Search for dissertations about: "user learning"
Showing result 1 - 5 of 256 swedish dissertations containing the words user learning.
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1. Adding Challenge to a Teachable Agent in a Virtual Learning Environment
Abstract : The topic of this thesis concerns what happens when challenging behavior is added to a teachable agent in a virtual learning environment. The aim of adding challenging behavior to teachable agents is to encourage students to engage in learning behaviors, improve their motivation and engagement, which may result in a deeper level of comprehension and an improved learning experience. READ MORE
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2. Virtual Learning Environments in Higher Education : A Study of User Acceptance
Abstract : The aim of the thesis was to create knowledge about factors influencing acceptance of virtual learning environments among academic staff and students in blended learning environments. The aim was operationalised by four research questions. READ MORE
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3. Learning Computing at University: Participation and Identity : A Longitudinal Study
Abstract : Computing education has struggled with student engagement and diversity in the student population for a long time. Research in science, technology, engineering, and mathematics (STEM) education suggests that taking a social, long-term perspective on learning is a fruitful approach to resolving some of these persistent challenges. READ MORE
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4. Learning text talk online : Collaborative learning in asynchronous text based discussion forums
Abstract : The desire to translate constructivist and sociocultural approaches to learning in specific learning activities is evident in most forms of training at current, not least in online education. Teachers worldwide are struggling with questions of how to create conditions in this fairly new realm of education for learners to contribute to the development of a good quality in their own and others' learning. READ MORE
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5. Visual Analytics for Explainable and Trustworthy Machine Learning
Abstract : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. READ MORE