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The increasing rates of illicit behaviors, particularly financial crimes, e.g., bank fraud and tax evasion, adversely affect national economies. In such cases, using nondestructive methods, scientists must evaluate relevant documents carefully to preserve their value as evidence. When forensic laboratories analyze paper as evidence, they typically investigate its origin and date of manufacture. If a document’s date is earlier than the earliest availability of the paper used in its creation, then this anachronism indicates that the document has been backdated. This study investigated weave marks and drainage marks for forensic purposes. Machine learning models for forensic document examination were developed and evaluated. The partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural network (ANN) classification models achieved F1-scores of 0.903, 0.952, and 0.931, respectively. In addition, to enhance model effectiveness and construct a robust model, variables were selected using the VIP scores generated by the PLS-DA model. As a result, the SoftMax classifier in the ANN model maintained its performance with an F1-score of 0.951 even with a 50% reduction in the number of input variables.

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields.

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts. [ABSTRACT FROM AUTHOR]

The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R packages to use to conduct a meta-analysis for each category presented in this paper. In practice, research novices may not know where to begin to learn a topic of meta-analysis for their research. Furthermore, they may not know how the topic of meta-analysis progresses from basics to advanced. This makes it almost next to impossible to use meta-analysis in their research. Certainly, this is problematic. Thus, this paper intends to help research novices overcome this problem. To achieve this, the three objectives mentioned at the beginning were implemented. To propose a synthesis of common categories of meta-analysis along with their respective definitions, a literature review was used as a method to synthesize and define the common categories of meta-analysis presented in this paper. Here, synthesizing meta-analysis categories in one figure would make it much easier for research novices to see the breadth of the subject in question. To propose an easy way to understand the concepts of meta-analysis through learning from their respective generic conceptual frameworks, personal experience was used as a method to make that offering. Here, it is encouraged that research novices should go beyond the definitions of categories of meta-analysis. They can try to frame their research by using example conceptual frameworks presented in the paper as a starting point. To point out which R packages to use with what categories of meta-analysis, a literature review was used as a method to identify those appropriate R packages available. Here, it would be useful for research novices to know which R packages they may need for their meta-analysis research. In summary, the contributions of the paper here would make research novices see and understand the common categories of meta-analysis from the bottom to the top of the pyramid, also offers an easy way to understand the common categories of meta-analysis by illustrating how to develop conceptual frameworks for the common categories of meta-analysis, and finally recommending the R packages to be used with the categories of meta-analysis. Ultimately, we hope that research novices will be able to use meta-analysis successfully in their research.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

This paper compares website usability--specifically library users' understanding of library terms--for fifty-one original research studies between 2012-2021, with the findings of John Kupersmith's 2011 white paper "Library Terms That Users Understand." Studies reported approximately twice as many terms that users didn't understand than terms users did understand, with some terms appearing in both categories. Analysis of the findings suggests a majority of Kupersmith's guidelines remain applicable to today's online environment, with some adjustments related to technology advances. We propose an additional guideline that acknowledges the role non-library websites play in guiding how users interact with library terminology.

Observation is widely regarded as a fundamental way of studying nature. However, due to its complexity, proposing a comprehensive characterization is challenging. Observation is a method of studying nature that can be divided into several dimensions. Each of these dimensions can be presented as a pair of terms, which are elaborated in this paper based on the epistemological, philosophical, and educational literature. In addition, the dimensions can be assigned to two levels, which can be used to group the individual dimensions. This epistemological perspective on the method is enriched by historical examples from different periods in physics and astronomy to substantiate and illustrate the classifications. The aim of the paper is to provide an overview of the epistemological features of the method in terms of these dimensions, which may be helpful for discussions in teacher training courses.

Labeled data are fed to learning models of classification tasks to help them learn to classify unlabeled data. Massive papers are hinged by citations to a few influential papers, much smaller than the total size, which, if labeled, would cause the spread of label information to the most of the papers. We utilized the co-reference relationship between papers since the references cited by a paper dataset usually cannot be contained by the dataset. We stated the problem of optimal paper labeling as the problem of picking a given fraction of nodes from a co-reference network to maximize the number of their neighbors, which is a submodular maximization problem with a cardinality constraint, NP-hard for general networks. We approximately solved it by picking nodes according to the ranks of specific network centralities. We further proved that labeling papers according to the rank of degree, the lowest-complexity centrality, can give a near-optimal solution with specific constraints on the maximum degree of co-reference network and labeling proportion. Experimental results show that our method brings a significant improvement in the accuracy of classification. • A model-agnostic optimization objective is proposed to improve paper classification accuracy. • The optimization objective is maximizing a paper set's neighborhood in a co-reference network. • Labeling high-centrality nodes is a labor-saving and approximative way to the maximizing problem. [ABSTRACT FROM AUTHOR]

