To cope with changing demands from society, higher education institutes are developing adaptive curricula in which a suitable integration of workplace learning is an important factor. Automated feedback can be used as part of formative assessment strategies to enhance student learning in the workplace. However due to the complex and diverse nature of workplace learning processes, it is difficult to align automated feedback to the needs of the individual student. The main research question we aim to answer in this design-based study is: ‘How can we support higher education students’ reflective learning in the workplace by providing automated feedback while learning in the workplace?’. Iterative development yielded 1) a framework for automated feedback in workplace learning, 2) design principles and guidelines and 3) an application prototype implemented according to this framework and design knowledge. In the near future, we plan to evaluate and improve these tentative products in pilot studies. https://link.springer.com/chapter/10.1007/978-3-030-25264-9_6
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Background: This follow-up study investigated the year-round effects of a four-week randomized controlled trial using different types of feedback on employees’ physical activity, including a need-supportive coach intervention. Methods: Participants (n=227) were randomly assigned to a Minimal Intervention Group (MIG; no feedback), a Pedometer Group (PG; feedback on daily steps only), a Display Group (DG; feedback on daily steps, on daily moderateto-vigorous physical activity [MVPA] and on total energy expenditure [EE]), or a Coaching Group (CoachG; same as DG with need supportive coaching). Daily physical activity level (PAL; Metabolic Equivalent of Task [MET]), number of daily steps, daily minutes of moderate to vigorous physical activity (MVPA), active daily EE (EE>3 METs) and total daily EE were measured at five time points: before the start of the 4-week intervention, one week after the intervention, and 3, 6, and 12 months after the intervention. Results: For minutes of MVPA, MIG showed higher mean change scores compared with the DG. For steps and daily minutes of MVPA, significantly lower mean change scores emerged for MIG compared with the PG. Participants of the CoachG showed significantly higher change scores in PAL, steps, minutes of MVPA, active EE, total EE compared with the MIG. As hypothesized, participants of the CoachG had significantly higher mean change scores in PAL and total EE compared with groups that only received feedback. However, no significant differences were found for steps, minutes of MVPA and active EE between CoachG and PG. Conclusions: Receiving additional need-supportive coaching resulted in a higher PAL and active EE compared with measurement (display) feedback only. These findings suggest to combine feedback on physical activity with personal coaching in order to facilitate long-term behavioral change. When it comes to increasing steps, minutes of MVPA or active EE, a pedometer constitutes a sufficient tool. Trial registration: Clinical Trails.gov NCT01432327. Date registered: 12 September 2011
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Presenting is considered as a core skill for the higher-educated professional (De Grez, 2009). However, many graduated students often fail to show effective presentation behaviors (Chan, 2011) and suffer from presentation anxiety Smith & Sodano, 2011). The development of presentation skills, therefore, is a crucial objective in higher education. While previous research emphasized the essence of practice and feedback opportunities for fostering students’ presentation skills and overcoming presentation anxiety (Van Ginkel et al., 2015), issues have been reported in educational practice that prevent the optimal development of the time consuming skill. These issues involve, amongst others, time constrains and the high workload of teachers (Adubra et al., 2019). Interestingly, studies have shown that innovative technologies such as Virtual Reality (VR) are valuable for offering practice opportunities and delivering personalized, automated feedback within presentation tasks (Van Ginkel et al., 2019). However, the previously studied automated feedback consisted of quantitative feedback reports which had to be interpreted by a teacher. Nowadays, technological developments allow the conversion of quantitative information into qualitative feedback messages that are constructed based on high-quality feedback criteria (Hattie & Timperley, 2007). Therefore, this experimental study aims to investigate the impact of qualitative automated feedback messages on students’ presentation skills (post-test only) and the development of presentation anxiety (pre-test post-test design). This experimental condition is compared with a validated control condition in which a teacher interprets quantitative, automatedfeedback reports. For data collection, validated rubrics and questionnaires are adopted. Besides, perceptions towards the utility of the feedback are assessed. The results of this study reveal no significant difference in presentation skills scores between the two feedback conditions. Moreover, students in both groups perceived the feedback and the feedback source as equally valuable for their presentation skills development. Interestingly, a significant decrease in presentation anxiety was determined from pre-test to post-test, without a significant differential impact. Findings of this study suggest that the integration of qualitative feedback messages in VR is effective for students’ presentation skills development. Moreover, practicing a presentation in VR and receiving automated feedback significantly decreases presentation anxiety. Insights from this study contribute to reducing the workload of teachers and challenging teachers in professionalizing to their new roles as coaches supporting students’ learning processes (Adubra et al., 2019). Future studies should focus on how effectively integrating peer-to-peer learning in VR-based education could further support teachers in constructing skills education within the digital era.
