University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting.
University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting. https://www.scienceguide.nl/2021/06/leren-van-docentteams-vraagt-om-gezamenlijk-leiderschap/
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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.
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.
Wil je als docententeam meer zicht krijgen op het gehele toetsprogramma? Wil je kritisch kijken naar mogelijke verbeterpunten? Of ben je bezig met herontwerp? Met KIT2.0 kijk je als opleidingsteam vanuit de principes van programmatisch toetsen naar de inrichting van de opleiding.Doel Met KIT2.0 willen we opleidingsteams helpen om kritisch naar het curriculum en het toetsprogramma te kijken. Dit doen we aan de hand van vijf kwaliteitscriteria: fitness for purpose, validiteit, leerfunctie, beslisfunctie en condities. Resultaten Op de website van KIT2.0 vind je informatie en filmpjes met verdere uitleg. Via de website kun je ook (gratis) inloggen en zelf aan slag met KIT2.0. Op de website www.husite.nl/toetsing vind je informatie en praktijkvoorbeelden over programmatisch toetsen Blog over interview met Liesbeth Baartman over KIT2.0. Korte uitleg van dr. Liesbeth Baartman (2017) programmatisch toetsen. Toetsbijeenkomst Hogeschool van Rotterdam. Keynote van dr. Liesbeth Baartman (2017) met een inleiding over toetsprogramma’s. Fontys Toetscongres. Baartman, L.K.J., Kloppenburg, R., & Prins, F.J. (2017). Kwaliteit van toetsprogramma’s. In H. van Berkel, A. Bax, & D. Joosten-ten-Brinke (Red.). Toetsen in het Hoger Onderwijs, pp.38-49. Bohn Stafleu van Loghum Van der Vleuten, C.P.M., Schuwirth, L.T.W., Driessen, E., Dijkstra, J., Tigelaar, D., Baartman, L.K.J., & Van Tartwijk, J. (2012). A model for programmatic assessment fit for purposes. Medical Teacher, 34, 205-214. Dronkers, J., de Kwant, E., Kruitwagen, C., & Baartman, L. (2017). Kwantitatieve analyse van een toetsprogramma. Examens, 3, augustus. Looptijd 01 september 2018 - 01 september 2020 Aanpak KIT2.0 is gebaseerd op wetenschappelijk onderzoek naar programmatisch toetsen. De oorsprong van KIT2.0 ligt in de promotieonderzoeken van dr. Liesbeth Baartman en dr. Raymond Kloppenburg (waaruit KIT1.0 voortkwam). KIT2.0 is ontwikkeld op basis van nieuwste inzichten in de wetenschappelijk literatuur over programmatisch toetsen én 10 jaar praktijkervaringen. KIT2.0 is ontwikkeld in valideringsrondes met opleidingen en wetenschappers. Meedoen? Wil je als opleiding meedoen aan het onderzoek naar KIT2.0? Neem dan contact op met Liesbeth Baartman. We werken aan de evaluatie en verbetering van KIT2.0 op basis van praktijkervaringen.
Aanleiding De luchtvaart wordt steeds veiliger. Toch zijn er alleen al in Nederland jaarlijks zo'n 11.000 issues met luchtvaartveiligheid. Wereldwijd vinden er elke dag ongelukken plaats die leiden tot schade aan vliegtuigen. Om de veiligheid verder te verbeteren is er nieuwe internationale regelgeving opgesteld. Onder deze regels moeten de maatschappijen alle incidenten en ongelukken analyseren en zo veiligheidsrisico's identificeren nog voordat deze zich voordoen. Het probleem is dat kleine en middelgrote luchtvaartmaatschappijen onvoldoende vliegbewegingen maken om genoeg goede data hiervoor te hebben. Doelstelling De centrale vraag die de onderzoekers in dit RAAK-project willen beantwoorden: Wat is de relatie tussen veiligheidsmanagement en veiligheidsperformance van luchtvaartmaatschappijen? Het onderzoek wil kleine en middelgrote luchtvaartmaatschappijen helpen bij het meten van de veiligheid van hun bedrijf, zonder dat ze grote hoeveelheden veiligheidsdata tot hun beschikking hebben. Het onderzoek zal geschikte veiligheidsindicatoren identificeren, een longlist ontwikkelen met meetwaarden voor safetymanagement, en een shortlist genereren en valideren van bruikbare meetwaarden. Deze kennis wordt vertaald in een online dashboard voor de industrie, zodat de veiligheid objectiever beoordeeld kan worden. Beoogde resultaten Een concreet resultaat van dit project is een online dashboard waarmee kleine en middelgrote luchtvaartmaatschappijen hun veiligheid kunnen beoordelen, inclusief handleiding. Er zullen masterclasses veiligheid worden georganiseerd voor de luchtvaartindustrie. Het projectteam zal de opgedane kennis verspreiden via wetenschappelijke artikelen in relevante peer-reviewed tijdschriften, een website, presentaties bij bedrijven en tijdens bijeenkomsten, en een afsluitende conferentie.
