mobile learning (eML) has a potential to transform and improve teaching worldwide and particularly so in resource-limited settings. To fully realize this potential, there is a need for a validated eML framework.
Goals: To build, implement and test an eML framework applicable to a complex body of knowledge using drug prescribing as a study case.
Execution: Functionalities of leading learning apps were analyzed for relationships with learning theories (LTs). A consensus selection of functionalities related to adaptive, interactive and personalized learning were implemented and tested for learning outcome in the drug prescribing app PharmaFrog.
Outcomes: The resulting learning framework of PharmaFrog has at its core learning activities derived from Cognitivism, followed by Behaviorism and Constructivism. They provide the learner with an interactive, personalized and adaptive learning experience. The specific, implemented elements include chunking, personalization, interactivity, reinforcement, repetitions, case scenarios, adaptive learning, summary, associations, linking concepts, cues, discrimination, feedback and sequenced learning. Altogether, they form the basis of the four learning modes of PharmaFrog. App usage data provided a preliminary, but robust evidence for a learning effect.