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An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students

  • Education & Training
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Abstract

This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student’s progress to ensure appropriate training.

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Acknowledgments

This research is part of the GEODAS-REQ project (TIN2012-37493-C03-02) financed by both the Spanish Ministry of Economy and Competitiveness and European FEDER funds.

Authors’ contributions

José Luis Fernández Alemán contributed to the following: the conception and design of the study, acquisition of data, analysis and interpretation of data, drafting the paper and approval of the version submitted. Laura López González, Ofelia González Sequeros, made the following contributions to the study: design of the four i-SIDRA tests and their feedback, acquisition of data, analysis and interpretation of data, drafting the paper and approval of the version submitted. Chrisina Jayne and Juan José López Jiménez provided support to integrate the Snap-Drift Neural Network into i-SIDRA, drafted and reviewed the paper, and approved the version submitted. Juan Manuel Carrillo de Gea and Ambrosio Toval reviewed the paper and approved the version submitted.

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Authors and Affiliations

  1. Faculty of Computer Science, Department of Informatics and System, University of Murcia, Murcia, Spain

    José Luis Fernández-Alemán, Juan José López-Jiménez, Juan Manuel Carrillo-de-Gea & Ambrosio Toval

  2. Faculty of Medicine, Department of Human Anatomy, University of Murcia, Murcia, Spain

    Laura López-González & Ofelia González-Sequeros

  3. Robert Gordon University, Aberdeen City, Scotland, UK

    Chrisina Jayne

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  1. José Luis Fernández-Alemán
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Correspondence to José Luis Fernández-Alemán.

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Fernández-Alemán, J.L., López-González, L., González-Sequeros, O. et al. An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students. J Med Syst 40, 85 (2016). https://doi.org/10.1007/s10916-016-0440-6

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