Learning analytics to explore the motivational profiles of non-traditional practical nurse students: a mixed-methods approach

Abstract

AbstractLearning analytics provides a novel means to examine various aspects of students’ learning and to support them in their individual endeavors. The purpose of this study was to explore the potential of learning analytics to provide insights into non-traditional, vocational practical nurse students’ (N = 132) motivational profiles for choosing their studies, using a mixed-methods approach. Non-traditional students were somewhat older learners than those following a more straightforward educational pathway and had diverse educational or professional backgrounds. Institutional admission data and analytics were used to identify their specific study motives and distinct motivational profiles, and to illustrate the connections between the motives emerging in the motivational profiles. Furthermore, the association between the motivational profiles and study performance was examined. The results of qualitative content analysis indicated that non-traditional practical nurse students pursued such specialized training for various reasons, and that pragmatic, professional rationales were emphasized over prosocial, altruistic factors. Through the adoption of person-centered latent class analysis, three motivational profiles were identified: self-aware goal-achievers, qualification attainers, and widely oriented humanitarians. Additionally, the analyses of epistemic networks for the profiles showed the complex interplay between the motives, confirming that some motive connections appear to be more prominent than others. Moreover, the findings indicated that study motives reported at admission did not seem to dictate students’ later study performance, as no statistically significant associations were found between the motivational profile and the students’ final grade point average or study dropout. This investigation paves the way for more-targeted motivational support and the use of learning analytics in the context of vocational education and training.