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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.

It is generally perceived that a substantial number of engineering faculty are still unaware of alternative educational methods, and many who are aware of them choose not to incorporate them into their approach to teaching. There are several likely reasons for this inertia, aside from the inevitable human resistance to change. The primary focus of imparting information is restricted to fulfilling the course requirements and, the upcoming term end examination. It is imperative to adopt a change from teaching to learning paradigm engaging the students in Higher Order Thinking Skills. Knowledge and technological advancements are changing the role of engineering and engineering faculty in the society. Engineering Education reforms need to focus on inductive teaching and stimulated learning. Students should be taught critical thinking skills and creative thinking skills to keep pace with the rapidly changing engineering profession. This paper reports the study done to test and explore the faculty awareness of Bloom's Taxonomy of Educational Objectives in the cognitive domain and Higher Order Thinking Skills (HOTS) and to evaluate question papers set by engineering faculty with respect to HOTS as proposed by Bloom's Taxonomy. Various active learning strategies to enhance critical thinking skills and creative thinking skills of the students are recommended for use by the faculty in their interactions with the students.

Computer assisted assessment is becoming more and more common through further and higher education. There is some debate about how easy it will be to migrate current assessment practice to a computer enhanced format and how items which are currently re-used for formative purposes may be adapted to be presented online. This paper proposes an evaluatory framework to assess and enhance the practicability of large-scale CAA migration for existing items and assessments. The framework can also be used as a tool for exposing compromises between delivery mechanism and validity--exposing the limits of validity of modified paper based assessments and highlighting the crucial areas for transformative assessments. (Contains 1 note, 5 tables, and 1 figure.)

The anatomy of the human shoulder predisposes the neurovascular bundle to compression at different levels of the thoracic outlet during abduction of the arm. There are four possible levels of compression at the thoracic outlet pathway: at the costo-clavicular angle, the inter-scalenic angle, under the pectoralis minor muscle or at the level of the humeral head. The positional thoracic outlet compression (TOC) often remains completely asymptomatic. When symptomatic, compressions are collectively referred to as thoracic outlet syndrome (TOS) and may require surgery if physical therapy fails to improve symptoms. The “thoracic outlet compression with complications or sequelae” (the acronym of which is “TOC-CS”, which can be simplified as “TOX”) will almost invariably lead to surgery to release the compression, and other possible treatment targeting the complications as required. There is a continuum between TOC, TOS and TOX, which are simply different clinical stages of the same mechanical issue, just like the Rutherford grades represent different stages of lower extremity arterial disease. We believe that discriminating between TOC, TOS and TOX clarifies clinical definitions and their respective treatment options. TOC is to be considered as a physiological positional phenomenon, TOS requires medical or surgical treatment and surgery should be considered as a primary option in TOX.

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 present study is intended to critically examine metadiscourse markers in 24 master thesis abstracts. Twelve of them are written by non-native Iraqi female students and the rest by native American female students. To do so, the researchers have set two aims: examining the types and subtypes of metadiscourse markers in terms of nativity and major and comparing the usages of metadiscourse markers' types and subtypes in terms of nativity. To achieve the present aims, Hyland's model (2005) is adopted. It aids in classifying the types and subtypes of metadiscourse markers in both data. The findings show that the Iraqi and American researchers use the interactive resources more than the interactional ones but the American researchers are capable of engaging their readers since their use of the interactional markers is higher than their counterparts. The field has no effect on the use of metadiscourse markers. There is diversity in the usage of the interactive resources in Iraqi data. Accordingly, the researchers recommend metadiscourse markers to be added to M.A courses as part of abstract writing exercise and the instructors of fourth-year college students to include metadiscourse markers in the writing of their research papers for coherence and clarity.

