Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Education & Training
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Hohlfelder, B., Stashek, C., Anger, K., and Szumita, P., Utilization of a Pharmacy Clinical Surveillance System for Pharmacist Alerting and Communication at a Tertiary Academic Medical Center. J. Med. Syst. 40(1):1–7, 2015.

    Google Scholar 

  2. Martín, D., Alcarria, R., Sánchez-Picot, Á., and Robles, T., An ambient intelligence framework for end-user service provisioning in a hospital pharmacy: A case study. J. Med. Syst. 39(10):1–10, 2015.

    Article  Google Scholar 

  3. Satyanarayanajois, S. D., Active-learning exercises to teach drug-receptor interactions in a medicinal chemistry course. Am. J. Pharm. Educ. 74(8):147, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stewart, P. D. W., Brown, S. D., Clavier, C. W., and Wyatt, J., Active-learning processes used in US pharmacy education. Am. J. Pharm. Educ. 75(4):68, 2011.

    Article  PubMed  Google Scholar 

  5. Prince, M., Does active learning work? A review of the research. J. Eng. Educ. 93(3):223–231, 2004. doi:10.1002/j.2168-9830.2004.tb00809.x.

    Article  Google Scholar 

  6. Blasco-Arcas, L., Buil, I., Hernández-Ortega, B., and Sese, F. J., Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Comput. Educ. 62:102–110, 2013. doi:10.1016/j.compedu.2012.10.019.

    Article  Google Scholar 

  7. Cain, J., and Robinson, E., A primer on audience response systems: Current applications and future considerations. Am. J. Pharm. Educ. 72(4):77, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bruff D (2009) Teaching with classroom response systems: creating active learning environments. Jossey-Bass. doi:citeulike-article-id:9759093

  9. Schick, P., Abramson, S., and Burke, J., Audience response technology: Under-appreciated value of post hoc analysis. Med. Educ. 45(11):1157–1158, 2011. doi:10.1111/j.1365-2923.2011.04084.x.

    Article  PubMed  Google Scholar 

  10. Latessa, R., and Mouw, D., Use of an audience response system to augment interactive learning. Fam. Med. 37(1):12–14, 2005.

    PubMed  Google Scholar 

  11. Pradhan, A., Sparano, D., and Ananth, C. V., The influence of an audience response system on knowledge retention: An application to resident education. Am. J. Obstet. Gynecol. 193(5):1827–1830, 2005. doi:10.1016/j.ajog.2005.07.075.

    Article  PubMed  Google Scholar 

  12. Gauci, S. A., Dantas, A. M., Williams, D. A., and Kemm, R. E., Promoting student-centered active learning in lectures with a personal response system. Adv. Physiol. Educ. 33(1):60–71, 2009.

    Article  PubMed  Google Scholar 

  13. Nájera, A., Villalba, J. M., and Arribas, E., Student peer evaluation using a remote response system. Med. Educ. 44(11):1146–1146, 2010. doi:10.1111/j.1365-2923.2010.03837.x.

    Article  PubMed  Google Scholar 

  14. Bhargava, P., Lackey, A. E., Dhand, S., Moshiri, M., Jambhekar, K., and Pandey, T., Radiology education 2.0—on the cusp of change: Part 1. Tablet computers, online curriculums, remote meeting tools and audience response systems. Acad. Radiol. 20(3):364–372, 2013. doi:10.1016/j.acra.2012.11.002.

    Article  PubMed  Google Scholar 

  15. Garbutt, J. M., DeFer, T. M., Highstein, G., McNaughton, C., Milligan, P., and Fraser, V. F., Safe prescribing: An educational intervention for medical students. Teach. Learn. Med. 18(3):244–250, 2006. doi:10.1207/s15328015tlm1803_10.

    Article  PubMed  Google Scholar 

  16. Turban, J. W., The audience response system: A modality for course evaluation. Med. Educ. 43(5):488–489, 2009. doi:10.1111/j.1365-2923.2009.03348.x.

