Search results

Preparing undergraduate students for lifelong learning

This thesis presents an exploration of ‘how entrepreneurship education pedagogy can enhance undergraduate business students’ autonomous motivation for self-directed learning’. It has twin, equally valuable, purposes: to make an original theoretical contribution and to improve professional practice in this area. The work addresses the lack of pedagogical research in entrepreneurship education that focuses on learner development, with a specific aim at development of self-directed learning skills for lifelong learning. The research is approached with a concurrent, mixed methods design, comparing pre- and a post-EE, self-assessment survey results from 245 students, enrolled in a Young Enterprise venture creation programme, and a control group at a Dutch university. With the use of open-question surveys among the same population, during and after the EE modules, as well as from focus group discussions with a selection of participating students and teachers, explanation was sought for the observations drawn from the quantitative study. Significant relationships were found between students’ self-reported maturity of autonomy, self-efficacy, and motivation for learning, and in how these relate to self-directed learning readiness. Entrepreneurship education was found to significantly moderate the relationship between the learning characteristics and self-directed learning, and to strengthen of the students’ perceived readiness for self-directed learning. Explanation for the impact of EE were found to be related to the stage-wise, mixed pedagogy approach to learning, that combines authentic learning with a hierarchical approach to competence development, and supportive team dynamics. The research contributes to practice with a proposed conceptual framework for understanding how to prepare for self-directed learning readiness and a teaching-learning framework for its development in formal educational settings. It contributes to knowledge with its deeper understanding of how students experience learning in EE and how that affects their willingness to pursue learning opportunities.

Cluttering identified: differential diagnostics between cluttering, stuttering and speech impairment related to learning difficulties

Supporting Learning Analytics Adoption

Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.

The immediate influence of implicit motor learning strategies on spatiotemporal gait parameters in stroke patients

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.

Learning apart or together?

The present study aims to contribute to the body of knowledge on HRD in small businesses by providing a detailed investigation of the role that owner-managers play in enabling social learning and performance in small firms. The investigation focusses particularly on the specific relationships of the social-interdependence orientation and social competence of owner-managers with their social learning behaviour, as well as with the performance of their smallbusinesses within the pig-production sector in the Republic of Korea. A survey was conducted amongst nearly 200 Korean ownermanagers of pig farms. The results indicate that social interdependence orientations and social competencies have a significant relationship with social learning behaviour. Self-promotion and a cooperative orientation are especially important, with selfpromotion taking precedence for social learning behaviour of a more ‘internal’ nature, and a cooperative attitude being more important social learning behaviour of a more ‘external’ nature. Social competence and social interdependence did not have a significant relationship with performance, but social learning behaviour did. The results further highlight the importance of individual social characteristics to social learning behaviour occurring outside highly structured educational settings, in addition to demonstrating that the competence and attitudes required are determined by the type of interaction partner.

Where Is the Learning in Learning Analytics?

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.