Large Classes Research Articles

Conformable evolution inclusions with causal operators

In this paper, we study local and nonlocal problems of conformable semilinear evolution inclusions with causal operators, which include time lag system and a large classes of parabolic multivalued differential equations. We first investigate the problems with the help of measure of noncompactness and afterwards when the right-hand side is Lipschitz.

Assessment of group formation methods on performance in group-based learning activities

Research on teaching and learning has demonstrated many benefits from team-and group-based learning for improving content comprehension and meeting learning outcomes in a wide range of disciplines. We help to expand on this research, by shedding additional light on the impact of group formation decisions on group performance during group learning activities in a higher education classroom setting. Our objective is to assess the impact of different group formation strategies, including random assignment, self-selection, and methods based on individual metrics, such as academic performance and individual skill levels. Using an experimental design approach, we find that GPA-based and math-ability based group formation strategies may provide some additional benefit to group performance on the margin when initially forming groups. Self-selection of groups may be more beneficial after students learn more about each other (e.g., skills sets and work ethic) during the progression of the course. Our study contributes to the literature by providing an assessment of different group formation strategies that are accessible to a wide range of instructors for small to large classes, as well as guidance on use of different group formation strategies within classes, specifically for classes with more quantitative and analytical tasks.

Demographic Factors Influencing Lecturers’ Protean Career Orientation in North-Central Universities, Nigeria

The elements of a basic protean career orientation are basic elements of human needs for growth and meaningful occupational productivity. However, a significant percentage of lecturers in Nigerian universities find it difficult in practicing their value driven-career attitude and self-directed behavior. Thus, this descriptive study sought to assess the demographic factors that influence lecturers’ dimensions of protean career orientation: value driven-career attitude and self-directed behavior in North-central Universities, Nigeria. Four hundred lecturers were purposely selected from Kogi State University, Anyigba, Benue State University Makurdi and Federal University of Lafia, Nigeria as respondents using self-structured questionnaires. Data were analyzed using descriptive statistics. The study found age and gender (70.2%) as influencing lecturers' value-driven career attitude while educational level and gender influences their self-directed attitude. The study discovered further that job and career satisfaction, organizational commitment, task performance, self-management behavior and career productivity were the influence protean career orientation had on lecturers work outcome in North-central universities, Nigeria (97.2%). However, 70.2% of lecturers in the study settings could not practice their protean career orientation effectively. Poor motivation, lack of adequate infrastructural facilities, teaching of large classes, poor staff development and strike actions were the barriers identified (68.4%). The study recommends among others that the Nigerian universities should provide organizational support that actively engages employees in the management of their own career as well as formal training and education on value-driven and self-directed attitudes so as to encourage positive values among lecturers.

Permutation-equivariant quantum convolutional neural networks

Abstract The Symmetric group $S_{n}$ manifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. Subgroups of $S_{n}$ arise, among many other contexts, to describe label symmetry of classical images with respect to spatial transformations, such as reflection or rotation. Equipped with the formalism of geometric quantum machine learning, in this study we propose the architectures of equivariant quantum convolutional neural networks (EQCNNs) adherent to $S_{n}$ and its subgroups. We demonstrate that a careful choice of pixel-to-qubit embedding order can facilitate easy construction of EQCNNs for small subgroups of $S_{n}$. Our novel EQCNN architecture corresponding to the full permutation group $S_{n}$ is built by applying all possible QCNNs with equal probability, which can also be conceptualized as a dropout strategy in quantum neural networks. For subgroups of $S_{n}$, our numerical results using MNIST datasets show better classification accuracy than non-equivariant QCNNs. The $S_{n}$-equivariant QCNN architecture shows significantly improved training and test performance than non-equivariant QCNN for classification of connected and non-connected graphs. When trained with sufficiently large number of data, the $S_{n}$-equivariant QCNN shows better average performance compared to $S_{n}$-equivariant QNN . These results contribute towards building powerful quantum machine learning architectures in permutation-symmetric systems.

