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This paper discusses how the William G. Squires Library conducted a usability study on their LibGuides to identify student design preferences. The results of the study support previous research concerning best practices for LibGuide creation. This paper offers insight into how librarian-centric design hinders student success and provides suggestions for how to best develop a student-centric template design that offers consistency and increased user proficiency across all guides. [ABSTRACT FROM AUTHOR]

The article explores What role does Romanticism play in the history of sexuality? How did British and European Romantic texts and practices participate in the emergence of modern sexuality? How anachronistic is it to use the term "sexuality" when describing Romanticism? This session is open to considering such topics as (among others) the relation between gender experimentation and sexual experimentation; Romantic "sexology"

Differentiation in education can be seen as a means of responding to student diversity in order to meet the vision of a school for all. Differentiation has been widely addressed within a western context, and it appears to be a versatile phenomenon as it occurs under various guises and with a variety of terms and modes of operationalizations. The aim of this configurative review is to investigate how differentiation appears in the international context and to contribute to a much-needed overview of the concept. Analysis of 28 scientific papers representing a broad range of national affiliations resulted in two main findings. First, differentiation is a complex idea that appears to be presented either as differentiating students or differentiating teaching. Four perspectives for approaching differentiation further illustrate the complexity of the phenomenon: differentiation as individualization, differentiation as adaptation to specific groups, differentiation as adaptations within diverse classrooms, and differentiation in a system perspective. Second, the analysis revealed that there are almost no studies transcending the focus on teachers and the classroom by addressing the organizational or system/policy level. This review argues for the benefits that a more system-oriented perspective of differentiation would provide. [ABSTRACT FROM AUTHOR]

Service learning is not a new concept in Australia, although it could be better harnessed in rural Australian health student placements for the betterment of rural communities, students and rural health workforce development. The service-learning literature offers several concepts worth considering further in the Australian context. This paper addresses three common barriers to health professional service-learning uptake in rural Australian communities by drawing on service-learning concepts from the international literature. It offers rural Australian health organisations innovative strategies to consider when hosting health professional students on service-learning placement. [ABSTRACT FROM AUTHOR]

Campus placement is an essential aspect of a student's academic career, as it determines their entry into the workforce. Predicting students' campus placement can help universities and colleges identify students who are likely to be successful in their chosen career paths and provide them with the necessary support to secure a job. Application of machine learning algorithms to forecast students' placement on campuses has gained traction in recent years. This research paper will explore the different approaches to predicting students' campus placement, the factors that influence campus placement, and the benefits and limitations of using machine learning algorithms for prediction. Machine learning is capable of adaptability and with the use of statistical models and algorithms they are able to draw inferences from patterns in data. Using ML algorithms forecasting can be done about the campus placement of students. Three ML algorithms viz, Naïve Bayes, Random Forest and Decision Trees are used to forecast the job/campus placement of students and evaluation of the aforesaid algorithms are performed with respect to accuracy of the classifier[11]. [ABSTRACT FROM AUTHOR]

Background: Staff shortages limit access to health services. The bidirectional benefits of allied health clinical placements are understood in the domains of student learning, health service delivery, and future workforce development. Still, the benefits to current workforce outcomes remain unknown. This review provides insights into the effects of allied health student placements in acute and primary care settings, particularly on healthcare staff's knowledge and procedural skills. Methods: This search was based on the integrative review process established by Whittemore and Knafl in 2005. In October 2023, the first author (MH) searched five major electronic databases: Medline-EBSCO, PubMed, CINAHL, Embase, and Scopus. The CLUSTER model was used to track additional references. The first three authors (MH, SM, and SC) were involved in screening, quality appraisal, and synthesis of the studies. Data were thematically synthesised and analysed. Results: MeSH headings and keywords were used in key search areas: health education, health professional training, clinical placements, and allied health professions. The systematic search yielded 12 papers on allied health student placements across various healthcare settings in rural and metropolitan areas, with no high-quality methodologies measuring student placements' impact on staff knowledge and skills. Four main themes were identified from the analysis: meaningful student integration in service delivery, targeted educational support to healthcare staff, development of staff procedural skills and confidence, and the mechanisms of why student placements work in this aspect. Conclusions: This review suggests that offering allied health student placement could be a promising approach to supporting rural healthcare staff in performing patient assessments and treatments proficiently and collaboratively. However, this requires further investigation to confirm. [ABSTRACT FROM AUTHOR]

For multi-object behaviour recognition in classroom scenes, crowded objects have heavy occlusion, invisible keypoints, scale variation, which directly overwhelms the recognition performance. Due to the dense student objects and similar student behaviours, multi-object behaviour recognition brings great challenges. Therefore, we proposed multi-object behaviour recognition based on object detection cascaded image classification. Specifically, object detection extracts student objects, followed by Vision Transformer (ViT) classification of student behaviour. To ensure the accuracy of behaviour recognition, it is first necessary to improve the detection performance of object detection. This paper proposes the Shallow Auxiliary Module for object detection to assist the backbone network in extracting hybrid multi-scale feature information. The multi-scale and multi-channel feature information is fused to alleviate object overlap and scale variation. We propose a Scale Assignment Fusion Mechanism that non-heuristically guides objects to learn the optimal feature layer. Furthermore, the Anchor-free Dynamic Label Assignment can suppress the prediction of low-quality bounding boxes, stabling training and improving detection performance. The proposed student object detector achieves the state-of-the-art mAP 50 of 88.03 and AP l of 57.64, outperforming state-of-the-art object detection methods. Our multi-object behaviour recognition method achieves the recognition of four behaviour classes, which is significantly better than the results of other comparison methods. [ABSTRACT FROM AUTHOR]