This paper presents an analysis of papers delivered at the ICALT 2004 conference, in order to understand the current research issues relating to Learning Objects (LOs). The major research results are summarized, and the papers are classified according to the definition of LO used and the approach taken (technical, pragmatic or pedagogic). The technologies employed, and the features present in the papers, are analyzed. (Contains 1 table and 5 figures.)

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 examines the different types of writing errors performed by 16 international postgraduate students undertaking an intensive English course at a public university in Malaysia. It was mandatory for international postgraduate students who obtained less than IELTS Band 6 to undertake an Intensive English Course (IEC) offered by the University, prior to entering their respective faculties' academic programs. The students were required to write a 3-5 page term paper assignment on a topic related to their field of study. Mixed methodology approach was employed to examine and analyze corpus of students' term papers. The errors in the term papers were identified and classified accordingly. The results of the study revealed that four most common English language errors committed by the participants were sentence structure, articles, punctuation and capitalization. This study also shed light on the manner in which students assumed the rules of English to that of their native language. Such insight is useful for both instructors and students because it provides significant information on the building blocks experienced by English language learners in academic writing.

The purpose of this research is to analyze the mission statements of business colleges operating in the United Arab Emirates. To this end, written and visual documents in the websites of business colleges were surveyed to collect data on components of mission such as philosophy, contribution, markets, and distinctiveness. Content analysis was used to analyze the data and the components of mission were classified or measured on a simple scale. The cost of tuition was also examined as, considered together with other findings, it can help point to the business strategy followed by colleges. The findings reveal clusters of colleges sharing similar characteristics and following strategies of differentiation or cost leadership in a segmented and crowded business education market. Predominantly privately-owned and characterized by a market philosophy but often with little distinctiveness business colleges are essentially teaching-oriented institutions. The need for return on investment and profit in a competitive education market has resulted in high tuition fees especially among better-known and internationally-accredited colleges. International accreditation is becoming important as the quality of education receives more attention. One limitation of this research is that conclusions are drawn on the basis of material found in publicly available documents only. The framework used in this research to analyze organizational mission of colleges of business in UAE can be helpful in carrying out similar research of tertiary institutions elsewhere.

Adult observers have surprisingly low calculation efficiencies for letter recognition (see, e.g., Pelli, Burns, Farell, & Moore-Page, 2006). Here, we examine the possibility that this is partly due to observers' neglecting paper features (e.g., the absence of ascenders and descenders in 'o'). Each of 16 observers completed 5,000 trials of a single-letter two-alternative forced-choice detection task. Using a combination of classification image analyses and Bayesian statistical analyses, we argue that between 60% and 75% of our participants indeed neglected paper features., (© 2014 ARVO.)

Questions are used to obtain information, stimulate thinking, and redirect reasoning. Academics in higher education use questions on a daily basis to stimulate thinking and reasoning in students. Final examination papers are used by academics to assess the retention and application skills of students. The assumption, however, exists that questions relating to application skills at universities of technology should start to dominate the higher academic levels in education, with a subsequent drop in questions regarding retention skills. These questions may be categorized as either higher order or lower order questions. This article attempts to distinguish between these two types of questions in light of Bloom's taxonomy, with similar concepts such as deep and surface learning being examined. The literature review is applied to an electrical engineering module titled Electronics, which serves as the case study. The results of this study indicate that a high percentage of the final examination papers dealt with the objective "Application," where students had to make use of numerous mathematical equations to solve various unknowns. The results also indicated that academics in electronics are using more lower order than higher order questions in their final examination papers. A balance is suggested between these two types of questions for various academic levels at universities of technology. (Contains 3 tables and 1 figure.)

The restricted time allocated to courses and the immediate need to read literature in English necessitates the teaching of selective reading in many English as a foreign language contexts. This paper reports on one element of an expeditious (quick, effective, efficient and selective) reading course for learners in an English for Academic Purposes (EAP) context. It details how postgraduate students of chemistry in a Tunisian university are taught to read titles of empirical research papers, as this becomes as important as reading abstracts for fast-tracking relevant data. Analysis of the literature in the field of technical writing identifies four types of titles. Based on this classification, the course component aims at raising the learners' awareness of the linguistic exponents that typify titles, and making them appreciate the importance of titles and their classification. The paper details how this single strategy fits into an expeditious approach to the teaching of reading.

