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Moving to remote, online learning to meet health guidelines during the COVID-19 pandemic posed a challenge for college science courses due to the interactive nature of laboratories. We developed and implemented a remote/in-person lab partner model where in-person students were paired with remote students in natural science courses during the 2020–2021 academic year. The goal was to provide an opportunity for students to attend lab in person, as well as an interactive lab experience for students who elected to attend remotely. Students in each course completed a questionnaire assessing their perceptions of their learning. Learning outcomes were assessed and compared between in-person and remote modalities in a general biology course. In-person students perceived that attending class in person increased their learning but that having remote partners hindered learning. Conversely, remote students perceived that attending class remotely hindered their learning overall but that having in-person partners enhanced their experience. We found that learning outcomes were not significantly different between in-person and remote students. Although the data suggest the model was not successful in its initial iteration, we provide a way to offer a remote laboratory option to make classes more inclusive and accessible. [ABSTRACT FROM AUTHOR]

How can primary literacy teachers effectively identify, adapt, and incorporate developmentally appropriate technologies to support their students' reading development? This article reports results from a case study attempting to digitize a classroom literacy practice—close reading—for Year 2 students. It suggests that digital tools can effectively support close reading practices and students' literacy learning. Results also identify some unique affordances that digitization offers. The author provides suggestions and tips for practitioners interested in incorporating technology into their regular close reading routines. [ABSTRACT FROM AUTHOR]

Hybrid project-based learning effectively develops academic writing skills in preparation for a gradual return to face-to-face instruction after a two-year pandemic triggered by Coronavirus Disease 2019 (COVID-19). Since this hybrid project-based learning initiative is a pioneering effort, it is essential to reflect on the program's successes and identify areas for improvement. This case study aims to explore the lecturers' and students' reflections on using hybrid project-based learning in academic writing courses. In-depth interviews and end-of-semester reflection were utilized to obtain data. Thematic analysis was conducted using the qualitative data analysis (QDA) miner lite software for data reduction. the findings revealed three prominent themes that emerged in this lecturers' reflections: i) integrating varied learning modalities; ii) fostering critical thinking and involvement; and iii) improving competencies, as they were also crucial to boosting professional development. Meanwhile, students portray hybrid project-based learning as challenging and rewarding and fosters a sense of value and acceptability among students. This research has transformative implications for curriculum development and instructional practices in higher education, especially academic writing. [ABSTRACT FROM AUTHOR]

Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery's production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms. This paper introduces a novel multiobjective optimization approach driven by a diffusion model (named DMO), which is designed specifically for gasoline blending scheduling. To address integer constraints and generate feasible schedules, the diffusion model creates multiple intermediate distributions between Gaussian noise and the feasible domain. Through iterative processes, the solutions transition from Gaussian noise to feasible schedules while optimizing the objectives using the gradient descent method. DMO achieves simultaneous objective optimization and constraint adherence. Comparative tests are conducted to evaluate DMO's performance across various scales. The experimental results demonstrate that DMO surpasses state-of-the-art multiobjective evolutionary algorithms in terms of efficiency when solving gasoline blending scheduling problems. [ABSTRACT FROM AUTHOR]

The present research introduces an optimum performance soft computing model by comparing deep (multi-layer perceptron neural network, support vector machine, least square support vector machine, support vector regression, Takagi Sugeno fuzzy model, radial basis function neural network, and feed-forward neural network) and hybrid (relevance vector machine) learning models for estimating the pile group settlement. Six kernel functions have been used to develop the RVM model. For the first time, the single (mentioned by SRVM) and dual (mentioned by DRVM) kernel function-based RVM models have been employed for the reliability analysis of settlement of pile group in clay, optimized by genetic and particle swarm optimization algorithms. For that purpose, a database has been collected from the published article. Sixteen performance metrics have been implemented to record the model's performance. Based on the performance comparison and score analysis, models MS3, MS9, MS17, MS23, and MS25 have been recognized as the better-performing models. Furthermore, the regression error characteristics curve, Uncertainty analysis, cross-validation (k-fold = 10), and Anderson–Darling test reveal that model MS23 is the best architectural model in reliability analysis of pile group settlement. The comparison of model MS23 with published models shows that model MS23 has outperformed with a performance index of 1.9997, a20-index of 100, an agreement index of 0.9971, and a scatter index of 0.0013. The compression index, void ratio, and density influence the pile group settlement prediction. Also, the problematic multicollinearity level (variance inflation for > 10) significantly affects the performance and accuracy of the deep learning model. [ABSTRACT FROM AUTHOR]

Critical thinking skill is one of the abilities that must be possessed by students in the 21st century. Students are currently not trained to always think critically, which could be caused by the learning styles of the students themselves. The learning style here is a combination of a person's state of being able to absorb, be able to organize, and be able to process information. This study aims to analyze the implementation and relationship of learning styles with critical thinking skills after the hybrid learning model is applied to the work and energy materials. The method used correlation analysis with product-moment correlation. The sample of this research was the students of class X MIPA, totaling 16 students who were selected by using the saturated sampling technique. The instruments used were a learning style questionnaire (an authentic assessment based on teaching and learning trajectory) with student activity sheet (AABTLT with SAS), and critical thinking skills test questions. The results showed that the average percentage of learning implementation using the hybrid learning model was 91% with a very effective category. The results of the hypothesis test using the product-moment correlation test, then count (0.110) < table (0.497) or count less than stable, so Ha is rejected and H0 is accepted. The results of this study can conclude that there is no significant relationship between learning styles and critical thinking skills of students of class X MIPA on the matter of work and energy. [ABSTRACT FROM AUTHOR]

