Abstract
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.

Abstract

Abstract
Self and co-regulation of learning (SCRL) are strategies that students can adopt to become more active and committed to their learning. Encouraging students to adopt these strategies is a challenge for teachers that can be met by using narratives as a teaching resource. To support teachers in this process, we present a method for evaluating, classifying, and reflecting on excerpts from immersive narratives for SCRL, so they objectively base their decision-making. The method was developed as an artifact of Design Science Research (DSR). In the Design stage of DSR, a 4-stage scheme was developed, and 38 criteria were described to identify and classify narratives that guide or encourage students to adopt SCRL strategies. In the DSR demonstration stage, we tested the method in an asynchronous e-learning curricular unit in Portuguese higher education, which uses a narrative-oriented immersive learning approach for SCRL, called e-SimProgramming. The results show that the graphic visualization of the classification made it possible to perceive the occurrence of the SCRL categories in the narratives, enabling the teacher to be inspired and reflect on the categories to be enhanced for necessary changes in the narrative in line with their pedagogical objectives.

Abstract
Obstructive sleep apnea (OSA) is a sleep disorder marked by repeated episodes of airway obstruction, leading to apneas (complete blockage) or hypopneas (partial blockage) during sleep. The standard diagnostic metric, the apnea-hypopnea index (AHI), quantifies the number of these events per hour of sleep but has limitations, such as its dependence on manual interpretation and lack of attention to event duration, which can be clinically significant. To address these issues, this study developed an algorithm to detect respiratory events from nasal airflow signals and measure their duration, using data from 22 patients at St. Vincent's University Hospital, sourced from the PhysioNet dataset. Signal processing techniques, including filtering and envelope analysis, were applied to extract features, and apnea/hypopnea events were identified based on American Academy of Sleep Medicine (AASM) guidelines. Events were classified by duration into three groups: 10-20 seconds, 20-40 seconds, and over 40 seconds. Preliminary results showed detection accuracy of 60% for apnea and 93% for hypopnea events. The study also explored relations between event duration and demographic factors, such as age, gender, body mass index (BMI), and Epworth Sleepiness Scale (ESS) scores, to assess whether longer events were linked to greater severity. These findings suggest that incorporating event duration and automated detection into OSA diagnosis could improve accuracy and provide better insight into the condition, potentially leading to more personalized treatments.

Abstract
This paper discusses the potential effects of Attention-Capture Damaging Patterns (ACDPs) in designing socially and culturally sensitive interfaces based on their mechanisms and psychological impacts on users. Building on the concept of “dark patterns” and examining how they contribute to social polarization, this study explores the intersection between digital interface design, digital wellbeing, and polarization. The paper analyzes several examples of ACDPs present in popular social media apps and platforms such as Instagram, TikTok, WhatsApp, and Facebook, proposing a new taxonomic approach based on three main categories. In addition, a set of alternative design strategies that promote healthier interactions on digital platforms are discussed to mitigate the negative effects of these patterns and promote a more balanced digital environment.

Abstract
This work introduces a web application for extracting, processing, and visualizing data from sleep studies reports. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP), the pipeline extracts over 75 key data points from four types of sleep reports. The web application offers an intuitive interface to view individual reports' details and aggregate data from multiple reports. The pipeline demonstrated 100% accuracy in extracting targeted information from a test set of 40 reports, even in cases with missing data or formatting inconsistencies. The developed tool streamlines the analysis of OSA reports, reducing the need for technical expertise and enabling healthcare providers and researchers to utilize sleep study data efficiently. Future work aims to expand the dataset for more complex analyses and imputation techniques.