This paper reports on the development of an analysis engine for the Research Writing Tutor (RWT), an AWE program designed to provide genre and discipline-specific feedback on the functional units of research article discourse. Unlike traditional NLP-based applications that categorize complete documents, the analyzer categorizes every sentence in Introduction section texts as both a communicative move and a rhetorical step. We describe the construction of a cascade of two support vector machine classifiers trained on a multi-disciplinary corpus of annotated texts. This work not only demonstrates the usefulness of NLP for automated genre analysis, but also paves the road for future AWE endeavors and forms of automated feedback that could facilitate effective expression of functional meaning in writing.

Objective: to analyse the artistic bindings of the eighteenth-century prints stored in the Historical-artistic Binding Database of the Royal Library, in order to locate the endpapers made with decorated papers, and to classify and describe them according to the proposals of the specialist literature. Methodology: The records from the database were retrieved using the "date" field, and the information is extracted from the "endpapers" and "notes" fields. The reproductions of the endpapers were analysed, identified and described following the classification model of decorated papers inspired by the works of Professor Carpallo Bautista, consisting of five techniques and their variants. Finally, a comparative analysis was carried out. Results: Among the 382 eighteenth-century prints with artistic bindings, ten were located and analysed that include endpapers decorated with xylographic plates, 135 with marbled or mottled papers (with designs corresponding to seven variants), four with paste-glued papers, three with dotted papers, 18 with gold and embossed papers, and four of other kinds such as silver or percaline paper. [ABSTRACT FROM AUTHOR]

The International League Against Epilepsy (ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century.

Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from: (1) the breadth and detail of the issues that have been identified; and (2) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.

Abstract Background Functional somatic symptoms and disorders are common and complex phenomena involving both bodily and brain processes. They pose major challenges across medical specialties. These disorders are common and have significant impacts on patients’ quality of life and healthcare costs. Main body We outline five problems pointing to the need for a new classification: (1) developments in understanding aetiological mechanisms; (2) the current division of disorders according to the treating specialist; (3) failure of current classifications to cover the variety of disorders and their severity (for example, patients with symptoms from multiple organs systems); (4) the need to find acceptable categories and labels for patients that promote therapeutic partnership; and (5) the need to develop clinical services and research for people with severe disorders. We propose ‘functional somatic disorders’ (FSD) as an umbrella term for various conditions characterised by persistent and troublesome physical symptoms. FSDs are diagnosed clinically, on the basis of characteristic symptom patterns. As with all diagnoses, a diagnosis of FSD should be made after considering other possible somatic and mental differential diagnoses. We propose that FSD should occupy a neutral space within disease classifications, favouring neither somatic disease aetiology, nor mental disorder. FSD should be subclassified as (a) multisystem, (b) single system, or (c) single symptom. While additional specifiers may be added to take account of psychological features or co-occurring diseases, neither of these is sufficient or necessary to make the diagnosis. We recommend that FSD criteria are written so as to harmonise with existing syndrome diagnoses. Where currently defined syndromes fall within the FSD spectrum – and also within organ system-specific chapters of a classification – they should be afforded dual parentage (for example, irritable bowel syndrome can belong to both gastrointestinal disorders and FSD). Conclusion We propose a new classification, ‘functional somatic disorder’, which is neither purely somatic nor purely mental, but occupies a neutral space between these two historical poles. This classification reflects both emerging aetiological evidence of the complex interactions between brain and body and the need to resolve the historical split between somatic and mental disorders.

