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Abstract We apply ambient noise tomography to a seismic array from the Trans‐Haiti project to obtain a 2‐D shear wave velocity (Vs) across Haiti. We perform multi‐component noise cross‐correlation, measure Rayleigh wave phase velocity and its horizontal‐to‐vertical amplitude ratio (H/V) between periods of 3–18 s, and jointly invert both measurements into Vs for the crustal structures of Haiti. Both H/V and phase velocity measurements exhibit consistent patterns related to the geologic units. Sedimentary basins—CSE and Plateau Central basins—show higher H/V values, while mountain areas—Massif de la Selle, Chaine des Matheux, Montagnes Noires and Massif de Nord—exhibit lower H/V. Regarding phase velocity, higher velocities are observed in northern and southern Haiti, likely reflecting the thinner crust compared to the thicker crust showing lower velocities in the central part. While our Vs model is consistent with previous model that suggested thinner crustal thickness in the northern and southern Haiti, with thickening in the center, the Moho interface in the central domain might be shallower than previously thought.

Abstract In the evolving landscape of higher education, challenges such as the COVID-19 pandemic have underscored the necessity for innovative teaching methodologies. These challenges have catalyzed the integration of technology into education, particularly in blended learning environments, to bolster self-regulated learning (SRL) and higher-order thinking skills (HOTS). However, increased autonomy in blended learning can lead to learning disruptions if issues are not promptly addressed. In this context, OpenAI's ChatGPT, known for its extensive knowledge base and immediate feedback capability, emerges as a significant educational resource. Nonetheless, there are concerns that students might become excessively dependent on such tools, potentially hindering their development of HOTS. To address these concerns, this study introduces the Guidance-based ChatGPT-assisted Learning Aid (GCLA). This approach modifies the use of ChatGPT in educational settings by encouraging students to attempt problem-solving independently before seeking ChatGPT assistance. When engaged, the GCLA provides guidance through hints rather than direct answers, fostering an environment conducive to the development of SRL and HOTS. A randomized controlled trial (RCT) was employed to examine the impact of the GCLA compared to traditional ChatGPT use in a foundational chemistry course within a blended learning setting. This study involved 61 undergraduate students from a university in Taiwan. The findings reveal that the GCLA enhances SRL, HOTS, and knowledge construction compared to traditional ChatGPT use. These results directly align with the research objective to improve learning outcomes through providing guidance rather than answers by ChatGPT. In conclusion, the introduction of the GCLA has not only facilitated more effective learning experiences in blended learning environments but also ensured that students engage more actively in their educational journey. The implications of this study highlight the potential of ChatGPT-based tools in enhancing the quality of higher education, particularly in fostering essential skills such as self-regulation and HOTS. Furthermore, this research offers insights regarding the more effective use of ChatGPT in education.

Abstract In the field of Science, Technology, Engineering, and Mathematics (STEM) education, which aims to cultivate problem-solving skills, accurately assessing learners' engagement remains a significant challenge. We present a solution to this issue with the Real-time Automated STEM Engagement Detection System (RASEDS). This innovative system capitalizes on the power of artificial intelligence, computer vision, and the Interactive, Constructive, Active, and Passive (ICAP) framework. RASEDS uses You Only Learn One Representation (YOLOR) to detect and map learners' interactions onto the four levels of engagement delineated in the ICAP framework. This process informs the system's recommendation of adaptive learning materials, designed to boost both engagement and self-efficacy in STEM activities. Our study affirms that RASEDS accurately gauges engagement, and that the subsequent use of these adaptive materials significantly enhances both engagement and self-efficacy. Importantly, our research suggests a connection between elevated self-efficacy and increased engagement. As learners become more engaged in their learning process, their confidence is bolstered, thereby augmenting self-efficacy. We underscore the transformative potential of AI in facilitating adaptive learning in STEM education, highlighting the symbiotic relationship between engagement and self-efficacy.

Abstract Background In the realm of Science, Technology, Engineering, and Mathematic (STEM) education, computer programming stands as a vital discipline, amalgamating cross-disciplinary knowledge and fostering the capacity to solve real-world problems via fundamental concepts and logical methodologies inherent to computer science. Recognizing the important of computer programming, numerous countries have mandated it as a compulsory course to augment the competitiveness of K-12 learners. Nevertheless, the inherent complexity of computer programming for K-12 learners often goes unacknowledged. Constraints imposed by the course format, coupled with a low instructor–learner ratio, frequently inhibit learners’ ability to resolve course-related issues promptly, thereby creating difficulties in the affective domain. While precision education tools do exist to ascertain learners’ needs, they are largely research-oriented, thereby constraining their suitability for deployment in pragmatic educational settings. Addressing this issue, our study introduces the precision education-based timely intervention system (PETIS), an innovative tool conceived to enhance both programming skills and affective learning in K-12 learners. Our research investigates the influence of PETIS on learners’ performance and evaluate its efficacy in facilitating computer programming education in K-12 environments. Results Quantitative results demonstrate that the application of the precision education-based timely intervention system (PETIS) proposed by this research significantly improves programming skills and affective-domain learning objectives for K-12 learners. Similarly, qualitative results indicate that PETIS is beneficial for both teaching and learning in K-12 computer programming courses. Conclusions These results not only confirm that timely intervention and feedback improve K-12 learners’ programming skills and affective-domain learning objectives in computer programming courses, but also yield implications as to the feasibility of applying precision education in real-world STEM scenarios.

