An empirical study of neural network-based audience response technology in a human anatomy course for pharmacy students.
Juan Manuel Carrillo-de-Gea
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.
FERNANDEZ-ALEMAN, J.L., LOPEZ-GONZALEZ, L., GONZALEZ-SEQUEROS, O., JAYNE, C., LOPEZ-JIMENEZ, J.J., CARRILLO-DE-GEA, J.M. and TOVAL, A. 2016. An empirical study of neural network-based audience response technology in a human anatomy course for pharmacy students. Journal of medical systems [online], 40(4), article number 85. Available from: https://doi.org/10.1007/s10916-016-0440-6
|Journal Article Type||Article|
|Acceptance Date||Jan 11, 2016|
|Online Publication Date||Jan 27, 2016|
|Publication Date||Apr 30, 2016|
|Deposit Date||Apr 14, 2016|
|Publicly Available Date||Jan 28, 2017|
|Journal||Journal of Medical Systems|
|Peer Reviewed||Peer Reviewed|
|Keywords||ELearning; Human anatomy; Neural network; Experiment|
FERNANDEZ-ALEMAN 2016 An empirical study of neural network-based
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