There are steps formally initiated by the Government of India to maintain its adherence to the norms of Inclusive Education. Such schemes, acts and policy drafts are brought into discussion in this paper along with an appraisal of their draft and scope. This include the Sarva Shiksha Abhiyan which became operational from the year 2001, The Right to Education Act, which is implemented from the year 2009 and the Action Plan for Inclusion in Education of Children and Youth with Disabilities of 2005, which was revised in the year 2009. The discussion is anchored around, though not limited to the aspect of 'Diversity' which is very pertinent to the Indian scenario and the philosophy of Inclusion. Cultural and geographical diversity of the Indian sub-continent provoke challenges in 'Doing Diversity' in classrooms and at the same time it is contributing for the teacher and the taught. Considering the scope of the concept of social inclusion and inclusive education, the paper argues that at an implementation level, inclusive education should be one that is responsive to diversities in a classroom and cultural space. Rather than reproducing social structures pertaining to the above mentioned social categories of class, gender, language etc., policy drafting and implementation of inclusive education should make certain attempts which are pro-inclusive. They need to be less privileging towards certain ways of learning and certain ways of evaluation, considering the physical and cultural diversities of the learners. In our discussion on these schemes and their scope in ensuring diversity in classrooms, questions pertaining to certain disadvantaged sections are covered including Scheduled Castes, Scheduled Tribes, Gender nonbinaries, migrants etc.

In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to understand and discuss the main approaches for providing and evaluating feedback used in the learning of novice programmers in the problem-solving activity. Thus, according to a formal protocol, an automatic search was performed for papers from 2016 to 2021. As a result, 39 studies were selected for the final analysis. As a result, we propose three different categorizations: the main approaches to providing feedback, the main methods used in the evaluation and the main aspects and effects of the evaluated feedback.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

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.

The Fraser Institute "Report Card" of school rankings has won the hearts of parents and the press. For over a decade, the rankings have been particularly burdensome for low-ranking (usually low socio-economic status, high-poverty) schools when parents of high-achieving children move them to higher-ranking schools. In February 2010, after defending parents' rights to access the rankings, Victoria's "Times-Colonist" newspaper decided not to publish them. Using critical discourse analysis, this article explores the rankings' long media reign and the "Times-Colonist"'s abrupt decision to stop publishing them. Discourse about the rankings is shaped by multiple factors including the relationship between the press and educators, as well as the nature of societal discourse--in particular, how powerful institutions create what Foucault calls "regimes of truth." (Contains 4 footnotes.)

With the rapid development of the decorative papers industry on a worldwide scale, the aesthetic assessment of decorative papers has evolved as one of the major fields for industrial production. This study was performed to investigate the ability of visible spectroscopy and NIR spectroscopy coupled with the soft independent modeling of class analogy (SIMCA) to reflect the surface characteristics of decorative paper and to classify decorative papers with different visual characteristics. The results showed that visible spectroscopy has a higher relationship with the surface characteristics of decorative papers than the NIR data during PCA analysis due to larger variations. Additionally, when using visible spectroscopy (400 to 780 nm), the classification accuracy reached 94% to 100%, a more accurate result than could be achieved based on color data. In the results of the NIR spectroscopy (780 to 2500 nm), the classification accuracy decreased to the range 1% to 56%, except for a value of 95% for the samples that were grained with a slightly dark color, and a greater number of samples were assigned to more than one class. There were significant differences in the performance of the models built with visible spectroscopy and NIR spectroscopy, so it can be concluded that visible spectroscopy coupled with SIMCA is more useful to classify the different types of decorative papers than NIR spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Uncertainty is unavoidable when dealing with data. The errors in measurements, limitations of measuring tools, or imprecise definition of linguistic variables may result in different types of uncertainty. These ambiguities may be due to vagueness in data which results from the imprecise boundaries of data sets; inconsistency that reflects conflict and contradiction between sets; qualitative description of data which sometimes taken by expertise; or some other type. Ignoring dealing with these types of uncertainty affects the reliability of research and the validity of the results. This article presents three approaches to treat uncertainty using fuzzy logic, intuitionistic logic, and neutrosophic logic and their methodologies in treating these kinds of ambiguity. Fuzzy logic and neutrosophic logic are used in building Rule-based Classification Systems. Different comparisons are presented to illustrate the importance of choosing the suitable logic to tackle the uncertainty in different data sets. These approaches are applied on six real world data sets; Iris, Wine, Wisconsin Diagnostic Breast Cancer, Seeds, Pima, and Statlog (Heart); which are available on UCI Machine Learning Repository web site. The results show that the type of uncertainty in the data set plays a great role in choosing the appropriate logic.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area. [ABSTRACT FROM AUTHOR]

