Replication Of Type Research Articles

Grammar and Composition Questions of Chinese Language Teacher Candidates’ Tests: 1997-2020

This text deals with question items set in the grammar & composition writing section of the Chinese teacher recruitment examinations for a total of 24 years (1997-2020) for the purpose of research. It is very important to look at the evaluation methods and evaluation details of the appointment examination to select excellent teachers. The main results of the study by question type and evaluation contents through analysis of a total of 168 questions based on 112 questions and sub-question criteria are as follows. First, as a result of analyzing the percentage of questions according to the question types: narrative type 41.2% , selective type 25%, short answer type 4%, essay type 6.1%, complete type 4.3%, and integrated type 20%. Second, the analysis result according to the evaluation contents: the typical problem type of grammar was error correction, and the representative problem type of composition writing was word order arrangement. The most frequently asked part of speech was the verb with 17 times questions, and the sentence component was the complement with 34 times questions. We were able to understand the changes and the current status of the overall test trends and examine the detailed evaluation factors.

Discovering America: Unpacking Popular Social Q&A for Prospective Chinese International Students

The global pandemic and recent shift to online learning has heightened the need to better understand how to support international students, especially across online and virtual platforms. However, a review of literature reveals a paucity of studies dedicated to international student use of internet-based platforms and services for seeking information and sharing experiences. One avenue for further research is Social Question & Answer Communities (SQACs), which are vast conduits of shared experiences and knowledge connecting hundreds of thousands of pre-application and pre-arrival students. In this study, content analysis was utilized to qualitatively observe the contents of the “Overseas Studies in The United States” section of Zhihu, and quantitatively count their features and characteristics. The study found that 58% of the questions and answers were devoted to Academic issues such as testing, admissions, learning and research, with another 13.0% on Crime, Law and Safety, and the remaining 29% of the questions were associated to a diverse array of topics associated with living and working in American society. The most popular answers were made up of mainly 4 main types: Sharing One’s Experience (32.0%), Advice (26.0%), Opinion (22.0%) and Critique (15.0%). Content analysis of three main answer features, namely the use of Imagery, Digital Resources, and Social feature, indicated that the Advice and Critique answer types contain the richest variety of features and that question context, textual styles and use of digital resources are important factors for understanding the answer popularity in SQACs.

Knowledge, Attitude and Practices (KAP) about biomedical waste management among hospital staff– A crossectional study in a tertiary care hospital, Andhra Pradesh, India

Introduction: Hospitals produce waste, which is increasing over the years in its amount and type. Hospital waste is a special category of waste which needs to be handled appropriately with precautions because in addition to the risk for patients and personnel who handle them, they also pose a threat to public health and environment. Objectives: To assess Knowledge, Attitude and Practices about Biomedical waste management among staff of a tertiary care hospital, Andhra Pradesh. Materials and Methods: A questionnaire with multiple choice questions type of answers to assess the Knowledge, Attitude and Practices of Biomedical waste management was given to the 3 categories of hospital staff in selected high risk areas like operation theatre, laboratories, medical ward and surgical ward, casualty, outpatient departments. The staff included Nurses including O.T. Staff, Sanitary Staff and Laboratory Staff. The answers were analyzed using Percentagesand Chi-square test. Results: The knowledge was significantly better among the nurses while it was poor among the sanitary staff. Conclusions: It is important to improve the knowledge regarding Bio-medical waste among all levels of hospital staff because this forms the foundation for effective and proper Bio-Medical waste management. Keywords: Bio-medical waste management, KAP, Tertiary care hospital, Andhra Pradesh.

Концепт ЖЕНА/WIFE в языковом сознании носителей русского и английского языков

This paper aimed to study the content of the concept of wife in Russian and English-speaking linguocultures. The research will broaden the understanding of linguistic consciousness of Russian and English native speakers as well as identify some peculiarities of their world image. It is important to study the national world image today, as it can help to improve mutual understanding between cultures and simplify the assimilation process for those who decide to immigrate for some reason or other. The author utilized the methods of linguocultural and comparative analysis, as well as of associative-verbal nets study suggested by Yu.N. Karaulov. They include comparing associative-verbal nets by respondents’ first two reactions, identifying the most frequent types of answers, and grouping the responses into semantic zones. Further, the article analyses the synonymic chains of the word wife and explains the significance of this concept in Russian and English-speaking linguocultures, as well as identifies common semantic groups for the concept (wedding, household) and determines a common response to the stimulus in the two linguocultures. In addition, the paper traces differences in the perception of the concept of wife and singles out various semantic zones and their key elements. The research found that for many Russian native speakers, this concept contains such features as faith and belonging to someone, while in English linguistic consciousness it includes gender and partnership.