Utilising Blended Learning for Large Classes to Deliver an Introductory Programming Course

Higher education institutions are constantly pushed to increase the student intake and produce industry-ready graduates. The pressure generally arises from the governing bodies, originating from socio-political factors. It is particularly evident in the field of Information Communication Technology (ICT) where there is a growing need for quality graduates. Undergraduate programmes in the field of ICT generally offer introductory computer programming courses that are mandatory at early stages of the degree programme. Consequently, the number of students registering for such courses can increase drastically. The students are coming from diversified backgrounds. While some students may have prior experience in computer programming, a larger majority may not have any prior knowledge or experience. Increased class size meets the constraint of the limited availability of physical classroom space and would require the class to be separated into smaller groups for face-to-face activities. Large classes are also challenged with the increased man-power requirement in terms of lecturers, instructors, and teaching assistants. Maintaining student engagement and interactivity becomes increasingly difficult in a large class. Grading of assessments becomes time-consuming and needs and requires increased manpower. Based on the observations and learning from an ongoing introductory programming course, this study evaluates the procedure for overcoming the challenges faced by the teaching staff, especially with a class size exceeding 1000. To cover the theoretical components, recorded video lectures are provided. The class is divided into groups consisting of approximately 100 students and live, online, interactive group discussions are conducted. The live, online smaller group discussions allow students to clarify doubts regarding the content. This also allows the distribution of the limited teaching and assistant staff. Students who need further assistance are encouraged to meet the teaching assistants physically. Thus, the workload is distributed effectively, and it helps ensure the dedicated support and attention is provided to the students. Auto graded programming assignments are effectively employed for formative and summative assessments, which reduces the grading workload significantly. The findings of this study provide insights on course design to effectively deliver the content and conduct assessments overcoming the challenges presented by increased class size.

Conformally invariant random fields, Liouville quantum gravity measures, and random Paneitz operators on Riemannian manifolds of even dimension

AbstractFor large classes of even‐dimensional Riemannian manifolds , we construct and analyze conformally invariant random fields. These centered Gaussian fields , called co‐polyharmonic Gaussian fields, are characterized by their covariance kernels k which exhibit a precise logarithmic divergence: . They share a fundamental quasi‐invariance property under conformal transformations. In terms of the co‐polyharmonic Gaussian field , we define the Liouville Quantum Gravity measure, a random measure on , heuristically given as and rigorously obtained as almost sure weak limit of the right‐hand side with replaced by suitable regular approximations . In terms on the Liouville Quantum Gravity measure, we define the Liouville Brownian motion on and the random GJMS operators. Finally, we present an approach to a conformal field theory in arbitrary even dimension with an ansatz based on Branson's ‐curvature: we give a rigorous meaning to the Polyakov–Liouville measure and we derive the corresponding conformal anomaly. The set of admissible manifolds is conformally invariant. It includes all compact 2‐dimensional Riemannian manifolds, all compact non‐negatively curved Einstein manifolds of even dimension, and large classes of compact hyperbolic manifolds of even dimension. However, not every compact even‐dimensional Riemannian manifold is admissible. Our results concerning the logarithmic divergence of the kernel rely on new sharp estimates for heat kernels and higher order Green kernels on arbitrary closed manifolds.

Exploring the role of project status information in effective code smell detection

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection.

Approximation properties of torsion classes

AbstractWe strengthen a result of Bagaria and Magidor (Trans. Amer. Math. Soc. 366 (2014), no. 4, 1857–1877) about the relationship between large cardinals and torsion classes of abelian groups, and prove that the Maximum Deconstructibility principle introduced in Cox (J. Pure Appl. Algebra 226 (2022), no. 5) requires large cardinals; it sits, implication‐wise, between Vopěnka's Principle and the existence of an ‐strongly compact cardinal. While deconstructibility of a class of modules always implies the precovering property by Saorín and Šťovíček (Adv. Math. 228 (2011), no. 2, 968–1007), the concepts are (consistently) nonequivalent, even for classes of abelian groups closed under extensions, homomorphic images, and colimits.

