Search

Personalized gamification aims to address shortcomings of the one-size-fits-all (OSFA) approach in improving students' motivations throughout the learning process. However, studies still focus on personalizing to a single user dimension, ignoring multiple individual and contextual factors that affect user motivation. Unlike prior research, we address this issue by exploring multidimensional personalization compared to OSFA based on a multi-institution sample. Thus, we conducted a controlled experiment in three institutions, comparing gamification designs ("OSFA" and "Personalized" to the learning task and users' gaming habits/preferences and demographics) in terms of 58 students' motivations to complete assessments for learning. Our results suggest no significant differences among OSFA and Personalized designs, despite suggesting user motivation depended on fewer user characteristics when using personalization. Additionally, exploratory analyses suggest personalization was positive for females and those holding a technical degree, but negative for those who prefer adventure games and those who prefer single-playing. Our contribution benefits designers, suggesting how personalization works; practitioners, demonstrating to whom the personalization strategy was more or less suitable; and researchers, providing future research directions.

Intelligent Tutoring Systems (ITS) have been widely used to enhance math learning, wherein teacher's involvement is prominent to achieve their full potential. Usually, ITSs depend on direct interaction between the students and a computer. Recently, researchers started exploring handwritten input (e.g., from paper sheets) aiming to provide equitable access to ITSs' benefits. However, research on math ITSs ability to handle handwritten input is limited and, to our best knowledge, no study has summarized its state of the art. This article fulfills that gap with a scoping review of handwritten recognition methods, characteristics, and applications of math ITSs compatible with handwritten input. Based on a search of 11 databases, we found eight primary studies that met our criteria. Mainly, we found that all ITSs depend on receiving handwritten input from a touchscreen interface, in contrast to recognizing solutions developed on paper. We also found that most ITSs focus on similar audiences (e.g., English speakers students), subjects (e.g., algebraic questions), and applications (e.g., in-class to understand student perceptions). Thus, towards enabling equitable access to ITSs, we propose ITS Unplugged (i.e., ITSs that i) run on low-cost, resource-restricted devices with little to no internet connection and ii) receive as well as return information in the format target users usually use) and contribute a research agenda concerning challenges of developing such ITSs.

Abstract Lymphocytic myocarditis (LM) is primarily triggered by various factors including viral infections and subsequent immune responses. While rare, some patients with LM experience recurrence with a life‐threatening fulminant form. Although combining steroids and immunosuppressants, such as azathioprine and mycophenolate mofetil, has demonstrated favourable outcomes in patients with LM, their efficacy is limited to the chronic phase. Indeed, various immunosuppressants have been used for LM with fulminant manifestation; however, their evidence remains lacking. In our case series, two patients with LM experienced fulminant relapses during steroid tapering, and another presented persistent cardiac enzymes elevation despite steroid therapies. Consequently, we initiated calcineurin inhibitors alongside steroids, resulting in well‐controlled clinical courses without further recurrence of LM and significant adverse effects. Our cases suggest calcineurin inhibitors as therapeutic options for managing steroid‐resistant LM with fulminant relapse.

Game designers and researchers have sought to create gameful environments that consider user preferences to increase engagement and motivation. In this sense, it is essential to identify the most suitable game elements for users' profiles. Designers and researchers must choose strategies to classify users into predefined profiles and select the most appropriate game elements for each user. This activity may challenge designers, learning designers, and researchers since they must base their choice on personal aspects that require a deep understanding. Therefore, this article aims to assist game designers, learning designers, and researchers in selecting user classification strategies to customize and personalize game-based and gamified learning environments. By conducting systematic literature mapping, we consolidate the most common strategies and explore their applications in games and gamification. Our analysis, based on 25 publications, reveals that we can classify the strategies according to user interaction, user personality, learning style, and motivation for learning. Strategies based on user interactions emerge as the most popular, while questionnaires and log data systems are commonly used instruments for identifying user profiles. The findings of this SLM offer valuable knowledge for game designers and researchers to define the criteria that will be used to evaluate the effect of games and gamified environments in educational contexts.