Governments, businesses, and educational institutions need to collaborate and make significant investments in order to address the growing digital skills crisis. In Europe, hundreds of digital skills initiatives have been launched with different forms of government and private industry support in the last five years alone. Consequently, digital skills initiatives have come to encompass a wide array of interventions. In this context, this paper proposes a typology of digital skills initiatives that was developed based on the analysis of over 300 initiatives listed in the European Commission's repository of best digital skills initiatives. The proposed typology consists of four categories: target group, digital skills, learning format, and sponsoring organization. In terms of target group, digital skills initiatives tend to target one or more of five distinct groups: the general public, educators, adults, seniors and youth. In terms of digital skills, digital skills initiatives tend to focus on general digital skills or specialized digital skills. In terms of learning format, digital skills initiatives tend to offer training and/or a learning resource. In terms of sponsoring organization, the initiatives tend to be sponsored by organizations that are either affiliated or unaffiliated with a technology vendor. The typology is followed by a presentation of mini cases, which highlight different archetypes of the proposed typology. The paper closes with a discussion of practical implications for policy makers, administrators, and scholars interested in digital skills initiatives and the future of work.

With the change in socio-political and educational contexts in global academia, the trend of writing academic papers has arisen among Nepali students and educators in higher education. However, many of them are still unfamiliar with the basic standard to be incorporated in an academic paper. In this context, this study examined the post-graduate students' errors in writing research proposals. A textual analysis method was employed, where quantitative phase of analysis was followed by qualitative, and the data were collected from 24 purposively selected research proposals and from the interview with 10 students. The quantitative data were gathered from content analysis of the research proposals and qualitative data were collected from the unstructured interview. The collected proposals were studied, erroneous expressions were listed and categorized in to four parameters like grammatical errors, lexical semantic errors, mechanic errors and syntactic errors. The information from the interview was recorded, noted and analyzed. The results exhibited that (1) article and preposition usages were more persistent grammatical errors; (2) run-ons and fragmentation were more reiterated syntactic errors; and (3) uses of single lexical items were more common lexical-semantic errors and punctuation was the more regular mechanical errors. These findings reveal that students have insufficient knowledge and skills for academic writing. Therefore, they need academic writing courses, training, or workshops from the early level of their schooling. The findings of this study are useful for curriculum designers, policymakers, instructors, and students because it provides significant information on the building blocks experienced by English language learners in writing research paper.

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]

This paper focuses on the most significant empirical studies on the topic of challenges facing the communicative language teaching approach in Saudi Arabia. The communicative language teaching method has been used for more than two decades in Saudi Arabia and research conducted during this period offers some insight into the main challenges of the implementation. The main goal of this paper is to synthesize findings from research studies conducted in the past and by doing so, suggest the possible avenues for tackling these challenges. Four broad categories of challenges have been adopted from Li's (1998) comprehensive framework in an attempt to structure the challenges under meaningful labels. This, in turn, enabled us to categorize the challenges according to their source which helped our understanding of how to resolve them. The findings suggest that the most frequently observed policy-related challenges are the current exam system, overcrowded classes, no AV equipment, and low quality of in-house training programs for teachers. Teacher-related challenges mostly relate to conflicting ideas about CLT, low confidence in these methods, preference for the traditional learning model, focus on developing skills that feature in the exams, and deficiencies in teachers' English. Student-related challenges are found to be their low-level proficiency, passive learning style, lack of motivation to learn the target language, and fear of their marks being negatively affected if they are paired with weaker peers during communicative activities. This paper concludes that these challenges are all interconnected, and a systematic approach is essential in trying to tackle these with the government intervention being a potential first step.

