This study explores the responses of junior high school mathematics educators in the Carcar City Division to learning loss. The loss was brought about by the disruptions caused by the COVID-19 pandemic. It aims to understand the strategies they implemented to address these challenges. Employing a qualitative design grounded in Straussian Grounded Theory, the research draws insights from key informant interviews and focused group discussions with teachers and administrators from the division’s three largest public schools. Findings reveal a range of innovative and context-responsive strategies such as peer tutoring, differentiated instruction, remedial interventions, and the use of blended learning modalities. The institutional support also plays a vital role in the success of learning recovery programs as well as the support coming the community. These two sources of support create a strong foundation for addressing learning gaps. Hence, the study culminates in the development of the substantive theory: Adaptive Student Learning Recovery, which outlines three interrelated pillars for effective learning recovery: pedagogical adaptation, social learning support, and institutional intervention. Pedagogical adaptation includes responsive teaching strategies tailored to students’ needs; social learning support highlights the roles of parents, peers, and community in sustaining student engagement; and institutional intervention refers to systemic efforts in leadership, resource allocation, and policy support. Despite persistent challenges—including technological disparities, limited instructional time, and socio-economic barriers—educators demonstrated resilience and a strong commitment to equitable learning recovery. The study offers a framework to guide future interventions and policymaking in post-pandemic mathematics education.

International Synchronous Hybrid Learning Spaces, in which local students and students from abroad participate synchronously online and on campus, offer opportunities for digital mobility and internationalisation at home. While research on Synchronous Hybrid Learning Spaces has expanded considerably, students’ perceived social challenges arising from the intersection of international diversity and multimodal participation remain underexplored. Addressing this gap, this study investigates students’ perceptions of social challenges in International Synchronous Hybrid Learning Spaces and derives design implications to mitigate them. To this end, a qualitative content analysis is conducted based on interview data with five students studying adult education at the Julius-Maximilians-Universität Würzburg, Germany. The findings reveal that group cohesion and translocal collaboration are constrained by structural heterogeneity in access, experiences of alienation linked to participation modes, and lecturers’ divisive verbal and non-verbal practices. To counter these challenges, the study highlights the importance of fostering opportunities for reflection and informal exchange, jointly negotiating group norms, and actively involving students as co-creators of the learning environment. Overall, the study contributes to the literature by demonstrating how the intersection of international diversity and multimodal participation amplifies social heterogeneity, structuring engagement, roles, and relationships in International Synchronous Hybrid Learning Spaces.

This study proposes a theoretically grounded conceptual framework to enhance student engagement in virtual engineering laboratories. The framework is validated through expert review rather than empirical testing. To address the challenges of online and blended learning environments, the model integrates two established theories: the extended Technology Acceptance Model (TAM2) and Self-Determination Theory (SDT). TAM2 captures extrinsic motivational factors, including perceived usefulness and ease of use, while SDT focuses on intrinsic psychological needs, particularly autonomy and competence. This paper presents an initial theoretical model that has been validated by experts and is intended to precede future empirical testing with students. Expert validation was conducted using a mixed-methods approach involving eight specialists in engineering education and educational technology. Quantitative evaluation employed the content validity ratio (CVR) and item-level content validity index (I-CVI), while qualitative feedback was analyzed using inductive thematic coding. The results showed strong agreement among experts on key components such as system usability, learner engagement, and feedback processes. However, some conceptual overlap was identified between the gamification and enjoyment constructs, suggesting the need for further clarification. The validated framework provides a foundation for future empirical studies to examine the proposed relationships among its constructs. By linking pedagogical design with digital system features, the framework contributes to a deeper understanding of student motivation and engagement in virtual engineering learning environments.

Multiscale characterization of micro fracture connectivity and gas migration in volcanic reservoirs using µCT and hybrid learning segmentation.

Course scheduling in higher education institutions is a complex combinatorial optimization problem involving numerous constraints such as lecturer availability, room capacity, time slots, and course distribution across semesters. Manual scheduling practices often result in conflicts, inefficient resource utilization, and prolonged preparation time. This study proposes a hybrid course scheduling system that integrates a Genetic Algorithm (GA) and Constraint Programming using the CP-SAT solver from OR-Tools. The GA is employed in the first phase to generate optimal course sections based on student enrollment, lecturer workload, and capacity constraints. The best solution produced by the GA is then refined using CP-SAT to generate a conflict-free timetable that satisfies all hard constraints, including lecturer, room, and time conflicts, while also optimizing selected soft constraints. The proposed system is implemented as a web-based application deployed on Microsoft Azure, enabling scalability and accessibility. Experimental results using real academic data demonstrate that the hybrid approach successfully produces feasible schedules with zero conflicts and significantly reduces scheduling time compared to manual methods. The results confirm that the integration of GA and CP-SAT provides an effective and flexible solution for university course scheduling problems.

