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
The rising rate of preprints and publications, combined with persistent inadequate reporting practices and problems with study design and execution, have strained the traditional peer review system. Automated screening tools could potentially enhance peer review by helping authors, journal editors, and reviewers to identify beneficial practices and common problems in preprints or submitted manuscripts. Tools can screen many papers quickly, and may be particularly helpful in assessing compliance with journal policies and with straightforward items in reporting guidelines. However, existing tools cannot understand or interpret the paper in the context of the scientific literature. Tools cannot yet determine whether the methods used are suitable to answer the research question, or whether the data support the authors’ conclusions. Editors and peer reviewers are essential for assessing journal fit and the overall quality of a paper, including the experimental design, the soundness of the study’s conclusions, potential impact and innovation. Automated screening tools cannot replace peer review, but may aid authors, reviewers, and editors in improving scientific papers. Strategies for responsible use of automated tools in peer review may include setting performance criteria for tools, transparently reporting tool performance and use, and training users to interpret reports.
Background:An eHealth tool that coaches employees through the process of reflection has the potential to support employees with moderate levels of stress to increase their capacity for resilience. Most eHealth tools that include self-tracking summarize the collected data for the users. However, users need to gain a deeper understanding of the data and decide upon the next step to take through self-reflection.Objective:In this study, we aimed to examine the perceived effectiveness of the guidance offered by an automated e-Coach during employees’ self-reflection process in gaining insights into their situation and on their perceived stress and resilience capacities and the usefulness of the design elements of the e-Coach during this process.Methods:Of the 28 participants, 14 (50%) completed the 6-week BringBalance program that allowed participants to perform reflection via four phases: identification, strategy generation, experimentation, and evaluation. Data collection consisted of log data, ecological momentary assessment (EMA) questionnaires for reflection provided by the e-Coach, in-depth interviews, and a pre- and posttest survey (including the Brief Resilience Scale and the Perceived Stress Scale). The posttest survey also asked about the utility of the elements of the e-Coach for reflection. A mixed methods approach was followed.Results:Pre- and posttest scores on perceived stress and resilience were not much different among completers (no statistical test performed). The automated e-Coach did enable users to gain an understanding of factors that influenced their stress levels and capacity for resilience (identification phase) and to learn the principles of useful strategies to improve their capacity for resilience (strategy generation phase). Design elements of the e-Coach reduced the reflection process into smaller steps to re-evaluate situations and helped them to observe a trend (identification phase). However, users experienced difficulties integrating the chosen strategies into their daily life (experimentation phase). Moreover, the identified events related to stress and resilience were too specific through the guidance offered by the e-Coach (identification phase), and the events did not recur, which consequently left users unable to sufficiently practice (strategy generation phase), experiment (experimentation phase), and evaluate (evaluation phase) the techniques during meaningful events.Conclusions:Participants were able to perform self-reflection under the guidance of the automated e-Coach, which often led toward gaining new insights. To improve the reflection process, more guidance should be offered by the e-Coach that would aid employees to identify events that recur in daily life. Future research could study the effects of the suggested improvements on the quality of reflection via an automated e-Coach.
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.