Self-regulation helps students develop various cognitive, metacognitive, and affective strategies to regulate their learning process and maximize learning gains. However, self-regulation demands i) an encouraging environment and ii) student motivation. First, adding Open Learner Models (OLM) to learning environments encourages self-regulation by providing clear feedback on which skills students need to prioritize. Second, gamifying these environments holds the potential to maximize student motivation. Nevertheless, empirical evidence on whether gamification improves learning self-regulation in OLM-based applications is lacking to our best knowledge. Therefore, we conducted a one-factor (gamification: yes or no) experimental study (n = 39) to test if gamification improves the self-regulation and performance of learners who used an OLM-based Intelligent Tutoring System to complete multiple-choice questions. Surprisingly, we found no significant differences in self-regulation and performance when comparing the gamified and non-gamified groups. Considering prior research did not investigate gamification in similar settings, the fact that gamification's effect varies depending on the context and learning outcome likely explains our findings. Thus, practitioners must be cautious in using gamification amid the lack of empirical evidence on its effectiveness in a given context/learning outcome. Researchers need to understand how gamification affects different learning outcomes in varied contexts. Accordingly, future research must investigate and ground how self-regulation, OLM, and gamification work together.

Abstract Aims A considerable proportion of candidates for transcatheter aortic valve implantation (TAVI) have underlying heart failure (HF) with preserved ejection fraction (HFpEF), which can be challenging for diagnosis because significant valvular heart disease should be excluded before diagnosing HFpEF. This study investigated the long‐term prognostic value of the pre‐procedural H2FPEF score in patients with preserved ejection fraction (EF) undergoing TAVI. Methods and results Patients who underwent TAVI between October 2013 and May 2017 were enrolled from the Optimized CathEter vAlvular iNtervention–Transcatheter Aortic Valve Implantation Japanese multicentre registry. After excluding 914 patients, 1674 patients with preserved EF ≥ 50% (median age: 85 years, 72% female) were selected for calculation of the H2FPEF score and were dichotomized into two groups: the low H2FPEF score [0–5 points; n = 1399 (83.6%)] group and the high H2FPEF score [6–9 points; n = 275 (16.4%)] group. Patients with high H2FPEF scores were associated with a higher prevalence of New York Heart Association Functional Class III/IV (59.3% vs. 43.7%, P

Programming online judges (POJs) have been increasingly used in CS1 classes, as they allow students to practice and get quick feedback. For instructors, it is a useful tool for creating assignments and exams. However, selecting problems in POJs is time consuming. First, problems are generally not organized based on topics covered in the CS1 syllabus. Second, assessing whether problems require similar effort to be completed and map onto the same topic is a subjective and expert-dependent task. The difficulty increases if the instructor must create variations of these assessments, e.g., to avoid plagiarism. Thus, here, we research how to support CS1 instructors in the task of selecting problems, to compose one-size-fits-all or personalized assignments/exams. Our solution is to propose a novel intelligent recommender system, based on a fine-grained data-driven analysis of the students' effort on solving problems in the integrated development environment of a POJ system, and automatic detection of topics for CS1 problems, based on problem descriptions. Data collected from 2714 students are processed to support, via our artificial intelligence (AI) method recommendations, the instructors' decision-making process. We evaluated our method against the state of the art in a simple blind experiment with CS1 instructors (N = 35). Results show that our recommendations are 88% accurate, surpassing our baseline (p = 0.05). Finally, our work paves the way for novel POJ smart learning environments, wherein instructors define learning tasks (assignments/exams) supported by AI.

Abstract Gene-knockout animal models with organ-deficient phenotypes used for blastocyst complementation are generally not viable. Animals need to be maintained as heterozygous mutants, and homozygous mutant embryos yield only one-fourth of all embryos. In this study, we generated organ-deficient embryos using the CRISPR-Cas9-sgRNAms system that induces cell death with a single-guide RNA (sgRNAms) targeting multiple sites in the genome. The Cas9-sgRNAms system interrupted cell proliferation and induced cell ablation in vitro. The mouse model had Cas9 driven by the Foxn1 promoter with a ubiquitous expression cassette of sgRNAms at the Rosa26 locus (Foxn1 Cas9 ; Rosa26_ms). It showed an athymic phenotype similar to that of nude mice but was not hairless. Eventually, a rat cell-derived thymus in an interspecies chimera was generated by blastocyst complementation of Foxn1 Cas9 ; Rosa26_ms mouse embryos with rat embryonic stem cells. Theoretically, a half of the total embryos has the Cas9-sgRNAms system because Rosa26_ms could be maintained as homozygous.

