Feedback is one of the most powerful tools teachers can use to enhance student learning. In 2006, the Dutch Inspectorate of Education concluded from classroom observations that it is difficult for Dutch teachers to give their students good feedback in order to stimulate students' learning process and developmental progress. Similar problems were revealed in other school levels and countries, for example in secondary education and in Finland. Giving feedback during active learning may be even more troublesome for teachers. During active learning, students are working in small groups on different learning goals and undertake different learning activities at the same time. They need to achieve task-related goals as well as to develop the meta-cognitive knowledge and skills that are needed for active learning. Yet, teachers often seem unable to provide the feedback that is needed and they do not know how to support the development of meta-cognitive knowledge and skills.Therefore, this research project focused on ways to improve primary school teachers' feedback giving practices during active learning. The central research question is: How can primary school teachers learn to give optimal feedback to pupils during active learning? To answer this question, five studies have been conducted. In the first study, knowledge regarding teachers' feedback practices was gathered. A category system was developed based on the literature and empirical data. A total of 1465 teacher-student interactions of 32 teachers who practiced active learning in the domain of environmental studies in the sixth, seventh or eighth grade of 13 Dutch primary schools were videotaped and assessed using this system. Results showed that about half of the teacher-student interactions contained feedback. This feedback was usually focused on the tasks that were being performed by the students and on the ways in which these tasks were processed. Only 5% of the feedback was explicitly related to a learning goal. In their feedback, the teachers were directing (rather than facilitating) the learning processes. During active learning, however, feedback on meta-cognition and social learning is important. Feedback should be explicitly related to learning goals. In practice, these kinds of feedback appear to be scarce. In the second study, the problems these 32 primary school teachers perceive and the beliefs they hold regarding the provision of feedback were investigated. A writing task and an interview were conducted. It appeared that teachers believed that conditional teacher skills, especially time management, hindered them most from giving good feedback. The most widely held belief was that 'feedback should be positive'. Teachers also believed that it is important to adopt a facilitative way of giving feedback, but they found this difficult to implement. Only some teachers believed goal-directedness and a focus on student meta-cognition were important during active learning and teachers did not perceive problems regarding these aspects. In the third study, a professional development program (PDP) was developed, implemented and evaluated. The goals and content of the PDP were based on a review of the literature regarding feedback and active learning and on the results of the preceding studies. The design of the PDP was based on the extant literature regarding the features which are considered to be important for PDPs, including structural features, goal setting and characteristics of the professional development activities that are part of the program. Effects of this PDP on 16 primary schoolteachers' knowledge, beliefs, perceived problems and classroom behavior were examined via observations, a writing task and a questionnaire prior and twice after the program was implemented. Results showed that several aspects of feedback during active learning were improved, both in the short and in the long term. For example, teachers learned to believe that feedback must be goal-directed and that learning goals need to be communicated to students. In the classrooms, teachers related their feedback more often explicitly to the learning goals. In the fourth study, the extent to which teachers attributed the success of the PDP to each of the purposefully implemented features of the PDP was examined. The 16 teachers that participated in the PDP completed a questionnaire and four focus group interviews were conducted. Results indicated that teachers value most features quite highly; all features contributed to teachers' professional development according to the teachers themselves. The qualitative data was used to illustrate and specify the theoretical knowledge regarding the features that appeared to be effective in PDP's. Finally, in the fifth study, the learning process of two of the participating teachers was described in detail. Written reflections, as well as videotaped reflections during the video interaction training meetings were analyzed and related to the effects of the PDP on both teachers' knowledge, beliefs, perceived problems and classroom behavior during te course of the PDP. By relating the learning processes of these two teachers to the literature regarding professional development, we aimed for a rich understanding of the impact of the PDP on teachers' professional development.
In higher education, students often misunderstand teachers’ written feedback. This is worrisome, since written feedback is the main form of feedback in higher education. Organising feedback conversations, in which feedback request forms and verbal feedback are used, is a promising intervention to prevent misunderstanding of written feedback. In this study a 2 × 2 factorial experiment (N = 128) was conducted to examine the effects of a feedback request form (with vs. without) and feedback mode (written vs. verbal feedback). Results showed that verbal feedback had a significantly higher impact on students’ feedback perception than written feedback; it did not improve students’ self-efficacy, or motivation. Feedback request forms did not improve students’ perceptions, self-efficacy, or motivation. Based on these results, we can conclude that students have positive feedback perceptions when teachers communicate their feedback verbally and more research is needed to investigate the use of feedback request forms.
