The main purpose of the research was the development and testing of an assessment tool for the grading of Dutch students' performance in information problem solving during their study tasks. Scholarly literature suggests that an analytical scoring rubric would be a good tool for this.Described in this article are the construction process of such a scoring rubric and the evaluation of the prototype based on the assessment of its usefulness in educational practice, the efficiency in use and the reliability of the rubric. To test this last point, the rubric was used by two professors when they graded the same set of student products. 'Interrater reliability' for the professors' gradings was estimated by calculating absolute agreement of the scores, adjacent agreement and decision consistency. An English version of the scoring rubric has been added to this journal article as an appendix. This rubric can be used in various discipline-based courses in Higher Education in which information problem solving is one of the learning activities. After evaluating the prototype it was concluded that the rubric is particularly useful to graders as it keeps them focussed on relevant aspects during the grading process. If the rubric is used for summative evaluation of credit bearing student work, it is strongly recommended to use the scoring scheme as a whole and to let the grading work be done by at least two different markers. [Jos van Helvoort & the Chartered Institute of Library and Information Professionals-Information Literacy Group]
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Formative assessment (FA) is an effective educational approach for optimising student learning and is considered as a promising avenue for assessment within physical education (PE). Nevertheless, implementing FA is a complex and demanding task for in-service PE teachers who often lack formal training on this topic. To better support PE teachers in implementing FA into their practice, we need better insight into teachers’ experiences while designing and implementing formative strategies. However, knowledge on this topic is limited, especially within PE. Therefore, this study examined the experiences of 15 PE teachers who participated in an 18-month professional development programme. Teachers designed and implemented various formative activities within their PE lessons, while experiences were investigated through logbook entries and focus groups. Findings indicated various positive experiences, such as increased transparency in learning outcomes and success criteria for students as well as increased student involvement, but also revealed complexities, such as shifting teacher roles and insufficient feedback literacy among students. Overall, the findings of this study underscore the importance of a sustained, collaborative, and supported approach to implementing FA.
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The purpose of this study was to provide insight into the interplay between student perceptions of competence-based assessment and student self-efficacy, and how this influences student learning outcomes. Results reveal that student perceptions of the form authenticity aspect and the quality feedback aspect of assessment do predict student self-efficacy, confirming the role of mastery experiences and social persuasions in enhancing student self-efficacy as stated by social cognitive theory. Findings do not confirm mastery experiences as being a stronger source of self-efficacy information than social persuasions. Study results confirm the predictive role of students’ self-efficacy on their competence outcomes. Mediation analysis results indicate that student’s perceptions of assessment have an indirect effect on student’s competence evaluation outcomes through student’s self-efficacy. Study findings highlight which assessment characteristics, positively influencing students’ learning, contribute to the effectiveness of competence-based education. Limitations of the study and directions for future research are indicated.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
The Dutch Environmental Vision and Mobility Vision 2050 promote climate-neutral urban growth around public transport stations, envisioning them as vibrant hubs for mobility, community, and economy. However, redevelopment often increases construction, a major CO₂ contributor. Dutch practice-led projects like 'Carbon Based Urbanism', 'MooiNL - Practical guide to urban node development', and 'Paris Proof Stations' explore integrating spatial and environmental requirements through design. Design Professionals seek collaborative methods and tools to better understand how can carbon knowledge and skills be effectively integrated into station area development projects, in architecture and urban design approaches. Redeveloping mobility hubs requires multi-stakeholder negotiations involving city planners, developers, and railway managers. Designers act as facilitators of the process, enabling urban and decarbonization transitions. CARB-HUB explores how co-creation methods can help spatial design processes balance mobility, attractiveness, and carbon neutrality across multiple stakeholders. The key outputs are: 1- Serious Game for Co-Creation, which introduces an assessment method for evaluating the potential of station locations, referred to as the 4P value framework. 2-Design Toolkit for Decarbonization, featuring a set of Key Performance Indicators (KPIs) to guide sustainable development. 3- Research Bid for the DUT–Driving Urban Transitions Program, focusing on the 15-minute City Transition Pathway. 4- Collaborative Network dedicated to promoting a low-carbon design approach. The 4P value framework offers a comprehensive method for assessing the redevelopment potential of station areas, focusing on four key dimensions: People, which considers user experience and accessibility; Position, which examines the station's role within the broader transport network; Place-making, which looks at how well the station integrates into its surrounding urban environment; and Planet, which addresses decarbonization and climate adaptation. CARB-HUB uses real cases of Dutch stations in transition as testbeds. By translating abstract environmental goals into tangible spatial solutions, CARB-HUB enables scenario-based planning, engaging designers, policymakers, infrastructure managers, and environmental advocates.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
