In Nederland wordt jaarlijks voor drie miljard euro aan bedrijfsopleidingen aangeboden. De meeste van deze opleidingen pretenderen de deelnemer werkelijk iets blijvends te leren. Transfer, beklijving van opleidingsresultaten, is hierin een cruciaal begrip. Onderzoek wijst uit dat de mate van transfer laag is: korte tijd na de opleiding ligt dit nog op zestig procent, echter de mate van transfer neemt met de tijd sterk af, na langere tijd blijkt deze waarde op tien procent te liggen. Schrikbarend laag! Hier is dus veel winst te behalen. In dit artikel bespreken we welke factoren een rol spelen bij het optimaliseren van de transfer. Tevens beschrijven we een pilotstudie naar transfer bij trainingen op het gebied van interpersoonlijke vaardigheden.
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Purpose - Focusing on management training, this study aimed to establish whether identical elements in a training program (i.e. aspects resembling participants' work situation) can improve training transfer and whether they do so beyond the contribution of two well-established predictors -- motivation to learn and expected utility. In an effort to establish mechanisms connecting identical elements with training transfer, we proposed and tested motivation to transfer as a mediator. Design/methodology/approach - Data were collected online from 595 general managers who participated in a management training program. Structural equations modeling was used to test the model. Findings - Identical elements, expected utility and motivation to learn each had a unique contribution to the prediction of training transfer. Whereas motivation to learn partly mediated these relationships, identical elements and expected utility also showed direct associations with training transfer. Research limitations/implications - Identical elements represent a relevant predictor of training transfer. In future research, a longitudinal analysis from different perspectives would be useful to better understand the process of training transfer. Practical implications - Participants may profit more from management training programs when the training better resembles participants' work situation. Organisations and trainers should therefore apply the concept of identical elements in their trainings, in order to increase its value and impact. Originality/value - This study contributes to the training literature by showing the relevance of identical elements for transfer, over and above established predictors.
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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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An important goal of educational designers is to achieve long-term transfer of learning that is the learner's application of newly acquired competencies. Extensive research during more than a century shows that especially in formal educational settings this fundamental aspect of education often occurs poorly or not at all, leading to what is called a Transfer Problem. To address this transfer problem, the present study examines intentions to transfer learning to multiple contexts; this focus on multiple transfer contexts extends previous research focusing on a single transfer context, typically the workplace. The present study aimed to estimate the influence of five organizational variables (peer support, supervisor support, opportunity to use, openness to change, and feedback) on pre-training intention to transfer prospective learning in two different transfer contexts: study and work. Participants were 303 students at an open university starting a digital course in information literacy. The model was tested using structural equation modelling. The results indicated that before starting the course supervisor support and feedback were considered the strongest predictors of intention to transfer new learning in both the study and the work contexts. This research is amongst the first in the training literature to address multicontextuality and examines intentions to transfer generic competences to the two transfer contexts study and work within one single study.
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In this study, the impact of a training program focusing on the deliberate use of interventions during coaching dialogues with prospective teachers was investigated. Video recordings were analyzed of coaching dialogues carried on in the workplace by 28 teachers in primary education with the prospective teachers under their guidance, both before and after they participated in the training program. The main goal of this program was to broaden the repertoire of interventions which coaches use in their dialogues with student teachers. The video recordings made were transcribed verbatim, coded by three independent researchers and analyzed using descriptive statistics and t tests for paired observations. Coaches repertoires of interventions were found to consist of an average of six types of interventions. This average remained stable throughout the training program. After training, a shift from directive towards non-directive interventions was observed. The length of the coaches speaking time decreased, while the number of their interventions increased. After training, coaches structured dialogues to a greater extent. Considerable interindividual variability existed between coaches. The relevance of these findings is that the deliberate use of interventions during coaching dialogue can be influenced through training with results noticeable in the workplace. The findings of this study suggest that the training program studied can serve relatively large numbers of teacher coaches, as its setup requires a feasible amount of effort from schools and participants.
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Manual crack inspection is labor-intensive and impractical at scale, prompting a shift toward AI-based segmentation methods. We present a novel crack segmentation model that leverages the Segment Anything Model 2 (SAM 2) through transfer learning to detect cracks on masonry surfaces. Unlike prior approaches that rely on encoders pretrained for image classification, we fine-tune SAM 2, originally trained for segmentation tasks, by freezing its Hiera encoder and FPN neck, while adapting its prompt encoder, LoRA matrices, and mask decoder for the crack segmentation task. No prompt input is used during training to avoid detection overhead. Our aim is to increase robustness to noise and enhance generalizability across different surface types. This work demonstrates the potential of foundational segmentation models in enabling more reliable and field-ready AI-based crack detection tools.
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Purpose – Training is considered a cornerstone intervention in the adoption and implementation of continuous improvement (CI). Yet despite the extensive investment of resources required, the purpose and content of CI training programs as mechanisms for CI implementation have received little attention. The purpose of this study is to identify approaches to integrating training in CI implementation. Design/methodology/approach – Q-methodology, a research approach that combines quantitative and qualitative methods was used in combination with semi-structured interviews to identify patterns in the viewpoints on the role of training in CI implementation. Based on a sample of 41 organizations, each represented by key informants, three perspectives on training activities in the context of CI implementation were identified. Findings – Our study contributes to CI implementation research by specifying the mechanisms through which training contributes to successful CI implementation. First, training can be used to promote organizational change, by linking the why of CI to the organizational strategy. Second, training can be used as a mechanism for knowledge transfer, when CI is primarily implemented through projects. Third, training can be an avenue for inducing change through leadership. Originality/value – The study shows how the context of CI implementation should inform the purpose, timing, audience and content of the training program, so that training activities contribute effectively to the success of CI implementation. These findings contribute to CI literature by elucidating the role that training plays in the implementation of CI and linking it to contextually relevant variables.
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BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
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The quality of mentoring in teacher education is an essential component of a powerful learning environment for teachers. There is no single approach to mentoring that will work in the same way for every teacher in each context. Nevertheless, most mentor teachers hardly vary their supervisory behaviour in response to varying mentoring situations. Developing versatility in mentor teachers' use of supervisory skills, then, is an important challenge. In this chapter, we discuss the need for mentor teacher preparation and explain the focus, content, and pedagogy underlying a particular training programme for mentor teachers, entitled Supervision Skills for Mentor teachers to Activate Reflection in Teachers (SMART). Also, findings from several studies assessing mentor teachers' supervisory roles and use of supervisory skills in mentoring dialogues, before and after the SMART programme, are presented. In addition, implications and perspectives for mentor teacher development and preparation are discussed.
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