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This paper examines how the Durkheimian approach to the 'ideal' delineates a possible way of straddling the dilemma between the normative orientation of 'powerful knowledge' accounts and the critical orientation of 'knowledge of the powerful' accounts. It argues that the normative aims are embedded in the fabrics of the sociological description with which the Durkheimian notion of 'elementary form' is concerned. To see where this enterprise can lead, this paper turns to the sociology of education of Bourdieu and Bernstein. Both draw on Durkheim's writings on primitive classifications in education and society, working towards uncovering the regularities of the world of knowledge classifications. Keeping in line with Bourdieu and Bernstein, this paper argues that one has to make the same refusal to the advocates of an abstract ideal of educational knowledge that is dissociated from its social conditions of historical realization in pedagogic contexts, and to the advocates of a cynical relativism of ideal that rejects any necessities socially established.

Underlying thematic analysis are a few fundamental human cognitive processes, such as categorizing, prototyping and metaphorical mapping. By unpacking these basic processes of human cognition, this paper hopes to provide a cognitive basis for thematic analysis as a foundational method in data analysis for qualitative research. In particular, it hopes to address the gap between qualitative methodologists' assumption of thematic analysis as a subjective, creative and flexible process and editors/reviewers' expectation that thematic analysis shall be objective, reliable and rigorous. By consciously and purposefully applying these cognitive processes, thematic analysis can be subjective and yet disciplined, creative and yet rigorous, flexible and yet reliable. The ultimate goal of this paper is to demystify, delineate and further demarcate the thematic analysis process for young and novice qualitative researchers.

Background: Through changing the way games are represented, classification systems have increased possibilities for teaching game forms beyond structured adult and singular official versions of popular sports. At the time of inception, the four-game form approach to classification (target, net/wall, striking/fielding, and invasion games) enabled modified, small sided conditioned games to be adapted to suit individual attributes, whilst reinforcing a core set of tactical and technical elements for transfer. With global shifts in patterns of sport participation, it is timely to review this dominant classification frame and the role it plays within physical education (PE). Purpose: This paper proposes changes to classification boundaries around games and sports in PE to establish a classification system that is inclusive of a wider range of games and sports and growing forms of participation that we contend are important for PE. The paper also argues for a broader conceptualisation of tactics, with social, environmental and affective dimensions a focal point for a revised, contemporary classification framework. Methods: The paper draws on Bernstein's notions of classification and frame to explore the pedagogical utility of the dominant games and sport classification used in PE and ways in which it could be revised. Each author independently undertook a structured mapping exercise designed to facilitate exploration and potential classification of a wide range of contemporary activities potentially relevant for physical education. Results: The paper proposes the addition of lap or circuit sports, route or journey sports, rush or action sports, stunts or tricking sports and rhythmic or aesthetic sports to broaden the learning and participation possibilities for young people and tune teachers into contemporary movement forms. The paper proposes sample questions that extend the tactical foci inherent in classification to encompass meaning, social, and ecological considerations for learning in PE.

Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.

Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). Aims: To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. Methods & Procedures: We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. Main Contribution: The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). Conclusions & Implications: Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.

Colleges are important providers of vocational education and training and in some countries they are the major provider. Although the international literature on colleges has grown considerably in the past two decades, it still consists primarily of qualitative descriptions of college sectors in different countries. Quantitative studies of differences in the activity mix of colleges in different countries could improve knowledge of international variation in the roles of colleges and provide a stronger foundation for study of the sources and consequences of variation in college roles. After reviewing different methodological frameworks for comparative analysis of college activity, the research reported here employs one of these frameworks to analyse differences in the activity mix of colleges in five countries. In addition to finding some noteworthy differences among the five countries, the paper also identified several problems of comparability of college data from different countries. The paper concludes that the development of internationally comparable data on colleges would require leadership by international organisations and agencies and is an undertaking well worth pursuing both for the benefits that it could bring to those whom colleges serve and for its contribution to the advancement of comparative study of vocational education and training.

