Heterogeneity of students' perceptions of e-learning platform quality: a latent profile analysis

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E-learning platform quality is an important subject in the community of researchers and practitioners. Quality assessment approaches have different perspectives and differ in many respects, such as: objective, target groups and methods used. In recent years, the utility of advanced person-centered methods has become increasingly obvious. Person-centered approaches assume that samples come from heterogeneous populations and focus on identifying unobservable subpopulations that include individuals or similar cases. In this category are included both traditional techniques (e.g. cluster analysis) and advanced techniques (e.g. Latent Class Analysis, Latent Profile Analysis). In the present study, the e-learning platform quality is a general concept defined through three dimensions. The overall perception of students regarding the e-learning platform quality is based on the assessment of the following dimensions: e-learning system quality, information quality, and service quality. The aim of this paper is to extend previous research by adopting a person-oriented approach (e.g. LPA-latent profile analysis) and examine the heterogeneity of students' perceptions of e-learning platform quality. A sample of 385 students from five Romanian universities participated in the study. The data was analyzed with Mplus package version 7.4. The results of LPA revealed the presence of three profiles, reflecting different levels of e-learning platform quality perceptions: low, moderate, and high. The resulting profiles are distinct with respect to e-learning system quality, information quality, and service quality. A one-way multivariate analysis of variance (MANOVA) showed that the perceptions of e-learning platforms quality differed significantly across the three profiles. Finally, the article presented the practical implications of the results. The results contribute, on one hand, to identifying groups of students who shared similar perception patterns when dealing with e-learning platform quality. On the other hand, the results are useful in developing a strategy of improving the e-learning platforms by including the specific needs of each student categories identified.
E-learning platform quality, System quality, Information quality, Service quality, Latent profile analysis, Unobserved heterogeneity