In deze rapportage staat het functioneren en beoordelen van professionals in het hbo centraal. In het project Experimental Learning Labs: Functioneren en Beoordelen in Teams zijn we, mede dankzij de steun vanuit de stimuleringsregeling van Zestor, op zoek gegaan naar andere en innovatieve manieren om de HR-cyclus vorm te geven en onderlinge feedback in teams te stimuleren, op een manier die beter aansluit bij de ontwikkelingen in de organisatie, de sector en bij de wensen en behoeften van medewerkers. Het project is uitgevoerd door en met medewerkers van de Hogeschool van Amsterdam (HvA). Aan het project deden professionals in 3 teams, HR professionals en een aantal medewerkers van het lectoraat Samenwerkende Professionals (voorheen Teamprofessionalisering) mee. Door in ‘Experimental Learning Labs’ in een drietal verschillende teams met nieuwe vormen van aanspreken en feedback geven te experimenteren1, heeft het project input gegeven en inspiratie opgeleverd voor een meer passende kijk op en aanpak van de HR-cyclus binnen de hogeschool. Deze Experimental Learning Labs zijn uitgevoerd onder begeleiding van het lectoraat Samenwerkende Professionals in samenwerking met de HR-werkgroep ‘Functioneren & Beoordelen’
Voortdurende maatschappelijke veranderingen en uitdagingen vragen om samenwerking en een leven lang ontwikkelen. Vaak gebeurt dit in learning communities (innovatieve leerwerkomgevingen) waar organisaties grensoverstijgend samenwerken aan complexe vraagstukken. Bruggenbouwers (brokers) hebben een sleutelpositie in het ontwikkelen van deze learning communities om mensen en organisaties met elkaar te verbinden. Een veelzijdige rol die zich moeilijk laat definiëren. Bovendien voorzien organisaties niet altijd bewust in ondersteuning en ontwikkeling van deze bruggenbouwers. Op basis van een mixed-methodsbenadering voorziet dit onderzoek in de behoefte van een generieke rolbeschrijving met zeven vaardigheden. Hierbij wordt de invloed van kennis, ervaring en persoonskenmerken belicht. Bruggenbouwers werken intersectoraal over grenzen van organisaties heen en ondersteunen betrokken professionals en organisaties in hun samenwerking door politiek bewust en strategisch te handelen. Zij stimuleren kennisdeling en vertalen kennis naar diverse betrokkenen en contexten en onderzoeken daarbij de beroepspraktijk systematisch. Deze rolbeschrijving en de gewenste ondersteuning hierin biedt concrete handvatten om bruggenbouwers beter te selecteren, te waarderen en ook gerichter te investeren in hun professionele ontwikkeling. Deze investering is van cruciaal belang omwille van de katalyserende werking van de rol als bruggenbouwer om het voortdurend leren en ontwikkelen bij organisaties mogelijk te maken
Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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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).
