In this study data-feedback in a cyclic model of data-driven teaching was used to enhance the teaching behavior of students registered in a master course for teachers. Differences between pre- and post-test measures in a simple one-group pre-test post-tests design proved to be significant with effect sizes ranging from d = 0.29 to d = 0.76. Improving teaching behavior in a time span of only six weeks on average is remarkable since earlier studies indicated that it takes over 15 years to master complex teaching skills with a ‘natural development’ of teaching skills of about 25% of a standard deviation.
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In higher education, students often misunderstand teachers’ written feedback. This is worrisome, since written feedback is the main form of feedback in higher education. Organising feedback conversations, in which feedback request forms and verbal feedback are used, is a promising intervention to prevent misunderstanding of written feedback. In this study a 2 × 2 factorial experiment (N = 128) was conducted to examine the effects of a feedback request form (with vs. without) and feedback mode (written vs. verbal feedback). Results showed that verbal feedback had a significantly higher impact on students’ feedback perception than written feedback; it did not improve students’ self-efficacy, or motivation. Feedback request forms did not improve students’ perceptions, self-efficacy, or motivation. Based on these results, we can conclude that students have positive feedback perceptions when teachers communicate their feedback verbally and more research is needed to investigate the use of feedback request forms.
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Studenten ervaren maar beperkt dat ze regie kunnen nemen op hun eigen leerproces. Regie nemen op je leerproces vraagt van studenten dat zij zelfregulerende vaardigheden bezitten. Ontwikkelingsgerichte feedback biedt enorme kansen om de zelfregulatie van studenten te ontwikkelen. De processen die ten grondslag liggen aan feedback en zelfregulatie kennen grote overeenkomsten. Wil feedback bijdragen aan zelfregulatie, dan moet de student een actievere rol krijgen in het feedbackproces. Om het gesignaleerde probleem van te weinig zelfregulatie door studenten en een te weinig actieve rol van studenten in het feedbackproces aan te pakken, zijn in dit project een aantal interventies ingezet gericht op het ontwikkelen van feedbackgeletterdheid bij studenten. De innovatie in dit project bestaat uit een feedbacktraining die wordt uitgevoerd in het propedeusejaar van een hbo opleiding. Met de training leert de student in het feedbackproces vier activiteiten: de student leert (1) de feedback te begrijpen, (2) de feedback te gebruiken, (3) op de feedback te reageren en (4) gericht te vragen naar feedback. Om de invloed van de training te bepalen is de feedbackgeletterdheid en zelfregulatie van studenten gemeten.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.
In het RAAK-project, genaamd Groningen MAPS, is er veel data en kennis vergaard van waaruit antwoorden zijn geformuleerd op verschillende vragen rondom belasting en belastbaarheid van (top)sporters. Het onderzoek naar de factoren die invloed hebben op de prestaties en het blessurerisico van sporters heeft opgeleverd dat we nu meer inzicht hebben in de informatie die nodig is om gericht te zoeken naar verbanden tussen belasting en belastbaarheid. We hebben echter nog niet gekeken naar de data vanuit een datamining perspectief. Datamining is het gericht zoeken naar verbanden in een database met als doel het opstellen van profielen. Deze profielen kunnen nieuwe inzichten geven waardoor sporters van nog betere feedback voorzien kunnen worden. Het doel van het Top-up project is om kennis te ontwikkelen over het automatiseren van de verwerking en analyse van datastromen. Dit zal leiden tot een datasysteem wat automatisch analyses uitvoert achter de schermen. Met dit datasysteem kan de Groningen MAPS-data verder geanalyseerd worden (door middel van datamining) om nieuw inzicht te verkrijgen op het gebied van patronen in belasting en belastbaarheid van (top)sporters.