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness. [ABSTRACT FROM AUTHOR]

A strategic priority for university educators is to ensure graduates have developed a range of transferable skills. An important skill required by employers is the development of digital capabilities. The COVID-19 pandemic necessitated the move of courses into blended formats which provided an opportunity for educators to redesign their modules to embed digital skills. During this challenging context, the Biological Sciences Department at Royal Holloway, University of London, in collaboration with the E- Learning and Careers Teams, capitalised on existing Microsoft technologies to launch an innovative online group project for a first-year module. This collaborative approach to curriculum design increased capacity to pivot quickly, and readily support students, as well as ensuring that learnings from the design process could be widely disseminated. The virtual collaborative assessment had high engagement and supported the development of transferable skills such as teamwork, digital literacy, and communication. The incorporation of a guided self-reflective exercise into the assessment allowed students to evidence the transferable skills they had acquired. By frontloading these foundational skills into an assessment early in their education, we increased capacity to build on these skills in future years across the wider programme. The design, which used asynchronous online technologies, also increased the capacity of staff to fit marking and student queries around their schedules. Our findings demonstrated that through online group work and guided self-reflective assessment, students developed transferable skills that would benefit them in their future careers. This transformative approach could be utilised across other departments, both in remote and hybrid environments. [ABSTRACT FROM AUTHOR]

This study intends to determine the perceptions of the in-service Teacher profession education program (PPG) students towards Indonesian network learning system (SPADA) hybrid learning. This study employed a survey method with a qualitative approach. Subjects in this study were 82 of 87 PPG Daljab 1 Batch 1 students. The technique of collecting data was through the instruments of questionnaires and interviews. The questionnaire was used to obtain data about the quality of online content and the assessment of online learning that has been carried out. Interviews were conducted to confirm the results of the questionnaire data to see the compatibility between the two data. Data analysis techniques in this study were conducted qualitatively and quantitatively by considering to the research objectives. The results showed that online learning had sharp quality content, can be accepted and well understood, easy to operate and use, overcomes distance and time constraints, and has benefits that are in line with the objectives of PPG implementation. [ABSTRACT FROM AUTHOR]

Synchronous hybrid learning (SHL) is a new instructional approach that allows on-site and remote students to participate in learning activities simultaneously. Investigating metaphorical perceptions about new learning environments may offer insights into the ways in which they are perceived by various stakeholders. Yet, research is missing a thorough investigation into metaphorical perceptions about hybrid learning environments. Hence, our goal was to determine and compare the metaphorical perceptions of higher education instructors and students regarding their roles in face-to-face versus SHL environments. When asked about SHL, participants were asked to refer separately to the on-site and remote student roles. Following the mixed-methods research design, data were obtained from 210 higher education instructors and students who responded to an online questionnaire during the 2021 academic year. Findings showed that both groups perceived their roles differently in face-to-face versus SHL. For instructors, the "guide" metaphor was replaced with the "juggler" and the "counselor" metaphors. For students, the "audience" metaphor was replaced with different metaphors for each cohort of learners. The on-site students were described as an active audience, while the remote students were described as outsiders or observers. The meaning of these metaphors will be discussed in light of the impact of the COVID-19 pandemic on teaching and learning in contemporary higher education. [ABSTRACT FROM AUTHOR]

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score. [ABSTRACT FROM AUTHOR]

Online anatomical resources are rising in popularity since the COVID‐19 pandemic, but the pedagogical principles and effectiveness of their use remain unclear. This article aims to demonstrate evidence‐informed ways in which fellow educators can create engaging online learning resources in clinical neuroanatomy and compare the effectiveness of text‐based and online learning resources. Data were analyzed from the Soton Brain Hub (SBH) YouTube page. Separately, a cross‐sectional study comparing the learning gain of using text‐based and video resources was done. The knowledge gain and retention were compared between groups using a pre‐teaching and post‐teaching multiple choice questions. YouTube analytics showed the average time a viewer spends on a video was found to be highly correlated to the length of the video, r = 0.77, p < 0.001 (0.69–0.82). The cross‐sectional study indicated a significant difference in mean normalized learning gain of video resources 61.9% (n = 53, CI 56.0–67.7%) versus text resources 49.6% (n = 23, CI 39.1–60.1%) (p = 0.030). However, there was no difference in retained learning gain between video resources 39.1% (n = 29, CI 29.2–49.0%) versus text‐based 40.0% (n = 13, CI 23.9–56.1%) (p = 0.919). Students engage most with short videos less than 5 min which reduces the intrinsic load of learning. Online resources are as effective as text‐based resources in providing learning gain and retention. In the future, the continued rise in popularity of online learning resources may result in further reduction in traditional face‐to‐face teaching. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the impact of hybrid learning on the satisfaction, effectiveness and academic performance of faculty and students in private universities in Kuwait. The study specifically addresses the challenges and experiences confronted by students and faculty members in utilising hybrid learning, assesses whether hybrid learning leads to improvements in academic performance and proposes measures for providing quality hybrid learning models in post-coronavirus disease 2019 (COVID-19) situations. Design/methodology/approach: This study employed a quantitative method by collecting data via a questionnaire targeting private university students and faculty members in Kuwait. A total of 311 participants completed the survey. Findings: The results show that students were in favour of the hybrid learning method and viewed this as being efficient when coming to satisfaction, academic performance and overall effectiveness; whilst faculty members have voiced the members' disfavour of the hybrid model of teaching as the members believe that the system is unjust, especially when coming to student's academic performance. Originality/value: Although research related to hybrid learning in different parts of the world has existed for many years, the research only recently gained prominence in Kuwait due to the COVID-19 pandemic. This paper will likely be of interest to academics, policymakers, the government and universities as the pandemic continues to make hybrid learning more popular and, therefore, would increase awareness with regards to students' and faculty members' opinions about the system and proposed ways to resolve any issues. [ABSTRACT FROM AUTHOR]