School district administrators must address structural racism and inequitable access to advanced learning opportunities in their school districts. District administrators in one large district in the northwestern part of the United States sought research-based advice by asking the authors to provide the answer to the "million-dollar" question, " What is the most effective and equitable way to deliver services to advanced learners? " This paper shares the response provided to the school district and discusses the complexity of the question. Based on a systematic literature review of best practices in the field of gifted education, we share our findings in three categories: (1) administrative structures, (2) pedagogy of gifted education, and (3) social and emotional considerations for advanced learners. We conclude by offering recommendations drawn from this review to develop a holistic view of programming and serving all students with advanced academic needs. [ABSTRACT FROM AUTHOR]

One of the key challenges in work-integrated learning (WIL) courses is how to account for learning that takes place away from campus, where the work space, daily routines and emergent actions within an organisation shape the possibilities for student learning. What do students do on placement to open the possibilities of working and learning in temporary sites of work? Using a practice-based approach, this paper outlines an ethnographic study of learning on placement. The paper draws on Gherardi's ([2019]. How to Conduct a Practice-Based Study: Problems and Methods. 2nd ed. Cheltnham: Edward Elgar Publishing) work on learning and knowing in practice to demonstrate how students learn to participate in situated practices to accomplish work tasks. It employs a rock-climbing metaphor to illuminate what students do in order to continue on with work when they encounter something surprising, make an error or have a question. The findings suggest student learning relies on the sociomaterial practices that produce and are produced by each unique workplace situation. The paper shows how learning on placement comprises the identification and use of distinct 'handhold' activities performed through simultaneous processes of knowing, doing and inventing. [ABSTRACT FROM AUTHOR]

Featured Application: As students are usually characterized by more than one learning style, multi-label classification methods may be applied for the diagnosis of a composite students' learning style, based on each learner's activities in the virtual learning environment. For data sets with weakly correlated learning activities, Shapley values present the explanations for the predicted student's multi-label learning style. In this way, the model assists teachers in better understanding the cognitive traits of the learners in terms of learning activities, enabling teachers to prepare the relevant learning objects for the personalization of virtual learning environments. The current paper attempts to describe the methodology guiding researchers on how to use a combination of machine learning methods and cognitive-behavioral approaches to realize the automatic prediction of a learner's preferences for the various types of learning objects and learning activities that may be offered in an adaptive learning environment. Generative as well as discriminative machine learning methods may be applied to the classification of students' learning styles, based on the student's historical activities in the e-learning process. This paper focuses on the discriminative models that try to learn which input activities of the student(s) will correlate with a particular learning style, discriminating among the inputs. This paper also investigates several interpretability approaches that may be applicable for the multi-label models trained on non-correlated and partially correlated data. The investigated methods and approaches are combined in a consistent procedure that can be used in practical learning personalization. [ABSTRACT FROM AUTHOR]

Purpose: Self-initiated international placements by students have been largely ignored in the literature on outward mobility in higher education. The support given to self-initiated international placement students, if any, has received even less attention. This study aims to address this lacuna. Design/methodology/approach: Drawing on insights from global mobility literature, we conducted a survey of UK university students who engaged in self-initiated international placements to various countries such as France, China, Brazil and Ghana. Data were analysed using descriptive statistics and thematic content analysis. Findings: Findings reveal that these "voluntary" placements can improve language fluency, increase self-confidence, renew stress management abilities and enhance cross-cultural competencies and intercultural sensitivity. The study problematises the lack of support given to these students particularly in terms of career development. Originality/value: Our paper is one of the first to bring this under-studied population to the attention of career guidance scholars. We propose that scholarly attention should be directed toward the agency of self-initiated international placement students and that targeted career guidance must be provided through more inclusive career services. [ABSTRACT FROM AUTHOR]

Assessing student engagement in educational settings is critical for monitoring and improving the learning process. Traditional methods that classify video-based student engagement datasets often assign a single engagement label to the entire video, resulting in inaccurate classification outcomes. However, student engagement varies over time, with fluctuations in concentration and interest levels. To overcome this limitation, this paper introduces a frame-level student engagement detection approach. By analyzing each frame individually, instructors gain more detailed insights into students' understanding of the course. The proposed method focuses on identifying student engagement at a granular level, enabling instructors to pinpoint specific moments of disengagement or high engagement for targeted interventions. Nevertheless, the lack of labeled frame-level engagement data presents a significant challenge. To address this challenge, we propose a novel approach for frame-level student engagement classification by leveraging the concept of knowledge transfer. Our method involves pretraining a deep learning model on a labeled image-based student engagement dataset, WACV, which serves as the base dataset for identifying frame-level engagement in our target video-based DAiSEE dataset. We then fine-tune the model on the unlabeled video dataset, utilizing the transferred knowledge to enhance engagement classification performance. Experimental results demonstrate the effectiveness of our frame-level approach, providing valuable insights for instructors to optimize instructional strategies and enhance the learning experience. This research contributes to the advancement of student engagement assessment, offering educators a more nuanced understanding of student behaviors during instructional videos. [ABSTRACT FROM AUTHOR]