Operations Research (OR) is a branch of applied mathematics that deals with optimization problems arising from different real contexts. The solving process of its problems is based on the construction and resolution of mathematical models, showing the possible connections between mathematics and the real world. Nevertheless, OR is not typically included in most curricula of higher secondary schools (i.e. Grades 9-12), but it is usually presented mainly at university level. To show how OR could be significant for these school students' education, the authors of this paper developed an educational project consisting of three teaching units. In this paper, we share the result of the teaching experiment related to the first unit, addressed to Grade 10. Qualitative and quantitative analyses show how it is appropriate to include OR and its typology of problems in regular school mathematics lectures. Second, these data also show how modelling and problem-solving skills, developed working with OR, can be fostered by implementing a collaborative way of working, also by making use of digital technologies. Last but not least, we demonstrate the positive impact such activities have on students' appreciation of OR.

In an upcoming coverage-comprehension study, we plan to assess learners' meaning-recall knowledge of words as they occur in the study's reading passage. As several meaning-recall test formats exist, the purpose of this small-scale study (N = 10) was to determine which of three formats was most similar to a criterion interview regarding mean score and the consistency of correct/incorrect classifications (match rate, k = 30). In Test 1, the prompt consisted of only the target item, and a written translation of its meaning was elicited. In Test 2, the prompt was a short sentence in which a target item was highlighted, and a written translation of only that target item was requested. In Test 3, the prompt was the same sentence as in Test 2, but the target item was unhighlighted, and participants were requested to translate the entire sentence. Finally, in the criterion interview, participants were asked to demonstrate their understanding of the target items in the same prompt sentences as in Tests 2-3. The results indicated that Test 3 produced a mean score and match rate most similar to the interview, followed by Test 2, with Test 1 being the least similar. The paper discusses several factors explaining differences in test performance that were explored during the interview.

The intricate and tricky case of semantic analysis, especially of the enigmatic realm of nouns, the red thread of linguistics and literary exploration. This academic research explores the rich sphere of Russian literature in-depth to determine connections between function and the embodiment of theme through proper names. This research is aiming at studying the role of proper nouns in the literary masterpieces by focusing on the ideas of character development, thematic cohesiveness, and the narrative flow. This papers deals with profound significance of proper nouns as linguistics pointers that take you beyond their literal meaning to different levels of connotative value. It takes most of its ideas from the late linguistics and literary critique. This research gets down to serious business by cherry-picking and analysing a set of Russian masterpieces. An intensive method of semantic analysis was used by the study to properly identify and categorize all the structural features of proper nouns. Also, it conducts an examination of the usage of language by these instrumentalities so as to discover their common features and themes which lead to the invention of the main narrative. The results of this study add to our comprehension of proper noun semantics in Russian literature and provide us with a stepping-stone to broader discussions on the linguistic theory and literary studies. This cognitive effort exceeds the linguistic borders, as it takes into consideration the cultural and historical value of the proper nouns being presented. The report shows why it is necessary to reconsider the theoretical design to account for the distinctive linguistic and particular cultural features of the Russian literary heritage. This research is a step toward unravelling noun semantics' mystery and adds to the already existing linguistic aesthetics and cultural semiotics debates.

Despite undeniable advances in research on language learning strategies in the last several decades, empirical investigations of actions and thoughts that learners engage in to better understand and use grammar structures in different contexts, or grammar learning strategies (GLS), remain scarce. Moreover, there is a paucity of studies examining the effects of pedagogical interventions in this area, both with respect to the use of different types of GLS and the mastery of grammar structures in terms of explicit and implicit (automatized) knowledge. There is also no empirical evidence concerning the mediating role of individual difference (ID) factors in this respect. The paper outlines a study currently in progress that seeks to address all of these gaps. The first part is devoted to a brief overview of key issues related to the definition and classification of GLS as well as a synthesis of available research in this area. This is followed by a description of an intervention-based research project which explores the effects of strategies-based instruction (SBI) targeting GLS by English majors in Poland, also taking into account the moderating impact of selected ID variables. In particular, the methodology of the study is presented and the envisaged contributions of the intervention are considered.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

Considering the importance of subject retrieval for scientific visibility, and the need to guide authors in self-archiving their papers in institutional repositories of university libraries, this study observed the patterns and strategies used by authors while indexing for keyword assignment. The study examined four categories of analysis: criteria for keyword assignment; use of controlled vocabulary for keyword assignment; understanding of the importance of keywords; and ordering criteria and function of assigned keywords. The study found that, while assigning keywords, authors: consider fundamental concepts for representing significant content of the text; act as domain expert indexers; and are unaware that keyword assignment is an indexing process that requires controlled vocabularies. The research suggests that institutional repositories implement a hybrid information representation and retrieval system to allow for both the representation of more specific subjects of knowledge domains, as well as controlled vocabulary indexing terms.