    Article  PubMed  Google Scholar 

  17. Carrion, I., Fernandez Aleman, J., and Toval, A., Personal health records: New means to safely handle our health data? IEEE Comput 45(11):27–33, 2012. doi:10.1109/mc.2012.74.

    Article  Google Scholar 

  18. Zapata, B., Fernández-Alemán, J., Idri, A., and Toval, A., Empirical studies on usability of mHealth apps: A systematic literature review. J. Med. Syst. 39(2):1–19, 2015.

    Article  PubMed  Google Scholar 

  19. Ouhbi, S., Fernández-Alemán, J., Toval, A., Idri, A., and Pozo, J., Free blood donation mobile applications. J. Med. Syst. 39(5):1–20, 2015. doi:10.1007/s10916-015-0228-0.

    Article  Google Scholar 

  20. Fernández-Alemán, J., Seva-Llor, C., Toval, A., Ouhbi, S., and Fernández-Luque, L., Free web-based personal health records: An analysis of functionality. J. Med. Syst. 37(6):1–16, 2013. doi:10.1007/s10916-013-9990-z.

    Article  Google Scholar 

  21. Ozdalga, E., Ozdalga, A., and Ahuja, N., The Smartphone in medicine: A review of current and potential use among physicians and students. J Med Internet Res 14(5):e128, 2012. doi:10.2196/jmir.1994.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Juanes, J., and Ruisoto, P., Computer applications in health science education. J. Med. Syst. 39(9):1–5, 2015.

    Article  Google Scholar 

  23. Fernández-Alemán, J. L., Sánchez-García, A. B., López-Montesinos, M. J., and Jiménez-Lopez, J. J., Examining the benefits of learning based on an audience response system when confronting emergency situations. CIN-Comput Inform Nurs 32(5):207–213, 2014.

    Article  Google Scholar 

  24. Kay, R. H., and LeSage, A., A strategic assessment of audience response systems used in higher education. Aust. J. Educ. Technol. 25(2):235–249, 2009.

    Google Scholar 

  25. Lee SW, Palmer-Brown D, Tepper JA, Roadknight CM Snap-drift: real-time, performance-guided learning. In: Neural Networks. Proceedings of the International Joint Conference on, 20–24 July 2003 2003. pp 1412–1416. doi:10.1109/ijcnn.2003.1223903, 2003.

  26. Fernandez-Aleman, J. L., Palmer-Brown, D., and Jayne, C., Effects of response-driven feedback in computer science learning. IEEE Trans. Educ. 54(3):501–508, 2011. doi:10.1109/te.2010.2087761.

    Article  Google Scholar 

  27. Brown DP, Draganova C, Sin Wee L Snap-drift neural network for selecting student feedback. In: Neural Networks. IJCNN 2009. International Joint Conference on, 14–19 June 2009 2009. pp 391–398. doi:10.1109/ijcnn.2009.5178859, 2009.

  28. Lee, S. W., Palmer-Brown, D., and Roadknight, C. M., Performance-guided neural network for rapidly self-organising active network management. Neurocomputing 61(0):5–20, 2004. doi:10.1016/j.neucom.2004.03.001.

    Article  Google Scholar 

  29. Lee SW, Palmer-Brown D, Roadknight C Reinforced snap-drift learning for proxylet selection in active computer networks. In: Proceedings of IEEE International Joint Conference on Neural Networks, 25–29 July 2004 2004. pp 1545–1550. doi:10.1109/ijcnn.2004.1380185

  30. Palmer-Brown, D., and Jayne, C., Self organisation and modal learning: Algorithms and applications. In: Bianchini, M., Maggini, M., and Jain, L. C. (Eds.), Handbook on neural information processing, Intelligent systems reference library, vol. 49. Springer, Berlin Heidelberg, pp. 379–400, 2013.

    Chapter  Google Scholar 

  31. Robertson, L., Twelve tips for using a computerized interactive audience response system. Med Teach 22(3):237–239, 2000.

