Predicting Academic Performance of Medical Students in Iran University of Medical Sciences based on Martin Ford’s Theory of Incentive Systems

AUTHORS

Azam Norouzi 1 , Jalil Koohpayezade 2 , * , Ladan Fata 3 , Seyed Kamran Soltani-Arabshahi 4 , Eshagh Moradi 1

1 Ph.D. Student of Medical Education, Center for Educational Research in Medical Science, School of Medicine, Iran University of Medical Science, Tehran, Iran

2 Associate Professor of Community Medicine, Department of Community Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

3 Professor of Clinical Psychology and Medical Education, Center for Educational Research in Medical Science, School of Medicine, Iran University of Medical Science, Tehran, Iran

4 Professor of Medicine & Medical Education, Center for Educational Research in Medical Sciences (CERMS), Iran University of Medical Sciences, Tehran, Iran

How to Cite: Norouzi A, Koohpayezade J, Fata L, Soltani-Arabshahi S K, Moradi E. Predicting Academic Performance of Medical Students in Iran University of Medical Sciences based on Martin Ford’s Theory of Incentive Systems, J Med Edu. 2018 ; 17(2):e105612. doi: 10.22037/jme.v17i2.21172.

ARTICLE INFORMATION

Journal of Medical Education: 17 (2); e105612
Published Online: October 19, 2018
Article Type: Research Article
Received: April 23, 2018
Accepted: June 25, 2018
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Abstract

Background: academic performance as the basis for judging students’ knowledge ans skills in a specific period is based on the education development program. We aimed to predict the performance of medical students based on the Martin Ford motivational systems theory.Methods: This study was a cross-sectional study on 170 medical students in science education, training and internship at Iran University of Medical Science, who were selected through random sampling.Motivational Strategies for Learning Questionnaire (MSLQ) was completed by all participants.Grade point average (GPA) was selected as academic performance. Pearson’s correlation and stepwise regression were used for data analysis.Results: There was a moderate relationship between responsive environment and academic performance (R=0.43, P<0.001), determined 19 percent (R2=0.191, P<0.001). Considering the regression coefficientof the predictor variable, the weight of responsive environment (B=0.662) indicates that the variable could predict the changes related to the students’ academic performance. So, the weight of responsiveenvironment would be in a more conservative state in generalizing the sample group to the statistical population.Conclusion: For predicting academic performance, responsive environment should be considered as hidden curriculum and educational planner should pay more attention to it.

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  • © 2018, Journal of Medical Education. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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