Problem solving skills must be familiar to students in any condition. However, during the pandemic, students have not optimally mastered this ability. At the end of the pandemic, case study-based hybrid learning is highly recommended in higher education. The objective of this research is to analyze the effectiveness of case study-based hybrid learning on problem solving skills which are detailed: (1) analyzing the completeness of students' problem solving ability of case study-based hybrid learning classically, (2) analyzing the completeness of students' problem solving ability of case study-based hybrid learning based on Mathematics Initial Ability (MIA), (3) analyzing the difference in the completeness of students' problem solving ability of case study-based hybrid learning with problem-based hybrid learning, (4) analyzing the difference in students' problem solving ability of case study-based hybrid learning with problem-based hybrid learning based on MIA. This quantitative research took 6th-semester students of the Mathematics Education Study Program of Universitas Negeri Semarang who took the Statistics of Education course in the 2021/2022 academic year as the population. Of the 6 class, 2 class were selected as samples, by random sampling. The research design used a post-only control design. How to collect data with a problem solving ability test. Data were analyzed using the z-test, and independent t-test. From the research obtained the results (1) the problem solving ability of students of Hybrid learning based on case study problems is classically complete both by mean and proportion, (2) students with high MIA, and medium, are complete by mean and proportion, while students with low MIA have not completed by mean and proportion, (3) there is no difference in the completeness of the problem solving ability of students of hybrid learning based on case study problems with hybrid learning based on problems, (4) there is a difference in the problem solving ability of medium MIA, there is no difference in low and high MIA in both learning models. Kemampuan pemecahan masalah harus dibiasakan bagi mahasiswa dalam kondisi apapun. Namun pada masa pandemi, mahasiswa belum secara maksimal menguasai kemampuan tersebut. Di masa pandemi berakhir, pembelajaran hybrid berbasis studi kasus sangat dianjurkan di perguruan tinggi. Tujuan penelitian ini adalah untuk menganalisis efektivitas pembelajaran hybrid berbasis studi kasus terhadap kemampuan pemecahan masalah yang dirinci: (1) menganalisis ketuntasan kemampuan pemecahan masalah mahasiswa pada pembelajaran hybrid berbasis studi kasus secara klasikal, (2) menganalisis ketuntasan kemampuan pemecahan masalah mahasiswa pada pembelajaran hybrid berbasis studi kasus berdasarkan Kemampuan Awal Matematika (KAM), (3) menganalisis perbedaan ketuntasan kemampuan pemecahan masalah mahasiswa pada pembelajaran hybrid berbasis studi kasus dengan pembelajaran hybrid berbasis masalah, (4) menganalisis perbedaan kemampuan pemecahan masalah mahasiswa pada pembelajaran hybrid berbasis studi kasus dengan pembelajaran hybrid berbasis masalah berdasar KAM. Penelitian kuantitatif ini mengambil mahasiswa semester 6 Program Studi Pendidikan Matematika Universitas Negeri Semarang yang mengambil mata kuliah Statistika Pendidikan tahun ajaran 2021/2022 sebagai populasinya. Dari 6 kelas dipilih 2 kelas sebagai sampel, dengan cara random sampling. Desain penelitian menggunakan post-only control design. Cara pengambilan data dengan tes kemampuan pemecahan masalah. Data dianalisis menggunakan uji z, dan uji t independen. Dari penelitian diperoleh hasil (1) kemampuan pemecahan masalah mahasiswa pada pembelajaran Hybrid berbasis permasalahan studi kasus tuntas secara klasikal baik mean maupun proporsinya, (2) mahasiswa dengan KAM tinggi dan sedang tuntas secara mean dan proporsinya, sedangkan mahasiswa dengan KAM rendah belum tuntas secara mean dan proporsi, (3) tidak terdapat perbedaan ketuntasan kemampuan pemecahan masalah mahasiswa yang pembelajaran hybrid berbasis masalah studi kasus dengan pembelajaran hybrid berbasis masalah, (4) terdapat perbedaan kemampuan pemecahan masalah KAM sedang, tidak terdapat perbedaan KAM rendah dan tinggi pada kedua model pembelajaran.

