Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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An investigation in the learning effects of integrated development projects. In two subsequent semesters the students were asked how they rated their competencies at the start of the project as well as at the end of it. The students voluntarily filled out a questionnaire. After the last questionnaire a number of students were also interviewed in order to learn more about their perceptions. It was a remarkable outcome of these interviews that a lot of students tended to give themselves lower ratings in the end if they met any difficulties in for instance communication or co-operation during the project. Then the questionnaire showed a decrease in the student's ratings, while anyone else would say the student did learn something after recognizing these difficulties. It required a different interpretation of the outcomes of the questionnaires. The investigation showed that co-operating in general and in multidisciplinary teams in particular, co-operating with companies and also working according to plans are the four objectives that are recognized mostly by the students. The factors that actually contribute to, or block, the learning effects remained unknown yet.
Lifelong learning is necessary for nurses and caregivers to provide good, person-centred care. To facilitate such learning and embed it into regular working processes, learning communities of practice are considered promising. However, there is little insight into how learning networks contribute to learning exactly and what factors of success can be found. The study is part of a ZonMw-funded research project ‘LeerSaam Noord’ in the Netherlands, which aims to strengthen the professionalization of the nursing workforce and promote person-centred care. We describe what learning in learning communities looks like in four different healthcare contexts during the start-up phase of the research project. A thematic analysis of eleven patient case-discussions in these learning communities took place. In addition, quantitative measurements on learning climate, reciprocity behavior, and perceptions of professional attitude and autonomy, were used to underpin findings. Reflective questioning and discussing professional dilemma's i.e. patient cases in which conflicting interests between the patient and the professional emerge, are of importance for successful learning.
MULTIFILE
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
The growing use of digital media has led to a society with plenty of new opportunities for knowledge exchange, communication and entertainment, but also less desirable effects like fake news or cybercrime. Several studies, however, have shown that children are less digital literate than expected. Digital literacy has consequently become a key part within the new national educational policy plans titled Curriculum.nu and the Dutch research and policy agendas. This research project is focused on the role the game sector can play in the development of digital literacy skills of children. In concrete, we want to understand the value of the use of digital literacy related educational games in the context of primary education. Taking into consideration that the childhood process of learning takes place through playing, several studies claim that the introduction of the use of technology at a young age should be done through play. Digital games seem a good fit but are themselves also part of digital media we want young people to be literate about. Furthermore, it needs to be taken into account that digital literacy of teachers can be limited as well. The interactive, structured nature of digital games offers potential here as they are less dependent on the support and guidance of an adult, but at the same time this puts even more emphasis on sensible game design to ensure the desired outcome. The question is, then, if and how digital games are best designed to foster the development of digital literacy skills. By harnessing the potential of educational games, a consortium of knowledge and practice partners aim to show how creating theoretical and practical insights about digital literacy and game design can aid the serious games industry to contribute to the societal challenges concerning contemporary literacy demands.