Guillain-Barré syndrome (GBS) is a rare but severe complication of COVID-19 vaccination. We report two cases of GBS following vaccination with the adenovirus vector vaccine ChAdOx1 nCoV-19 (Vaxzevria, AstraZeneca) and review the relevant literature. Relevant studies published between December 2020 and May 2022 including 881 patients with GBS were reviewed. GBS incidence and the need for mechanical ventilation were reported at a higher level among patients receiving Vaxzevria (n = 400). However, incidence cannot be accurately estimated from case reports. Thus, the true GBS rates following COVID-19 vaccination should be determined by population-based data.

Background: Science, Technology, Engineering, and Mathematics (STEM) education in Asian universities struggles to integrate Knowledge, Skills, and Attitudes (KSA) due to large classes and student reluctance. While ChatGPT offers solutions, its conventional use may hinder independent critical thinking. Objectives: This study introduces PA-GPT, using ChatGPT as a "virtual peer" in peer assessments to promote active learning and enhance knowledge, higher-order thinking skills (HOTS), and attitudes--the core of KSA in STEM. Methods: A randomised controlled trial involved 61 first-year engineering students (43 males, 18 females) from a university in Southern Taiwan enrolled in "Network Embedded Systems and Applications." Participants, all with prior ChatGPT experience but no programming background, were purposively sampled. They were randomly assigned to the experimental group (n = 31) using PA-GPT or the control group (n = 30) using traditional ChatGPT. Over 8 weeks, data were collected using pre- and post-tests: a knowledge construction test (20 items, [alpha] = 0.85); a HOTS scale ([alpha] = 0.78-0.83) measuring critical thinking, problem-solving, and creativity; and the S-STEM questionnaire ([alpha] >0.80) assessing attitudes towards STEM subjects and 21st-century learning. ANCOVA analysed the data, controlling for pre-test scores, and Levene's test checked homogeneity of variances. Results and Conclusions: ANCOVA results showed that PA-GPT significantly outperformed traditional ChatGPT in enhancing knowledge construction (F = 9.89, p = 0.002), critical thinking (F = 37.00, p < 0.001), problem-solving (F = 9.40, p = 0.003), creativity (F = 7.22, p = 0.009), and attitudes towards mathematics (F = 25.52, p < 0.001), engineering/technology (F = 16.06, p < 0.001), and 21st-century learning (F = 26.38, p < 0.001). These findings demonstrate that PA-GPT effectively addresses challenges in student engagement and HOTS development in STEM education by simulating peer interactions. Peer Assessment with ChatGPT (PA-GPT) promotes active learning and self-reflection, potentially revolutionising AI-assisted education in large class settings. This study provides pioneering evidence for the effectiveness of AI-driven peer assessment in enhancing comprehensive STEM competencies, offering a promising direction for future educational technology integration.

Virtual Reality (VR) has demonstrated significant advantages in various educational fields as it allows learners to actively learn in high interactivity and realism under their learning pace. However, for low-achieving students, VR learning environments may introduce some challenges, which are problems encountered during their learning process, which lead to difficulties in self-regulating their learning progress and becoming disoriented in the lack of guidance. This study aims to explore the improvement of self-regulated VR learning environment based on the Cognitive Theory of Multimedia Learning and enhance the performance of low-achieving students. We designed a VR course on electronic circuit hardware and programming, providing participants with a series of learning aids in the VR learning environment, including feedback, hints and guidance. Feedback aids provide correct or incorrect feedback after each task or test, guidance aids display progress following each task or test and hint aids offer guidance when inactivity is detected. A quasi-experiment was conducted by using questionnaires and practical tasks to assess the participants' cognitive levels, practical hands-on skills, self-regulated learning abilities and learning engagement after VR learning. The results demonstrated significant improvements in all learning indicators for low-achieving students.