Nowadays Machine Learning is the most useful area for students and researchers for classification and prediction by using the machine learning algorithms. In this four kinds of algorithms are available. Those are supervised, unsupervised, semi-supervised and reinforcement algorithms. In this paper is compared the already existed KNN algorithm and a new Dynamic KNN algorithm with some case studies in order to show the accuracy. Dynamic KNN algorithm is an improvised version of KNN only. One of the significant factors that affect the accuracy of the model is the value of K. Fundamentally; the value of K is fixed for all data points. In this study, the value is calculated at the local level for all test points. This study performs a comparative study between suggested model and the traditional KNN model. [ABSTRACT FROM AUTHOR]

Purpose: The study of sustainability within universities is recognized as essential for debates and research; in the long term, the "sustainable university" concepts can contribute to sustainability from a larger perspective. This study aims to propose a conceptual model for evaluating the students' sustainability considering their interactions with the university and the environment. The proposed model is titled Sunshine model. It is applied to students of the La Salle University, Peru. Design/methodology/approach: The model combines academic performance, happiness and the ecological footprint to quantify university students' sustainability. A structured questionnaire survey was elaborated and applied to get the raw data that feeds the three methods. The students' average grades evaluate academic performance. Happiness is quantified by the happiness index method, and the ecological footprint is measured by the demand for food, paper, electricity, transport and built-up areas. Results are evaluated under both approaches, overall group performance and clusters. Findings: The proposed model avoids misleading interpretations of a single indicator or discussions on sustainability that lack a conceptual model, bringing robustness in assessing students' sustainability in universities. To have a low ecological footprint, the student needs to need up to 1 planet for their lifestyle, be considered happy with at least 0.8 (of 1) for happiness index, and have good academic performance with at least a grade of 7 (of 10) in their course. Regarding the case study, La Salle students show a high academic grade degree of 7, a high level for happiness index of 0.8 and low performance for ecological footprint by demanding 1.8 Earth planets, resulting in an "environmentally distracted" overall classification for students with 2019 data. From a cluster approach, 81% of evaluated students (n = 603) have low performance for ecological footprint, whereas 31% have low performance for indicators of recreational activities of happiness. Changing lifestyles and making more recreational activities available play crucial roles in achieving higher sustainability for the La Salle students. Research limitations/implications: The happiness assessment questionnaire can be subject to criticism, as it was created as a specific method for this type of audience based on existing questionnaires in the literature. Although it can be seen as an important approach for diagnoses, the proposed model does not consider the cause–effect aspect. The decision-maker must consider the sociocultural aspects before implementing plan actions. Practical implications: University managers can better understand why university students have high or low sustainability performance and provide more effective actions toward higher levels of students' sustainability. Originality/value: The proposed model, Sunshine model, overcomes the single-criteria existing tools that access the sustainability of universities. Rather than focusing on university infrastructure, the proposed model focuses on the students and their relationship with the university. [ABSTRACT FROM AUTHOR]

Online knowledge distillation breaks the pre-determined strong and weak teacher-student models, it provides a new way of thinking about knowledge distillation. However, the current online methods often use the Logits-based prediction distribution, and the features containing rich semantic information are rarely used. Even if the feature-based methods are used, they only operate on the last layer of the network, without further exploring the representation knowledge of the middle layer feature map. To address the above issues, we propose an innovative feature early fusion and reconstruction (FEFR) method for online knowledge distillation which entails four essential components: multi-scale feature extraction and intermediate layer feature early fusion, reconstruction of features, dual-attention and overall fusion module in this paper. We propose early fusion by "sum" operation for feature matrices between different layers and advance fusion to improve the feature map representation. In order to enhance the communication ability between groups to obtain features, the features were reconstructed. We create a dual-attention to enhance the critical channel and spatial regions adaptively in order to collect more accurate information. The previously processed feature maps are combined and fused using feature fusion, which also aids in student models training. A study of the network architectures of CIFAR-10, CIFAR-100, CINIC-10 and ImageNet 2012 shows that FEFR provides more useful characterization knowledge for refinement and improves accuracy by about 0.5% compared to other methods. [ABSTRACT FROM AUTHOR]

With the popularity of Internet of Things (IoT) devices, IoT botnets like Mirai have been infecting as many devices as possible such as IP cameras and home routers. Because of the sheer volume and continual operation of many vulnerabilities (many users do not pay much attention to IoT update alerts and leave the configurations by default) of IoT devices, the population of an IoT botnet becomes increasingly tremendous. The growing population, though making a botnet powerful, results in an increased risk of exposure. Specifically, once a bot is captured, the command and control (C&C) channel may be cracked and then tracked, potentially rendering more bots being discovered. To solve this problem, this paper proposes an automated approach to group management of large-scale IoT bots. The basic idea of the proposed approach is to establish a reliable and unsuspicious social network-based C&C channel capable of automatically grouping bots, wherein a group of bots have a unique ID that is against cross-group tracking. The Diffie–Hellman key exchange method is leveraged for efficiently generating the unique group ID, thereby scaling up automatic bot grouping. We refer to the botnet proposed in this paper as a multichannel automatic grouping botnet (MCG botnet) and conduct verification experiments using social networks and more than 2,000 docker nodes. The experimental results show that the MCG botnet has the ability of automatic grouping and antitracking. [ABSTRACT FROM AUTHOR]

