Search

BackgroundNarcolepsy is a chronic sleep disorder with a broad variety of symptoms. Although narcolepsy is primarily characterized by excessive daytime sleepiness and cataplexy (loss of muscle control triggered by emotions), patients may suffer from hypnagogic hallucinations, sleep paralysis, and fragmented night sleep. However, the spectrum of narcolepsy also includes symptoms not related to sleep, such as cognitive or psychiatric problems. Symptoms vary greatly among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the whole symptom spectrum and may not capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy symptoms over time to evaluate their burden and the effect of treatment. ObjectiveThis study aimed to describe the design, development, implementation, and evaluation of the Narcolepsy Monitor, a companion app for long-term symptom monitoring in narcolepsy patients. MethodsAfter several iterations during which content, interaction design, data management, and security were critically evaluated, a complete version of the app was built. The Narcolepsy Monitor allows patients to report a broad spectrum of experienced symptoms and rate their severity based on the level of burden that each symptom imposes. The app emphasizes the reporting of changes in relative severity of the symptoms. A total of 7 patients with narcolepsy were recruited and asked to use the app for 30 days. Evaluation was done by using in-depth interviews and user experience questionnaire. ResultsWe designed and developed a final version of the Narcolepsy Monitor after which user evaluation took place. Patients used the app on an average of 45.3 (SD 19.2) days. The app was opened on 35% of those days. Daytime sleepiness was the most dynamic symptom, with a mean number of changes of 5.5 (SD 3.7) per month, in contrast to feelings of anxiety or panic, which was only moved 0.3 (SD 0.7) times per month. Mean symptom scores were highest for daytime sleepiness (1.8 [SD 1.0]), followed by lack of energy (1.6 [SD 1.4]) and often awake at night (1.5 [SD 1.0]). The personal in-depth interviews revealed 3 major themes: (1) reasons to use, (2) usability, and (3) features. Overall, patients appreciated the concept of ranking symptoms on subjective burden and found the app easy to use. ConclusionsThe Narcolepsy Monitor appears to be a helpful tool to gain more insight into the individual burden of narcolepsy symptoms over time and may serve as a patient-reported outcome measure for this debilitating disorder.

Programming and creative thinking are important skills for the twenty-first century. A large body of evidence suggests that a playful approach to learning helps children engage deeply with programming, improves their creative thinking skills, and shapes a positive attitude towards programming. However, such research has rarely considered how differences in socioeconomic background impact the way children experience such programming activities. The theoretical perspective of science capital suggests that children from high income families will hold more positive attitudes towards science and technology and will perform better in programming than children from lower income families based on their generally higher exposure to experiences involving computing technology. To examine this assumption, we designed and implemented single-occasion programming workshops lasting two hours that followed the Lifelong Kindergarten Approach and investigated differences in children's attitudes, their learning outcomes (measured by a pre-post-test, perceived learning, and task-based performance), and the fun they experienced during the workshops. We collected data from three primary schools in three distinct socioeconomic neighborhoods (i.e., high-, middle-, and low-income areas), involving, in total, 138 children. Findings indicate that the workshops had a positive effect on the children's attitude towards programming in the middle- and low-income schools only. The self-reported learning was similar in the three schools, but children from the low-income school significantly outperformed children from the high-income school in their task-based performance. Children from the middle-income school had the most fun, however, the experience of fun only significantly affected the low-income school children's perceived learning scores. We conclude that children from the middle- and low-income schools profited the most from the playful programming workshop and call on future research to investigate further underlying factors of perception, acceptance, and enjoyment of out-of-school programming activities in relation to participants' socioeconomic background when studying children's participation of programming in school.

Educational settings are emotional places where students experience diverse emotions in relation to academic activities and their outcomes. Emotions, in turn, greatly influence students' learning process and engagement. Research on emotions in Design-Based Learning (DBL) has so far been coarse-grained examining how students evaluate their overall feelings towards the DBL project. As yet, little is known regarding how specific DBL activities influence students' emotional experience. Therefore, we report a three-month field study of a DBL project involving 30 middle school students (aged 13-14) addressing dual research purposes: (1) to faithfully reconstruct the manner and sequence of activities during DBL from a fine-grained perspective; and (2) to examine the relationship between these activities and students' emotional experience. This study used a mixed research method and collected multiple data sources, including experience sampling surveys, classroom observations, and interviews. The research outcomes in this study are multiple. First, this paper reveals detailed inspection regarding the types of task students performed, the strategies of shifting, and executing tasks during the process student experienced. Second, this paper identifies specific types of activities that have a significant positive or negative relationship with students' emotional experiences. Derived from reported empirical evidence in the present study, this paper furtherly proposes an Activity-and-Affect model of DBL. This model provides a fine-grained description of DBL activity as continuous along three dimensions: task (design thinking process, project management, social interaction) task strategy (single-tasking vs. multitasking), and collaboration strategy (individual or group). Our analysis highlights the variability in how different DBL activities can be associated with different emotions.

Researchers and practitioners in learning sciences, educational technology and child-computer interaction often argue that fun is an essential element of learning. Therefore, researchers in the above fields aim to explore how learning activities could be made more enjoyable in order to facilitate engagement in the learning process and to improve the learning outcomes. Despite such wide interest, there has been little systematic effort to define and measure fun. The herein introduced research aims to (a) define the term 'fun' and (b) to create a tool for the reliable measurement of it. In the first study testing the initial item pool 75 students ([mu].sub.age = 11.78); in the think-aloud study testing the comprehensibility of the items six 11-year-old children and in the final validation study, 128 students ([mu].sub.age = 12.15) participated. We applied a deductive scale development approach. For the model testing, CFA was used and second-order latent variable models were fitted. In this paper, we conceptualize the term of fun and introduce the final 18-item version of the FunQ that consists of six dimensions (Autonomy, Challenge, Delight, Immersion, Loss of Social Barriers and Stress) and bears with the appropriate validity and reliability measures ([omega].sub.overall = 0.875 and [omega].sub.partial = 0.864; RMSEA = 0.052 and SRMR = 0.072). We contribute with (a) a review of the literature regarding the concept of fun, (b) a conception of fun as a multidimensional theoretically motivated concept, (c) a multidimensional instrument for assessing experienced fun-the FunQ-and (d) a psychometric evaluation of the proposed instrument. FunQ is put forward as a reliable and much-needed addition to the current palette of instruments., Author(s): Gabriella Tisza [sup.1] , Panos Markopoulos [sup.1] Author Affiliations: (1) grid.6852.9, 0000 0004 0398 8763, Department of Industrial Design, Eindhoven University of Technology, , 5612 AE, Eindhoven, The Netherlands [...]

Design-based learning (DBL) is gaining increasing acceptance as a motivating and practical approach that can be used to prepare young people for the challenges of the twenty-first century. Emotions are known to influence a student's academic performance in traditional learning, which raises the question as to what role emotions can play in DBL. This article presents a systematic survey of literature published in the last 20 years (searching from 1998 to 2019) and indexed in the Scopus, ERIC, and PsycINFO databases which contribute to our understanding of children's emotions in DBL. This review coded a total of 34 papers that met the inclusion criteria. Findings that reported on children's emotions are structured under three themes: (1) the affective DBL components, (2) the labeled emotions, and (3) the impact of emotions in DBL. Based on this evidence, we make recommendations for future research and compile a set of guidelines for designing DBL activities, taking into account students' emotions that can aid their learning.

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network's utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ± 10 % of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method's average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications. [ABSTRACT FROM AUTHOR]