Purpose: This study explores how synchronous and asynchronous tools contribute to the development of oral communication skills by investigating their impact on fluency, interpersonal understanding, anxiety reduction, and repeated practice. Research Methodology: A mixed-methods approach was used, combining quantitative data from surveys and performance assessments with qualitative insights from interviews, classroom observations and student reflections. Results: Synchronous tools such as Zoom and Microsoft Teams enhanced students' fluency, spontaneity, and interaction skills. In contrast, asynchronous tools, such as Flipgrid and online discussion boards, offered a reflective space that reduced anxiety, allowing for thoughtful and precise contributions. Conclusions: This study emphasizes the importance of hybrid learning methods that combine the strengths of both synchronous and asynchronous approaches to foster confident and accurate communication skills. Limitations: This study was limited to undergraduate students and focused on specific tools, potentially limiting its generalizability to other contexts or educational levels. Contributions: This research highlights the complementary role of synchronous and asynchronous tools in developing oral communication skills, advocating hybrid pedagogies in digital learning environments.

The Smart Grid (SG), a sophisticated electrical network that uses digital technology to monitor and regulate power flow, is susceptible to cyberattacks like eavesdropping, data spoofing, and data falsification. Although there are cryptographic solutions, managing certificate revocation lists (CRLs) is still difficult, and public-key cryptography (PKC) is sometimes unfeasible for inexpensive, power-constrained IoT devices. By taking advantage of hardware flaws in RF devices, Radio Frequency Fingerprint Identification (RFFI) has become a viable non-cryptographic security method. However, for practical implementation, strong deep-learning architectures and efficient deep signal preprocessing are needed. For the first time, we combine XGBoost and BiLSTM in this study to present a hybrid classification framework for RFFI. The accuracy of a Support Vector Classifier (SVC) trained on 15,000 data was 92.6%, whereas the BiLSTM-XGBoost model obtained 97.5% accuracy on 5,000 samples. Furthermore, 97% accuracy was obtained when XGBoost was applied to channel-estimated and equalized wireless data. These findings show how well hybrid deep learning techniques work to strengthen Smart Grid security against online attacks.

Vocational physical education faces challenges including resource limitations, safety risks, and rigid teaching models. This study explores virtual reality (VR) as a web-based immersive solution to enhance instruction. Empirical results show that VR overcomes spatial constraints by simulating realistic environments—such as skiing and martial arts scenarios—and enables personalized learning through data-driven feedback. It supports a “pre-class, in-class, post-class” hybrid model, improving skill acquisition, focus, and safety. Despite limitations like user discomfort, lack of tactile feedback, and high initial costs, VR significantly boosts engagement and teaching efficiency. The study recommends policy support, teacher training, and iterative scenario design to promote the sustainable integration of VR in vocational physical education.

The government of Ethiopia has invested over 20 million dollars to improve the quality of education through digital technology. However, the effectiveness of such huge investment on education is overlooked. The purpose of this study was to assess the effectiveness of School Network (SN) in promoting students’ English Language performance. The researcher deployed a descriptive research design and a mixed research approach to collect data on the implementation processes and other contextual factors potentially influencing the process. The researcher also utilized UNESCO’s input, process, and output logic model to set standards of effectiveness. The research was conducted in the City Government Administration of Addis Ababa, specifically in four selected sub-cities, based on their achievement in SN implementation. The participants of the study included one English language expert, four school principals, 20 male and 10 female English Language teachers (a total of 30 teachers), six ICT/plasma technicians, and 160 pre-college students (80 males and 80 females). Five instruments were used to collect data from primary and secondary sources. Classroom observations were conducted to assess the delivery of School Network instruction in teaching English. Moreover, focus group discussions were held, and questionnaire was administered to examine the role of the School Network on students’ English language performance. Additionally, these instruments were used to identify challenges related to School Network usage in ELT. Furthermore, explorative interview was conducted and documents were analyzed to prove whether there was difference in effectiveness of the School Net instruction in teaching English language based on sex and stream(natural and social science students) or not. Besides, these instruments were utilized to identify challenges related to School Net usage in ELT. Both quantitative and qualitative approaches were employed to analyze and interpret the data gathered through these tools. The findings of the study indicated that SN has the potential to be accessed through platforms like plasma, e-learning, and cloud computing, and it provides video on demand (VOD). This enabled interactive blended language learning (IBLL), mobile-assisted language learning (MALL), and computer-assisted language learning (CALL) and Technology Assisted Language Learning (TALL). However, factors such as minimal teacher’s involvement, low level of students’ participation, lack of support services and facilities, frequent network disconnections, electric power fluctuations causing damage and theft of accessories such as set-top boxes, adapters, batteries and remotes; and school rules restricting students from using electronic devices in school, hindered its effectiveness. Hence, concerned authorities should evaluate the program's effectiveness and address the identified problems to help students benefit from the potential of School Network platforms to learn the target language (English).