Current paper reports the findings of a content analysis of Open Educational Resources. Twenty three studies were collected for the analysis. The studies were collected from different journals depending on open access policy. Every study was categorised by author, year of publication, title of the study, author characteristics and analysis. The analysis refers to the information about Open Educational Resources variables. Therefore the investigators categorize the research papers based on OER. An analysis of research papers was formed in conclusion and finalizing the common dimensions of content analysis of OER studies.

The field of molecular genetics has many roles in biodiversity assessment and conservation. I summarize three of those standard roles and propose logical extensions of each. First, many biologists suppose that a comprehensive picture of the Tree of Life will soon emerge from multilocus DNA sequence data interpreted in concert with fossils and other evidence. If nonreticulate trees are indeed valid metaphors for life's history, then a well dated global phylogeny will offer an opportunity to erect a universally standardized scheme of biological classification. If life's history proves to be somewhat reticulate, a web-like phylogenetic pattern should become evident and will offer opportunities to reevaluate the fundamental nature of evolutionary processes. Second, extensive networks of wildlife sanctuaries offer some hope for shepherding appreciable biodiversity through the ongoing extinction crisis, and molecular genetics can assist in park design by helping to identify key species, historically important biotic areas, and biodiversity hotspots. An opportunity centers on the concept of Pleistocene Parks that could protect "legacy biotas" in much the same way that traditional national parks preserve special geological features and historical landmarks honor legacy events in human affairs. Third, genetic perspectives have become an integral part of many focused conservation efforts by unveiling ecological, behavioral, or evolutionary phenomena relevant to population management. They also can open opportunities to educate the public about the many intellectual gifts and aesthetic marvels of the natural world.

The eighteenth century books from the collection of the old digital library of the Library University Seville are analyzed to locate the parts made with decorated papers, in order to classify and describe them according to the proposals of specialized literature. For this, the records included in the repository of the digital collection are recovered, the digital reproductions of the books are visualized, the parts with decorated papers are identified and described following the decorated papers classification model, previously elaborated, based on the research of Professor Carpallo Bautista. The model is made up of 6 techniques and their variants. Of the more than 6.700 records from the 18th century in the collection of the digital ancient collection, 511 include decorated papers in their bindings. [ABSTRACT FROM AUTHOR]

This paper discussed the relationship between language, ecology, and culture, and claimed that the study of linguistic communication as pragmatics should not be confined to the traditional context, but should focus on a broader ecological environment. It analyzed the context of practical communication from the perspective of language ecology beginning with the discussion of the ecological crisis in communication and found that language, like plants and animals in nature, needed the support of the external environment with certain "soil fertility". This paper classified ecological context into two types: internal ecological context (psychological-cognitive context) and external ecological context (natural environment and social environment). Based on this classification, the ecological context of pragmatics was further divided into environment-friendly context, addressee-friendly context, and speaker-friendly ecological context. This paper was an exploratory analysis of language ecology in pragmatics, aiming at helping communicative participants find their ecological niche and adopt appropriate strategies to maintain the ecological balance in pragmatic communication.

