Zoekresultaten

Building connections for success

For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.

Overcoming Privacy-Related Challenges for Game Developers

Design and development practitioners such as those in game development often have difficulty comprehending and adhering to the European General Data Protection Regulation (GDPR), especially when designing in a private sensitive way. Inadequate understanding of how to apply the GDPR in the game development process can lead to one of two consequences: 1. inadvertently violating the GDPR with sizeable fines as potential penalties; or 2. avoiding the use of user data entirely. In this paper, we present our work on designing and evaluating the “GDPR Pitstop tool”, a gamified questionnaire developed to empower game developers and designers to increase legal awareness of GDPR laws in a relatable and accessible manner. The GDPR Pitstop tool was developed with a user-centered approach and in close contact with stakeholders, including practitioners from game development, legal experts and communication and design experts. Three design choices worked for this target group: 1. Careful crafting of the language of the questions; 2. a flexible structure; and 3. a playful design. By combining these three elements into the GDPR Pitstop tool, GDPR awareness within the gaming industry can be improved upon and game developers and designers can be empowered to use user data in a GDPR compliant manner. Additionally, this approach can be scaled to confront other tricky issues faced by design professionals such as privacy by design.

AI-based exhaust gas temperature

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.

Vertrouwen als uitgangspunt - Versterken van de kracht van de organisatie

Development and internal validation of a multivariable prognostic model to predict chronic pain after a new episode of non-specific idiopathic, non-traumatic neck pain in physiotherapy primary care practice

Objective To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors. Design A prospective cohort study with a 6-month follow-up between January 2020 and March 2023. Setting 30 physiotherapy primary care practices. Participants Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset. Baseline measures Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude. Outcome measures Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain. Results 62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer–Lemeshow test yielded a p-value of 0.7167, indicating a good model fit. Conclusion This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.

Trust in Clinical AI: Expanding the Unit of Analysis

From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.