Blended learning offers a learner-centred approach that employs both in-class learning and digital technology to facilitate online learning. Such an approach is especially advantageous to adult-learners in higher education as it meets their educational needs. However, adult-learners’ participation in blended learning programmes remains challenging due to a general lack of online interaction, and no clear teaching strategies that address this concern. Literature relating to adult-learners’ educational needs and online interaction was consulted in order to design teaching strategies that foster adult-learners’ online interaction. The aim of this study is to further validate these teaching strategies, hence a multiple case study was carried out using a mixed method approach. As such, eight teachers and sixteen students from four courses across three universities in Belgium and the Netherlands were interviewed. Additionally, a questionnaire testing a pre-defined set of variables was distributed to 84 students. The results lead to a set of validated teaching strategies that help teachers to further develop their professional skills and expertise. The teaching strategies can be grouped into three categories, namely 1) the teacher's online presence, 2) collaborative learning activities and preparatory learning activities, and 3) the distribution of learning content and learning activities across online and in-class learning. An elaborate set of validated teaching strategies is included. This study aids towards teacher professional development and adds evidence-based knowledge to teaching strategies and instructional frameworks for adult-learners in higher education.
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This study examines the relationships between students’ perceptions of heavy study load, time spent on learning, study strategies, and learning outcomes. Student’s study strategies were measured with a short version of Vermunt’s Inventory of Learning Styles. It was possible to replicate 5 processing and 5 regulation strategies. The higher order dimensions meaning directed learning style (relate and structure, concrete processing, critical processing) and reproduction directed learning style (memorize and repeat, analyze, self-regulation of contents, process and results, external regulation of the learning process) differed from Vermunt. The scales showed differences across groups, which is in line with previous research. Linear structural analysis showed that reproduction directed learning precedes meaning directed learning. Only meaning directed learning affected GPA, the influence of the two learning styles on ECs was not evidenced in this study. Contact hours influenced ECs, but this effect was tempered through its negative association with a heavy study load. The limitations, implications for practice, and directions for further research and development will be discussed in the round table.
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Purpose Supervisors are responsible to train students in healthcare placements. Although there is knowledge about workplace learning and supervision in general, little is known about supervisors’ pedagogic strategies in specific healthcare placements. In this study, we identify how supervisors’ reasoning and interrelated actions manifest in physiotherapy and nursing work settings. Methods Following the stimulating recall approach, we conducted 16 interviews with supervisors at seven work settings. Using a theoretical framework of workplace supervision, we performed a deductive template analysis. Results Four configurations of pedagogic strategies reveal how supervision manifests in healthcare placements. The results provide unique insights into specific supervision moments, and elucidate the situatedness of the supervisors’ strategies. Conclusions The present study illustrates the variation in aims and focus of supervisors in placements. Supervisors’ pedagogic strategies were found to be mainly based on (A) role modelling, (B) overall support, (C) trust, and (D) letting go. Further research is needed to investigate the interplay between supervisors and students in learning situations within work settings.
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Objectives: To investigate immediate changes in walking performance associated with three implicit motor learning strategies and to explore patient experiences of each strategy. Design: Participants were randomly allocated to one of three implicit motor learning strategies. Within-group comparisons of spatiotemporal parameters at baseline and post strategy were performed. Setting: Laboratory setting. Subjects: A total of 56 community-dwelling post-stroke individuals. Interventions: Implicit learning strategies were analogy instructions, environmental constraints and action observation. Different analogy instructions and environmental constraints were used to facilitate specific gait parameters. Within action observation, only videotaped gait was shown. Main measures: Spatiotemporal measures (speed, step length, step width, step height) were recorded using Vicon 3D motion analysis. Patient experiences were assessed by questionnaire. Results: At a group level, three of the four analogy instructions (n=19) led to small but significant changes in speed (d=0.088m/s), step height (affected side d=0.006m) and step width (d=–0.019m), and one environmental constraint (n=17) led to significant changes in step width (d=–0.040m). At an individual level, results showed wide variation in the magnitude of changes. Within action observation (n=20), no significant changes were found. Overall, participants found it easy to use the different strategies and experienced some changes in their walking performance. Conclusion: Analogy instructions and environmental constraints can lead to specific, immediate changes in the walking performance and were in general experienced as feasible by the participants. However, the response of an individual patient may vary quite considerably.
