Abstract
Objective This paper presents an empirical study of a formative mobile-based assessment approach that can be used to provide students with intelligent diagnostic feedback to test its educational effectiveness. Method An audience response system called SIDRA was integrated with a neural network-based data analysis to generate diagnostic feedback for guided learning. A total of 200 medical students enrolled in a General and Descriptive Anatomy of the Locomotor System course were taught using two different methods. Ninety students in the experimental group used intelligent SIDRA (i-SIDRA), whereas 110 students in the control group received the same training but without employing i-SIDRA. Results In the students’ final exam grades, a statistically significant difference was found between those students that used i-SIDRA as opposed to a traditional teaching methodology (T(162) = 2.597; p = 0.010). The increase in the number of correct answers during the feedback guided learning process from the first submission to the last submission in four multiple choice question tests was also analyzed. There were average increases of 20.00% (Test1), 11.34% (Test2), 8.88% (Test3) and 13.43% (Test4) in the number of correct answers. In a questionnaire rated on a five-point Likert-type scale, the students expressed satisfaction with the content (M = 4.2) and feedback (M = 3.5) provided by i-SIDRA and the methodology (M = 4.2) used to learn anatomy. Conclusions The use of audience response systems enriched with feedback such as i-SIDRA improves medical degree students’ performance as regards anatomy of the locomotor system. The knowledge state diagrams representing students’ behavior allow instructors to study their progress so as to identify what they still need to learn.
Original language | English |
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Pages (from-to) | 172-181 |
Number of pages | 10 |
Journal | International Journal of Medical Informatics |
Volume | 94 |
DOIs | |
Publication status | Published - 1 Oct 2016 |
Bibliographical note
Funding Information:This research is part of the project GINSENG (TIN2015-70259-C2-2-R) supported by the Spanish Ministry of Economy and Competitiveness and European FEDER funds .
Publisher Copyright:
© 2016 Elsevier Ireland Ltd
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.