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several Machine Learning (ML) models to perform classification tasks on the dataset to predict student performance, including Linear Regression, Logistic Regression, Random Forests, K-Nearest Neighbours, and more. Secondly, to evaluate the efficacy of these ML models and identify those which are best suited to this task. The categories of data utilised in achieving these aims include (i) demographic information, (ii) academic background, and (iii) interaction with MOOC course materials. The research procedure comprises five phases: data exploration to analyse the dataset, feature engineering which involves discerning the most important features and converting them into a format decipherable by the ML models, model building, model evaluation by measurement of accuracy, and subsequent comparative evaluation between the different models. The results achieved in this study are expected to have implications on how MOOC platforms utilise data to improve user experience. As indicated by the findings of this study, the data collected by these platforms may be used to predict performance with accuracy of over 77%; this extracted information can be exploited to enhance educational theory or practices in the context of MOOCs, for instance by implementing varying teaching methodologies or providing different types of resources based on predicted performance.

Multi-class data classification is distinguished as a significant and challenging research topic in contemporary machine learning, particularly when concerning imbalanced data sets. Hence, a thorough investigation of multi-class imbalanced data classification is becoming increasingly pertinent. In this paper, an overview of multi-class imbalanced data classification was generated via conducting a systematic mapping study, which endeavors to analyze the state of contemporary multi-class imbalanced data classification, with the primary goal of ascertaining the corpus of research undertaken in machine learning. To achieve this aim, 7,164 papers were assessed and the 147 prominent ones were selected from five digital libraries, which were further categorized according to techniques, issues, and types of datasets. After a thorough review of these papers, a taxonomy of multi-class imbalanced data classification techniques is proposed. Based on the results, researchers widely employ algorithmic-level, ensemble, and oversampling strategies to address the issue of multi-class imbalance in medical datasets, primarily to mitigate the impact of challenging data factors. This research highlights an urgent need for more studies on multi-class imbalanced data classification. [ABSTRACT FROM AUTHOR]

Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

Categorical growth models describe examinee growth in terms of performance-level category transitions, which implies that some percentage of examinees will be misclassified. This paper introduces a new procedure for estimating the classification accuracy of categorical growth models, based on Rudner's classification accuracy index for item response theory-based assessments. Results of a simulation study are presented to provide evidence for the accuracy and validity of the approach. Also, an empirical example is presented to demonstrate the approach using data from the Indiana Student Performance Readiness and Observation of Understanding Tool growth model, which classifies examinees into growth categories used by the Office of Special Education Programs to monitor the progress of preschool children who receive special education services.

Whilst teaching about the nature of science (NOS) is a significant goal of science education, there remains debate about specifying the NOS and appropriate pedagogies and approaches to researching the NOS. A neglected, but conceptually and practically significant, problem is the proliferation of NOS-related learning goals such as NOS beliefs, views, understandings and knowledge. In this theoretical paper, we argue that such goals are often poorly defined, and different goals cohere with different pedagogical and research strategies. We propose a novel three-part taxonomy of NOS learning goals (as NOS beliefs, knowledge and understandings) and contend that different approaches are appropriate for teaching and assessing NOS beliefs, views and knowledge. An NOS belief refers to a positive attitudinal stance towards some proposition that lacks justificatory support or cannot easily be judged true or false. NOS knowledge indicates justified true beliefs related to the NOS. NOS understanding denotes a grasping of how a collection of NOS knowledge is related. The goals vary by the extent to which they can be judged true or false and the degree of justification they require. For NOS beliefs, a range of stances is acceptable; NOS knowledge must be a true and justified belief; in the case of NOS understanding, teaching and assessment should focus on the appreciation of relationships between justified true beliefs. The novel taxonomy brings needed clarity to a confused aspect of NOS research and may lead to the development of NOS pedagogies and assessment tools more precisely targeted to well-defined learning goals.

The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency-Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching-learning activities are aligned with regulatory body expectations.

European countries differ widely in terms of vocational education and training (VET) tradition and the delivery of VET at the upper secondary level. A statistical approach to build a classification of VET systems in Europe is presented in the present article on the grounds of the size of the vocational enrollment, on the one hand, and the percentage of vocational enrollment in programs in which work and school are combined, on the other hand. Twenty-two European countries with full available information are considered in the study: 18 European Union (EU) countries, three Schengen Area (non-EU) countries (Iceland, Norway, and Switzerland), and the UK (non-EU state outside Schengen). Cluster analysis is a quantitative form of classification. The country groupings emerging from the K-means analysis performed in this paper allow us to distinguish mainly between vocational-oriented countries with high vocational specificity (e.g. Germany, Switzerland, and Austria), highly vocational-oriented countries with traditionally school-based VET programs (e.g. the Czech Republic and Slovakia), and less vocationally-oriented countries (general education-oriented countries such as Estonia and Spain).

