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. 2020 Feb 24;8(1):26.
doi: 10.3390/pharmacy8010026.

Assessing Performance and Engagement on a Computer-Based Education Platform for Pharmacy Practice

Kelly Grindrod  1 , Katherine Morris  2 , Rosemary Killeen  1
Affiliations

Assessing Performance and Engagement on a Computer-Based Education Platform for Pharmacy Practice

Kelly Grindrod et al. Pharmacy (Basel). .

Abstract

A computer-based education platform was developed using a theory-based approach to help Canadian pharmacy professionals adopt their full scope of practice. Data from the platform were used to identify factors that impacted user performance and engagement. A de-identified dataset included response data for 21 unique modules, including quiz responses and self-reflection questions. Outcome measures included user performance (mean quiz score) and engagement (completion rate for attempted modules). Analysis of variance (ANOVA), multivariate regression modelling, and machine learning cluster analysis were used to analyze the data. Of the 5290 users, 68% were pharmacists, 11% were technicians, 13% were pharmacy students, and 8% were pharmacy technician students. Four clusters were identified separately for pharmacists and technicians. Clusters with the higher performance and engagement tended to have more users practicing in community pharmacies while the lower performing clusters tended have more internationally trained users. In the regression modelling, pharmacists performed better than technicians and students while students were more engaged (p < 0.0001). Further, internationally trained pharmacists had slightly lower scores but similar engagement compared to domestically trained pharmacists (p < 0.0001). Users demonstrated higher performance on modules related to scope of practice than on clinical topics, and were most engaged with topics directly impacting daily practice such as influenza vaccinations and new and emerging subjects such as cannabis. The cluster analysis suggests that performance and engagement with a computer-based educational platform in pharmacy may be more related to place of practice than to personal demographic factors such as age or gender.

Keywords: computer-based education; continuing professional development; online education; pharmacy practice.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Histogram of the distribution of the mean overall pharmacist quiz score (performance) in the four clusters.
Figure A2
Figure A2
Histogram of the distribution of the number of quizzes completed (persistence) by pharmacists in the four clusters.
Figure A3
Figure A3
Scatterplot of the distribution of the mean overall quiz score (performance) and the number of quizzes completed (persistence) in the four pharmacist clusters.
Figure A4
Figure A4
Histogram of the distribution of the mean overall technician quiz score (performance) in the four clusters.
Figure A5
Figure A5
Histogram of the distribution of the number of quizzes completed (persistence) by technicians in the four clusters.
Figure A6
Figure A6
Scatterplot of the distribution of the mean overall quiz score (performance) and the number of quizzes completed (persistence) in the four technician clusters.
Figure A7
Figure A7
Performance and engagement by user type.
Figure A8
Figure A8
Performance and engagement by location of training.
Figure A9
Figure A9
Performance and engagement by practice type.
Figure 1
Figure 1
Clusters using the partitioning around medoids (PAM) approach for pharmacists.
Figure 2
Figure 2
Clusters using the partitioning around medoids (PAM) approach for technicians.
Figure 3
Figure 3
Overall mean quiz score in each module.
See this image and copyright information in PMC

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