    Article  Google Scholar 

  32. Allen, D., and Tanner, K., Infusing active learning into the large-enrollment biology class: Seven strategies, from the simple to complex. Cell Biol. Educ. 4(4):262–268, 2005. doi:10.1187/cbe.05-08-0113.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Caldwell, J. E., Clickers in the large classroom: Current research and best-practice tips. CBE Life Sci. Educ. 6(1):9–20, 2007.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Guo, R., Palmer-Brown, D., Lee, S. W., and Cai, F. F., Intelligent diagnostic feedback for online multiple-choice questions. Artif. Intell. Rev. 42(3):369–383, 2014. doi:10.1007/s10462-013-9419-6.

    Article  Google Scholar 

  35. Hunter, J. E., and Schmidt, F. L., Fixed effects vs. random effects meta-analysis models: Implications for cumulative research knowledge. Int. J. Sel. Assess. 327(8):272–292, 2000.

    Google Scholar 

  36. Hedges, L., and Olkin, I., Statistical methods for meta-analysis. Academia Press, Orlando, 1985.

    Google Scholar 

  37. Kampenes, V. B., Dyba, T., Hannay, J. E., and Sjøberg, D. I., A systematic review of effect size in software engineering experiments. Inf. Softw. Technol. 49(11–12):1073–1086, 2007.

    Article  Google Scholar 

  38. Votta, R. J., and Benau, E. M., Sources of stress for pharmacy students in a nationwide sample. Curr. Pharm. Teach. Learn. 6(5):675–681, 2014. doi:10.1016/j.cptl.2014.05.002.

    Article  Google Scholar 

  39. Clauson, K. A., Alkhateeb, F. M., and Singh-Franco, D., Concurrent use of an audience response system at a multi-campus college of pharmacy. Am. J. Pharm. Educ. 76(1):6, 2012. doi:10.5688/ajpe7616.

    Article  PubMed  PubMed Central  Google Scholar 

  40. McLaughlin, J. E., Gharkholonarehe, N., Khanova, J., Deyo, Z. M., and Rodgers, J. E., The impact of blended learning on student performance in a cardiovascular pharmacotherapy course. Am. J. Pharm. Educ. 79(2):24, 2015. doi:10.5688/ajpe79224.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Medina, M. S., Medina, P. J., Wanzer, D. S., Wilson, J. E., Er, N., and Britton, M. L., Use of an audience response system (ARS) in a dual-campus classroom environment. Am. J. Pharm. Educ. 72(2):38, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Slain, D., Abate, M., Hodges, B. M., Stamatakis, M. K., and Wolak, S., An interactive response system to promote active learning in the doctor of pharmacy curriculum. Am. J. Pharm. Educ. 68(5):1–9, 2004.

    Article  Google Scholar 

  43. Lymn, J. S., and Mostyn, A., Audience response technology: Engaging and empowering non-medical prescribing students in pharmacology learning. BMC Med. Educ. 10:73–73, 2010. doi:10.1186/1472-6920-10-73.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Cain, J., Black, E., and Rohr, J., An audience response system strategy to improve student motivation, attention, and feedback. Am. J. Pharm. Educ. 73(2):21, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Poirier, T. I., A seminar course on contemporary pharmacy issues. Am. J. Pharm. Educ. 72(2):30, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Trapskin, P., Smith, K., Armitstead, J., and Davis, G., Use of an audience response system to introduce an anticoagulation guide to physicians, pharmacists, and pharmacy students. Am. J. Pharm. Educ. 69(2):190–197, 2005.

    Article  Google Scholar 

  47. Landin, M., and Pérez, J., Class attendance and academic achievement of pharmacy students in a European university. Curr. Pharm. Teach. Learn. 7(1):78–83, 2015. doi:10.1016/j.cptl.2014.09.013.

    Article  Google Scholar 

  48. Cor, M. K., and Peeters, M. J., Using generalizability theory for reliable learning assessments in pharmacy education. Curr. Pharm. Teach. Learn. 7(3):332–341, 2015. doi:10.1016/j.cptl.2014.12.003.

    Article  Google Scholar 

  49. Cleland, J., Arnold, R., and Chesser, A., Failing finals is often a surprise for the student but not the teacher: Identifying difficulties and supporting students with academic difficulties. Med. Teach. 27(6):504–508, 2005. doi:10.1080/01421590500156269.

