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 sampling 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.

Acknowledging crossing-avoidance heuristic violations when solving the Euclidean travelling salesperson problem.

If a salesperson aims to visit a number of cities only once before returning home, which route should they take to minimise the total distance/cost? This combinatorial optimization problem is called the travelling salesperson problem (TSP) and has a rapid growth in the number of possible solutions as the number of cities increases. Despite its complexity, when cities and routes are represented in 2D Euclidean space (ETSP), humans solve the problem with relative ease, by applying simple visual heuristics. One of the most important heuristics appears to be the avoidance of path crossings, which will always result in more optimal solutions than tours that contain crossings. This study systematically investigates whether the occurrence of crossings is impacted by geometric properties by modelling their relationship using binomial logistic regression as well as random forests. Results show that properties, such as the number of nodes making up the convex hull, the standard deviation of the angles between nodes, the average distance between all nodes in the graph, the total number of nodes in the graph, and the tour cost (i.e., a measure of performance), are significant predictors of whether crossings are likely to occur.

Sense of direction and conscientiousness as predictors of performance in the Euclidean travelling salesman problem.

A salesperson wishes to visit a number of cities before returning home using the shortest possible route, whilst only visiting each city once. This optimization problem, called the Travelling Salesman Problem, is difficult to solve using exhaustive algorithms due to the exponential growth in the number of possible solutions. Interestingly, when presented in Euclidean space (ETSP), humans quickly find good solutions. Past studies, however, are in disagreement whether human solutions are impacted by the participant's ability to process figural effects in the graph geometry. In this study, we used principal component analysis to combine two correlated [r = 0.37, p < 0.01] self-assessed personality measures, i.e., a participant's sense of direction and a participant's level of conscientiousness, onto a single impulsiveness/cautiousness dimension. We then showed, using simple linear regression, that this new dimension is a significant predictor [R2 = 0.12, p < 0.01] of the number of edge crossings that occur in human ETSP solutions, a key metric of graph optimality. Our study provides evidence to suggest that human solutions to the ETSP are significantly affected by individual differences, including personality and cognitive traits.