This research aims to develop a framework for a hybrid teaching factory model that combines face-to-face and online learning with information and communication technology to improve employability skills in the new normal era. The research method uses the ADDIE Research and Development model, which consists of five stages: analysis, design, development, implementation, and evaluation. The research was conducted in several vocational high schools in DKI Jakarta Region, Indonesia. The findings in the research prove that the implementation of hybrid learning has an impact on students and teachers. Hybrid learning teaching factory is proven to be able to overcome frustrations and limitations between teachers and students in the learning process through online facilities, making teaching factory learning more innovative because there are variations of learning to interact and discuss, and making the classroom atmosphere conducive because students become happy and active in learning and skilled in working. The success of a hybrid teaching factory is considered mutually beneficial for both students and teachers who complete one of the teaching factory learning curriculums while industry instructors develop and work on collaborative platforms that provide useful services such as augmented reality and virtual reality-based applications.

The contemporary system of sociological education undergoes all processes of digital transformation; therefore, the key challenges to the teaching community and risks associated with new functions and a decrease in the quality of education require serious rethinking in the context of digital transformation. Moreover, the tasks of identifying the capabilities of EdTech and constructive ways of using them in sociological education are also updated. The article considers the trends in the development of EdTech in education and conditions, technologies and directions for its use in sociological education. The empirical basis of the article is the results of sociological research conducted by international and Russian organizations on the use and development prospects of EdTech. The introduction and use of technologies in the education system determine the task of developing a new digital infrastructure and of the corresponding training/retraining of teaching staff. On the other hand, the education system should respond to students’ requests for emotional inclusion, support, psychological well-being and pedagogy of care in the digitally mediated environment. The author argues that the key tasks to be solved under the introduction of EdTech into sociological education are as follows: formation of a meaningful approach to the use of technology by all participants of the educational process; training in new methods for the analysis of transforming social reality; identification of legitimate ways to use artificial intelligence in education and research. The article emphasizes the dynamism of digital transformation in education, which determines the need for monitoring research and for a comprehensive (infrastructural, personnel, content) digital transformation strategy taking into account the features of educational programs.

This paper is a study on the effectiveness of hybrid learning; a blend of traditional and technology-based training, vis-à-vis the second language (L2) learner of English pronunciation. Specifically, our motivation is centred on the functionality of combining the habitual classroom teaching style with the Telegram app (a mobile-based technological tool) for studies relating to speech production and perception. The methodical procedure and design for this study is both quantitative and descriptive; employing the use of a multiple-choice questionnaire in data collection from 401 Nigerian undergraduate and postgraduate students in the University of Benin, Nigeria, to ascertain the effectiveness of digital designs and stratagems. Pie charts are employed to display multiple divisions of the study's data comparison. This study emphasizes the Online-Driver Blended Learning Model; such a pedagogical approach exposes the L2 learner to a more involved and effective way of understanding how to avoid imposing an unfamiliar accent upon the target language. Consequently, we see a merger of online practical exercises and exposure to uploaded multimodal texts and native speakers' spoken data (audio-visual recordings/voice notes) with scheduled direct classroom interludes. Findings confirm that exploiting such a hybrid model enhances students' cognitive abilities and retentive capacity regarding English pronunciation. Such digital audio-visual tools are paramount phonetic strategies for learning how to avoid unacceptable phonetic alterations. Thus, we see the effectiveness of integrating the Telegram app into the traditional brick-and-mortar educational method in handling problems associated with speech sound production and second-language phonological interference.

Automatic modulation recognition (AMR) is an essential topic of cognitive radio, which is of great significance for the analysis of wireless signals and is one of the current research hotspots. Traditional AMR approaches predominantly utilize raw in-phase/quadrature symbols (I/Q), amplitude/phase (A/P), or pre-processed data (e.g., high-order cumulates, spectrum images, or constellation diagrams) as inputs for the recognition model. However, it is difficult to achieve superior performance with only a single type of data as input. This paper proposes a novel multi-channel hybrid learning framework that integrates convolutional layers, Long Short-Term Memory (LSTM) layers, fully connected layers and classification layers. The model is built for modeling spatial-temporal correlations from four signal cues (including I/Q signals, A/P signals, I, and Q signals), which aims to explore various differences and leverage the complements from multiple data-form. Two functions employed during the data conversion process further enhance the non-linear representational capacity of the model, thereby boosting the recognition accuracy of the model. Experimental results demonstrate that the proposed framework effectively addresses the classification challenges of QAM16 and QAM64. For the RAML2016A dataset, our model achieves an impressive recognition accuracy of 95% at an SNR of 0 dB. Extensive experiments indicate that the proposed framework outperforms other current networks in terms of recognition accuracy.

