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.
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Education for sustainability scholarship argues that sustainability competence is more than cognitive domain learning that is traditionally (over) focused on reason, knowledge application and testing. Affective domain is missing from the education curricula in general (Sowel, 2005, Dernikos et al, 2020), and in Higher Education in Sustainability (HES) (Shepard, 2008). Yet, “it is possible to construct an argument that the essence of education for sustainability is a quest for affective outcomes” (Shepard, 2008). For example, there is a link between personal values and sustainability performance (Potocan 2021), and emotional intelligence has been seen to be “the foundation of a more cooperative and compassionate [sustainable] society” (Estrada, Rodriguez, Moliner, 2021).
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Conference Paper From the article: Abstract Learning analytics is the analysis and visualization of student data with the purpose of improving education. Literature reporting on measures of the effects of data-driven pedagogical interventions on learning and the environment in which this takes place, allows us to assess in what way learning analytics actually improves learning. We conducted a systematic literature review aimed at identifying such measures of data-driven improvement. A review of 1034 papers yielded 38 key studies, which were thoroughly analyzed on aspects like objective, affected learning and their operationalization (measures). Based on prevalent learning theories, we synthesized a classification scheme comprised of four categories: learning process, student performance, learning environment, and departmental performance. Most of the analyzed studies relate to either student performance or learning process. Based on the results, we recommend to make deliberate decisions on the (multiple) aspects of learning one tries to improve by the application of learning analytics. Our classification scheme with examples of measures may help both academics and practitioners doing so, as it allows for structured positioning of learning analytics benefits.
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