Produced water management is one of the major environmental concerns in the Niger Delta, where the operation of oil production generates enormous volumes of effluents with complex chemical characteristics. In this study, hybrid, physics-based, and machine learning models were formulated and compared for the prediction of main produced water quality parameters of pH, Total Dissolved Solids (TDS), Oil and Grease (O&G), Heavy Metal Concentration (HMC), and Chemical Oxygen Demand (COD). Historical monitoring data from 2010 to 2023 were fitted using five types of models: Autoregressive Integrated Moving Average (ARIMA), ARIMA–Long Short-Term Memory (ARIMA–LSTM), Physics-Informed LSTM (PI–LSTM), Random Forest (RF), and a physics-based process model. Model performance was compared using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R²), and probabilistic forecast intervals. Amongst models, the hybrid PI–LSTM consistently performed better than the rest in terms of prediction accuracy (RMSE = 12.6, MAE = 8.8, R² = 0.87) in terms of seasonal variability and long-term dependency capture for all parameters. The physics-based model provided interpretive insights into water–hydrocarbon interactions and production system dynamics. Overall, results indicate that the integration of physical principles into deep learning models enhances predictive performance and interpretability of water quality predictions generated. Results have significant implications for Niger Delta environmental monitoring, regulatory decision-making, and sustainable produced water management.

Technology in nowadays’ era has made leaps and bounds in offering a lot to both society and education. Teachers are asked to undertake a new role and respond to the new technology related challenges. The Covid-19 pandemic brought distance education to the surface of the academic world. Educational robotics has infiltrated the field of education recently, but it has not been explored and utilized as it could be, especially in the field of primary education. This study aims to discuss how the combination of educational robotics and school distance education can benefit education in an effective and innovative way. The study employed literature review and a qualitative research approach utilizing a semi-conducted interview as the research tool. Ten (10) experienced teachers in the fields of Distance Education and Educational Robotics participated. The results revealed that Educational Robotics utilizing several tools and platforms can effectively support and enhance school distance education, especially within a framework of blended learning.

Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management.

Electrocardiography is a cornerstone in the diagnosis of cardiovascular diseases; however, accurate interpretation demands expert knowledge and is often impeded by data scarcity and annotation costs. To address these challenges, we propose SimCardioNet, a hybrid self-supervised and supervised deep learning framework for multi-class electrocardiography image classification. SimCardioNet leverages a custom multi-scale convolutional neural network backbone enhanced with residual connections and multi-head self-attention, pretrained via a modified SimCLR contrastive learning strategy that integrates a hybrid loss combining InfoNCE and cosine similarity. Following self-supervised pretraining, the model undergoes supervised fine-tuning with progressive layer unfreezing to mitigate overfitting and preserve meaningful representations. We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses. On Dataset I, SimCardioNet achieves 0.975 accuracy, 0.973 precision, 0.973 recall, and 0.972 F1-score under 3-fold cross-validation. On Dataset II, the model demonstrates perfect classification performance (1.00 accuracy, precision, recall, and F1-score), highlighting strong generalization. On the PTB-XL dataset (Dataset III), SimCardioNet attains 0.921 accuracy and 0.921 F1-score, outperforming current state-of-the-art models including dual-branch CNNs, entropy-enhanced CNNs, and Bi-GRU architectures. Ablation studies confirm the critical contributions of self-supervised pretraining, attention mechanisms, and domain-specific augmentations. Grad-CAM visualizations further validate the model's focus on clinically relevant Electrocardiography regions. Our results underscore SimCardioNet's potential to reduce reliance on labeled data while delivering robust, interpretable, and clinically viable Electrocardiography classification especially valuable in resource-constrained settings.

This paper review relevant literature on the concept and design of blended learning technology for underdeveloped area. First, authors define blended learning in this context and explained an activity system as an analytical framework such as needing of challenges of cultural background, technoware, humanware, infoware, orgaware. The methodology in this research, using PRISMA Framework for flow diagram systematic review. The procedure of this systematic review consists of several steps, namely 1) compete Background and Purpose (Background and objectives). 2) Research Question. 3) Searching for the literature. 4) Selection Criteria. 5) Data Extraction Strategy. 6) Assess Quality of Primary Studies. 7) Data Synthesis. Use 2 Research Questions and 3 Quality Assessment. Found 55 papers which are related and meet to this topic, and only 9 papers are relevant and processed to the nest result.