The smooth transition of young people to the labor market and their competency in successfully planning and developing their careers constitute key aims of all modern education systems. The implementation of work placements plays an important role in the realization of these aims by enhancing the communication between the education providers and the world of work, and by contributing to the development of professionally oriented competences by young learners. The paper focuses on traineeships, a particular type of work placement, which is implemented by post-secondary education institutions in Greece. A traineeship includes a variety of training processes with clear objectives and predetermined assessment strategies, which help trainees to gain professional skills and experience through an experiential process. Its ability to exercise a strong influence on the professional prospects of young people led many education institutions to integrate traineeship opportunities in their study program either as a compulsory component or as a non-mandatory option. The paper analyses the traineeship component of the study programs of three post-secondary education institutions in Greece, i.e. Institutes of Vocational Training (IVTs), Higher Education Institutions (HEIs) and Technological Educational Institutes (TEIs). More analytically, the paper investigates the legislative provisions concerning the organization and implementation of student traineeships, and records the evolution of the particular learning option over the years. In addition, it attempts to identify indications of interaction between post-secondary education institutions as regards the organization and the particular features of student traineeship schemes, which may imply the development of mutual learning. The paper concludes by articulating some remarks regarding the implementation of traineeships in Greek post-secondary institutions and the improvement of their organizational and operational characteristics. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic changed the way we work, spend, live, and learn. The impact was felt in the health sector where hospitals cancelled elective surgery, put on hold outpatient services, and implemented new social distancing procedures and telehealth systems, to enable hospitals to increase bed capacity. For medical students, these factors meant significant disruption to their clinical placements, remote delivery of their education, cessation of international and interstate placements, complicated by significant travel restrictions and border closures. There were concerns that final year students might be unable to graduate that year due to this lack of clinical exposure. Innovation: As a result of this disruption in late March 2020 we developed an innovative 6 week 'COVID-19 e-lective' rotation, consisting of online modules, virtual clinical tutorials and a COVID project totalling the equivalent of 200 h of work. Results: An evaluation was undertaken that found it to be remarkably successful in meeting the students' learning needs and alleviating concerns about disrupted placements. It was also conducted during 2021 for all Year 4 students to help expand clinical placement opportunities. Outcomes: This paper describes the e-lective, its innovations, its challenges, and its evaluation findings, for others to learn from. [ABSTRACT FROM AUTHOR]

Agent-based models present an ideal tool for interrogating the dynamics of communication and exchange. Such models allow individual aspects of human interaction to be isolated and controlled in a way that sheds new insight into complex behavioral phenomena. This approach is particularly valuable in settings beset by confounding factors and mixed empirical evidence. The political communication setting of deliberation is one such salient setting: in business, politics, and everyday life, individuals with varying opinions, experience, and information attempt to collaborate and make decisions. Empirical evidence suggests that such collaborative reasoning can lead to good decisions, yet there are numerous deliberative failures which may frequently cause groups to reach bad outcomes. Using the substantive setting of deliberation, this paper presents an agent-based model aimed at disambiguating the individual factors which influence decision-making conversations. We model this communicative process as a deliberative game of "giving and asking for reasons." Agents share beliefs around possible policy initiatives and attempt to enact "good" policies through a process of mutual exchange and consideration. The model considers an interconnected policy landscape in which implementing or not implementing a policy mediates the value of other policies. Within this framework, the paper considers the impacts of three canonical failures of deliberation: limited cognitive capacity, group factions, and tendencies to make poor judgments when accepting or rejecting others' views. We find that cognitive capacity can significantly decrease the ability of a group to reach a good decision. However, this effect appears to be mitigated for groups of opposing factions. Indeed, polarized groups do surprisingly well at identifying optimal policy solutions, suggesting that heterogeneous agents can achieve good outcomes if they are willing to talk and learn from each other. [ABSTRACT FROM AUTHOR]

Predicting students' performance in distance courses is a very relevant task to help teachers identify students who need reinforcement or extension activities. Nevertheless, identifying the student's progress is highly complicated due to the large number of students and the lack of direct interaction. Artificial intelligence algorithms contribute to overcoming this problem by automatically analyzing the features and interactions of each student with the e-learning platform. The main limitations of the previous proposals are that they do not consider a ranking between the different marks obtained by students and the most accurate models are usually black boxes without comprehensibility. This paper proposes to use an optimized ordinal classification algorithm, FlexNSLVOrd, that performs a prediction of student's performance in four ranking classes (Withdrawn < Fail < Pass < Distinction) by generating highly understandable models. The experimental study uses the OULA dataset and compares 10 state-of-the-art methods on 7 different courses and 3 classical classification metrics. The results, validated with statistical analysis, show that FlexNSLVOrd has higher performance than the other models and achieves significant differences with the rest of the proposals. In addition, the interpretability of FlexNSLVOrd is compared with other rule-based models, and simpler and more representative rules are obtained. [ABSTRACT FROM AUTHOR]

This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on "descriptions" (21 Boolean categories) and "codes" (11 Boolean Categories) of regions' RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group's ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions. [ABSTRACT FROM AUTHOR]

