title: Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.
abstract: BACKGROUND
  Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics.
  
  
  METHODS
  This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course.
  
  
  RESULTS
  Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001).
  
  
  CONCLUSIONS
  This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Natasa M
authors: NM
authors: Milic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Goran Z
authors: GZ
authors: Trajkovic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Zoran M
authors: ZM
authors: Bukumiric
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Andja
authors: A
authors: Cirkovic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Ivan M
authors: IM
authors: Nikolic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Jelena S
authors: JS
authors: Milin
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Nikola V
authors: NV
authors: Milic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Marko D
authors: MD
authors: Savic
authors: Department of Public Health, Medical Faculty, University of Pristina, Kosovska Mitrovica, Serbia.
authors: Aleksandar M
authors: AM
authors: Corac
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Jelena M
authors: JM
authors: Marinkovic
authors: Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
authors: Dejana M
authors: DM
authors: Stanisavljevic
mesh_terms: D003194:Computer-Assisted Instruction; D003479:Curriculum; D004501:Education, Medical / Q000379:methods; D004521:Educational Measurement; D005260:Female; D006801:Humans; D008297:Male; D017145:Models, Educational; D011446:Prospective Studies; D013223:Statistics as Topic / Q000193:education*; D013663:Teaching / Q000379:methods