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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.
Het project FIXAR richt zich op het beantwoorden van de vraag: Hoe kan de luchtvaart- en windenergiesector composietenreparaties middels geautomatiseerde technologieën economisch verantwoord maken? Deze vraag komt voort uit eerdere ervaringen in RAAK-mkb projecten op het gebied van composietfabricage, oriëntatie op de nationale en internationale markt en uit de feedback van het betrokken mkb. Het mkb staat voor de uitdaging kennis en ervaring met automatiseringsoplossingen op te doen en nieuwe inspectietechnologieën in te voeren, wil het de groeiende behoefte aan composietenreparaties het hoofd bieden. De doelstelling van het project is dan ook, het door praktijkgericht onderzoek ontwikkelen van geautomatiseerde methoden voor duurzame geautomatiseerde composietenreparaties die technisch- en economisch haalbaar zijn. Om dit doel te bereiken wordt door Hogeschool Inholland samengewerkt met een aantal kennisinstituten en mkb-partners. Het project is opgebouwd rondom vier deelonderzoeken. Hiermee zijn alle aspecten van composietenreparaties gedekt; hulpmiddelen voor geautomatiseerde reparaties, inspectie en validatie, materiaalonderzoek en opleiding van medewerkers. Gelet op de state of the art-kennis, ligt de focus op luchtvaart en windenergie. Het zijn namelijk juist deze twee sectoren die het meest van elkaar kunnen profiteren. Binnen de deelonderzoeken komen state of the art-zaken aan bod als drones en Augement Reality. Aangezien het onderzoek zich richt op actuele problemen bij de bedrijven, zal een deel van het onderzoek bij de bedrijven zelf plaatsvinden en kunnen deze bedrijven direct profiteren van de resultaten van het onderzoek. In het onderwijs komen stage- en afstudeerplekken beschikbaar voor de studenten van de deelnemende hogescholen. Daarnaast vindt er een duurzame vertaalslag plaats van de projectresultaten en bevindingen middels het realiseren van onderwijsmateriaal t.b.v. de curricula van de opleidingen aviation, luchtvaarttechnologie, werktuigbouwkunde, en technische informatica. Het project heeft een blijvende impact op de beroepspraktijk omdat het deelnemende mkb met de resultaten uit dit project hun kennis van reparatieprocessen op hoger niveau brengt.
Het analyseren van grote gegevensbestanden om de kwaliteit van het onderwijs te verbeteren is een hot item. De toepassing van learning analytics kan het onderwijs verbeteren. Wij doen onderzoek naar learning analytics en de vaardigheden die gebruikers daarbij nodig hebben.Doel Wij onderzoeken wat de gevolgen zijn van databewerking op de uitkomsten van learning analytics. En welke vaardigheden hebben gebruikers nodig om deze systemen zinvol te gebruiken? Learning analytics Learning analytics is het meten, verzamelen, analyseren en rapporteren van data van studenten en hun omgeving om het leren en de leeromgeving te begrijpen en te verbeteren. Het gebruik van learning analyticssystemen Het realiseren van grote delen van de onderwijsvisie van Hogeschool Utrecht is sterk verbonden met de succesvolle uitvoering van analyses op studentniveau. Het gebruik van learning analyticssystemen is niet vanzelfsprekend. De ontwerpers en ontwikkelaars van deze systemen moeten helder zijn over hun ontwerpkeuzes (zoals manieren van databewerking en de werking van algoritmes). Anderzijds moeten studenten en docenten beschikken over datavaardigheden om deze systemen op een zinvolle manier te gebruiken. Resultaten Dit onderzoek loopt. Na afloop vind je hier een samenvatting van de resultaten. In juli 2019 verscheen het volgende artikel van de onderzoekers: Automated Feedback for Workplace Learning in Higher Education. Looptijd 01 september 2017 - 31 december 2020 Aanpak We hebben eerst verkennend onderzoek gedaan door een case study waarin onderzocht is wat de effecten zijn van verschillende keuzes in de data cleaning op de uitkomsten van de data-analyse. Vanaf september 2019 gaan we onderzoeken welke datavaardigheden studenten nodig hebben om learning analytics-systemen effectief te gebruiken.