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

With the continuous evolution of technology, XR products are expected to witness significant breakthroughs and advancements in these aspects, ultimately enhancing the overall user experience. However, careful consideration and optimization of these characteristics based on the intended purpose and usage scenarios of the product remain an area of ongoing research. The primary goal of this paper is to provide a comprehensive reference framework for researchers and product developers to facilitate further research and development in XR technology. By analyzing and discussing the existing literature, this review aims to synthesize the current understanding and state‐of‐the‐art progress in XR technology, serving as a valuable resource for researchers and developers seeking to innovate in this rapidly growing field. [ABSTRACT FROM AUTHOR]

Shadowing is the act of vocalizing the speech one is listening to as simultaneously as possible. The primary function of shadowing is learners' listening skill and pronunciation skill development. Despite the importance of second language (L2) listening skills, this pedagogical technique has not received focal attention in the field. In this paper, shadowing is situated in the framework of systematic and deliberate practice, which is supported by an L2 acquisition theory called the skill acquisition theory. Based on this framework, a total of 16 different primary types of shadowing are classified into shadowing for phonological processing and shadowing for intake through meaning-focused processing. The proposed classification and guideline will be useful for teachers to adapt a variety of shadowing techniques for their teaching context and learners as well as highlighting shadowing as an important pedagogical tool of teaching bottom-up listening skills.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. [ABSTRACT FROM AUTHOR]

Research classification is an important aspect of conducting research projects because it allows researchers to efficiently identify papers that are in line with the latest research in each field and relevant to projects. There are different approaches to the classification of research papers, such as subject-based, methodology-based, text-based, and machine learning-based. Each approach has its advantages and disadvantages, and the choice of classification method depends on the specific research question and available data. The classification of scientific literature helps to better organize and structure the vast amount of information and knowledge generated in scientific research. It enables researchers and other interested parties to access relevant information in a fast and efficient manner. Classification methods allow easier and more accurate extraction of scientific knowledge to be used as a basis for scientific research in each subject area. In this regard, this paper aims to propose a research classification model using data mining methods and techniques. To test the model, we selected scientific articles on digital workplace accessibility for the disabled retrieved from Scopus and Web of Science repositories. We believe that the classification model is universal and can be applied in other scientific fields. [ABSTRACT FROM AUTHOR]

Evaluative judgement is the ability to understand what constitutes the quality of a performance or product and to apply this understanding in the evaluation of one's own or another's task. It is, therefore, a key element in the development of learning to learn competences linked to professionalism. Experiences that explicitly encourage it in university studies are therefore essential. Peer feedback strategies could be one way of developing it. This paper presents the results of an intervention, based on a didactic sequence that included a peer assessment process, carried out in three courses with an iterative task of two feedback loops. A total of 1173 comments from 114 students were collected and analysed under a triple categorisation. A classification of peer comments is presented, which can be used and transferred to other work aiming at a similar analysis. From the first loop to the second, a higher percentage of comments classified as positive corrective feedback is observed, and an improvement in the quality of comments in all courses. In addition, the quality of comments differs greatly according to the type of assessment criteria. These results are discussed, outlining strategies for future designs.

In order to distinguish between language-related ideologies, Ruiz (1984) proposed three language orientations, namely 'language-as-problem', 'language-as-right' and 'language-as-resource'. Although this typology has been applied to various countries and regions around the globe, relevant research in China, a multi-ethnolinguistic country, remains limited. Through a review of China's minority language policies and relevant literature, this paper explores the application of Ruiz's theory to the context of China and proposes two possible directions which can complement this theory. Firstly, the study found that there are different layers, complexities, and even contradictions under a broad/unified language orientation, and that minority languages can be simultaneously positioned as a 'right', a 'resource' and a 'problem'. Secondly, by investigating a particular ethnic minority language (the Nuosu Yi language), the study found that the written (literacy) and spoken (oracy) skills of the language can be positioned differently in language-in-education policies and practices. We therefore propose that a critical 'biliteracy continua' lens (Hornberger 2004) can provide further analytical nuance to the language orientations put forward by Ruiz (1984).