The core purpose of integrating inquiry-based learning strategies into STEM activities is to create a challenging learning environment that stimulates students' active learning, thereby effectively enhancing their interest in learning, thinking skills, and practical application abilities. To achieve these goals, developing more technology-assisted tools and platforms to improve teaching efficiency and enrich the learning experience becomes crucial. Therefore, this research developed InquiryGPT, which guides students through the POEE inquiry process by imposing limitations, facilitating dialogue, and providing feedback, while avoiding over-reliance on ChatGPT. This promotes the cultivation of higher-order thinking skills through interactions with InquiryGPT, thereby enhancing students' knowledge construction and understanding. This study, based on the Technology Acceptance Model (TAM), examined the effectiveness of InquiryGPT. The results showed no significant difference between InquiryGPT and ChatGPT in terms of perceived usefulness and perceived ease of use. Additionally, through analysis of covariance (ANCOVA), the study further analyzed the impact of InquiryGPT on students' higher-order thinking skills and learning scores, proving that it significantly enhances students' creativity, problem-solving abilities, critical thinking, and knowledge construction.

IntroductionAlthough infection was the most common symptom in patients returning to the ED, whether intravenous antibiotic administration at the index visit could serve as an indicator of patients with infectious diseases at high risk for hospital admission after returning to the ED within a short period of time remains unclear. The study aimed to investigate the potential risk factors for hospital admission in patients returning to the ED within 72 hours with a final diagnosis of infectious diseases.Material and methodsThis retrospective cohort study analyzed return visits to the ED from January to December 2019. Adult patients aged >20 years who had a return visit to the ED within 72 hours with an infectious disease were included herein. In total, 715 eligible patients were classified into the intravenous antibiotics and non-intravenous antibiotics group (reference group). The outcome studied was hospital admission to general ward and intensive care unit (ICU) at the return visits.ResultsPatients receiving intravenous antibiotics at index visits had significantly higher risk-approximately two times-for hospital admission at the return visits than those did not (adjusted odds ratio = 2.47, 95% CI = 1.34-4.57, p = 0.004). For every 10 years increase in age, the likelihood for hospital admission increased by 38%. Other factors included abnormal respiratory rate and high C-reactive protein levels.ConclusionsIntravenous antibiotic administration at the index visit was an independent risk factor for hospital admission at return visits in patients with an infection disease. Physicians should consider carefully before discharging patients receiving intravenous antibiotics.

Background: Cerebral computed tomography (CT) and various severity scoring systems have been developed for the early prediction of the neurological outcomes of cardiac arrest survivors. However, few studies have combined these approaches. Therefore, we evaluated the value of the combination of cerebral CT and severity score for neuroprognostication. Methods: This single-center, retrospective observational study included consecutive patients surviving nontraumatic cardiac arrest (January 2016 and December 2020). Gray-to-white ratio (GWR), third and fourth ventricle characteristics, and medial temporal lobe atrophy scores were evaluated on noncontrast cerebral CT. Simplified cardiac arrest hospital prognosis (sCAHP) score was calculated for severity assessment. The associations between the CT characteristics, sCAHP score and neurological outcomes were analyzed. Results: This study enrolled 559 patients. Of them, 194 (34.7%) were discharged with favorable neurological outcomes. Patients with favorable neurological outcome had a higher GWR (1.37 vs 1.25, p < 0.001), area of fourth ventricle (461 vs 413 mm2, p < 0.001), anteroposterior diameter of fourth ventricle (0.95 vs 0.86 cm , p < 0.001) and a lower sCAHP score (146 vs 190, p < 0.001) than those with poor recovery. Patients with higher sCAHP score had lower GWR (p trend < 0.001), area of fourth ventricle (p trend = 0.019) and anteroposterior diameter of fourth ventricle (p trend = 0.014). The predictive ability by using area under receiver operating characteristic curve (AUC) for the combination of sCAHP score and GWR was significantly higher than that calculated for sCAHP (0.86 vs 0.76, p < 0.001) or GWR (0.86 vs 0.81, p = 0.001) alone. Conclusions: The combination of GWR and sCAHP score can be used to effectively predict the neurological outcomes of cardiac arrest survivors and thus ensure timely intervention for those at high risk of poor recovery.

The rise of precision education has encouraged teachers to use intelligent diagnostic systems to understand students' learning processes and provide immediate guidance to prevent students from giving up when facing learning difficulties. However, current research on precision education rarely employs multimodal learning analytics approaches to understand students' learning behaviors. Therefore, this study aims to investigate the impact of teachers intervene based on different modalities of learning analytics diagnosing systems on students' learning behaviors, learning performance, and motivation in STEM collaborative learning activities. We conducted a quasi-experiment with three groups: a control group without any learning analytics system assistance, experimental group 1 with a unimodal learning analytics approach based on image data, and experimental group 2 with a multimodal learning analytics approach based on both image and voice data. We collected students' image or voice data according to the experimental design and employed artificial intelligence techniques for facial expression recognition, eye gaze tracking, and speech recognition to identify students' learning behaviors. The results of this research indicate that teacher interventions, augmented by learning analytics systems, have a significant positive impact on student learning outcomes and motivation. In experimental group 2, the acquisition of multimodal data facilitated a more precise identification and addressing of student learning challenges. Relative to the control group, students in the experimental groups exhibited heightened self-efficacy and were more motivated learners. Moreover, students in experimental group 2 demonstrated a deeper level of engagement in collaborative processes and the behavior associated with constructing knowledge.