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De Tijdelijke Commissie Leraren onder leiding van Rinnooy Kan (2007) heeft gepleit voor een ster-kere rol van leraren bij het formuleren en bewaken van de professionele kwaliteit. Dat betekent dat (de beroepsgroep van) leraren een standaard moeten formuleren voor de professionele kwaliteit, voor de kennis en vaardigheden die de leden van het ‘gilde’ van leraren dienen te bezitten. Tegelijk moet de eigen professionele kwaliteit afgezet worden tegen die gildekennis.Dat geldt voor leraren, maar ook voor studenten. Ook zij moeten inzicht hebben in wat er aan professionele kwaliteit van leraren verwacht wordt en dat kunnen relateren aan het niveau van professionele kwaliteit dat zij op dat moment bezitten. Lerarenopleiders kunnen een bijdrage leveren aan het leren van studenten als zij bewuster omgaan met het geven van feedback. Door de rol van feedback in het leren van studenten te verkennen, en vragen te stellen over welke proces-sen gaande zijn bij studenten tijdens het ontvangen van feedback en welke kwaliteitseisen gesteld kunnen worden aan feedback, kunnen lerarenopleiders studenten beter ondersteunen in hun ontwikkeling. Dialoog is hierbij van essentieel belang.
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The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Veel mbo-opleidingen kiezen voor praktijkroutes, hybride leeromgevingen en gepersonaliseerde leerroutes. Dit levert dilemma’s op bij de afsluiting van de opleiding. Gebruikelijke examens passen vaak niet meer. Deze opleidingen willen informatie uit het onderwijs laten meewegen in de diplomabeslissing en een mix aan bewijzen gebruiken uit praktijk, werk en andere leeromgevingen.
In de schoonmaakbranche is de werkdruk hoog . Hierdoor worden gebouwen dagelijks niet goed genoeg schoongemaakt. Er heerst krapte op de arbeidsmarkt. Schoonmaakwerk is vooral handmatig werk en is ook zwaar werk. De schoonmaakbranche is dringend op zoek naar technologische oplossingen die het werk in de toekomst kunnen verlichten. Eén van die technologische oplossingen is de introductie van schoonmaakrobots , die op dit moment mondjesmaat op de markt worden gebracht. Schoonmaakorganisaties weten nog niet goed hoe deze robots efficiënt in te zetten, het vergt nog veel tijd om ze te kunnen gebruiken en schoonmaakmedewerkers zijn terughoudend om ermee te werken. Het project Assisted Cleaning Robots (ACR) richt zich op de volgende onderzoeksvraag: “hoe integreer je robottechnologie in het werkproces in de schoonmaakbranche, zodat een robot enerzijds zo optimaal mogelijk het werkproces ondersteunt, en anderzijds zo optimaal mogelijk met de mens samenwerkt.” Wat hierin optimaal is en hoe dit gemeten kan worden, is onderdeel van het onderzoek en is afhankelijk van de technologische mogelijkheden, de mensen die er mee werken, en de werkomgeving. In dit project werken Fontys Hogeschool Engineering, Fontys Hogeschool Techniek & Logistiek en de Haagse Hogeschool samen met schoonmaakorganisaties CSU en Hectas en andere bedrijven (toeleveranciers van schoonmaakrobots als ontwikkelaars), nationaal samenwerkingsverband Holland Robotics en brancheorganisatie Schoonmakend Nederland. Dit project kent een looptijd van twee jaar en gaat van start op 1 november 2021. In dit project worden nieuwe schoonmaakprocessen gedefinieerd en wordt op basis van deze processen technologie ontwikkeld (waar doorgaans eerst een nieuw product wordt ontwikkeld en daarna pas gekeken naar hoe dit product in te zetten). In dit project staat de mens die met de technologie in het proces moet gaan werken centraal. De technologie en het proces worden gevalideerd middels praktijktests met de betrokken schoonmaakorganisaties, op representatieve locaties. Hieruit worden lessen getrokken voor verbeteringen.
