Het welzijn van studenten in het hoger onderwijs staat onder druk. Binnen hogeschool Inholland wordt het welzijn van studenten gemeten met de jaarlijkse studentenwelzijnsmonitor. In de ronde van 2022 werd voor het eerst aan studenten die dat wilden een feedbackrapportage met persoonlijke scores, tips en hulpbronnen teruggekoppeld. Verschillen tussen studenten die wel of niet feedback wilden ontvangen zijn onderzocht en in een aanvullende studie is gekeken hoe studenten de feedbackrapportage evalueerden. Van de 1462 deelnemers van de studentenwelzijnsmonitor 2022, hebben 819 (56%) ervoor gekozen de persoonlijke feedback te ontvangen. Op het gebied van welzijn zijn geen verschillen gevonden tussen studenten die wel of geen feedback wilden ontvangen. Aan de aanvullende studie hebben 48 studenten deelgenomen. Zij die de feedbackrapportage wilden ontvangen (n= 30) waren nieuwsgierig naar de uitkomsten en persoonlijke verbeterpunten. Zij beoordeelden de feedback met een 7,2 gemiddeld. Deelnemende studenten die geen feedbackrapportage wilden ontvangen (n= 18) wisten de reden niet meer of gaven aan al te weten hoe het met het eigen welzijn gesteld is. Na het tonen van een voorbeeld van een dergelijke feedbackrapportage, gaf 62% aan volgende keer waarschijnlijk wel voor feedback te kiezen. De feedbackrapportages werden positief beoordeeld, maar er is volgens studenten ruimte voor verbetering in onder andere de vormgeving en uitleg over de scores. De feedback is meegenomen in de doorontwikkeling van de feedbackrapportages van de studentenwelzijnsmonitor 2023.
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.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
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.
