Search Results (278)

We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article

This research study investigated the composite magnetic fields of underwater vehicles in the presence of ocean waves under varying conductivity, analyzed their spatiotemporal characteristics, attenuation laws, and influence mechanism. We integrated the modeling of three types of magnetic fields to obtain a composite magnetic field: the magnetic anomaly field generated by a ferromagnetic vehicle was simulated with a hybrid ellipsoid–dipole model, the wake magnetic field generated by its motion, and the ocean wave magnetic field generated by wind-driven waves were derived from the velocity fields. Simulation results show that the magnetic anomaly and wake magnetic fields are mainly influenced by vehicle speed, course, and diving depth, while the ocean wave magnetic field is affected by wind speed and direction. The composite magnetic field’s intensity increases with vehicle and wind speed but decreases with the increase in diving depth. This study offers a comprehensive analysis of the composite magnetic fields of underwater vehicles in the presence of ocean waves, emphasizing the significant impact of vehicle motion and marine environmental parameters. These insights are essential to gaining a deeper understanding of the magnetic fields generated by underwater vehicles as they navigate ocean waves. Full article

Programming education traditionally requires extensive manual assessment of student assignments, which is both time-consuming and resource-intensive for instructors. Recent advances in large language models (LLMs) open opportunities for automating this process and providing timely feedback. This paper investigates the application of artificial intelligence (AI) tools for preliminary assessment of undergraduate programming assignments. A multi-phase experimental study was conducted across three computer science courses: Introduction to Programming, Programming 2, and Advanced Programming Concepts. A total of 315 Python assignments were collected from the Moodle learning management system, with 100 randomly selected submissions analyzed in detail. AI evaluation was performed using ChatGPT-4 (GPT-4-turbo), Claude 3, and Gemini 1.5 Pro models, employing structured prompts aligned with a predefined rubric that assessed functionality, code structure, documentation, and efficiency. Quantitative results demonstrate high correlation between AI-generated scores and instructor evaluations, with ChatGPT-4 achieving the highest consistency (Pearson coefficient 0.91) and the lowest average absolute deviation (0.68 points). Qualitative analysis highlights AI’s ability to provide structured, actionable feedback, though variability across models was observed. The study identifies benefits such as faster evaluation and enhanced feedback quality, alongside challenges including model limitations, potential biases, and the need for human oversight. Recommendations emphasize hybrid evaluation approaches combining AI automation with instructor supervision, ethical guidelines, and integration of AI tools into learning management systems. The findings indicate that AI-assisted grading can improve efficiency and pedagogical outcomes while maintaining academic integrity. Full article

With the increasing number of existing buildings, the implementation of durability-preserving repair procedures is becoming increasingly important. The chemical re-alkalization (CRA) enables the protection of reinforced concrete structures exposed to carbonation by maintaining or restoring the alkalinity in the concrete through the application of an alkaline mortar, such as hybrid alkali-activated binders (HAABs). However, the process of CRA is still insufficiently understood, which means that the requirements for the repair mortars can only be roughly formulated. This paper therefore investigates the process of CRA using laser-induced breakdown spectroscopy (LIBS). Based on the quantitative results of potassium transport in the composite system, a time-dependent attenuation factor can be determined that allows for the adaptation of Fick’s second law of diffusion previously used to predict CRA. The attenuation factor provides further insight into the course of potassium transport, which, based on the results, never follows an ideal diffusion process. Adjusting the diffusion law allows for an improved prediction of the maximum achievable re-alkalization depth depending on the repair mortar, where a potassium content of, e.g., 2.3 wt% leads to a complete re-alkalization of 16 mm. The present study demonstrates the potential of LIBS to quantitatively represent CRA for the first time thus providing new insights into potassium transport and the dynamics of the process. Full article

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

This study endeavors to project the trajectory of electric and hybrid vehicle adoption through 2030, operating under the premise that specific hybrid models can harness electricity from charging stations akin to fully electric counterparts. Employing the seasonal ARIMA (SARIMA) time series model, we preprocess the current counts of electric and hybrid passenger vehicles. Additionally, we use this model to forecast future counts. Our preprocessing findings suggest that Turkey currently experiences a deficit of approximately 26% in electric and hybrid vehicles, considering conventional market dynamics from 2018 to 2023. Furthermore, assuming the observed seasonal fluctuations in passenger vehicle sales will similarly influence electric and hybrid vehicle demand, a secondary preprocessing is conducted on the dataset. Applying this methodology, our projections indicate Turkey will approach a total of 2.6 million electric and hybrid vehicles by the close of 2029, offering insights for policymakers and private stakeholders in charting the course of charging infrastructure development. Full article

