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An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.
- Source :
- Journal of Medical Systems; Apr2016, Vol. 40 Issue 4, p1-12, 12p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
ANATOMY
COMPUTER assisted instruction
HEALTH occupations students
LEARNING strategies
META-analysis
ARTIFICIAL neural networks
PHARMACISTS
QUESTIONNAIRES
RESEARCH funding
SATISFACTION
STATISTICS
STUDENTS
T-test (Statistics)
DATA analysis
EFFECT sizes (Statistics)
MOBILE apps
DESCRIPTIVE statistics
Subjects
Details
- Language :
- English
- ISSN :
- 01485598
- Volume :
- 40
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Medical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 115925313
- Full Text :
- https://doi.org/10.1007/s10916-016-0440-6