Diabetic retinopathy (DR) can be defined as visual impairment caused by prolonged diabetes affecting the blood vessels in the retina. Globally, it stands as the primary contributor to blindness, impacting approximately 191 million individuals. While prior research has addressed DR classification using retinal fundus images, existing methods often focus on isolated lesion detection, lacking a comprehensive framework for the simultaneous identification of all lesions. Previous studies concentrated on early-stage features like exudates, aneurysms, hemorrhages, and blood vessels, sidelining severe-stage lesions such as cotton wool spots, venous beading, very severe intraretinal microvascular abnormalities (IRMA), diffuse intraretinal hemorrhages, capillary degeneration, highly activated microglia, and retinal pigment epithelium (RPE) damage. In this study, a deep learning approach is proposed to classify DR fundus images by severity levels, utilizing GoogleNet and ResNet models based on adaptive particle swarm optimizer (APSO), for enhanced feature extraction. The extracted features from the hybrid model are further used with different machine learning models like random forest, support vector machine, decision tree, and linear regression models. Experimental results showcased the proposed hybrid framework outperforming advanced approaches with a remarkable 94% accuracy on the benchmark dataset. This method demonstrates potential enhancements in precision, recall, accuracy, and F1 score for different DR severity levels.

The article discusses the relevance of digital transformation in the learning process in higher educational institutions. The authors of the article have identified the problem of digital transformation of higher education and established contradictions between the continuous increase in the requirement for the availability of various forms of education (blended, hybrid, distance learning) and the quality of professional training of students. The authors have identified the main problems associated with transformation in the field of modern education: the phenomenon of «clip thinking», the crisis of text culture. The purpose of the research is to identify the pros and cons of digital education at a university by analyzing the use of digital technologies in the design of new educational material. The method of theoretical analysis and pedagogical modeling have been used in the research. The authors have concluded that teachers have an ambiguous attitude towards digitalization in Pedagogy: on the one hand, it negatively affects the education system, on the other, on the contrary, it has a positive effect. In conclusion, the results of the study have shown that teaching students general academic skills in the context of the digital transformation of a university environment will improve the quality of the learning process and significantly improve the professional preparation of students.

Background: The COVID-19 pandemic forced school closures and rapid transitions to distance learning, which were widely associated with negative effects on educational attainment and mental health among youth. Research is now emerging about the relationship between distance learning and educational outcomes, as well as factors that sped or delayed the return of in-person learning in specific geographic regions. In the state of Massachusetts, in the United States, high schools (9th-12th grade) varied in the length of time that passed before in-person learning was offered. This study investigated (1) what factors were associated with the date at which schools implemented hybrid/in-person learning, and (2) what factors, including time in remote learning, were associated with loss of educational attainment. Methods: The sample included N = 267 regional/local high schools. Analyses investigated whether time to hybrid/in-person learning was associated with the percentage of students from low-income households and from minority ethnic/racial groups, local political affiliations and COVID incidence rate in September 2020, and the size of the district. The second set of analyses examined whether the high schools' observed losses in standardized math test scores between 2019 and 2021 were associated with the amount of time students remained in exclusively remote learning, as well as the percentage of students from low-income households and minority ethnic/racial groups, the COVID cumulative incidence rate in the region by April 2021, and the size of the school district. Results: Multiple linear regression analysis examining variance in the date at which hybrid/in-person learning was implemented was most strongly predicted by the size of the school district. Multiple linear regression analysis examining variance in the loss of educational attainment was most strongly predicted by the percentage of students from low-income households in the high school. Exploratory analyses comparing charter schools with regional public high schools found that charter schools showed significantly greater loss of educational attainment, contrary to hypotheses. Conclusion: Additional protections for students from larger school districts, lower-income families, and charter schools are needed in case of future population-level disruptions in education. [ABSTRACT FROM AUTHOR]

Hybrid learning is an alternative learning that can provide wider access for students to learn and increase student’s participation in learning. Student’s involvement in the hybrid learning model is an interesting and important issue to be researched. This study aims to examine whether there are differences in cognitive engagement, emotional engagement, social engagement, behavioral engagement, and collaborative engagement in hybrid learning. This research is an experimental research. A total of 52 accounting students participated in the research respondents were given treatment in the form of implementing hybrid learning. The t-test is used to determine whether there is differences in social engagement, cognitive engagement, emotional engagement, behavioral engagement, and collaborative engagement in hybrid learning. The results show that student engagement is different before and after the implementation of hybrid learning based on the five dimensions of engagement. Educators who intend to increase student engagement can use hybrid learning. [ABSTRACT FROM AUTHOR]

The study aimed to identify the extent of blended learning resources availability in the schools of Bethlehem Governorate from the teachers' perspective, and provide blended learning resources proposals. The descriptive analytical approach was employed, using a questionnaire distributed to a sample of (365) male and female teachers of public-school teachers in the Bethlehem Governorate, out of (2231) the total study population. The study main results showed an average availability degree of blended learning resources in public schools in Bethlehem Governorate, from their teachers' perspectives. There were statistical differences in the response of sample members due to gender variable, except for technology field, and in the school location variable in favor of the city compared to the village. As for level and major variables, there were no differences in the response averages of the study sample. The study recommends adopting future policies for developing physical technical environments in public schools, teacher continuous professional development and training in courses and workshops on school related issues in general and male schools in particular, to improve their skills, teaching methods and develop their technical abilities. [ABSTRACT FROM AUTHOR]