    Article  PubMed  Google Scholar 

  50. Yates, J., Development of a ‘toolkit’ to identify medical students at risk of failure to thrive on the course: An exploratory retrospective case study. BMC Med. Educ. 11:95–95, 2011. doi:10.1186/1472-6920-11-95.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Froncek, B., Hirschfeld, G., and Thielsch, M. T., Characteristics of effective exams—development and validation of an instrument for evaluating written exams. Stud. Educ. Eval. 43:79–87, 2014. doi:10.1016/j.stueduc.2014.01.003.

    Article  Google Scholar 

  52. Eckleberry-Hunt, J., and Tucciarone, J., The challenges and opportunities of teaching “generation Y”. J. Grad. Med. Educ. 3(4):458–461, 2011. doi:10.4300/jgme-03-04-15.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Berger, B., Baldwin, H., McCroskey, J., and Richmond, V., Communication apprehension in pharmacy students: A national study. Am. J. Pharm. Educ. 47(2):95–102, 1983.

    Google Scholar 

  54. Gazibara, T., Marusic, V., Maric, G., Zaric, M., Vujcic, I., Kisic-Tepavcevic, D., Maksimovic, J., Maksimovic, N., Denic, L., Grujicic, S., Pekmezovic, T., and Grgurevic, A., Introducing E-learning in epidemiology course for undergraduate medical students at the faculty of medicine, University of Belgrade: A pilot study. J. Med. Syst. 39(10):1–7, 2015.

    Article  Google Scholar 

  55. Menendez, E., Balisa-Rocha, B., Jabbur-Lopes, M., Costa, W., Nascimento, J. R., Dósea, M., Silva, L., and Lyra Junior, D., Using a virtual patient system for the teaching of pharmaceutical care. Int. J. Med. Inform. 84(9):640–646, 2015.

    Article  PubMed  Google Scholar 

  56. Reis, L. O., Ikari, O., Taha-Neto, K. A., Gugliotta, A., and Denardi, F., Delivery of a urology online course using moodle versus didactic lectures methods. Int. J. Med. Inform. 84(2):149–154, 2015.

    Article  PubMed  Google Scholar 

  57. Sowan, A. K., and Idhail, J. A., Evaluation of an interactive web-based nursing course with streaming videos for medication administration skills. Int. J. Med. Inform. 83(8):592–600, 2014.

    Article  PubMed  Google Scholar 

  58. Ozyurt, O., Ozyurt, H., and Baki, A., Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT. Expert Syst. Appl. 40(8):2914–2940, 2013. doi:10.1016/j.eswa.2012.12.008.

    Article  Google Scholar 

  59. Kobus, M. B. W., Rietveld, P., and van Ommeren, J. N., Ownership versus on-campus use of mobile IT devices by university students. Comput. Educ. 68(0):29–41, 2013. doi:10.1016/j.compedu.2013.04.003.

    Article  Google Scholar 

  60. Clark, R., Media will never influence learning. Educ. Teach Res. 42(2):21–29, 1994. doi:10.1007/bf02299088.

    Google Scholar 

  61. Hatziapostolou, T., and Paraskakis, I., Enhancing the impact of formative feedback on student learning through an online feedback system. Electron. J. E-Learn. 8(2):111–122, 2010.

    Google Scholar 

  62. Stuart, S. A. J., Brown, M. I., and Draper, S. W., Using an electronic voting system in logic lectures: One practitioner’s application. J. Comput. Assist. Learn. 20(2):95–102, 2004. doi:10.1111/j.1365-2729.2004.00075.x.

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. José Luis Fernández-Alemán
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura López-González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ofelia González-Sequeros
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chrisina Jayne
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juan José López-Jiménez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juan Manuel Carrillo-de-Gea
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ambrosio Toval
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Luis Fernández-Alemán.

Additional information

This article is part of the Topical Collection on Education and Training

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0440-6

Keywords

Search

Navigation