Prediction of Graduate Learners’ Academic Achievement in an Online Learning Environment Using a Blended Trauma Course

Abstract

Background

The concepts of online and blended education came into the limelight in the 19th century. Over time, the concepts expanded and reached a peak in 2021 in response to the COVID-19 lockdown. One of the challenges is the monitoring of the performance of distant learners. In face-to-face courses, an instructor can easily identify struggling learners during the regular meetings.

Aim of the Study

This study explored variables that can predict the academic achievement of learners early in online learning environments. Although there was no consensus, the factors were still hypothesized as predictors for academic achievement.

Methods

A quasi-experimental study was conducted to test the hypothesis. Thirty-three graduate learners were enrolled in a blended trauma course. The learners’ age, their previous experiences in online education, pre-test scores, and the number of logs to the online platform were studied. These elements were considered as predictors of academic achievement in the online aspect of the course.

Results

The findings revealed that there was no statistically significant correlation between the age, the previous experience in online education, the pre-test scores, and the number of logs in the first two weeks. However, there was a statistically significant correlation between the number of logs into the online platform in the first three weeks of study and the learners’ academic achievement. Additionally, the number of logs in the first three weeks was a statistically significant predictor for academic achievement in online education. This early prediction can help instructors to identify and support struggling learners.

Conclusion

The records of the online activity of learners in the first three weeks of study can help in early prediction of their academic achievement. Age, previous online education, and pretest scores were not statistically significant predictors.

Keywords:

• academic achievement
• prediction
• learning analytics
• online education
• graduate learning

Data Sharing Statement

Data are available on request.

Acknowledgments

We appreciate the support of the Orthopedic and traumatology department for their support. We deliver deep thanks for the postgraduate students who agreed to participate in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no conflict of interest.