Challenges in Maintaining Quality Assessments in Higher Learning Institutions: Experiences from Selected Tanzanian Universities

This study was conducted to assess the extent to which universities in Tanzania ensure quality in assessments and identify challenges facing the whole assessment process. Literature showed a poor linkage between university graduates and industry whereby some graduates were reported to be lacking essential skills expected of them beside of having good results. This situation motivated the researcher to conduct this study being guided by TQM theory. The study adopted a convergent parallel design which allowed a collection of both qualitative and quantitative at a go. The study targeted lecturers, examination officers, heads of departments and quality assurance directors from universities offering teacher education in Tanzania. Stratified convenience and purposeful sampling techniques were used to select 48 lecturers, 4 examination officers, 4 heads of departments and 4 quality assurance directors from four universities. Questionnaires and in-depth interview guides were used to collect data. Quantitative data were analyzed in frequencies and percentages while the analysis of qualitative data was done in themes and its presentation was done narratively being supported by direct quotations. The study found out universities to be striving to improve quality in final examinations but doing less in formative assessments. Large classes and workloads on the other side were found to be the challenges facing the assessment process. The study recommended for universities to extend the moderation process to the continuous assessments and develop policies that will make lecturers maintain quality in the assessment process.

Implementing creative dance activities for primary school children to improve health and wellbeing: a qualitative study in the North East England.

Evidence suggests that group arts activities with children build resilience and positive mental wellbeing. However, insufficient attention has been paid to how such activities can be implemented in practice across different contexts, particularly in socio-economically disadvantaged areas. Therefore, we explored the implementation of a dance-based intervention in two primary schools situated in an area of high economic deprivation in North East England. Our study explored Year 1 (age 5-6) and Year 5 (age 9-10) children, their parents, teachers and dance artists' views of a creative dance intervention (South Tees Arts project; STAR) in two schools in North East England, using interviews and focus groups, combined with innovative data capture activities (i.e. movement activities, graffiti walls, songs and Vox Pops). Children felt that STAR contributed positively to their emotional wellbeing and physical health. Teachers noticed improved confidence, engagement, literacy, and social and motor skills and less disruptive behaviour in class. Benefits continued beyond school, with children exercising at home to practice their dance moves. Several implementation barriers were identified ranging from limited time, large classes, dealing with challenging behaviours, the impact of COVID-19, stigma and anxiety. In response to these challenges, several solutions were developed during project delivery, such as artists and children working in pairs and role modelling by teachers and dance artists. We found three underlying mechanisms for successful implementation: (1) constant communication between teachers, dance artists and parents was essential to managing challenging behaviours, building personal relationships with children, and helping parents to get involved and support their children at home. (2) Linking dance activities to the school curriculum (using narratives from existing reading schemes) helped to support skill gaps. (3) A strong ethos of partnership between school, dance and arts providers and researchers ensured the adaptability and flexibility of projects.

Spatial variability of sedimentary assemblages reflects variations in bioerosion pressure of adjacent coral reefs.

The composition of coral-reef sediments is highly variable across space and time, and differences in the life histories of the dominant calcifying organisms on reefs contribute to the heterogeneity of reef sediments. Previous studies have suggested that variations in coral-reef bioerosion can influence spatial and temporal variations of sedimentary assemblages: elevated erosion rates of dead coral skeletons can trigger a pulse of coral-derived sediments and cause a shift in the dominance of sedimentary grains from coralline algae, such as Halimeda, to coral. We assessed the variability of the sedimentary composition and bioerosion rates of reefs at different spatial scales to determine the association between these two variables. We surveyed the benthic assemblages on reefs exhibiting different ecological states and collected samples of the associated sediments. We calculated the carbonate budget for each site and compared their variability at different hierarchical levels to the variability of their respective sedimentary assemblages. At the scale of sites (1-10 km), Halimeda cover was a significant predictor of the relative abundance of Halimeda grains. Both the relative abundance of coral grains and reef bioerosion rates varied significantly at the scale of locality (tens to hundreds of km), with high abundances of coral grains in the sediments coinciding with high rates of bioerosion. The main drivers of bioerosion at our localities were parrotfish assemblages dominated by large size classes of excavating species such as Sparisoma viride. Reef sediments may reflect the gross degree of bioerosion pressure that reefs experience, and historical changes in bioerosion rates could potentially be assessed by examining the sediments across temporal scales.

