University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting.
Background/Aims: Analogy learning, a motor learning strategy that uses biomechanical metaphors to chunk together explicit rules of a to-be-learned motor skill. This proof-of-concept study aims to establish the feasibility and potential benefits of analogy learning in enhancing stride length regulation in people with Parkinson’s. Methods: Walking performance of thirteen individuals with Parkinson’s was analysed using a Codamotion analysis system. An analogy instruction; “following footprints in the sand” was practiced over 8 walking trials. Single- and dual- (motor and cognitive) task conditions were measured before training, immediately after training and 4-weeks post training. Finally, an evaluation form was completed to examine the interventions feasibility. Findings: Data from 12 individuals (6 females and 6 males, mean age 70, Hoehn and Yahr I-III) were analysed, one person withdrew due to back problems. In the single task condition, statistically and clinically relevant improvements were obtained. A positive trend towards reducing dual task costs after the intervention was demonstrated, supporting the relatively implicit nature of the analogy. Participants reported that the analogy was simple to use and became easier over time. Conclusions: Analogy learning is a feasible and potentially implicit (i.e. reduced working memory demands) intervention to facilitate walking performance in people with Parkinson’s.
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.
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.