Background There is a scarcity of data on the prevalence and clinical impact of cerebrovascular disease detected on preprocedural computed tomography (CT) before aortic valve replacement (AVR) in patients with severe aortic stenosis. Methods and Results Among patients with severe aortic stenosis undergoing AVR, the authors compared clinical outcomes between patients with and without cerebrovascular disease detected on preprocedural CT, which was defined as chronic brain infarction or hemorrhage. The primary outcome measure in this study was a composite of all‐cause death or stroke. Among 567 study patients, 200 patients (35.3%) had cerebrovascular disease on preprocedural CT. Among 200 patients with cerebrovascular disease on preprocedural CT, only 28.5% of patients had a clinical history of symptomatic stroke. The cumulative 3‐year incidence of death or stroke was higher in patients with cerebrovascular disease on preprocedural CT than in those without cerebrovascular disease on preprocedural CT (40.7% versus 24.1%, log‐rank P

Emotions can influence cognitive development and are key elements to the teaching-learning process. Positive emotions (e.g., engagement) can improve the ability to solve problems, store information, and make decisions. On the other hand, negative emotions (e.g., boredom) reduce the capacity to process information at a deeper level, preventing learning to become effective. Therefore, students' emotions must be regulated to hinder negative and to promote positive emotions during learning. To support the choice of the best intervention to regulate individual emotions, this article proposes an algorithm based on simulated data considering different individual performances in solving Algebra exercises. The results suggest that the proposed model has high success rates (over 90%) in the choice of interventions and may be applied in real scenarios.

Education plays a crucial role in shaping societies and driving progress for future generations. Ensuring an education system that offers equal opportunities to all students is essential to promote equitable education. However, many students face disparities that impede their access to fair educational opportunities. This paper delves into the significance of using data-driven strategies to achieve a more equitable education system. We examine the barriers and inequalities hindering students’ access to high-quality education and explore how data can be instrumental in addressing these challenges. To this end, we leveraged open educational data from Brazil, identifying eight school-related factors associated with student performance. We present various visualizations and insights on how these factors can be employed to promote equity among schools in the country. Furthermore, we recommend the implementation of two public education policies aimed at improving digital governance in Brazilian education. In summary, this paper underscores the crucial role of data-driven approaches in advancing educational equity and offers practical guidance for researchers and policymakers alike.

Studies in the literature reported several positive benefits provided by the use of technology in online education, especially in the gamified tutoring system. However, despite the benefits of intelligent tutoring systems, recent studies indicate the presence of a gender gap not considered in the construction of the attributes present in the gamified tutoring system. To investigate this impact by observing users' behavioral changes in gamified online educational environments, the present study aims to investigate the effects of the stereotype threats using a quantitative experiment with a Factorial Design in three gamified environments (stereotypical male version, stereotypical female version and control environment). Was conducted an experiment with 150 individuals (high school and undergraduate students) without considering age, ethnicity, or social class. The results show that the participants allocated to the male learning environment present an increase in aggressiveness level. Furthermore, the results also show the stereotypical male and female learning environments increased the participants' performance level. Another finding was that the threatening condition provided a significant increase in the participants' flow level among males subjected to a threatening condition, which did not manifest in the case of females. In addition, this study also observed the effect of the stereotype threat on men and women in the threatening condition by division in the 34-year age group, resulting in a significant increase in the level of flow among men. This study showed previous results show that the gamified environment influences psychological variables as aggressiveness, intellectual performance, and flow level, they raise questions about the direction of these changes and the impact they may have on users' usability and performance in these systems.

Gamification has been widely used to design better educational systems aiming to increase students' concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students' needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students' gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.