Steeds meer leerlingen groeien op met een andere taal dan het Nederlands. Zij krijgen onderwijs in een taal die niet hun eerste taal is. Op het gebied van toetsing brengt deze situatie uitdagingen met zich mee. Meertalige leerlingen kunnen, bijvoorbeeld in het rekenonderwijs, niet hun volledige potentieel aantonen wanneer zij beoordeeld worden in een taal die zij nog aan het verwerven zijn. Functioneel Meertalig Assessment (FMA; De Backer et al., 2017) biedt een mogelijke oplossing voor dit validiteitsprobleem, omdat FMA kennis en vaardigheden van leerlingen via een pallet aan evaluatievormen (bijv. toetsen, observaties, gesprekken) en via hun meertalige repertoires zichtbaar maakt. De daadwerkelijke toepassing van FMA staat echter nog in de kinderschoenen (De Backer, 2020). In de huidige studie verkenden we daarom hoe onderwijsprofessionals vormgeven aan en leren over deze nieuwe benadering van toetsing en in hoeverre een Lesson-Study-aanpak daarbij ondersteunend is. Professionals (N=15) van vijf basisscholen die onderwijs verzorgen aan jonge nieuwkomers voerden samen met een procesbegeleider (N=4) een zogenaamde Assessment Study uit. Een analyse van de FMA-praktijken die deze Assessment-Study-teams ontwikkelen en inzichten uit reflecties van deelnemers op het Assessment-Study-proces laten zien dat een Lesson-Study-methodiek onderwijsprofessionals een effectief kader biedt bij het ontwikkelen van en leren over FMA.
AIM: This paper is a report of the development and testing of the psychometric properties of an instrument to measure the accuracy of nursing documentation in general hospitals.BACKGROUND: Little information is available about the accuracy of nursing documentation. None of the existing instruments that quantify accuracy of nursing diagnoses, interventions, and progress and outcome evaluations are suitable to measure documentation in general hospital environments, nor were they intended for this purpose.METHOD: The D-Catch instrument, based on the Cat-ch-Ing instrument and the Scale for Degrees of Accuracy in Nursing Diagnoses, was developed in 2007-2008. Content validity of the D-Catch instrument was assessed by two Delphi panels, in which pairs of independent reviewers assessed 245 patient records in seven hospitals in the Netherlands. Construct validity was assessed by explorative factor analysis with principal components and varimax rotation. Internal consistency was measured by Cronbach's alpha. The inter-rater reliability of the D-Catch instrument was tested by calculating Cohen's weighted kappa (K(w)) for each pair of reviewers. Results. Quantity and quality variables were used to assess the accuracy of nursing documentation. Three constructs were identified in the factor analysis. 'Accuracy of the nursing diagnosis' was the only variable with substantial loading on component two (0.907) and a modest loading on component one (0.230). Internal consistency (Cronbach's alpha) was 0.722. The inter-rater reliability (K(w)) varied between 0.742 and 0.896.CONCLUSION: The D-Catch instrument is a valid and reliable measurement instrument to assess nursing documentation in general hospital settings.
The quality of teaching has a clear impact on student success, but how can good teaching be defined? The European QualiTePE research project, funded by the Erasmus+ programme and involving ten European countries, seeks to adress this question specifically for Physical Education (PE). The QualiTePE instrument was designed for use in teacher training and further training to enable criteria-based observation and assessment of the quality of Physical Education lessons. The instrument is designed for diverse PE teaching and learning scenarios, alongside teacher resources, facilitating the practical assessment of teaching quality in PE. The QualiTePE instrument quantifies teaching quality by assessing specific, observable teaching characteristics via questionnaire items. Each assessment is conducted by three different population groups: 1) the students 2) the PE teacher 3) an observer. The comparative analysis of the data collected from these three perspectives enables systematic and criteria‐based feedback for (prospective) teachers, identifies areas of improvement, and informs content development for PE across Europe. The QualiTePE digital web-based evaluation tool for assessing the “Quality of Teaching in Physical Education” is now available in English, German, French, Italian, Spanish, Dutch, Swedish, Slovenian, Czech and Greek.
Inzet van serious games als scholingsinstrument voor zorgprofessionals of als patiëntinterventie neemt sterk toe. Serious games kunnen kosten besparen en zorgkwaliteit verbeteren. (Potentiële) afnemers vragen, in lijn met het medische onderzoeksparadigma, vaak naar de klinische effectiviteit (internal validity) van deze games. Het gros van de Nederlandse game-ontwikkelaars bestaat echter uit kleine ondernemingen die het aan middelen en expertise ontbreekt om de hiervoor benodigde longitudinale onderzoekstrajecten uit te voeren. Tegelijkertijd tonen mkb’ers, meestal zonder ervan bewust te zijn, tijdens het game-ontwikkelproces al verschillende validiteitsvormen aan volgens het design-onderzoeksparadigma (face validity, construct validity, e.d.). Door dit niet bij hun afnemers kenbaar te maken, komt een constructieve dialoog over validiteit moeilijk op gang en lopen mkb’ers opdrachten mis. Het ontbreekt hen aan een begrippenkader en praktische handvatten. Bestaande raamwerken zijn nog te theorie-gedreven. Om mkb’ers te helpen de 'clash' te overbruggen tussen het medische en het design-onderzoeksparadigma, ontwikkelen lectoraten ICT-innovaties in de Zorg (Hogeschool Windesheim, penvoerder) en Serious Gaming (NHL Stenden Hogeschool) samen met elf mkb’ers, afnemers, studenten en experts in een learning community drie hulpmiddelen: •Checklist: praktische mkb-richtlijnen voor het vaststellen van validiteit; •Beslisboom: op basis waarvan mkb’ers onderbouwd de juiste validatiemethode kunnenselecteren; •Serious game: om samen met (potentiële) afnemers te spelen, zodat verschillende soortenvaliditeit expliciet benoemd worden. De hulpmiddelen worden inhoudelijk gevoed door casestudies waarin mkb’ers gevolgd worden in hoe validiteit momenteel wordt vastgesteld en geëxpliciteerd in het ontwikkelproces. Vervolgens brengen we de ontworpen hulpmiddelen in de mkb-praktijk voor evaluatie. Opgeleverde hulpmiddelen stellen mkb’ers in staat werkbare validatiemethoden toe te passen gedurende het game-ontwikkelproces om acceptabele bewijslast op te leveren voor potentiële afnemers, waardoor hun marktpositie versterkt. Ook draagt het project bij aan operationalisering van bestaande raamwerken en kunnen de hulpmiddelen in game design-curricula worden geïncorporeerd.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
