Delivering and Assessing Learning Material through Gquest: A Case Study on Patient Education

Delivering and Assessing Learning Material through Gquest: A Case Study on Patient Education

Giordano Lanzola (Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy), Germana Ginardi (Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy), Paola Russo (Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy) and Silvana Quaglini (Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy)
Copyright: © 2014 |Pages: 17
DOI: 10.4018/ijmbl.2014070104
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Abstract

Gquest is a mobile platform for administering interviewing and learning material. It relies on a model that supports adaptivity in the dialog with its users and enforces consistency rules to constrain their input. Gquest downloads its modules over the air making them available to the users, then a synchronization engine collects any input provided and sends it to a server for evaluation purposes. Thus, Gquest supports learning about user behaviors or preferences by administering interviewing material and collecting answers. However, by reversing the conversation paradigm it also supports the delivery of learning material. In this paper we illustrate a case study in which both paradigms have been exploited. First we implemented a guide for training patients on a rare disease called amyloidois, and second we integrated a plain questionnaire at the end of that guide to assess the quality of learning perceived by the user.
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Introduction

The widespread diffusion of mobile devices has determined an increasing emphasis on Mobile Learning (m-Learning) that is defined as “learning delivered or supported solely or mainly by handheld and mobile technologies such as personal digital assistants, smart-phones or wireless laptop PCs” (Traxler, 2007). Thus m-Learning proposes a new model for education which is independent of time and environment (Holzinger, Nischelwitzer & Meisenberger, 2005; Peters, 2007; Breitwieser, Terbu, Holzinger, Brunner, Lindstaedt & Müller-Putz, 2013).

Medical education and clinical practice, due to the intensive use of knowledge they exert (Schreiber, van Heijst, Lanzola & Stefanelli, 1993) and the different forms involved (Lanzola & Stefanelli, 1993), represent two possible areas for the exploitation of m-Learning (Bloice, Simonic & Holzinger, 2013). Several studies point out, for example, that there is a high percentage of outpatients not taking prescribed medications (Vermeire, Hearnshaw, Van Royen & Denekens, 2003; Di Matteo, 2004) and in the majority of cases the cause is ascribed to their low literacy (Gazmararian, Kripalani, Miller, Echt, Ren & Rask, 2006). Thus, an important area of m-Learning addresses patient literacy and clinical practice, and applications are starting to appear both for patients and doctors. On the patient’s side they are often used to fill in diagnostic surveys involving outpatients who spend most of their time away from home, so that mobile devices prove to be an optimal solution (Holzinger, Kosec, Schwantzer, Debevc, Hofmann-Wellenhof & Frühauf, 2011). On the clinical staff side, apart from applications used to access patient records and laboratory reports, mobile appliances are beginning to be adopted as a means of teaching, learning and practicing. Some applications see mobile devices as multimedia textbooks which can be used to improve resident education and patient care while others see tablets being used to visualize and plan clinical activities such as surgeries (Sadri, Murphy & Odili, 2012; Davis, Garcia, Wyckoff, Alsafran, Graygo, Withum, & Schulman, 2012). Furthermore, the literature already reports that the use of specific applications by resident doctors helps them in learning faster and in improving both the perceived and the actual effectiveness of their actions (Patel, Chapman, Luo, Woodruff & Arora, 2012).

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