The preference of students in competence-based Pre-Vocational Secondary Education (PVSE) for information processing strategies and the development of their body of knowledge were measured in a study that was carried out with 31 participants. The students' information processing strategies were measured by means of semi-structured interviews, questionnaires and think-aloud sessions. 26 of the 31 participants had a preference for surface processing strategies when working in workplace simulation. The other 5 students preferred deep learning. The learning environment appeared to elicit this surface level processing. The development of the body of knowledge of the students was measured by means of the concept mapping technique. For most students, an improvement of the body of knowledge took place in the course of the project in workplace simulation that was researched. Their knowledge became more elaborate and better organized. No significant relations between information processing strategies and the development of the concept maps could be found for the students participating in the research.
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The purpose of this study was to investigate relations between goal orientations, information processing strategies and development of conceptual knowledge of pre-vocational secondary education students (n=719; 14 schools). Students' preferences for certain types of goals and information processing strategies were examined using questionnaires. Conceptual knowledge was investigated by having students create concept maps before and after a learning project. Structural analyses showed that student preferences for mastery and performance goals positively affected their preferences for the use of deep and surface information processing strategies. Preferences for work avoidance goals negatively influenced preferences for deep and surface processing. Use of surface information processing strategies negatively affected the development of conceptual knowledge. Remarkably, no relation was found between students' preferences for deep processing strategies and development of conceptual knowledge.
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The preference of students in competence-based Pre-Vocational Secondary Education (PVSE) for information processing strategies and the development of their body of knowledge were measured in a study that was carried out with 31 participants. The students' information processing strategies were measured by means of semi-structured interviews, questionnaires and think-aloud sessions. 26 of the 31 participants had a preference for surface processing strategies when working in workplace simulation. The other 5 students preferred deep learning. The learning environment appeared to elicit this surface level processing. The development of the body of knowledge of the students was measured by means of the concept mapping technique. For most students, an improvement of the body of knowledge took place in the course of the project in workplace simulation that was researched. Their knowledge became more elaborate and better organized. No significant relations between information processing strategies and the development of the concept maps could be found for the students participating in the research.
DOCUMENT
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).