Simple Summary: In this work, we explored computational methods for analyzing a color digital image of a wound and predicting (from the analyzed image) the number of days it will take for the wound to fully heal. We used a hybrid computational approach combining deep neural networks and decision trees, and within this hybrid approach, we explored (and compared the accuracies of) different types of models for predicting the time to heal. More specifically, we explored different models for finding the outline of the wound within the wound image and we proposed a model for computing the proportions of different types of tissues within the wound bed (e.g., fibrin slough, granulation, or necrotic tissue). Our work clarifies what type of model should be used for the computational prediction of wound time-to-healing and establishes that, in order to predict time-to-healing accurately, it is important to incorporate (into the model) data on the proportions of different types in the wound bed. Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a controlled study in dogs, as well as human wound images that are annotated for the tissue type composition. In the context of a hybrid-learning approach (neural network segmentation and decision tree regression) for the image-based prediction of time-to-healing, we tested whether explicitly incorporating tissue type-derived features into the model would improve the accuracy for time-to-healing prediction versus not including such features. We tested four deep convolutional encoder–decoder neural network models for wound image segmentation and identified, in the context of both original wound images and an augmented wound image-set, that a SegNet-type network trained on an augmented image set has best segmentation performance. Furthermore, using three different regression algorithms, we evaluated models for predicting wound time-to-healing using features extracted from the four best-performing segmentation models. We found that XGBoost regression using features that are (i) extracted from a SegNet-type network and (ii) reduced using principal components analysis performed the best for time-to-healing prediction. We demonstrated that a neural network model can classify the regions of a wound image as one of four tissue types, and demonstrated that adding features derived from the superpixel classifier improves the performance for healing-time prediction. [ABSTRACT FROM AUTHOR]

The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction. [ABSTRACT FROM AUTHOR]

The preference for paper-based learning (traditional) or audio-visual learning (electronic) brought about hybrid learning whose preference among students is much greater due to the interaction with digital and audio-visual media. The general objective is to analyze and describe the experiences of university students on paper-based learning called traditional and audio-visual learning called e-learning, for which a quantitative descriptive and correlational methodology was used with a measurement instrument through an anonymous and voluntary survey with 1240 participants. The results have acceptable statistics and are demonstrated with the confirmatory factor analysis, showing the preference for hybrid learning. Conclusion, students request that universities implement this methodology (hybrid) to work properly and have a quality education in the future. [ABSTRACT FROM AUTHOR]

Loki is a state-of-the-art adaptive bitrate algorithm for the transmission of real-time-communication (RTC) video. It fuses traditional heuristic methods with a learning-based model to maximize the quality of experience (QoE) under diverse network conditions. However, a recurring rebound pattern is observed in Loki’s decision-making process where the decision frequently oscillates between the two boundaries of the action space, making Loki fail to adapt to the fluctuating network bandwidth. To address this issue, we propose Loki+, which improves both the fusion mechanism and the design of the learning-based actor. Specifically, we replace the element-wise multiplication with a simple but effective trend fusion and further optimize the design of reward and loss functions for training Loki+. Extensive simulation results show that Loki+ significantly improves the QoE in the aspects of reducing the stall rate by 20% ∼ 60% and the frame delay by 3.5% ∼ 30.5% while maintaining a similar sending bitrate or video quality, compared with Loki. [ABSTRACT FROM AUTHOR]

Highlights: The Ministry of Education Research Center for Online Education conducted a national survey of university administrators of learning and teaching. The survey results showed that (a) the universities in eastern China had developed and introduced more massive open online courses (MOOCs) than those in the central and western regions; (b) of the various measures for blended learning, the universities tended to focus on supporting hardware development before providing training and incentive measures to teachers; and (c) the universities had widely used hybrid learning approaches. In relation to hybrid learning, Tsinghua University explored infrastructure upgrades, teacher training, and the optimal use of the rain classroom and its clone class, and launched the global hybrid classroom and global open courses. In the future, it will be necessary to further promote education equity in line with national policies and provide and perfect incentives to improve the quality of blended learning and expand hybrid learning while improving interaction through technology. [ABSTRACT FROM AUTHOR]

This study investigates the preparedness of school students in grades 9-12 to engage in physics project-based hybrid learning within the Sultanate of Oman. Using survey methodology, the research employed the online learning readiness scale and self-directed learning readiness scale following an evaluation of the questionnaire's reliability. A total of 383 physics students participated in the electronic survey conducted across 11 educational governorates in Oman, and descriptive statistics were employed for the analysis. Most participants, predominantly from grade 9, were situated in the Muscat Governorate, consistent with the targeted research population. The findings indicate a generally moderate level of readiness among school physics students (grades 9-12) for engaging in project-based hybrid learning. The study enhances the comprehension of student readiness dynamics amid evolving educational methods, specifically in physics projectbased hybrid learning in the Sultanate of Oman. [ABSTRACT FROM AUTHOR]

