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 paper describes some explorations on the concept of disassemblability as an important circularity indicator for products because of its severe impact on reuse value. Although usefulness of the concept for determining disassembly strategies and for improving circular product design clearly shows in earlier studies, the link with Industry 4.0 (I4.0)-related process innovation is still underexposed. For further technical development of the field of remanufacturing, research is needed on tools & training for operators, diagnostics, disassembly/repair instructions and forms of operator support. This includes the use of IoT and cobots in remanufacturing lines for automatic disassembly, sorting and recognition methods; providing guidance for operators and reduction of change-over times. A prototype for a disassembly work cell for a mobile phone has been developed together with researchers and students. This includes the removal of screws by means of a cobot using both vision & the available info in the product’s Bill-Of-Materials, the removal of covers, opening of snap fits and replacement of modules. This prototyping demonstrates that it is relatively easy to automate disassembly operations for an undamaged product, that has been designed with repairability in mind and for which product data and models are available. Process innovations like robotisation influence the disassemblability in a positive way, but current indicators like a Disassembly Index (DI) can’t reflect this properly. This study therefore concludes with suggestions for an evaluation of disassemblability by looking at the interaction between product, process and resources in a coherent way.
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Even learners with good language ability do not automatically engage in interactional encounters in the foreign language. Affective factors, such as speakers’ willingness to communicate (WTC), self-confidence and enjoyment of oral interaction play an important role in this (e.g. Dewaele & MacIntyre, 2014; MacIntyre, 2002). Little is known, however, about the effects of different instructional approaches on learner affect in oral interaction in the foreign language classroom. In a randomized experiment with Dutch pre-vocational learners (N = 147), we evaluated the effects of three newly developed instructional programmes for English as a foreign language (EFL). These programmes differed in instructional focus (form-focused vs interaction strategies-oriented) and type of task (pre-scripted language tasks vs information gap tasks). Multilevel repeated measures analyses revealed that learners’ enjoyment of EFL oral interaction was not affected by instruction, that WTC decreased over time, and that self-confidence was positively affected by combining information gap tasks with interactional strategies instruction. In addition, regression analyses revealed that development in learners’ WTC and enjoyment did not have predictive value for task achievement in EFL oral interaction, but that development in self-confidence did explain task achievement in trained interactional contexts. These results suggest that it is worthwhile for practitioners to address the development of self-confidence in their language lessons, and that they could do so my combining the use of information gap tasks with interactional strategy instruction that includes compensation-and meaning negotiation strategies.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