Combining virtual reality with asymmetric collaborative learning: a case study in chemistry education

The use of Virtual Reality (VR) in education is getting more and more common, especially when hands-on learning experiences have to be delivered. With VR it becomes possible, e.g., to simulate dangerous or costly procedures that could hardly be implemented in real settings. However, engaging large classes in immersive laboratory activities may be difficult, since VR kits are still rather expensive for quantity purchases and may require powerful PCs as well as proper spaces to work. A possible way to deal with these issues could be to combine VR with so-called asymmetric Collaborative Learning (CL). CL is a particularly interesting pedagogical approach, as it make learners work in team to achieve a common educational goal, promoting critical thinking and active learning. In asymmetric CL, in particular, learners use different technologies to interact. When combined with VR, asymmetric CL could be used, e.g., to let some learners get immersed in a virtual environment, while some others actively participate in the experience using a desktop interface. This configuration could allow, in principle, to involve more learners in the same amount of time and with the same number of VR kits, while also letting them benefit of the advantages of CL. Based on these considerations, this paper investigates the impact of CL on VR-based education by leveraging an immersive virtual environment designed to support a laboratory experience in a Chemistry course. A user study was conducted by involving 46 university students enrolled in the course. Objective and subjective metrics were used to compare two education methods, i.e. one in which the students experienced the VR environment in isolation, another one in which pairs of students collaborated with an asymmetric approach. Students’ knowledge acquisition was assessed by means of theoretical quizzes, whereas practical performance was automatically measured during the VR experience. The experimental results showed that trading off VR-based, individual learning for CL may have positive effects on the acquisition of theoretical knowledge, but may be detrimental to the achievement of practical abilities if sufficient exposure to technology cannot be guaranteed.

Enhancing Authentic Assessment in Large-Class Design Education Through Authentic Project-Based Learning

As global economies advance, the cultivation of critical thinking and creativity in higher education is increasingly important for societal and economic progress. Adopting traditional educational methods to larger class sizes where personalized attention is scarce is crucial. This research delves into the fusion of blended and authentic project-based learning by integrating online and face-to-face instruction to immerse students in practical scenarios. The study examines how these integrated methods can improve student engagement, critical thinking, creativity, and proposes an effective assessment system for large-class design education. Drawing from Herrington’s principles of authentic learning and utilizing a mixed-method approach, which includes a Likert scale survey of Design Management students, the research indicates a notable impact of blended and authentic learning on assessment outcomes. The study, presented within the ANABLE (Assessment strategy iN Authentic Blended Learning Environment) framework, emphasizes the enhancements in assessment practices. These improvements enable educators to track learning progress, identify areas needing enhancement, and gauge proficiency levels. The results imply that these pedagogical methods boost learning outcomes by involving students in complex, relevant problems, encouraging deep cognitive engagement, and offering personalized feedback. The findings suggest that these integrated strategies provide practical solutions for large classes, effectively equipping students for contemporary workforce requirements. Enhancing assessment techniques and promoting active learning enhance comprehension and memory retention, develop pertinent skills, foster deeper learning, facilitate fruitful collaboration, and enhance both assessment results and overall student contentment.

Relating behaviour of data-aware process models

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation

Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the years, however. For most of the history, ground state simulations of two-dimensional quantum lattice systems using (infinite) projected entangled pair states have relied on what is called a time-evolving block decimation. In recent years, multiple proposals for the variational optimization of the quantum state have been put forward, overcoming accuracy and convergence problems of previously known methods. The incorporation of automatic differentiation in tensor networks algorithms has ultimately enabled a new, flexible way for variational simulation of ground states and excited states. In this work we review the state-of-the-art of the variational iPEPS framework, providing a detailed introduction to automatic differentiation, a description of a general foundation into which various two-dimensional lattices can be conveniently incorporated, and demonstrative benchmarking results.

Optogenetic control of Drosophila neurons: a laboratory practical for undergraduates and outreach.

