Background: A significant part of neurological rehabilitation focuses on facilitating the learning of motor skills. Training can adopt either (more) explicit or (more) implicit forms of motor learning. Gait is one of the most practiced motor skills within rehabilitation in people after stroke because it is an important criterion for discharge and requirement for functioning at home. Objective: The aim of this study was to describe the design of a randomized controlled study assessing the effects of implicit motor learning compared with the explicit motor learning in gait rehabilitation of people suffering from stroke. Methods: The study adopts a randomized, controlled, single-blinded study design. People after stroke will be eligible for participation when they are in the chronic stage of recovery (>6 months after stroke), would like to improve walking performance, have a slow walking speed (<1 m/s), can communicate in Dutch, and complete a 3-stage command. People will be excluded if they cannot walk a minimum of 10 m or have other additional impairments that (severely) influence gait. Participants will receive 9 gait-training sessions over a 3-week period and will be randomly allocated to an implicit or explicit group. Therapists are aware of the intervention they provide, and the assessors are blind to the intervention participants receive. Outcome will be assessed at baseline (T0), directly after the intervention (T1), and after 1 month (T2). The primary outcome parameter is walking velocity. Walking performance will be assessed with the 10-meter walking test, Dynamic Gait Index, and while performing a secondary task (dual task). Self-reported measures are the Movement Specific Reinvestment Scale, verbal protocol, Stroke and Aphasia Quality of Life Scale, and the Global Perceived Effect scale. A process evaluation will take place to identify how the therapy was perceived and identify factors that may have influenced the effectiveness of the intervention. Repeated measures analyses will be conducted to determine significant and clinical relevant differences between groups and over time. Results: Data collection is currently ongoing and results are expected in 2019. Conclusions: The relevance of the study as well as the advantages and disadvantages of several aspects of the chosen design are discussed, for example, the personalized approach and choice of measurements.
Background: Differential learning (DL) is a motor learning method characterized by high amounts of variability during practice and is claimed to provide the learner with a higher learning rate than other methods. However, some controversy surrounds DL theory, and to date, no overview exists that compares the effects of DL to other motor learning methods.Objective: To evaluate the effectiveness of DL in comparison to other motor learning methods in the acquisition and retention phase.Design: Systematic review and exploratory meta-analysis.Methods: PubMed (MEDLINE), Web of Science, and Google Scholar were searched until February 3, 2020. To be included, (1) studies had to be experiments where the DL group was compared to a control group engaged in a different motor learning method (lack of practice was not eligible), (2) studies had to describe the effects on one or more measures of performance in a skill or movement task, and (3) the study report had to be published as a full paper in a journal or as a book chapter.Results: Twenty-seven studies encompassing 31 experiments were included. Overall heterogeneity for the acquisition phase (post-pre; I2 = 77%) as well as for the retention phase (retention-pre; I2 = 79%) was large, and risk of bias was high. The meta-analysis showed an overall small effect size of 0.26 [0.10, 0.42] in the acquisition phase for participants in the DL group compared to other motor learning methods. In the retention phase, an overall medium effect size of 0.61 [0.30, 0.91] was observed for participants in the DL group compared to other motor learning methods.Discussion/Conclusion: Given the large amount of heterogeneity, limited number of studies, low sample sizes, low statistical power, possible publication bias, and high risk of bias in general, inferences about the effectiveness of DL would be premature. Even though DL shows potential to result in greater average improvements between pre- and post/retention test compared to non-variability-based motor learning methods, more high-quality research is needed before issuing such a statement. For robust comparisons on the relative effectiveness of DL to different variability-based motor learning methods, scarce and inconclusive evidence was found.
Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
De analyse van data over het leren van studenten kan waardevol zijn. 'Learning analytics' gebruikt studentdata om het leerproces te verbeteren. Welke organisatorische vaardigheden hebben Nederlandse instellingen voor hoger onderwijs nodig om learning analytics succesvol in te zetten?Doel We onderzoeken welke organisatievaardigheden er nodig zijn om in het hoger onderwijs met 'learning analytics' te werken. Met learning analytics krijgen studenten, docenten en studiebegeleiders inzicht in het leerproces. Dit doen ze door data van studenten te analyseren. In de praktijk blijkt het lastig voor onderwijsinstellingen om hier over de hele breedte van de organisatie mee te gaan werken. We kijken in dit onderzoek welke vaardigheden er nodig zijn binnen een organisatie om 'learning analytics' slim in te zetten. Resultaten Dit onderzoek loopt. Tot nu toe hebben we drie wetenschappelijke artikelen gepubliceerd: A First Step Towards Learning Analytics: Implementing an Experimental Learning Analytics Tool Where is the learning in learning analytics? A systematic literature review to identify measures of affected learning From Dirty Data to Multiple Versions of Truth: How Different Choices in Data Cleaning Lead to Different Learning Analytics Outcomes Looptijd 01 december 2016 - 01 december 2020 Aanpak Het onderzoek bestaat uit literatuuronderzoek, een case study bij Nederlandse onderwijsinstellingen en een validatieproject. Dit leidt tot de ontwikkeling van een Learning Analytics Capability Model (LACM): een model dat beschrijft welke organisatorische vaardigheden nodig zijn om learning analytics in de praktijk toe te passen.
It is predicted that 5 million rural jobs will have disappeared before 2016. These changes do notonly concern farmers. In their decline all food chain related SMEs will be affected severely. Newbusiness opportunities can be found in short food supply chains. However, they can onlysucceed if handled professionally and on a proper scale. This presents opportunities on 4interconnected strands:Collect market relevant regional dataDevelop innovative specialisation strategies for SMEsForge new forms of regional cooperation and partnership based on common benefits andshared values.Acquire specific skillsREFRAME takes up these challenges. In a living lab of 5 regional pilots, partners willdemonstrate the Regional Food Frame (RFF) as an effective set of measures to scale up andaccommodate urban food demands and regional supplies. New data will reveal the regions’ ownstrengths and resources to match food demand and supply. REFRAME provides a supportinfrastructure for food related SMEs to develop and implement their smart specializationstrategies in food chains on the urban-rural axis. On their way towards a RFF, all pilots will use a5-step road map. A transnational learning lab will be set up in support of skill development andtraining of all stakeholders. REFRAME pools the know-how needed to set up these Regional FoodFrames in a transnational network of experts, each closely linked and footed in its own pilotregion.
