The principal aim of this study is to explore the relations between work domains and the work-related learning of workers. The article is intended to provide insight into the learning experiences of Dutch police officers during the course of their daily work. Interviews regarding actual learning events and subsequent changes in knowledge, skills or attitudes were conducted with police officers from different parts of the country and in different stages of their careers. Interpretative analyses grounded in the notion of intentionality and developmental relatedness revealed how and in what kinds of work domains police officers appear to learn. HOMALS analysis showed work-related learning activities to vary with different kinds of work domains. The implications for training and development involve the role of colleagues in different hierarchical positions for learning and they also concern the utility of the conceptualisation of work-related learning presented here.
In summarizing the research on collaborative learning, the quest for the holy grail of effective collaborative learning has not yet ended. The use of the GLAID framework tool for the design of collaborative learning in higher education may contribute to better aligned designs and hereby contribute to more effective collaborative learning. The GLAID framework may help monitor, evaluate and redesign projects and group assignments. We know that the perception of the quality of the task, and the extent to which students feel engaged, influences the perception of students of how much they learn from a GLA. However, perceptions alone are only an indication of what is learned. A next step is to study exactly what those learning outcomes are. This leads to a more difficult question: how can we measure the learning outcomes? Although a variety of research underlines the large potential of collaboration for learning outcomes, the exact learning outcomes of team learning can only be partly foretold. During collaborative learning students could partly achieve the same or similar learning outcomes, but as each individual learning internalizes what is learned from the collaborative learning by his/her given prior experiences and knowledge, the learning outcomes of collaborative learning are probabilistic (Strijbos, 2011), and therefore attaining specific learning outcomes is likely but not guaranteed. If learning outcomes are different per individual and are probabilistic, how can we measure those learning outcomes? Wenger, Trayner, & De Laat (2011) regard the outcomes of learning communities as value creations that have an individual outcome and a group outcome. This value creation induced by collaborative learning consists, for example, of changed behaviour in the working environment as well as the production of useful products or artefacts. Tillema (2006) also describes that communities of inquiry can lead to the design of conceptual artefacts: products that are useful for a professional working environment.
On the Open Research Amsterdam website, the Digital Production Research Group presented its main projects and achievements.--Dutch:Verbinding onderwijs, onderzoek en praktijkIn 2017 is het Robot Lab van de Hogeschool van Amsterdam (HvA) opgericht. Zij transformeren onder andere sloophout tot nieuwe meubels!In deze collectie leest u meer over het Robot Lab en de projecten en die hier worden uitgevoerd.
Carboxylated cellulose is an important product on the market, and one of the most well-known examples is carboxymethylcellulose (CMC). However, CMC is prepared by modification of cellulose with the extremely hazardous compound monochloracetic acid. In this project, we want to make a carboxylated cellulose that is a functional equivalent for CMC using a greener process with renewable raw materials derived from levulinic acid. Processes to achieve cellulose with a low and a high carboxylation degree will be designed.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
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.