The transformation of post-pandemic learning at Indonesian universities is becoming increasingly diverse. Several universities use hybrid learning models to implement hybrid learning practices. The purpose of this study is to assess the hybrid learning process at two Indonesian universities. The context input process product (CIPP) evaluation model is used in this study as part of a mixed method. Quantitative methods are used to get an overview of student and lecturer responses, whereas qualitative methods are used to dig deeper into data about hybrid learning practices. This study included 341 students and 22 lecturers from both universities. Techniques for collecting data include questionnaires, interviews, observation, and documentation studies. The data was analyzed using learning theories. The study’s findings indicate that hybrid learning practices are performing well. The readiness of university infrastructure, as well as the readiness of students and lecturers, determines the success of hybrid learning implementation. In practice, however, some lecturers tend to use face-to-face or online learning practices exclusively to fill gaps in the hybrid learning process. This study suggests that universities investigate an effective model for implementing hybrid learning. [ABSTRACT FROM AUTHOR]

Social presence, which refers to the psychological phenomenon of perceiving other persons in technology mediated communication as "real" and with whom one can connect, has gained an increasing interest by teachers and researchers involved in designing online, hybrid, and blended learning environments, particularly group learning settings known as computer-supported collaborative learning (CSCL). While some scholars attribute social presence primarily to the physical attributes of communication media, others emphasize the importance of social contextual and individual factors. Despite considering these factors, they still cannot fully explain the varying degrees of social presence experienced across different communication and collaboration modes and modalities. Consequently, there is a need for a more comprehensive theoretical account on the antecedents of social presence. In this article we propose such an account that integrates the social information processing (SIP) theory, construal level theory (CLT), and telepresence theory into one social presence framework. In line with CLT, we propose that social presence is also influenced by the impressions (construals) we construct from other persons not only through the accumulation of messages over time but also through the psychological distance we feel to those persons, which may be imposed by features of the communication media or realities of the learning context. Further, in line with telepresence theory, we propose that social presence is influenced by the sense of being "present" in the remote physical or virtual place, as this is where other salient persons "are." This comprehensive theoretical framework allows us to understand varying degrees of social presence while in (pseudo) realtime and asynchronous communication and collaboration using a variety of different communication media ranging from text-based (e.g., e-mail, instant text messaging) to immersive (e.g., 3D computer generated; a physical remote place). [ABSTRACT FROM AUTHOR]

With the official announcement of the new type of coronavirus-induced COVID-19 outbreak as a global pandemic, an extraordinary situation that no one has ever encountered has started. Just as life was about to return to normal in Türkiye, two devastating earthquakes, centered in Kahramanmaraş, affected ten different cities. Many global and national developments in various fields, which are expected to happen in the distant future, were completed within 3 years. One of these fields was undoubtedly education. Hybrid learning is seen as a trending educational approach combining face-to-face and online learning. Plastinated specimens come to the forefront for hybrid education with various advantageous features. They are not only non-toxic, dry, odorless materials, but also can simulate the natural anatomic appearance in detailed manner. With the help of new-generation acrylic paints and dyes which can penetrate into tissues, plastinates offer a unique natural look rather impressive than any other techniques. Due to the features mentioned above, plastinates are also convenient materials for handling, transportation or storage. These issues will be discussed in our article in terms of compatibility with hybrid learning. The aim of this article is to give ideas and make suggestions about how plastinates, which have been used efficiently in anatomy practices and professional training, can be used in hybrid veterinary anatomy education. [ABSTRACT FROM AUTHOR]

The semantic segmentation of drone LiDAR data is important in intelligent industrial operation and maintenance. However, current methods are not effective in directly processing airborne true-color point clouds that contain geometric and color noise. To overcome this challenge, we propose a novel hybrid learning framework, named SSGAM-Net, which combines supervised and semi-supervised modules for segmenting objects from airborne noisy point clouds. To the best of our knowledge, we are the first to build a true-color industrial point cloud dataset, which is obtained by drones and covers 90,000 m2. Secondly, we propose a plug-and-play module, named the Global Adjacency Matrix (GAM), which utilizes only few labeled data to generate the pseudo-labels and guide the network to learn spatial relationships between objects in semi-supervised settings. Finally, we build our point cloud semantic segmentation network, SSGAM-Net, which combines a semi-supervised GAM module and a supervised Encoder–Decoder module. To evaluate the performance of our proposed method, we conduct experiments to compare our SSGAM-Net with existing advanced methods on our expert-labeled dataset. The experimental results show that our SSGAM-Net outperforms the current advanced methods, reaching 85.3 % in mIoU, which ranges from 4.2 to 58.0 % higher than other methods, achieving a competitive level. [ABSTRACT FROM AUTHOR]