Teaching aspects of neuroscience to large undergraduate classes can be difficult in terms of the cost of equipment involved such as microscopes and electrophysiology equipment, the time taken to master techniques such as dissection or intracellular recording, and ethical concerns when using vertebrates. Here, I describe a practical that uses behavioral readouts and optogenetics on Drosophila that can be implemented with minimal cost as well as reduced ethical concerns and uses mostly observational techniques. The practical can be used to teach aspects of genetics and the tools for manipulating neuronal activity for ascribing neuronal function. The practical can be customized to fit different undergraduate levels and learning objectives.

Non-relativistic torque and Edelstein effect in non-collinear magnets

The Edelstein effect is the origin of the spin-orbit torque: a current-induced torque that is used for the electrical control of ferromagnetic and antiferromagnetic materials. This effect originates from the relativistic spin-orbit coupling, which necessitates utilizing materials with heavy elements. Here, we show that in magnetic materials with non-collinear magnetic order, the Edelstein effect and, consequently, a current-induced torque can exist even in the absence of the spin-orbit coupling. Using group symmetry analysis, model calculations, and realistic simulations on selected compounds, we identify large classes of non-collinear magnet candidates and demonstrate that the current-driven torque is of similar magnitude as the celebrated spin-orbit torque in conventional transition metal structures. We also show that this torque can exist in an insulating material, which could allow for highly efficient electrical control of magnetic order.

Valuative invariants for large classes of matroids

AbstractWe study an operation in matroid theory that allows one to transition a given matroid into another with more bases via relaxing a stressed subset. This framework provides a new combinatorial characterization of the class of (elementary) split matroids. Moreover, it permits to describe an explicit matroid subdivision of a hypersimplex, which, in turn, can be used to write down concrete formulas for the evaluations of any valuative invariant on these matroids. This shows that evaluations on these matroids depend solely on the behavior of the invariant on a tractable subclass of Schubert matroids. We address systematically the consequences of our approach for several invariants. They include the volume and Ehrhart polynomial of base polytopes, the Tutte polynomial, Kazhdan–Lusztig polynomials, the Whitney numbers of the first and second kinds, spectrum polynomials and a generalization of these by Denham, chain polynomials and Speyer's ‐polynomials, as well as Chow rings of matroids and their Hilbert–Poincaré series. The flexibility of this setting allows us to give a unified explanation for several recent results regarding the listed invariants; furthermore, we emphasize it as a powerful computational tool to produce explicit data and concrete examples.

Attitudes and Perceptions of University Students Towards Teachers' Strategies for Managing Large EFL Classrooms

This study explores students' perceptions and attitudes towards managing large classes in English as a Foreign Language (EFL) at Yogyakarta State University. Using a quantitative survey approach, the research involved 45 master's students from the English Education Department. It examined aspects of classroom management such as physical environment, teacher communication, and instructional strategies. Results show overall positive views on the learning environment and teacher effectiveness but reveal challenges in experiential learning and student-teacher interaction within large classes. The findings emphasize the need for innovative teaching methods, thorough preparation, and tailored lesson plans to address the specific needs of large EFL classrooms. This research offers insights for educators and policymakers to enhance teaching and learning in similar settings

Station-rotation blended learning model in science education a case study for rural classrooms

ABSTRACT This study investigates the implementation of the station-rotation blended learning model in science education within rural classrooms in Turkey. The research employs a case study approach to explore the learning experiences of 5th-grade students as they engage with this model. Throughout the implementation, teacher-led instruction, online instruction, and independent work instruction stations were established, and students rotated between these stations. The participants include eight 5th-grade students, comprising five girls and three boys, aged 10-11. Data were collected through focus group interviews, open-ended questionnaires, reflective diaries, and a researcher diary including researcher observations. The students expressed positive sentiments, stating that they learned better in the teaching process, found the lessons enjoyable, acquired independent learning skills, gained different perspectives, improved their peer teaching and collaboration skills, and experienced increased interest in the subjects. However, they also reported negative experiences, such as limited classroom space, issues during station transitions, and insufficient time. Additionally, students mentioned facing the most difficulty with the independent work station, finding the online station the most entertaining, and considering the teacher-led station to provide the most information. Finally, two models for the implementation of the station-rotation model in science classes, tailored for small and large classes, are proposed.