Al-Imlā’ Tiqniyyah fa’ālah li Kasyf al-Musykilāt wa al-Ḥulūl fi Ta’līm al-Lugah al-‘Arabiyyah

This research aims to extrapolate the weaknesses of students at SMP (Junior High School) Sadamiyyah-Jepara. The dictation test method is used to obtain data about error types of student answers. Data analysis is carried out through two phases: identifying errors, and then searching for their source. The research found several problems facing students at SMP Sadamiyyah in writing dictated texts. The most important are those related to writing Arabic letters, understanding vocabulary, grammars, and the overall meaning of sentences used in the dictation process. One causes these problems is the nature of Arabic writing, and the other is due to their lack of listening and their weakness in Arabic. The sound varies with its symbols in some Arabic words, the proximity of the source of some letters also inherits the difficulty of distinguishing between them. In addition, the lack of vocabularies and grammars hinders the overall understanding of the meanings of sentences. Dictation is a test of arabic comprehensive ability. Therefore, improving students’ ability in the dictation requires a lot of language practices. Students' poor in dictation becomes a starting point for reforming the Arabic language education process integratively.

Spam detection and high-quality features to analyse question –answer pairs

Purpose In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs. Design/methodology/approach This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions. Findings There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions. Originality/value The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.

Automatic Voter Recommendation Method for Closing Questions in Stack Overflow

Stack Overflow is the most popular programming question and answer community that continuously receives a large number of questions every day. To ensure the quality of questions, the community grants privileges for the moderators and a group of experienced users to review the quality of questions and close the low-quality ones (e.g. duplicate or irrelevant questions). The review process is a typical crowdsourcing job that relies on users’ volunteer participation, and the current practices of closing questions in Stack Overflow face two aspects of challenges: (1) an obvious increase in both the absolute number and the percentage of “closed” questions; (2) a considerable decrease in participation willingness of experienced users to close questions. In order to solve the problem, we present a novel model of user willingness for reviewing and voting questions by incorporating four types of user activity history, including questions, answers, comments and votes of closing questions. Then we propose an automatic recommendation method based on the model to assign experienced users proper questions, to utilize the forces of them to close questions. The evaluation shows that the successful recommendation probability in the top 5, top 10, top 20, top 30, top 40, top 50 users are 48.23%, 58.93%, 68.83%, 74.27%, 78.13% and 81%, respectively.

RNN based question answer generation and ranking for financial documents using financial NER

Organizations, governments and many entities deal with an expanse of voluminous financial documents and this necessitates a need for a financial expert system which, given a financial document, extracts finance-related questions and answers from it. This expert system helps us to adequately summarize the document in the form of a question-answer report. This paper introduces the novel idea of generating finance-related questions and answers from financial documents by introducing a custom Financial Named Entity Recognizer, which can identify financial entities in a document with an accuracy of 92%. We have introduced a method of generating finance-based questions using a sample document to obtain a set of generalized questions that we can feed to any similar financial document. We also record the expected answer type during the question generation phase, which helps to develop a robust mechanism to verify that we always generate the correct answers during the answer extraction stage.

Need for Closure: Measure Adaptation to Brazil and Relation with Moral Foundations and Prosociality

Need for closure (NFC) is the need people have for any type of answers on a given topic, compared to confusion and ambiguity. Despite being relevant to the study of concepts such as authoritarianism, prejudice, and hostile behaviors, its use has not been identified in the Brazilian context, and adaptation studies have not yet been carried out in the country. Therefore, this study sought to adapt and evaluate the psychometric properties of the brief NFC scale for Brazil, as well as to explore its relationship with the Moral Foundations Theory and Prosociality. The sample consisted of 427 people, aged 18 to 66 years (M = 26.6). Through Confirmatory Factor Analysis, a one-dimensional model was confirmed with adequate levels of adjustment and a good index of internal consistency (α = 0.81). The measure also presented convergent and divergent validity, in addition to the Binding Moral Foundations having emerged as significant correlates, but not the prosociality measures. Finally, it was concluded that the scale presented acceptable preliminary evidence regarding the psychometric parameters and can begin to be used to assess NFC in the Brazilian context.