Background: Virtual reality (VR) offers significant potential for hands-on learning environments by providing immersive and visually stimulating experiences. Interacting with such environments can bring numerous benefits to learning, including enhanced engagement, knowledge construction, and higher-order thinking. However, many current VR studies in hands-on learning tend to utilise one-way learning materials, limiting participants' ability to interact with and reflect on their actions and learning experiences. Moreover, these studies often neglect to assess learners' capacity to transfer their skills from VR learning to real-world scenarios. Therefore, this study aims to investigate participants' engagement, learning outcomes, higher-order thinking, and their ability to apply acquired skills in real-world contexts. Method and Objective: Participants were immersed in a hands-on learning environment in VR, where they received feedback and engaged in reflection during the learning process. This study recruited 68 university students to participate in the experiment, employing a quasi-experimental design. Participants experienced learning units both with and without reflection on the feedback. The theme of these learning units was related to embedded systems, encompassing topics such as the assembly of embedded components and Python programming. The investigation aimed to assess whether reflection in VR could enhance cognitive levels, engagement, real-world hands-on tasks abilities, and higher-order thinking. Results: The research results indicate that in the VR hands-on learning environment, participants engage in reflection through the provided feedback, effectively enhancing their engagement, cognitive levels, and hands-on task abilities. Simultaneously, it also successfully assists participants in developing problem-solving skills and critical thinking in higher-order thinking. Conclusions: Participants actively engaged in reflection on their own learning tasks and actions within the hands-on learning environment, enabling them to develop a deeper understanding of the learning material and enhance their real-world hands-on tasks abilities, we encourage the integration of reflection strategies in VR learning environments for hands-on learning, as they can yield positive benefits for learners.

Virtual reality (VR) provides a unique platform for interactive learning experiences, enhancing learning, particularly in hands-on courses. However, the visual load of VR and the lack of guidance and interaction from physical teachers or peers can pose challenges for learners in self-regulated learning (SRL) and learning motivation. This study designed feedback in the VR learning environment based on the signaling principles of Cognitive Theory of Multimedia Learning (CTML) to investigate learners' SRL, learning motivation, cognitive levels, and their abilities to handle real-world scenarios. We developed a VR hands-on learning course on the Artificial Intelligence of Things (AIoT) and conducted two real-world AIoT hands-on tasks, implementing a quasi-experimental study with 71 university students. Participants were randomly assigned to either the experimental group or the control group. The experimental group received three types of feedback signals (gaze, progress, and decision) designed based on signaling principles, while the control group received no feedback. The results showed that the experimental group exhibited significantly higher levels of SRL and learning motivation compared to the control group after VR learning. The feedback group also performed better in terms of cognitive levels and real-world hands-on tasks.

Science, Technology, Engineering, and Mathematics (STEM) education is essential for developing future-ready learners in both secondary and higher education levels. However, as students transition to higher education, many encounter challenges with independent learning and research. This can negatively impact their Higher-Order Thinking Skills (HOTS), engagement, and practical expertise. This study introduces a solution: Computational Thinking Scaffolding (CTS) in the Jupyter Notebook environment, designed to enhance STEM education at the tertiary level. CTS incorporates five phases: Decomposition, Pattern Recognition, Abstraction, Algorithm Design, and Evaluation. Utilizing a quasi-experimental method, we assessed the impact of CTS on the HOTS, engagement, and practical skills of undergraduate and postgraduate students. Our findings hold substantial relevance for university educators, academic advisors, and curriculum designers aiming to enhance students' HOTS and hands-on capabilities in STEM disciplines. The results validate the effectiveness of CTS in elevating tertiary STEM learning outcomes, and they spotlight the adaptability of the Jupyter Notebook as a valuable tool in higher education. In conclusion, our research underscores the merits of CTS for improving outcomes in higher STEM education and sets a benchmark for future endeavors in this domain.

A new type of glycan array covalently or noncovalently attached to aluminum oxide-coated glass (ACG) slides was developed to study of enzymatic reactions and protein binding. Compared to the preparation of glycan arrays on glass slides and other surfaces, the developed method of arraying using phosphonic acid reacting with ACG is more direct, convenient, and effective and represents a new platform for the high-throughput analysis of protein-glycan interactions.