The rapid development of smart home and building technologies requires educational methods that facilitate the integration of theoretical knowledge with practical, system-level design skills. Computer-assisted tools play a crucial role in this process by enabling students to experiment with complex Internet of Things (IoT) and building automation ecosystems in a risk-free, iterative environment. This paper proposes a pedagogical framework that integrates simulation-based prototyping with collaborative and spatial design tools, supported by elements of design thinking and blended learning. The approach was implemented in a master’s-level Smart Building Systems course, to engage students in interdisciplinary projects where virtual modeling, digital collaboration, and contextualized spatial design were combined to develop user-oriented smart space concepts. Analysis of project outcomes and student feedback indicated that the use of simulation and visualization platforms may enhance technical competencies, creativity, and engagement. The proposed framework contributes to engineering education by demonstrating how computer-assisted environments can effectively support practice-oriented, user-centered learning. Its modular and scalable structure makes it applicable across IoT- and automation-focused curricula, aligning academic training with the hybrid workflows of contemporary engineering practice. Concurrently, areas for enhancement and modification were identified to optimize support for group and creative student work. Full article

This study examines the transformative role of educational technologies in higher education, with a focus on their impact on student engagement and collaboration in online and hybrid learning environments. It draws on data from 11 educators at an Australian university across Education, Health Sciences, and Humanities disciplines. Utilising the online tool Padlet, these educators facilitated interactive activities that enhanced teaching and learning. This article analyses Padlet’s unique features and how they were employed to optimise student engagement and learning outcomes. Semi-structured interviews reveal how Padlet supported multimedia presentations, group work, and discussions. The findings underscore the versatility of Padlet in promoting critical thinking and knowledge sharing, ultimately enhancing the student experience in both online and hybrid learning settings. This study encourages educators to adopt innovative strategies that incorporate Padlet and similar technologies to enhance their teaching practices. Full article

With the proliferation of online learning platforms, selecting appropriate artificial intelligence (AI) courses has become increasingly complex for learners. This study proposes a novel hybrid AI course recommendation framework that integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) for robust textual feature extraction, enhanced by a Random Forest classifier to improve recommendation precision. A curated dataset of 2238 AI-related courses from Udemy was constructed through multi-session web scraping, followed by comprehensive data preprocessing. The system computes semantic and lexical similarity using cosine similarity and fuzzy matching to handle user input variations. Experimental results demonstrate a high recommendation accuracy = 91.25%, precision = 96.63%, and F1-Score = 90.77%. Compared with baseline models, the proposed framework significantly improves performance in cold-start scenarios and does not rely on historical user interactions. A Flask-based web application was developed for real-time deployment, offering instant, user-friendly recommendations. This work contributes a scalable and metadata-driven AI recommender architecture with practical deployment and promising generalization capabilities. Full article

Background/Objectives: “Accelerated Recovery after Surgery” (ARAS) programs for esophagectomy aim to shorten the perioperative course without increases in morbidity or mortality. In such programs, the prediction and early detection of perioperative complications is essential, as ICU observation times are limited. We evaluated two potential laboratory markers as predictors for postoperative complications: shedding of the endothelial glycocalyx and the veno-arterial CO₂-gap as indicators of microcirculatory disturbances. Methods: In total, 26 patients undergoing hybrid Ivor Lewis esophagectomy within an ARAS program were included. Macrocirculatory conditions were kept stable by enhanced hemodynamic monitoring (PiCCO). Glycocalyx shedding parameters (Syndecan-1, heparan sulfate, hyaluronic acid) and a panel of inflammatory mediators were measured preoperatively, upon ICU-admission, and on the first postoperative day. The veno-arterial CO₂-gap was calculated at induction of anesthesia, during laparoscopy, and upon admission to the ICU. Results: Complications (Dindo-Clavien ≥3) occurred in n = 16 (62%) patients. From preoperatively to admission to the ICU, Syndecan-1 (29 pre-op to 56 ng/mL at ICU-admission) and Interleukins 1b (1.2 to 1.4 pg/mL), 6 (1.3 to 19.9 pg/mL), 8 (5.2 to 19.9 pg/mL), and 10 (0.50 to 1.33 pg/mL) increased, indicating a temporary increase in inflammation and glycocalyx shedding during surgery. A difference between patients with or without complications could not be detected. There was also no difference in the veno-arterial CO₂-gap between the two groups (median of 6.8 mmHg in all patients, 6.7 in patients with complications, 7.8 in patients without complications). Conclusions: Signs of microcirculatory dysfunctions and inflammation occurred during esophagectomy within an ARAS protocol with tightly controlled hemodynamics. Increases in Syndecan-1 and the veno-arterial CO₂-gap could not predict perioperative complications. Full article

