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iFractions: Using Games to Teach Children Fractions
Get PDFFractions are fundamental concepts in mathematics education and can be applied to various everyday tasks. However, teaching and learning fractions remain challenging due to their abstract nature. To mitigate this challenge, this paper introduces an open-source collection of educational minigames to support the teaching of fractions through interactive and visual approaches. At the same time, to evaluate its effectiveness, a quasi-experimental study was conducted using pre- and post-tests. The results demonstrated a significant improvement in students’ hit ratio, with an average normalized gain of 0.767. With this work, we contribute to the teaching of mathematics by presenting an open-source tool that can facilitate the teaching and learning of fractions
Introduction to the Minitrack on Digital Innovations for Inclusive Health and Well-Being
No full textIntroduction of Gabor Transform Features for an Internet of Things Security Paradigm
Get PDFAs the proliferation of Internet of Things (IoT) devices spreads across critical infrastructure sectors, the threat likewise increases as attack vectors become proportionally more serious. It is necessary to balance protection complexity and operational disruption while providing resilient and effective security solutions. Distinct Native Attribute (DNA) fingerprinting using RF Signal Gabor transform (GTX) features has proven to be computationally inexpensive and robust in physical (PHY) layer applications. This work combines GTX discrimination gains with data dimensional reduction using GTX-derived images to achieve effective DNA fingerprinting—this is done using lower-dimensional images of Radio Frequency (RF) signal Gabor transform responses to perform Image Domain DNA (ID-DNA) Fingerprinting. ID-DNA Fingerprinting performance includes accurate device classification of %C = 90% and reliable detection of rogue devices at a Rogue Rejection Rate (RRR) of RRR = 90%
Toward an AI Maturity Model in Healthcare: Identifying Core Dimensions and Critical Success Factors
Get PDFArtificial Intelligence is increasingly recognized as a critical enabler for transforming hospital operations and improving healthcare delivery. However, the absence of healthcare-specific maturity models limits the systematic adoption of AI in clinical settings. This study addresses this gap by conducting a structured literature review of existing AI maturity models and critical success factors across domains. The analysis identifies six core dimensions: technology, data, strategy, people, organization, and regulations. These findings highlight the multifaceted nature of AI integration and underscore the need for a tailored approach in complex healthcare environments. By providing a conceptual foundation, this work advances the development of future AI maturity models to support healthcare leaders in assessing AI readiness, ensuring strategic alignment, and facilitating structured AI integration within hospital settings. Further empirical validation is needed to refine the framework for practical application
What Are You Craving? Using Wearables to Distinguish Food and Drug Cravings During Treatment with Extended-Release Buprenorphine
Get PDFCraving, or the subjective, strong desire to use a substance, is a central factor in addiction, and part of the diagnostic criteria for substance use disorders (SUDs). Cravings can also occur for other triggers such as food, and cravings for food and drugs have been found to activate distinct neural pathways in the brain. Recently, physiologic signals from wearable devices have been applied to digitally detect cravings in patients with SUDs. But to date, no studies have explored digital detection of cravings by subtype. We collected continuous physiologic sensor data from N = 12 participants with opioid use disorder, treated with extended-release buprenorphine (BUP-XR). Data were analyzed to assess whether sensor signals carried differential information that could distinguish between food-, drug- and mixed-craving types. Accelerometer, heart rate and heart rate variability features significantly differed between drug, food and mixed trigger cravings. Cross validated models trained with these features distinguished each type of craving with area under ROC curve ranging from 75%-80%. These findings support the ability of wearable sensor-based digital biomarkers to distinguish craving subtypes in individuals with SUD
Introduction to the Minitrack on Qualitative and Mixed-Method Research in Organizational Systems and Technology
No full textMind the Gap: Gender Differences in Generative AI Adoption at Work
Get PDFDespite the growing relevance of generative AI in the workplace, a significant gender gap in its adoption persists. This study investigates why women are less likely than men to use generative AI tools at work and identifies predictors that explain this difference. Combining a cross-sectional survey (n = 200) with a one-week diary study (n = 76, 266 daily observations), we examine both the intention to use and actual daily use of generative AI. Across both studies, women reported lower usage intentions and spent significantly less time using generative AI. Drawing on the UTAUT, we find that performance expectancy is the strongest predictor—particularly among women—followed by social influence. In contrast, effort expectancy and facilitating conditions appear less relevant. Additional factors such as AI literacy and job demands further explain AI use. Our results highlight the need for gender-sensitive interventions to reduce the gender gap in generative AI use
Reframing China in Digital Discourse: U.S. Representations across Platforms after the TikTok Ban
Get PDFThis study treats TikTok as an unintended arena of Chinese public diplomacy and examines how China’s image is constructed in U.S. discourse surrounding the TikTok ban. Using qualitative framing analysis, it compares elite newspapers (The New York Times, The Wall Street Journal), tech blogs (Wired, Gizmodo), and TikTok user-generated videos. Cross-platform analysis identifies six recurring frames: elite newspapers reproduce state-centric and threat-based portrayals; tech blogs introduce reflexive, platform-oriented critiques; TikTok users contribute emotionally resonant, culturally connective, and satirical expressions that diversify China’s image. The findings suggest that national image construction is becoming increasingly decentralized. In this fragmented discursive ecology, China’s public diplomacy must adapt by emphasizing affective engagement and incorporating non-state narratives
