Many studies report changes taking place in the field of higher education, changes which present considerable challenges to educational practice. Educational science should contribute to developing design guidance, enabling practitioners to respond to these challenges. Design patterns, as a form of design guidance, show potential since they promise to facilitate the design process and provide common ground for communication. However, the potential of patterns has not been fully exploited yet. We have proposed the introduction of a task conceptualization as an abstract view of the concept chosen as central: the task. The choice of the constituting elements of the task conceptualization has established an analytical perspective for analysis and (re)design of (e)learning environments. One of the constituting elements is that of ‘boundary objects’, which has added a focus on objects facilitating the coordination, alignment and integration of collaborative activities. The presented task conceptualization is deliberately generic in nature, to ease the portability between schools of thought and make it suitable for a wide target audience. The conceptualization and the accompanying graphical and textual representations have shown much promise in supporting the process of analysis and (re)design and add innovative insights to the domain of facilitating the creation of design patterns.
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In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff (tmdiff), a technique to support model differencing for textual languages. tmdiff requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, tmdiff can then determine which elements are the same across revisions, and which are added or removed. As a result, tmdiff brings the benefits of model differencing to textual languages.
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Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common- Sense Model of Self-Regulation (E-CSM). Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard deviation 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout.
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