We are living in an ever-changing world. The changes in science, technology and economics constantly impact areas closely related to human life, such as education and health. Organisations must rely on innovation to remain relevant and effective in a constantly evolving society. Educational institutions, namely schools, colleges or even training institutions, contribute to product innovation when they produce new and significantly different products and services such as new syllabi, new resources or new educational experiences like e-learning. They carry out process innovation when they bring about significant changes in how they provide their service, for instance, changes in the way their teachers work together, communicate with parents or offer services in collaboration with other institutions. With the onset of the medical catastrophe of COVID-19 and the pandemic thereafter, the education sector plunged itself into adapting to the use of technology to keep its service functional, albeit with significant challenges faced in its effective implementation. In this article, the focus is on studying technology implementation in an early childhood care teacher-training institute to establish service innovation in education in the post-pandemic era. Multiple perspectives were derived from all the stakeholders, such as student-teachers, teacher educators and heads of schools (as future employers) regarding teacher preparedness and student-teacher readiness. The inputs helped us to develop the conceptual model for future teaching-learning. The conceptual model A.D.O.P.T. (Act with growth mindset, Deliver Quality, Open to Adaptation, Prioritize with Precision, Team Engagement) highlights how the stakeholders move from makeshift 'jugaad' innovation to sustainable development of the educational system through grassroots innovation. [ABSTRACT FROM AUTHOR]

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This literature review traces the historical development and changing educational delivery modalities to examine the evolution, application, and effects of remote, online, and hybrid learning models. It offers a theoretical framework outlining important educational ideas and techniques, concentrating on how different learning styles are considered within these paradigms and their difficulties and advantages. The paper emphasizes how significant technology developments have influenced remote learning, especially when combining synchronous and asynchronous learning techniques in hybrid models. Effective course design, learner engagement, technology infrastructure, faculty training, and institutional support are all factors that contribute to successful implementation. The study also contains case studies demonstrating successful and failed implementation initiatives, emphasizing the techniques, instructional approaches, technological integrations employed, and results and outcomes obtained. The analysis suggests best practices and suggestions for educators, institutions, and legislators to improve the effective implementation of various learning models, address possible issues, and offer enough faculty and student support. This thorough literature analysis provides insights into the dynamic interaction between pedagogy, technology, and institutional support in distant, online, and hybrid learning, serving as a significant resource in education and learning. [ABSTRACT FROM AUTHOR]

Stock prediction is prominent in the field of Artificial Intelligence. Stock prediction problems are handled either as a regression or classification task. Studies in the literature have also shown success for hybrid learning to stock prediction. But little attention is paid to finding out the effect of spatial feature extraction/distortion over the temporal effect of the deep neural network and vice versa for the problem under study. The paper, therefore, proposes a hybrid long short-term memory (LSTM) network over a convolutional neural network (CNN) called LSTM-CNN as against the popular CNN-LSTM model. The daily price movement of the S&P 500 index data is utilized. A sliding window technique is considered to obtain a balanced data of 20-days window data from the S&P 500. The proposed stock prediction model is investigated further for an optimal set of hyperparameters using the Bayesian optimization (Bo) technique. In addition, the proposed model is compared with optimized CNN, LSTM, and CNN-LTSM models. The optimized LSTM-CNN model is found to outperform the other models with accuracy, precision, and recall values of 0.9741, 0.9684, and 0.9800, respectively. The proposed model is established to provide a better stock trend prediction. [ABSTRACT FROM AUTHOR]

The covid-19 outbreak is spreading in the world fast, forcing states worldwide to transform into digital services, including education services. This study aims to investigate the design and challenges of Islamic universities in implementing HL. Hybrid Learning (HL) denotes a mixture of face-to-face (FTF) learning in the classroom with online learning through application assistance. In this study, the researcher used a qualitative descriptive method using open questions, documentation, and observation. In collecting data, researchers used critical reflection and triangulation. The study's results delineate that the HL design in Islamic universities in West Nusa Tenggara is imposed with FTF and online learning. Online learning design is prescribed using various features such as maximizing various applications, including zoom applications, LMS, Google Classroom, Whatsapp Group, etc. Students who savor online learning are students in the fifth semester and up, while students who undergo the FTF learning system are freshman and second-year students. This arrangement is because students in that semester have never felt the stall and orientation of lectures. Meanwhile, students who can make online appointments and FTF are those who are undergoing the final project. The challenges in HL include lecturers having difficulty constructing learning tools, students' low motivation and willingness to learn, unstable and unevenly distributed internet access, and students' economic conditions who experience difficulties. In FTF, lecturers and students have difficulty interacting because government policies prohibit crowds, social distancing, and must wear facemasks. Therefore, students, parents, and colleges must synergize to make the HL process successful. [ABSTRACT FROM AUTHOR]

After the COVID-19 pandemic was over, the teaching and learning process in the world of education underwent several changes. The COVID-19 pandemic forced the global learning community to transition within a very short period of time from the traditional in-class method of learning to a mode of online learning. After the pandemic started to subside, even though it had not wholly disappeared, learning was carried out using hybrid learning. Hybrid learning was continued to prevent the re-emergence of the Covid virus by reducing the number of students in each face-to-face class. The purpose of this study is to determine whether there are differences in learning outcomes between online learning and offline (face-to-face) learning. Based on normality and homogeneity tests as well as different tests on pretest and posttest scores, the results show that there are differences in posttest learning outcomes for the Online and Offline Groups, which can be seen from the P Value (Sig.) (0.005) < 0.05. Differences also occur in the results of the work; students in the Online Group were more courageous in trying to solve things in a different way from what the educator teaches compared to students from the Offline Group. With an awareness of these differences, future research can create a learning model that combines online and offline learning to improve student learning outcomes. [ABSTRACT FROM AUTHOR]