Research investigating individual device preference and e-learning quality perception: can a one-solution-fits-all e-learning solution work?

Background: COVID-19 caused a paradigm shift for educators, and raised many questions about the future of technology in the delivery of educational content. Literature highlights numerous benefits of using e-learning solutions, yet many still consider ‘online learning’ as the cheap/‘low-quality’ alternative to traditional ‘face-to- face’ models. In this research we ask two questions that are critical to the effective development of future e- learning solutions: Do students prefer face-to-face (traditional) learning methods or e-learning technology enabled solutions? Does perception of e-learning, and/or device preference, vary between individuals? Methods: A three part, quantitative questionnaire was developed, based on previously used questionnaire items, which collected: demographic data, student preference concerning learning, and individual variance - via use of the Cultural Value (CV) Scale dimension test. Data was collected from 518 participants using convenience sam- pling from a range of universities in Pakistan. EFA and CFA showed that questions and factor loading was good. CV Scale results show clear loading and model fit at the individual level, allowing application of results beyond Pakistan. Results: By considering the CV Scale dimensions, our results highlighted three distinct technology preference clusters: i) students, with a high-power distance score, who prefer traditional face-to-face teaching methods; ii) students with low power distance and high uncertainty avoidance scores, who prefer use of e-learning on their mobile devices, and iii) students with low power distance and low uncertainty avoidance scored, who prefer to use laptop devices. Conclusions: This paper highlights that the majority of students are happy to engage with online blended learning solutions, however a one-solution fits all approach to technology use in education fail to satisfy the interaction preferences need of all student groups. Only by embracing flexible and mixed blend delivery models, supporting interaction across a range of pervasive devices, can we maximize student perception towards education service provision.