This study investigates the perceptions of economics students from Romania’s Central Region regarding the global phenomena of globalization, localization, and slowbalization (GLS), analyzed through the lens of environmental, economic, and educational sustainability. The research highlights a high level of awareness and understanding of globalization and localization, while the concept of slowbalization remains relatively unfamiliar and often perceived with uncertainty or neutrality. Most respondents view globalization as the most sustainable model for long-term economic development, emphasizing its contributions to international trade, market expansion, investment flows, and access to global education and research. At the same time, localization is recognized for its role in preserving cultural identity, strengthening local economies, and addressing pressing environmental issues through low-carbon solutions. Regarding educational sustainability, students support a hybrid model that balances global exposure with the appreciation of local knowledge and traditions—a glocal approach particularly endorsed by master’s students. The study also reveals statistically significant differences between undergraduate and graduate respondents, indicating more mature perspectives among those in advanced studies. The paper could help in course design and lesson engagement and concludes by recommending curricular reforms in economic education and proposing future interdisciplinary, comparative, and qualitative research to deepen understanding of GLS dynamics, particularly in the context of emerging global trends and technological transformations. Full article

Research on the complex dimensions of engagement in large, lecture-based courses remains scarce. Lecture-based courses are often characterized by passive learning environments, raising concerns about their capacity to foster motivation. This study investigates how motivational factors shape multiple dimensions of engagement—cognitive, behavioral, emotional, and agentic—in introductory sociology courses. A quantitative, cross-sectional survey was conducted with 434 first-year students enrolled at seven public universities in North Rhine–Westphalia, Germany. All participants had completed the Abitur at the Gymnasium and experienced hybrid learning during their final years of secondary education due to the COVID-19 pandemic. The study formulated three hypotheses: (1) mastery (self-improvement) goals positively predict emotional, behavioral, and cognitive engagement (validated); (2) perceived autonomy support increases emotional engagement (validated); and (3) performance goals (motivation to outperform peers) have a stronger effect on emotional than cognitive engagement (rejected). Results indicate that performance goals neither enhance emotional engagement nor exert a stronger influence on emotional than on cognitive engagement, challenging common assumptions about the role of competitive motivation in large lecture settings. Additionally, despite low levels of agentic engagement—attributed to the structural constraints of large lecture-based learning environments—students’ internal engagement was in line with other studies. These findings highlight the critical role of educational culture, particularly the emphasis on autonomy within the German school system, and the influence of learning spaces in shaping student engagement. We suggest that engagement is shaped by familiarity with hybrid formats that support autonomy, as well as by an academic culture in which active silent engagement is often the norm. In such contexts, mastery goals and autonomy-supportive backgrounds help foster more reactive dimensions of student engagement. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

Introduction: X-linked agammaglobulinemia (XLA) is a prototypical inborn error of immunity (IEI) caused by mutations in the BTK gene, leading to a profound deficiency of mature B cells and severe pan-hypogammaglobulinemia. The Epstein-Barr virus (EBV), which primarily infects B lymphocytes, is believed to be unable to establish persistence in these patients due to the lack of its natural reservoir. Indeed, current evidence supports that EBV infection is typically refractory in individuals with XLA. Methods: We describe the clinical and molecular characterization of a 10-year-old male patient with genetically confirmed XLA who developed EBV viremia, hemophagocytic lymphohistiocytosis (HLH), and EBV-positive cutaneous T cell lymphoma. Diagnosis was supported by flow cytometry, serology, quantitative PCR, EBER in situ hybridization, histopathology, and whole-exome sequencing. Results: Despite the complete absence of peripheral B cells, EBV was detected in leukocytes and multiple tissues, indicating active infection. The patient developed HLH and a T cell lymphoma with EBER-positive infiltrates. Genetic analysis revealed a nonsense mutation in BTK (1558C>T, R520*), confirming XLA. The clinical course included multiple episodes of neutropenia, viral and bacterial infections, and severe systemic inflammation. Conclusions: This is the first documented case of an XLA patient with confirmed BTK mutation presenting with clinical features more consistent with chronic active EBV infection. These findings challenge the prevailing paradigm that XLA confers protection against EBV-related diseases and further support the possibility of EBV noncanonical reservoirs leading to immune dysregulation. EBV should also be considered in the differential diagnosis of XLA patients presenting with systemic inflammation or lymphoproliferative disease. Full article

For the precise description of gas physical processes in high-voltage direct current (HVDC) transmission, an advanced and robust numerical framework for the simulation of transient particle densities in the course of corona discharges is developed in this work. The aim is the scalable and consistent modeling of the space charge density under realistic conditions. The core component of the framework is a discontinuous Galerkin method that ensures the conservative properties of the underlying hyperbolic problem. The space charge density at the electrode surface is imposed as a dynamic boundary condition via Lagrange multipliers. To increase the numerical stability and convergence rate, a homotopy approach is also integrated. For the experimental validation, a measurement concept was realised that uses a subtraction method to specifically remove the displacement current component in the signal and thus enables an isolated recording of the transient ion current with superimposed voltage stresses. The experimental results on a small scale agree with the numerical predictions and prove the quality of the model. On this basis, the framework is transferred to hybrid HVDC overhead line systems with a bipolar design. In the event of a fault, significant transient space charge densities can be seen there, especially when superimposed with new types of voltage waveforms. The framework thus provides a reliable contribution to insulation coordination in complex HVDC systems and enables the realistic analysis of electrohydrodynamic coupling effects on an industrial scale. Full article