Key digested message Grouping by ability is a contentious issue within research, yet this pedagogical practice continues to persist across education systems globally. The strong link between highly differentiated education systems and academic/social inequity requires deep consideration of the role of ability grouping in shaping how children experience teaching and learning in school. This mixed methodological study presents findings from analysis of data from a national survey with primary school teachers and three intensive case study schools. Key findings indicate that: ■ Ability grouping shapes the relational dynamics in the classroom, casting an 'invisible hand' over children's social worlds. ■ Demarcating the boundaries of ability in a classroom attributes value-ability to each group, accruing benefit for those assigned to the high ability group and resulting in exclusion for those in lower levels. ■ Grouping by ability maps out a geography of affect for how children feel and embody their identity as a learner, with pride attributed to being positioned as higher ability, and shame associated with being placed into lower ability groups. ■ The emotional labour in navigating the complex relational dynamics as shaped by the 'invisible hand' of ability grouping impacts on children's wellbeing, with profound implications for those assigned to the lower level. [ABSTRACT FROM AUTHOR]

This is a methodological critique of research by the Best Practice in Grouping Students (BPGS) project claiming teacher bias in allocating students to first‐year secondary school mathematics teaching sets (British Educational Research Journal, 45(4), 873–879). The research assumes that bias could be shown by non‐random relationships between 'misallocations' to sets and memberships of gender, ethnic and/or socio‐economic subgroups. This paper questions the authors' prescriptions for correct set placements and demonstrates two ways in which non‐random relationships between subgroup memberships and (alleged) misplacements could be generated despite all students with similar scores within a school being given the same chance of a set position deemed correct by the researchers. It suggests that the BPGS project results are largely artefacts of their evaluation approach. No claim is made herein that teacher bias plays no part in set allocation, merely that, if it does, the approach by Connolly et al. could not evidence it. A lesson from the critique is that, before drawing inferences from data aggregated from diverse clusters, it is wise for researchers to investigate the way the data are generated at source and patterned by aggregation. [ABSTRACT FROM AUTHOR]

With the transformation of the supply chain from factor driven to investment driven and to innovation driven, supply chain innovation has attracted more and more attention. This paper obtains the Bayesian prior probability of innovation and studies the supply chain innovation path from the perspective of strategy and behavior. The model divides the overall innovation capability of the supply chain into specific node tasks and assets (Capability Set), and the innovation demand of the supply chain is expressed in the form of the conditional probability of market demand. Under the conditions of minimum risk, minimum cost, and rapid market response, it determines who should lead the supply chain innovation first and what type of supply chain innovation should be carried out first. Finally, taking the supply chain of the professional market in Zhejiang Province as an example, this paper verifies the theoretical model of supply chain innovation decision-making. The innovation of the supply chain of Zhejiang's specialized market is decomposed into the capability set of each enterprise node of the supply chain. This paper transforms the group innovation ability of the supply chain in the professional market into the individual innovation ability of a single enterprise node and reveals the starting point and intensity of the demand innovation of products/services in the supply chain. [ABSTRACT FROM AUTHOR]

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With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student's historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students' data inside and outside the classroom, establish and improve college students' achievement index system, then deeply analyze and discuss the development of college students' achievement and ability, and finally give each student's achievement results. [ABSTRACT FROM AUTHOR]

In the context of the COVID-19 restrictions and the pivot to online teaching and learning, teacher educators were forced to consider new spaces for School Placement and the assessment of these new sites of practice. This paper explores the process of the redesigning of the assessment of school placement components from the perspective of ten teacher educators across five teacher education programmes in one university. Hybridity theory, 'third space', and figurational sociology allowed us to understand who and what influenced the redesigning of assessment practices. The three themes relating to assessment included: (i) A network of interdependent relationships influencing teacher educators' reimagining of assessment processes; (ii) The influence of external interdependent relationships and context(s); and (iii) The role(s) of assessor for the teacher educator. We explicitly argue for the need to continue to advocate and advance these practices to 'build Initial Teacher Education back better'. [ABSTRACT FROM AUTHOR]

While between-school tracking has been a well-explored topic especially with countries tracking their educational systems as early as at the age of 10, within-school ability grouping has received comparatively less analytical attention. This paper explores effects of within-school informal tracking practices at socioeconomically and ethnically mixed elementary schools. In Slovakia, where these practices were previously described as linked to segregation of Roma students, they result in academically, socioeconomically, and ethnically distinct separate classes in a school. We try to identify the early tracking practices of schools by exploring if two or more classes included in the TIMSS 2019 assessment in a particular school display extreme differences with regard to ethnic (Slovak vs. Roma) or socioeconomic (defined by parental education levels or occupation) composition. We employ a series of hierarchical linear models to assess the impact of early within-school informal tracking on mathematics and science test results of students from low-track classes. We check the robustness of our findings in a parallel propensity scores approach. Our results confirm that class-level segregation seems to have a very significant connection to academic performance of students from low-track classes. When compared to identical students from non-tracked classes, students from low-track classes have more than 15% lower test scores in mathematics and science. This points to the need to further explore early informal within-school tracking practices which have so far escaped analytical attention. While not a topic of cross-national assessment programs per se, this can be done using data from major international assessments. [ABSTRACT FROM AUTHOR]

