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Exploring the Impact of Spaced Repetition Through Anki Usage on Preclinical Exam Performance
Victoria Winter1, Emily Ames1, Marley Jacobs1
1Kirk Kerkorian School of Medicine at UNLV, Las Vegas, Nevada, USA.
Spaced repetition learning using Anki significantly improved medical student performance on the Comprehensive Basic Science Subject Exam (CBSE). Higher Anki usage, measured by mature and unsuspended cards, correlated with better exam scores, supporting its effectiveness for preclinical medical education.
Area of Science:
- Medical Education
- Learning Sciences
Background:
- Medical knowledge is rapidly expanding, while preclinical education phases are shortening.
- This presents a challenge for medical students to efficiently learn and retain vast amounts of information.
- Spaced repetition is a learning technique that can be implemented using the Anki application.
Purpose of the Study:
- To evaluate the impact of Anki usage on medical student performance in preclinical assessments.
- To determine if Anki usage correlates with scores on the Comprehensive Basic Science Subject Exam (CBSE).
Main Methods:
- 36 medical students provided deidentified Anki usage data (matured cards, study hours, unsuspended cards).
- Data were analyzed using independent t-tests, Pearson Correlation coefficients, and linear regressions.
- Anki metrics were correlated with CBSE exam scores.
Main Results:
- A significant positive correlation was found between the number of mature Anki cards and CBSE exam scores.
- Total study hours and the number of unsuspended cards also positively correlated with higher CBSE scores.
- Students with above-average mature card counts scored significantly higher on the CBSE (71.5%) compared to those below average (60.0%).
Conclusions:
- Increased Anki usage, indicated by more mature and unsuspended cards, is associated with improved performance on the CBSE.
- Spaced repetition via Anki appears to be an effective strategy for medical students to master and retain preclinical knowledge.
- Further research with larger sample sizes is recommended to validate these findings.
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