This research was triggered by the identified need in literature for large-scale studies about the kinds of designs that teachers create for mobile learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analyzing designs has been to manually classify them following high-level pedagogically guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of supervised machine learning (SML) to automatically classify the textual content of m-learning designs using pedagogically relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in the designs. Because not all SML models are transparent, but researchers often need to understand their behaviour, the second goal of this paper is to consider the trade-off between models' performance and interpretability in the context of design analytics for m-learning. To achieve these goals, we compiled a dataset of designs deployed using two tools, Avastusrada and Smartzoos. With this dataset, we trained and compared different models and feature extraction techniques. We further optimized and compared the best performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal with an illustrative case. We found that SML can reliably classify designs with accuracy > 0.86 and Cohen's kappa > 0.69.

Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of understanding "static," and (b) a collection of questions designed to serve as a learning/practice/diagnostic tool to address those aspects. Providing answers requires learners to apply higher-order cognitive skills and, hence, to advance their understanding of the essential meaning of the concept. Each question is analyzed according to three characteristics: (a) the "static" aspects that the question examines according to a detailed classification the paper provides; (b) identification of the question according: to Bloom's revised taxonomy, to the Structure of Observed Learning Outcome (SOLO) taxonomy; and to the problem-solving keywords used in the question's formulation. Several recommendations for teaching are presented.

Education institutions are rethinking their approaches as the world adjusts to a new normal after the pandemic. The e-Portfolio, an emerging tool in education that suits the current context, was reviewed in the study to aspire for better future implementation. Numerous studies have broadly investigated e-portfolios' use in teaching, learning, or assessment. It has risen to prominence, becoming commonplace. To narrow down the considerable volume of research, develop new knowledge, and detect gaps in the existing literature, this study conducted a systematic review of existing literature on e-portfolio use in education. This approach synthesises secondary publications during the past decade. A keyword search of eportfolio and reviews yielded 812 review papers. These articles were examined further to determine whether they met the predetermined criteria, and 12 review articles were identified. It was discovered that if successfully implemented, e-portfolios have promising benefits. Nevertheless, the implementation of e-portfolios also faces specific challenges. This article also synthesised the participants' perceptions of their e-portfolio experience. The focus of the paper is to offer implementation suggestions for practitioners. The diversity of technological e-portfolio platforms and related pedagogical frameworks were also discussed to inspire future implementation. Conclusions in this research advocate further longitudinal research into the pedagogical design of e-portfolio implementation.

Based on the quantitative analysis of L2 English texts from Korean undergraduates, the present paper demonstrates the possibility of distinguishing L2 speaking proficiency with differing measures of syntactic complexity in English writing. To this end, 14 measures of complexity were gauged using an L2 Syntactic Analyzer (Lu, 2010) in 89 EFL essays from a learner corpus to find out the best indicators of L2 speaking proficiency. As a result of the normality assumption test, the logistic regression analysis was applied and the model was significant in classification according to L2 proficiency and the classification was correct in 62.9% of the studied cases. Furthermore, it showed that the best predictor of L2 speaking proficiency was the complex nominal per T-unit (CNT). Specifically, upon the acquisition of CNT, EFL learners are 6.4 times more likely to achieve higher proficiency in speaking English. Taken together, this paper suggests that EFL students' quality of writing can be effectively used to distinguish their speaking ability.

This study analyzes the linguistic features of counter-arguments and support arguments using two computational linguistic tools: Coh-Metrix and Gramulator. The research question investigates whether counter-argument paragraphs and support paragraphs are different in terms of their linguistic features. To conduct this study, a corpus of 78 argumentative papers was collected. The paragraphs in the papers were categorized in terms of their function. The categories included functions of Support, Counter-argument, Expostulation, Counter-argument and Expostulation, Background, and Other. The paragraphs were analyzed for their readability and writing quality through Coh-Metrix. With the exception of the measure of Deep Cohesion, the Coh-Metrix results suggest minimal differences in terms of readability and writing quality between counter-argument and support paragraphs. Following the Coh-Metrix analysis, both counter-argument and support corpora were analyzed through Gramulator for their lexical features. The Gramulator results suggest the presence of causal language and fixed expressions in counterarguments, as well as some tagged language in support arguments.