This paper examines the role of precolonial class inequality systems in the intergenerational transmission of education processes amongst ethnic groups in Africa. Using ethnographic and household survey data from six African countries and grouping ethnic groups by the historical class system that existed within them, I observe variations in intergenerational persistence between them with varying levels of significance in the different countries included. The findings suggest that understanding intergenerational mobility within African countries should take into account the different historical ethnic group characteristics, although the mobility process does not evolve uniformly across countries. Country-specific colonial administrative systems and the immediate post-independence education policies are critical factors that also need to be taken into account to understand the changes in education-based intergenerational persistence from the precolonial to the contemporary period. [ABSTRACT FROM AUTHOR]

Although placing students in the appropriate proficiency levels of post-secondary English programs is crucial for optimal learning, the evaluation of placement tests (PTs) in terms of establishing their reliability and validity is relatively under-researched. This study assesses the validity, reliability, and effectiveness of an in-house online PT. The content validity was established through the internal and external moderation of the question papers and answer keys while criterion-related concurrent validity was established via IELTS benchmarking. New Student Survey was used to investigate the face validity. The internal consistency and reliability of the reading test items were measured using Cronbach's alpha while descriptive statistics were calculated for the listening test. Paired sample t-test (dependent t-test) was used to assess the inter-rater reliability of the speaking and writing tests which were double-marked. The data analysis revealed that the PT was effective in placing students at different levels of the foundation program (FP) and the statistical analyses conducted to test the reliability and validity showed positive results for most of the test versions. The study offers useful insights to test developers and policymakers regarding the authentication of in-house tests and the creation of guidelines for PT design and evaluation. [ABSTRACT FROM AUTHOR]

To be competitive, universities across the world are embedding an international perspective into every layer of an institution's operational structure. For higher education (HE) providers that offer sandwich degrees (4-year undergraduate courses with a compulsory placement after the second year), this allows students to choose a range of options. Students can enter the labour market for one year, or they can go overseas to study at a foreign institution. For some students, it might even be possible to do both. However, regarding final year degree performance, which option leads to higher student performance? In this paper, we aim to shed light on this empirical question. Our results are drawn from Aston University (UK) which is a world-leading University in Advanced Technology. Overall, using a large student dataset, we find that for students who have a compulsory placement built into their degree programme, the work placement has a more powerful impact on student performance compared to an international study placement abroad. Our findings have important implications for universities across the world that offer sandwich degrees to their students. [ABSTRACT FROM AUTHOR]

A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all "wicked problems" are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a system. This paper briefly introduces the concept of GCI as well as why it is a system of collective optimization, and explores why "wicked problems" from the perspective of GCI are collective optimization problems, and therefore why such problems might require GCI to be reliably solvable. [ABSTRACT FROM AUTHOR]

Human societies are constantly coping with global risks. In the face of these risks, people typically have two options, that is, to respond together as a whole (collective solution) or to respond independently (individual solution). Based on these two solutions, individuals have a variety of behavioral strategies. On the other hand, various regulatory bodies supported by the population limit people's choices and punish individuals who do not contribute to collective solutions. So with different risks, how do the two solutions, the various individual strategies, and the constraints from regulators affect the group's response to risk? This paper proposes an extended public goods game model involving opportunists and the regulator to explore the effectiveness of collective and individual solutions against risks. The results show that requiring individuals to invest more in the collective solution reduces the group' s success in resisting risk. To improve the group's ability to resist risk, investment in individual solution should be at least no less than that in collective solution. The establishment fund and punishment intensity of the regulatory agency have no significant effect on the success of collective and individual solutions. This inspires us to contemplate the role and measures of various types of authorities in coping with global risks. [ABSTRACT FROM AUTHOR]

While the innovative practicum project described in this paper was an emergency measure, it continues to enhance our pedagogy for preparing social work students for placement. The project was designed collaboratively with postgraduate students when their imminent placements in statutory agencies were indefinitely delayed by Aotearoa New Zealand's first Covid-19 lockdown, beginning late March 2020 and extending for 7 weeks. The prevailing uncertainty was stressful for our students, who needed to complete placement for degree completion and professional registration. As the staff team responsible for their practicum, we needed to quickly devise a robust alternative learning experience. This endeavour evolved into a codesigned, collegial knowledge-exchange project combining academic knowledge and practice wisdom gathered through students' consultation with academic and practice experts. The project's key components were focussed analysis of practice research, interviews with experienced social workers, production of a succinct "practice briefing", application of knowledge gained to a "real life" practice story, and sharing accumulated knowledge. From a pedagogical perspective, we highlight our learning about promoting professional communication to underpin collaborative work, and the usefulness of intensive orientation to practice context before students begin placement. [ABSTRACT FROM AUTHOR]

Three IRT diagnostic-classification-modeling (DCM)-based multiple choice (MC) item design principles are stated that improve classroom quiz student diagnostic classification. Using proven-optimal maximum likelihood-based student classification, example items demonstrate that adherence to these item design principles increases attribute (skills and especially misconceptions) correct classification rates (CCRs). Simple formulas compute these needed item CCRs. By use of these psychometrically driven item design principles, hopefully enough attributes can be accurately diagnosed by necessarily short MC-item-based quizzes to be widely instructionally useful. These results should then stimulate increased use of well-designed MC item quizzes that target accurately diagnosing skills/misconceptions, thereby enhancing classroom learning. [ABSTRACT FROM AUTHOR]

Background: Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts. Methods: Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum. Results: Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding. Conclusion: The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions. [ABSTRACT FROM AUTHOR]

