Purpose The purpose of this paper is to identify and present a global perspective of digital pedagogies in relation to technology and academic librarians. Design/methodology/approach The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was used in this study. Findings Based on the data, academic librarians must develop a foundational understanding of 21st century pedagogies and digital skills to teach in an online environment. Originality/value This review paper considers the emergent teaching role of the academic librarian within the digital environment. The themes in the findings highlight the importance of digital pedagogical knowledge and digital fluency of academic librarians as a teacher within the digital environment in higher education.

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This study investigated the possible factors that predict e‑learning integration into the teaching and learning of science subjects among preservice science teachers. A unified e‑learning integration model was developed in which factors such as attitude, intention, skill and flow experience served as precursors of e‑learning integration. This was done to help close the gap that no previous studies have developed a structural model to statistically explain the interactions among the most influential factors in various technology integration models. The survey method was used to gather data from 100 preservice science teachers and partial least square structural equation modelling technique was applied for structural path analysis and testing of the developed model. Results revealed a good model fit and hypotheses formulated in this study were faithfully supported. The results also revealed that all factors investigated were found to be significant predictors of e‑learning integration with skill standing out as the most significant and strongest factor that predicts the integration of e‑learning by preservice science teachers.

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.

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