The past decades have shown an accelerated development of technology-enhanced or digital education. Although an important and recognized precondition for study success, still little attention has been paid to examining how an affective learning climate can be fostered in online training programs. Besides gaining insight into the dynamics of affective learning itself it is of vital importance to know what predicts trainees’ intention to transfer new knowledge and skills to other contexts. The present study investigated the influence of five affective learner characteristics from the transfer literature (learner readiness, motivation to learn, expected positive outcomes, expected negative outcomes, personal capacity) on trainees’ pre-training transfer intention. Participants were 366 adult students enrolled in an online course in information literacy in a distance learning environment. As information literacy is a generic competence, applicable in various contexts, we developed a novel multicontextual transfer perspective and investigated within one single study the influence of the abovementioned variables on pre-training transfer intention for both the students’ Study and Work contexts. The hypothesized model has been tested using structural equation modeling. The results showed that motivation to learn, expected positive personal outcomes, and learner readiness were the strongest predictors. Results also indicated the benefits of gaining pre-training insight into the specific characteristics of multiple transfer contexts, especially when education in generic competences is involved. Instructional designers might enhance study success by taking affective transfer elements and multicontextuality into account when designing digital education.
MULTIFILE
The availability of time has played a pivotal role in the analysis of tourism. An examination of social theory and time suggests that tourists experience time in multiple ways, which has implications for the traditional temporal and spatial reference frame. This article calls for a better understanding of 'time' in tourism and sets the agenda for further research into time and the sustainable development of tourism. It analyses the role of time in shaping tourism consumption and illustrates the challenges posed by new temporal understandings and distance concepts to create less greenhouse-gas-dependent tourism in our society.
LINK
This paper presents user-friendly code for the implementation of a loss function for neural network time series models that exploits the topological structures of financial data. By leveraging the recently-discovered presence of topological features present in financial time series data, the code offers a more effective approach for creating forecasting models for such data given the fact that it allows neural network models to not only learn temporal patterns of the data, but also topological patterns. This paper aims to facilitate the adoption of the loss function proposed by Souto and Moradi (2024a) in financial time series by practitioners and researchers.
LINK
