We have developed a pedagogical approach wherein learners acquire systems thinking skills and content knowledge by constructing qualitative representations. In this paper, we focus on how learners learn about the biological mechanisms of calcium regulation by constructing such a representation, how they interact with the software, and the effect on learning outcomes. The software contains various functionalities to support learners, and a workbook guides them through the process. Cluster analysis of learners’ use of the software categorizes them into three styles, which we have labelled: exploratory, comprehensive, and efficient. Learning outcomes are evaluated through pre- and post-tests and show overall improvement on systems thinking skills and content knowledge. No significant differences in outcome are observed between the interaction styles of learners. This implies that constructing qualitative representations effectively increases learners’ systems thinking skills and understanding of calcium regulation, regardless of their interaction style.
This thesis presents an exploration of ‘how entrepreneurship education pedagogy can enhance undergraduate business students’ autonomous motivation for self-directed learning’. It has twin, equally valuable, purposes: to make an original theoretical contribution and to improve professional practice in this area. The work addresses the lack of pedagogical research in entrepreneurship education that focuses on learner development, with a specific aim at development of self-directed learning skills for lifelong learning. The research is approached with a concurrent, mixed methods design, comparing pre- and a post-EE, self-assessment survey results from 245 students, enrolled in a Young Enterprise venture creation programme, and a control group at a Dutch university. With the use of open-question surveys among the same population, during and after the EE modules, as well as from focus group discussions with a selection of participating students and teachers, explanation was sought for the observations drawn from the quantitative study. Significant relationships were found between students’ self-reported maturity of autonomy, self-efficacy, and motivation for learning, and in how these relate to self-directed learning readiness. Entrepreneurship education was found to significantly moderate the relationship between the learning characteristics and self-directed learning, and to strengthen of the students’ perceived readiness for self-directed learning. Explanation for the impact of EE were found to be related to the stage-wise, mixed pedagogy approach to learning, that combines authentic learning with a hierarchical approach to competence development, and supportive team dynamics. The research contributes to practice with a proposed conceptual framework for understanding how to prepare for self-directed learning readiness and a teaching-learning framework for its development in formal educational settings. It contributes to knowledge with its deeper understanding of how students experience learning in EE and how that affects their willingness to pursue learning opportunities.
MULTIFILE
Adversarial thinking is essential when dealing with cyber incidents and for finding security vulnerabilities. Capture the Flag (CTF) competitions are used all around the world to stimulate adversarial thinking. Jeopardy-style CTFs, given their challenge-and-answer based nature, are used more and more in cybersecurity education as a fun and engaging way to inspire students. Just like traditional written exams, Jeopardy-style CTFs can be used as summative assessment. Did a student provide the correct answer, yes or no. Did the participant in the CTF competition solve the challenge, yes or no. This research project provides a framework for measuring the learning outcomes of a Jeopardy-style CTF and applies this framework to two CTF events as case studies. During these case studies, participants were tested on their knowledge and skills in the field of cybersecurity and queried on their attitude towards CTF education. Results show that the main difference between traditional written exam and a Jeopardy-style CTF is the way in which questions a re formulated. CTF education is stated to be challenging and fun because questions are formulated as puzzles that need to be solved in a gamified and competitive environment. Just like traditional written exams, no additional insight into why the participant thinks the correct answer is the correct answer has been observed or if the participant really did learn anything new by participating. Given that the main difference between a traditional written exam and a Jeopardy-style CTF is the way in which questions are formulated, learning outcomes can be measured in the same way. We can ask ourselves how many participants solved which challenge and to which measurable statements about “knowledge, skill and attitude” in the field of cybersecurity each challenge is related. However, when mapping the descriptions of the quiz-questions and challenges from the two CTF events as case studies to the NICE framework on Knowledge, Skills and Abilities in cybersecurity, the NICE framework did not provide us with detailed measurable statements that could be used in education. Where the descriptions of the quiz-questions and challenges were specific, the learning outcomes of the NICE framework are only formulated in a quite general matter. Finally, some evidence for Csíkszentmihályi’s theory of Flow has been observed. Following the theory of Flow, a person can become fully immersed in performing a task, also known as “being in the zone” if the “challenge level” of the task is in line with the person’s “skill level”. The persons mental state towards a task will be different depending on the challenge level of the task and required skill level for completing it. Results show that participants state that some challenges were difficult and fun, where other challenges were easy and boring. As a result of this9 project, a guide / checklist is provided for those intending to use CTF in education.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Despite the recognized benefits of running for promoting overall health, its widespread adoption faces a significant challenge due to high injury rates. In 2022, runners reported 660,000 injuries, constituting 13% of the total 5.1 million sports-related injuries in the Netherlands. This translates to a disturbing average of 5.5 injuries per 1,000 hours of running, significantly higher than other sports such as fitness (1.5 injuries per 1,000 hours). Moreover, running serves as the foundation of locomotion in various sports. This emphasizes the need for targeted injury prevention strategies and rehabilitation measures. Recognizing this social issue, wearable technologies have the potential to improve motor learning, reduce injury risks, and optimize overall running performance. However, unlocking their full potential requires a nuanced understanding of the information conveyed to runners. To address this, a collaborative project merges Movella’s motion capture technology with Saxion’s expertise in e-textiles and user-centered design. The result is the development of a smart garment with accurate motion capture technology and personalized haptic feedback. By integrating both sensor and actuator technology, feedback can be provided to communicate effective risks and intuitive directional information from a user-centered perspective, leaving visual and auditory cues available for other tasks. This exploratory project aims to prioritize wearability by focusing on robust sensor and actuator fixation, a suitable vibration intensity and responsiveness of the system. The developed prototype is used to identify appropriate body locations for vibrotactile stimulation, refine running styles and to design effective vibration patterns with the overarching objective to promote motor learning and reduce the risk of injuries. Ultimately, this collaboration aims to drive innovation in sports and health technology across different athletic disciplines and rehabilitation settings.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
