Dropout from an online course in applied statistics for health research

Abstract

Introduction: Online education is becoming increasingly popular, although dropout rates in online courses are the primary challenge. There are few studies that have addressed this situation in statistics education for research.

Objective: To estimate the dropout probability in an online course on applied statistics in healthcare, considering course progress and its relationship with gender, age, and participants’ professions.

Method: A cohort study was conducted with 108 participants, including healthcare professionals and administrative staff from the National Institute of Child Health in San Borja (INSN-SB), in an 11-session virtual course over two months. A descriptive analysis was performed, as well as Kaplan-Meier curves to assess the likelihood of dropout based on gender, and Cox regression to adjust the results, all at a significance level of 5%.

Results: 68.5% dropped out of the course, with a decrease in the pass rate from 37.5% in 2021 to 26.6% in 2022. The majorities were women (70.7%), and the highest dropout rate was in 2022. Attendance among dropouts significantly decreased, especially in the first three classes (58.1%). Average scores increased in 2022.
Kaplan-Meier analysis showed a 46.3% dropout probability after the fifth session, with no significant gender differences. And the regression model does not show a significant association between desertion and age, gender, or profession.

Conclusion: The online statistics course at INSN-SB experienced a high dropout rate, with over two-thirds of the participants discontinuing their participation. The dropout rate was not associated with the age, gender, or profession of the participants.