This paper proposes a novel framework for systematic reviews, the "conceptual systematic review" (CSR), incorporating elements of content analysis that often implicitly precede synthesising research but are rarely made explicit. We argue that a CSR has the possibility to conceptually map a "tangled term," to prepare systematic reviews, to advance interdisciplinary communication, and to offer guidance in tangled fields of scientific discourse. The proposed methodological framework involves a six-stage procedure aimed at systematically unravelling tangled terms through the deductive--inductive coding of a systematically retrieved literature corpus, thereby going beyond the quantitative synthesis of research findings. We call the elements of such a framework "heuristics," "classifications," and "systematics"; they provide insight into the quality of term disentanglement and can be viewed hierarchically: heuristics do not (necessarily) satisfy any quality criteria, classifications adhere to "analytical quality criteria," and systematics also meet "empirical quality criteria." The six quality criteria through which the quality of a CSR can be made explicit are introduced to allow for professional scrutiny and critique. It is argued that making conceptual elements of systematic reviews explicit is especially relevant in the field of educational research. Finally, we discuss the relation of the CSR to similar methods and address weaknesses in the proposed framework.

The paper presents a framework for school guidance in the twenty-first century that focuses the guidance approach, on promoting lifelong learning and takes into account the impact of VUCA (volatility, uncertainty, complexity, and ambiguity). It outlines the goals, objectives, and key players involved in guidance approach and provides a structured approach based on six levels: actors, devices, practices, main issues, objectives, and general aim. Four types of practices are defined to address four main societal objectives, which are linked to current challenges facing society. The framework aims to provide a tool for schools to implement and structure an educational approach to guidance.

A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

The present paper explores approaches to the classification of ethnic identity. In the framework of research on comparative classifications, we analyse the contextual factors that influence classification in Hungarian education. We compared the number of students who self-reported as Roma with the respective number reported by the school heads (as experts) and examined the discrepancies between the two indicators. We also examined whether there was a correlation between the estimation by the head of the school and the views and attitudes of the teaching staff and school heads. One important finding to emerge was the more than the twofold difference between the external classifications reported by individuals belonging to the majority population, and the students' self-identification. In other words, the school heads tended to overestimate the proportion of Roma students in their schools compared to the students' own self-identification, which was related to their pedagogical practices, beliefs, and commitment. In particular, school heads overestimated the proportion of Roma students in schools where teachers perceived that there were lower expectations and where teachers attributed school failure to cultural differences.

Rurality is known to be associated with a number of weaker educational outcomes, from lower attainment through to lower social mobility. This is why so much policy and practitioner focus has been directed at addressing the rurality gap in educational outcomes. In this paper, we use pupil-level data for Scotland to contribute to two dimensions of this problem. First, we explore the relationship between socio-economic deprivation and educational mobility across urban and rural primary schools in Scotland. This provides new insights on the issue of rural disadvantage. Second, we use our dataset to explore the socio-economic makeup of urban and rural schools in Scotland, documenting that schools located in the highest and lowest SIMD areas are more homogeneous than those in the middle. This is important for the classification of schools in targeting educational interventions to improve social mobility.

Purpose: Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach: Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings: The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set's normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value: This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

This paper analyzes the impact of college athletic reclassification for educational institutions in the United States. Most of America's colleges and universities offer athletic opportunities for their students under NCAA governance. The level of competition and associated resource requirements range from relatively low (Division 3) to high (Division 1). In recent years, there has been an increase in the number of institutions that have either reclassified to a higher division, or publicly considered such a move. The resource obligations of reclassification are significant, and administrators must carefully weigh expected benefits and costs with respect to the institution's mission and vision. While several studies (Bell, 2017; Schwarzber, 2014; Frieder & Fulks, 2007; Tomasini, 2005) have focused on reclassification from Division 1-FCS to FBS--the highest level of competition--less attention has been paid to colleges in Division 2 and 3--which comprise two-thirds of NCAA membership. Our study addresses a gap in the literature by analyzing athletic reclassification among schools moving up from Division 2 to Division 1 (FCS and no-football) during the period of 2001-2018. Drawing upon multiple data sources, we construct a panel of 33 schools (14 treatments and 19 controls) to evaluate the impact of reclassification on applications and athletic department budgets. Results from quasi-experimental methods (difference in differences, event study, and synthetic control models) suggest that reclassification significantly increased basketball program revenues and athletic department expenses among the treatment schools. However, we do not find evidence that reclassification raises student applications in the short term.