This study aims to reveal the dynamics of learning difficulties faced by students in traditional Islamic boarding schools in the digital age, which are caught between the preservation of Islamic values and the demands of technological modernization. Using a qualitative approach with a case study strategy, data was collected through in-depth interviews and focus group discussions with 15 senior high school students from various majors and levels of education. The results of thematic analysis show four main findings: technological limitations that impact access to learning resources; conventional teaching methods that are less adaptive to digital learning styles; differences in students' views on the integration of technology in learning; and adaptive strategies used by students through collaboration, informal discussions, and the utilization of limited resources. These findings reinforce the relevance of the Digital Divide and Social Constructivism theories in the context of Islamic education, emphasizing that the digital divide in Islamic boarding schools is not merely a matter of infrastructure, but also a matter of values and learning culture. The novelty of this research lies in its focus on the agency of students as adaptive actors in the transformation of Islamic education, showing that Islamic boarding schools have the potential to develop hybrid learning models that harmonize tradition and technology.

This research examines the instructional strategies employed by English instructors to enhance students’ speaking skills in a blended learning environment. Despite the fact that blended learning has been widely adopted in formal higher education settings, few studies have investigated how blended learning operates in small private English courses with diverse young adult learners. The purpose of this study is to identify the strategies used in designing blended speaking lessons and to examine its perceived impacts on student performance. A qualitative case study design was carried out which involved two experienced instructors teaching five speaking classes. The researchers collected the data through classroom observations and semi structured interviews. The data were analyzed using thematic analysis. The results of this study reveal three dominant strategies: task-based speaking cycles across online and offline modes, structured asynchronous practice using digital platforms, and personalized feedback loops. These strategies were perceived to improve students’ fluency, confidence, and autonomy by providing low pressure rehearsal spaces and targeted support. These demonstrate that effective blended speaking instruction depends more on intentional pedagogical design than on technological sophistication, particularly in contexts where the resources are limited. This research expands the current understanding of blended learning practices beyond higher education environments. Keywords: Blended Learning, Speaking Skills, English Language Teaching, Instructor Strategies, Students Performance

The history and evolution of neurologic education span from Hippocratic concepts of the brain as the center of thought to modern innovations in digital and arts-based learning. Nineteenth-century pioneers established neurology as a distinct discipline, and contemporary advances in neuroimaging, genetics, and telemedicine continue to reshape both what and how we teach. In today's fast-paced clinical environments, educators can still be effective at teaching by using learner-centered strategies such as prebriefing, microteaching, and structured feedback frameworks like the One-Minute Preceptor and the SNAPPS (summarize, narrow, analyze, probe, plan, select) model. These methods are central to integrating high-impact instruction into demanding settings of clinical care. In addition to traditional methods of teaching, emerging modalities, including simulation, blended learning, and intentional team-based methods, further enhance diagnostic reasoning and interprofessional collaboration. Technology-driven innovations such as teleneurology, virtual reality, and gamification expand access and engagement, whereas arts and humanities approaches foster observation, empathy, and reflective capacity. Together, these strategies illustrate that even amid clinical demands, neurologic education can remain central to the work. By intentionally planning for and integrating education into our clinical care, we are preparing the next generation of neurologists to think critically, act compassionately, and teach adaptively.

LLM-based pedagogical agent for ICU simulation instructor training: A quasi-experimental study.

This study investigates the perceptions of future English as a Foreign Language (EFL) teachers in Bali, Indonesia regarding artificial intelligence (AI), particularly generative AI tools like ChatGPT and Gemini. Recognizing the potential of AI to enhance instructional practices, the research employs an embedded mixed-method design with 150 participants, utilizing surveys and semi-structured interviews. The instruments demonstrated content validity and reliability, with quantitative data analyzed for frequency distributions and qualitative data subjected to interactive model analysis. Findings reveal a predominantly positive perception of generative AI among EFL students in a blended learning context, who recognize its utility while expressing concerns about implementation. As prospective educators, participants are beginning to contemplate pedagogical strategies for integrating AI into their future classrooms. The study highlights the urgency of establishing AI policies grounded in critical digital pedagogy principles to optimize educational experiences. This research contributes to the discourse on AI in education, emphasizing the need for context-specific approaches to leverage AI’s potential while addressing pedagogical, ethical, and technical challenges.

A hybrid learning framework for real-time fire dynamics prediction using diffusion models and spiking neural networks