Taking affective learning in digital education one step further: trainees’ affective characteristics predicting multicontextual pre-training transfer intention.

Laurent Testers, A. Gegenfurtner, S. Brand-Gruwel

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)
    7 Downloads (Pure)

    Abstract

    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.

    Original languageEnglish
    Article number2189
    JournalFrontiers in psychology
    Volume11
    DOIs
    Publication statusPublished - 2020

    Keywords

    • affective learning
    • distance education
    • training
    • transfer of learning
    • multicontextual transfer
    • intention to transfer
    • information literacy

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