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This study explores learners’ emotional experiences and self-regulation and how they overcome stressful situations in a simulation-based learning environment (SBLE). We also examine what kinds of situations an AI tutor could be used to facilitate simulation-based learning. We will first present the theoretical background of the research dealing with self-regulated learning, followed by positive and negative emotions and situations in simulation-based learning. The research questions of the...
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With the accelerating growth of the academic corpus, doubling every nine years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text screening is done...
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Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications. Especially data sets derived from publication's full-text have recently gained attention. While several such data sets already exist, we see key shortcomings in terms of their domain and time coverage, citation network completeness, and representation of full-text content. To address these points, we propose a new version of the data set...
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Systematic approaches to evidence synthesis can improve the rigour, transparency, and replicability of a traditional literature review. However, these systematic approaches are time and resource intensive. We evaluate the ability of OpenAI’s ChatGPT to undertake two initial stages of evidence syntheses (searching peer-reviewed literature and screening for relevance) and develop a novel collaborative framework to leverage the best of both human and AI intelligence. Using a scoping review of...
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Common classroom teaching analysis, which has focused on counting and coding the teacher-student behaviors and discourse interactions, faces many difficulties as content-free, low efficiency, and small scale in analysis. To overcome these shortcomings, and foster high-quality classroom teaching, the current study proposes a blended human and Artificial Intelligence (AI) technology for classroom teaching analysis named as TESTII. It consists of five steps identifying teaching events,...
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The assessment of student wellbeing has been often static and lagged behind for the intervention/diagnostic purpose. In this chapter, we aim to introduce an automated school wellbeing scoring dynamic real-time system, School Day Wellbeing Model. With Artificial Intelligence (AI)-based item sampling methods and answers scoring and reporting systems, the School Day Wellbeing Model can collect wellbeing data at low cognitive cost, track wellbeing real time at multiple levels (e.g., individual-,...
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The active learning methods for prioritising systematic reviews have undergone significant progress and innovation in recent years. This rapid development, however, has inadvertently highlighted the disparity between the rapid development of these methodologies and their rigorous evaluation, stemming from constraints in simulation size, lack of infrastructure, and the use of few datasets. We embark on a large-scale simulation study involving over 27 thousand simulations and over 156 million...
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To foster optimal student learning, well-being, and overall health, it is vital that school environments prioritise comfort.
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