In the field of educational data mining, the classification of students' grades is a subject that receives widespread attention. However, solving this problem based on machine learning algorithms and deep learning algorithms is usually limited by large datasets. The privacy problem of educational data platforms also limits the possibility of building an extensive dataset of students' information and behavior by gathering small datasets and then carrying out the federated training model. Therefore, the balance of educational data and the inconsistency of feature distribution are the critical problems that need to be solved urgently in educational data mining. Federated learning technology enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. However, these methods are only applicable to the data environment with common characteristics or common samples under the alliance. This results in domain transfer between nodes. Therefore, in this paper, we propose a framework based on federated transfer learning for student classification with privacy protection. This framework introduces the domain adaptation method and extends the domain adaptation to the constraint of federated learning. Through the feature extractor, this method matches the feature distribution of each party in the feature space. Then, labels and domains are classified on each side, the model is trained, and the target model is updated by gradient aggregation. The federated learning framework based on this method can effectively solve the federated transfer learning on heterogeneous datasets. We evaluated the performance of the proposed framework for student classification on the datasets of two courses. We simulated four scenarios according to different situations in reality. Then, the results of only source domain training, only target domain training, and federated migration training are compared. The experimental results show that the heterogeneous federated transfer framework based on domain adaptation can solve federated learning and knowledge transfer problems when there are little data at the data source and can be used for students' grades classification in small datasets. [ABSTRACT FROM AUTHOR]

Nowadays engineering students are losing their campus placement opportunities provided by the institutes due to non-awareness of the market requirements. In this research, the authors try to develop an online platform for checking placement capability as well as level of skills required for the industry of engineering students in campus placement for IT Sector. Data was collected from the Final year engineering students of Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur. Primary data of students 'skills performance was collected by using pen and paper test and Secondary data i.e., placement record was collected directly from the respective institutes. The various models developed and finalized one based on research constraints and R2 value. Online platform was developed for Binary Logistic Regression Model to predict placement capability of engineering students. This online platform will support institutes as well as students to understand the level of preparation of students for upcoming campus placement season and make systematic plan to enhance required skills up to optimum level. [ABSTRACT FROM AUTHOR]

The study of information spreading based on the complex network theory and topological structure has become an important issue in complex networks. Plenty of infectious disease models are widely used for information diffusion research in complex networks. Based on these state-of-the-art models, a new epidemic dynamic model with dynamic evolution equations is proposed and performed on the homogeneous and heterogeneous networks, respectively, in this paper. Meanwhile, we divide the propagation states into two states: L and H (low propagation ability groups and high propagation ability groups) and consider the transformation of these two states in our model. Then, the equilibria and stability of the model are analyzed for both homogeneous and heterogeneous networks to verify the validity of the proposed model. Finally, simulation results illustrate that the proposed model and information propagation dynamic evolution equations are reasonable and effective. Experiments with effect factors also reveal the interaction mechanism and the diffusion process of the proposed model in complex networks. [ABSTRACT FROM AUTHOR]

Intelligence, as well as working memory and attention, affect the acquisition of mathematical competencies. This paper aimed to examine the influence of working memory and attention when taking different mathematical skills into account as a function of children's intellectual ability. Overall, intelligence, working memory, attention and numerical skills were assessed twice in 1868 German pre-school children (t1, t2) and again at 2nd grade (t3). We defined three intellectual ability groups based on the results of intellectual assessment at t1 and t2. Group comparisons revealed significant differences between the three intellectual ability groups. Over time, children with low intellectual ability showed the lowest achievement in domain-general and numerical and mathematical skills compared to children of average intellectual ability. The highest achievement on the aforementioned variables was found for children of high intellectual ability. Additionally, path modelling revealed that, depending on the intellectual ability, different models of varying complexity could be generated. These models differed with regard to the relevance of the predictors (t2) and the future mathematical skills (t3). Causes and conclusions of these findings are discussed. [ABSTRACT FROM AUTHOR]

While international research has demonstrated that ability grouping is inequitable, Australian research has often overlooked equity concerns, predominantly focusing on studying the benefits of grouping for students perceived to have high ability, especially in mathematics. This paper investigates the effect that mathematics ability grouping has on senior secondary mathematics participation for low and middle grouped students in New South Wales, Australia. We drew on interviews about mathematics participation with 85 students and 22 mathematics teachers from 11 government high schools. Our thematic analysis of these data identified that secondary school grouping practices often serve to entrench student interest and/or performance from primary school. This has consequences for many students, with a differentiated curriculum in Years 9 and 10 precluding low and middle grouped students from participating in high-level mathematics when they reach senior secondary school. These findings raise serious equity questions about the current push in Australia for universities to re-introduce high-level mathematics as a prerequisite to entering certain degrees. We argue that stakeholders need greater awareness of the consequences of ability grouping and that those seeking to lift post-compulsory mathematics participation should be targeting primary and early secondary school. [ABSTRACT FROM AUTHOR]

