Relating Student Engagement Indicators to Academic Performance Using Multiple Correspondence Analysis

A Bibliometric Analysis of the Literature on Norovirus Disease from 1991-2021

Norovirus (NoV) is one of the oldest recognized diseases and the leading causal pathogen for acute gastroenteritis (AGE) worldwide. Though numerous studies have been reported on NoV disease, limited research has explored the publication trends in this area. As a result, the objective of this work was to fill the void by conducting a bibliometric study in publication trends on NoV studies as well as discovering the hotspots. The Web of Science central assemblage database was hunted for publications from 1991 to 2021 with “norovirus” in the heading. Microsoft Excel 2016, VOSviewer, R Bibliometrix, and Biblioshiny packages were deployed for the statistical analysis of published research articles. A total of 6021 published documents were identified in the Web of Science database for this thirty-year study period (1991–2021). The analyses disclosed that the Journal of Medical Virology was the leading journal in publications on norovirus studies with a total of 215 published articles, the Journal of Virology was the most cited document with 11,185 total citations. The United States of America (USA) has the most significant productivity in norovirus publications and is the leading country with the highest international collaboration. Analysis of top germane authors discovered that X. Jiang (135) and J. Vinje (119) were the two top relevant authors of norovirus publications. The commonly recognized funders were US and EU-based, with the US emerging as a top funder. This study reveals trends in scientific findings and academic collaborations and serves as a leading-edge model to reveal trends in global research in the field of norovirus research. This study points out the progress status and trends on NoV research. It can help researchers in the medical profession obtain a comprehensive understanding of the state of the art of NoV. It also has reference values for the research and application of the NoV visualization methods. Further, the research map on AGE obtained by our analysis is expected to help researchers efficiently and effectively explore the NoV field.

Multiple Correspondence Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses

There have been manifold thrilling studies strikingly conducted in recent years to explore factors influencing student acceptance of massive open online courses (MOOCs). The principal goal was to determine future prediction and sustainable use of MOOCs for providing pervasive quality education services. This has led to the examination of different theoretical models tested on varying sample sizes for factor exploration. However, existing studies have reflected heterogeneous results caused by divergent sources not observed in the literature using the multiple correspondence analysis (MCA). This study aimed to apply the data science method of MCA to explore hidden associations amongst factors influencing student acceptance of MOOCs and heterogeneity sources of theoretical models and sample sizes to blur the literature hiatus. Results based on data extracted from 54 primary studies published from 2015 to 2021 with a total of 19,638 valid student responses generally conclude the existence of four main levels of associations. The four associations were respectively composed of single, blended, extended and complex theories and each level is associated with distinct categories and a combination cloud of similar categories. Moreover, results indicated that very small sample size is the most unusual under the basic assumption that none of the variables are correlated. It is practically germane to confirm hidden associations in a dataset of influencing factors to help reach a much greater understanding of the application and performance of MOOCs for sustainable education services.