The aim of this paper is to review some studies conducted with different learning areas in which the schemes of different participants emerge. Also it is about to show how mathematical proofs are handled in these studies by considering Harel and Sowder's classification of proof schemes with specific examples. As a result, it was seen that the examined studies we readdressed in the learning areas of Analysis, Geometry, Algebra, Linear Algebra, Elementary Number Theory, Probability, Combinatorics, and MIX. Students in early grades tend more towards external and empirical proof schemes. On the other hand, the characteristics of the proof schemes become more sophisticated as the participants' profiles change to pre-service teachers or as they become more specializing in mathematics. Some results are as follows: the academic achievement levels, genders, and grade levels of the participants in the studies examined in this paper have indicated that they have similar traces with the schemes they use. In addition, it has been determined that new perspectives such as examining Harel and Sowder's classification with new lenses, revealing the overlooked roles of some dynamics in proof, or improving the framework provide an important research area in terms of revealing students' potentials.

Many pragmaticists claimed that pragmatics developed mainly from 1970s and 1980s, taking the emergence of its series of pragmatic theories (e.g., Cooperative Principle, Conversation Implicature, Relevance Theory, etc.), methodology, and the official issue of "Journal of Pragmatics" (1977) in Amsterdam as its marks. However, few scholars reinstate the historical truth of the real development of pragmatics by comparing and reviewing the semiotic thoughts between Peirce and Morris so as to discover the development and prosperity of pragmatics in Morris's times (around 1930s). As one of the founders of modern semiotics, Morris's major contribution derives from his proposal of trichotomy theory of semiosis, that is, syntactics, semantics, and pragmatics, grounded on which Morris intends to establish an all-inclusive general theory of signs. Substantially, Morris's semiotic theory involves rich pragmatic thought which is inherited and developed from Peirce theory of signs that has logic and pragmaticism as its foundations. This paper aims to reveal the formation and development of pragmatics in Morris's behavioral semiotics by means of comparing the semiotic thoughts between Peirce and Morris from the perspective of dynamic history. Specifically, this paper involves the analysis of the philosophical foundations of pragmatics, disciplinary classification, the establishment of pragmatics, theory of sign inquiry, the relevant specific semiotic views, and three semiotic dimensions of Morris's pragmatics, which are conducive to explore the pragmatic origin and development from the perspective of Morris's semiotic thoughts. As such, Pragmatics in Morris's behavioral semiotics is established and developed in a systematic and formal way.

Introduction: Freezing cold injuries (FCI) are a common risk in extreme cold weather operations. Although the risks have long been recognised, injury occurrences tend to be sparse and geographically distributed, with relatively few cases to study in a systematic way. The first challenge to improve FCI medical management is to develop a common nomenclature for FCI classification. This is critical for the development of meaningful epidemiological reports on the magnitude and severity of FCI, for the standardisation of patient inclusion criteria for treatment studies, and for the development of clinical diagnosis and treatment algorithms. Methodology: A scoping review of the literature using PubMed and cross-checked with Google Scholar, using search terms related to freezing cold injury and frostbite, highlighted a paucity of published clinical papers and little agreement on classification schemes. Results: A total of 74 papers were identified, and 28 were included in the review. Published reports and studies can be generally grouped into four different classification schemes that are based on (1) injury morphology; (2) signs and symptoms; (3) pathophysiology; and (4) clinical outcome. The nomenclature in the different classification systems is not coherent and the discrete classification limits are not evidence based. Conclusions: All the classification systems are necessary and relevant to FCI medical management for sustainment of soldier health and performance in cold weather operations and winter warfare. Future FCI reports should clearly characterise the nature of the FCI into existing classification schemes for surveillance (morphology, symptoms, and appearance), identifying risk-factors, clinical guidelines, and agreed inclusion/exclusion criteria for a future treatment trial. [ABSTRACT FROM AUTHOR]