Taking Affective Learning in Digital Education One Step Further: Trainees’ Affective Characteristics Predicting Multicontextual Pre-training Transfer Intention
Autor: | Saskia Brand-Gruwel, Laurent Testers, Andreas Gegenfurtner |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
affective learning
training Instructional design Information literacy multicontextual transfer lcsh:BF1-990 Distance education Applied psychology Affective learning Structural equation modeling lcsh:Psychology ddc:370 Transfer of training distance education ComputingMilieux_COMPUTERSANDEDUCATION Psychology information literacy Transfer of learning intention to transfer Competence (human resources) General Psychology Original Research transfer of learning |
Zdroj: | Frontiers in Psychology Frontiers in psychology, 11:2189. Frontiers Media Frontiers in Psychology, Vol 11 (2020) |
ISSN: | 1664-1078 |
Popis: | 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. |
Databáze: | OpenAIRE |
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