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Blended learning environments (BLEs) have become an indispensable part of higher education and an essential part of course delivery. Although teachers need to be active agents in facilitating students’ self-regulation and interaction, little is known to what extent such support is provided. This study investigated the use of self-regulation strategies (SRS) and interactional methods of teachers and students in BLEs. In a cross-sectional design, 171 teachers and 331 students completed a questionnaire on the use of SRS and the application of human and non-human interactional methods. Results showed that, on average, teachers and students pay little attention to SRS and do not or hardly use interactional methods. Results also showed that experienced and inexperienced teachers did not differ in their attention to SRS, although a significant difference was found between teachers with and without online teaching experience. Teachers with more online teaching experience pay more specific attention to metacognitive and management strategies. A positive relationship was also found between the extent to which teachers use both human and non-human interactional methods and the extent to which they pay attention to SRS in the online component of BLEs. Finally, there was a positive relationship between the extent to which students utilize both human and non-human interactional methods and the extent to which they apply SRS. Outcomes of this research provide insight into the design of BLEs and emphasize the importance of teachers' attention to students’ SRS and the use of interactional methods.
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Het doel van dit is boek is om de lezer te wijzen op de gevarieerde en innovatieve methodes om de 'lessen' informatievaardigheden, die reeds geïntegreerd zijn in het curriculum, te beoordelen en na die beoordeling (eventueel) te verbeteren.
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This study focuses on students’ approaches to learning, particularly in innovative learning environments. A person-oriented research perspective was chosen to search for nuances and details that add to existing knowledge on secondary students’ learning. The relation between students’ goal orientations, learning strategies, and different learning environments was investigated via questionnaires and interviews. The questionnaire study revealed four profiles of 673 students’ (meta-)cognitive learning strategies. Differences in student learning were found between students of an innovative school and students of regular schools, indicating that learning strategies are elicited by the learning environments students are confronted with. The interview study with 20 students from the innovative school illustrated their learning in learning environments typical for this school. Results revealed how students from different profiles differed in their goal orientations and learning strategies and that these differences were related to students’ need for teacher support when learning. The results of this study provide qualitative and quantitative insight in (enhancing) secondary students’ learning, doing justice to the learners as persons and individual differences.
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Although self-regulation is an important feature related to students’ study success as reflected in higher grades and less academic course delay, little is known about the role of self- regulation in blended learning environments in higher education. For this review, we analysed 21 studies in which self-regulation strategies were taught in the context of blended learning. Based on an analysis of literature, we identified four types of strategies: cognitive, metacognitive, motivational and management. Results show that most studies focused on metacognitive strategies, followed by cognitive strategies, whereas little to no attention is paid to motivation and management strategies. To facilitate self-regulation strategies non-human student tool interactional methods were most commonly used, followed by a mix of human student-teacher and non-human student content and student environment methods. Results further show that the extent to which students actively apply self-regulation strategies also depends heavily on teacher's actions within the blended learning environment. Measurement of self-regulation strategies is mainly done with questionnaires such as the Motivation and Self-regulation of Learning Questionnaire.Implications for practice and policy:•More attention to self-regulation in online and blended learning is essential.•Lecturers and course designers of blended learning environments should be aware that four types of self-regulation strategies are important: cognitive, metacognitive, motivational and management.•Within blended learning environments, more attention should be paid to cognitive, motivation and management strategies to promote self-regulation.
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This article reports on a literature review on empirical research investigating learning for vocations in the context of vocational education. We included 36 studies in which learning for vocations is empirically studied. Learning for vocations is characterised based upon prevalent research traditions in the field and framed from the perspective of vocational education and organised learning practices. This framing and characterisation directed the search terms for the review. Results show empirical data on vocational learning and illustrate how learning processes for the functions of vocational education - vocational identity development, development of a vocational repertoire of actions, and vocational knowledge development - actually take place. The review further shows that, empirical illustrations of learning processes that occur in the context of vocational education and organised learning practices are relatively scarce. The findings can be typified in relation to our theoretical framework in terms of three learning processes, that is learning as a process of (a) belonging, becoming, and being, (b) recontextualization, and (c) negotiation of meaning and sense-making. We argue that more empirical research should be carried out, using the functions of vocational education and the three learning processes to better understand vocational learning.
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Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.
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