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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.
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.
Multiple Correspondence Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses
Cecilia Temilola Olugbara (author) / Moeketsi Letseka (author) / Oludayo O. Olugbara (author)
2021
Article (Journal)
Electronic Resource
Unknown
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