Analysis of participation and performance of MOOC learners via latent class analysis: A retrospective study based on the data of a nursing MOOC from 2018 to 2022

Abstract

BackgroundAlthough massive open online courses have been widely used in nurse education, few studies have evaluated MOOC learner behavioral characteristics. Understanding MOOC learners' participation and performance parameters is helpful for further development and administration of this educational approach. ObjectivesTo categorize nursing MOOC learners according to their different learning participation and to compare the differences in learning performance of different types of MOOC learners. DesignRetrospective. Settings and participantsParticipants evaluated in this study were learners of the Health Assessment MOOC on a Chinese MOOC platform for nine semesters from 2018 to 2022. MethodsVia latent class analysis, MOOC learners were categorized according to the number of times they participated in each topic test and the final exam. Differences in scores of each topic test and the final exam, case discussion number, and total evaluation score among different learners were compared. ResultsUsing latent class analysis, MOOC learners were categorized as committed (28.96 %), negative (16.08 %), mid-term dropout (12.78 %) and early dropout (42.18 %) learners. Committed learners performed best and no significant difference were found among other learner types on most topic tests and the final exam. Committed learners participated in case discussions most actively. According to total evaluations, committed, mid-term dropout, early dropout, and negative learners performed from best to worst. ConclusionHealth Assessment MOOC learners were categorized using five-years of data. Committed learners performed best. No significant difference in performance was found for other learners on most topic tests and the final exam. Understanding learner characteristics and educational behavior is critical for effective design and administration of future MOOC learning approaches.