The calibration accuracy of the Doppler Velocity Log (DVL) error directly affects the performance of the SINS/DVL integrated navigation system. Previous studies of DVL calibration have not dealt with DVL measurement outliers. Therefore, in this paper, a novel DVL calibration method is proposed to reduce the influence of outliers on calibration accuracy. Introducing the Lie group theory to the DVL calibration study for the first time, and taking advantage of the Special Orthogonal Group of order 3 [SO(3)] representation, a new DVL calibration model based SO(3) group with the ability to represent large misalignment angles is established. And an invariant extended Kalman filter (IEKF) design for the DVL calibration model is introduced. Then, a robust IEKF is proposed by combining the linear error propagation equation of the SO(3)-based DVL calibration model with the statistical similarity measure (SSM) theory. The proposed robust IEKF algorithm enriches the IEKF theory and has the ability to accurately estimate the state of the Lie group model in outlier environments. The simulation and lake trial are used to illustrate the effectiveness and superiority of the proposed DVL calibration method. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to discuss the "Q-Step in the Community" programme, part of the Q-Step Centre based in the Sociology Department at Manchester Metropolitan University, designed to help address the skills gap in quantitative methods (QM) that is evident across parts of the UK higher-education sector. "Q-Step in the Community" is a data-driven work-based learning programme that works in partnership with local organisations to provide placement opportunities for final year undergraduates and postgraduates. Students conduct a quantitative research project, which is typically identified by the placement provider. Design/methodology/approach: The authors use quantitative and qualitative feedback from students and placement providers, along with our own reflections on the process to evaluate the placement programme. Data were collected through a focus group and email interviews with placement providers, along with a questionnaire, which was distributed to "Q-Step in the Community" alumni. Findings: Data-driven work-based learning opportunities allow students to develop and demonstrate their quantitative skills and support networking opportunities whilst also developing valuable soft-skills experience of the workplace that develops their career-readiness. In addition, those opportunities provide valuable research for placement providers, which support their sustainability and enhance their service delivery. Research limitations/implications: The research focusses solely on one programme at one university offering quantitative data driven work-based learning opportunities at undergraduate and post-graduate level. It is not possible to make valid comparisons between those who do a placement with those who do not. Originality/value: Views of key stakeholders in the process have been sought for this research, which can be useful to consider for others considering developing similar programmes for their students. [ABSTRACT FROM AUTHOR]

Impact assessment (IA) involves complex interactions among societal actors with diverse knowledge systems and worldviews (ontological pluralism) that ideally combine to both define and support societal goals, such as sustainable development. An often acknowledged but rarely explored concept in these efforts is research capacity — the ability of a group to engage, produce, maintain and use knowledge — and associated implications for pluralistic process outcomes. This paper presents an embedded case study of the IA policy network in the Yukon Territory, Canada, to explore the various roles of research capacity in a well-established IA process where Indigenous and public representation are guaranteed, as is financial support for boundary spanning and knowledge brokering roles to support pluralism. Using Rapid Policy Network Mapping, we examine the formal and informal connections amongst IA policy actors and identify sources and flows of knowledge throughout the network. Results indicate that while research capacity is critical to well-functioning IA processes in the Yukon Territory, the ability of the IA policy network to source, disseminate and engage new knowledge is limited. Important boundary spanning 'choke points' can act as both facilitators and barriers, based on the capacity of the knowledge brokers occupying these spaces. The findings inform policy efforts to ensure inclusion and advance pluralism in IA processes. [ABSTRACT FROM AUTHOR]

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. The first part of this paper develops and justifies a Bayesian model of myside bias at the level of individual reasoning. In the second part, this Bayesian model is implemented in an agent-based model of group discussion among myside-biased agents. The agent-based model is then used to perform a number of experiments with the objective to study whether the myside bias hinders or enhances the ability of groups to collectively track the truth, that is, to reach the correct answer to a given binary issue. An analysis of the results suggests the following: First, whether the truth-tracking ability of groups is helped or hindered by myside bias crucially depends on how the strength of myside bias is differentially distributed across subgroups of discussants holding different beliefs. Second, small groups are more likely to track the truth than larger groups, suggesting that increasing group size has a detrimental effect on collective truth-tracking through discussion. [ABSTRACT FROM AUTHOR]

This paper aims to explore the Painting Classification in art teaching under Machine Learning. Based on Emotional Semantics and Machine Learning, the Emotional Semantics of the traditional image are expounded. Firstly, Emotional Semantics are applied to figure painting in art teaching. Then, the convolutional sparse automatic encoder model is introduced in Painting Classification. Finally, the accuracies of the Painting Classification of the Support Vector Machine classifier (SVMC) and that of the Naive Bayes classifier are compared, and the relevant conclusions are drawn. The accuracy of Painting Classification is positively correlated with the scale of painting. After analysis, the painting set is classified in a ratio of 2 : 1, with 2/3 as training set and 1/3 as test set, which is conducive to the good accuracy of classification. In Machine Learning, proper whitening can improve the accuracy of Painting Classification to a certain extent. However, when the whitening treatment coefficient is selected, it cannot be too large, and the average pooling is more accurate than maximum pooling. After the comparison of the new SVMC, the Naive Bayes classifier, and the convolutional sparse automatic encoder, the convolutional sparse automatic encoder has the highest accuracy of Painting Classification. Therefore, the Painting Classification in art teaching under Machine Learning is explored, which is of great help to the classification work of students or teachers in the future. [ABSTRACT FROM AUTHOR]

One of the challenges in e-learning is the customization of the learning environment to avoid learners' failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students' results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students' interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students' classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students' group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students' group relationships, the classification of college students' groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students' networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students' association classification. [ABSTRACT FROM AUTHOR]