Two Sides of the Same Coin or Two Different Currencies? Representations of Happiness and Unhappiness among Finnish Women

This paper presents results from a study exploring representations of “happiness” and “unhappiness.” Word associations with these concepts were produced by 16–18 and 29–34-year-old women from Finland, the country that the United Nation’s World Happiness Report has ranked the “happiest” in the world. Correspondence Analysis (CA) and Hierarchical Cluster Analysis show that participants in both age groups share three clusters of words associated with “happiness”: Tangible happiness, Affective happiness and Serene happiness. We noted more differences in the associations with “unhappiness,” for which the two groups share only two clusters: Loss and Everyday problems. A distinct third cluster, Affective unhappiness, emerged for the younger women, whereas older women’s associations are further differentiated into a more complex structure, including two more clusters: Dejection and Apprehension. Additionally, CA shows that in both age groups, self-reported happiness levels do not discriminate which words are associated with happiness and unhappiness. Finally, qualitative content analysis of a questionnaire item investigating how to reach complete happiness suggested that there are three recurring answer types: happiness can be improved through external changes, internal changes, or not at all because complete/permanent happiness does not exist. The study provides a methodological design which, unlike most happiness studies, allows participants the freedom to bring up the meaning of happiness and unhappiness. Thus, the study constitutes a contribution to a more nuanced understanding of happiness.

Does Training Improve Sanitary Inspection Answer Agreement between Inspectors? Quantitative Evidence from the Mukono District, Uganda

Sanitary inspections (SIs) are checklists of questions used for achieving/maintaining the safety of drinking-water supplies by identifying observable actual and potential sources and pathways of contamination. Despite the widespread use of SIs, the effects of training on SI response are understudied. Thirty-six spring supplies were inspected on two occasions, pre- and post-training, by an instructor from the research team and four local inspectors in the Mukono District of Uganda. SI score agreement between the instructor and each inspector was calculated using Lin’s concordance correlation coefficient. Average SI score agreement between the instructor and all inspectors increased post-training for the Yes/No answer type (0.262 to 0.490). For the risk level answer type (e.g., No, Low, Medium, High), average SI score agreement between the instructor and all inspectors increased post-training (0.301 to 0.380). Variability of SI scores between the four inspectors was calculated using coefficient of variation analysis. Average SI score variability between inspectors reduced post-training for both answer types, Yes/No (21.25 to 16.16) and risk level (24.12 to 19.62). Consistency of answer agreement between the four inspectors for each individual SI question was calculated using index of dispersion analysis. Average answer dispersion between inspectors reduced post-training for both answer types, Yes/No (0.41 to 0.27) and risk level (0.55 to 0.41). The findings indicate that training has a positive effect on improving answer agreement between inspectors. However, advanced training or tailoring of SI questions to the local context may be required where inconsistency of responses between inspectors persists, especially for the risk level answer type that requires increased use of inspector risk perception. Organisations should be aware of the potential inconsistency of results between inspectors so that this may be rectified with appropriate training and, where necessary, better SI design and customisation.

Cross-Cultural Differences in the Generation of Novel Ideas in Middle Childhood.

Innovation and creativity have recently been in the center of the debate on human cultural evolution. Yet, we know very little about childrens’ developing capacity to generate novel ideas, as a key component of innovation and creativity, in different cultural contexts. Here, we assessed 8‐ to 9-year-old children from an autonomous and a relational cultural context, namely Münster (urban Germany; n = 29) and Banten (rural Cameroon; n = 29). These cultural contexts vary largely in their ecology, social structure, and educational system, as well as the cultural models on children’s individual development and thinking. Therefore, they provide an optimal contrast to investigate cultural similarities and differences in development of creative capacities. We applied classical divergent thinking tasks, namely an alternative uses task and a pattern association task. In these tasks, children are asked to generate as many ideas as possible what an object could be used for or what a pattern could be. First, our study revealed a good internal consistency and inter-task correlations for the assessment of children’s fluency and the generation of unique ideas in both cultures. Second, and most critically, we found significantly higher levels of creative capacities in children from Münster in contrast to Banten. This was reflected in both a higher number of ideas (fluency) and a higher number of unique ideas (uniqueness). Third, looking at the type of answers that children gave in the alternative uses task, we found that children from Münster and Banten uttered a similar number of conventional ideas, but that children from Münster uttered more ideas to manipulate an object, invent novel things with an object, and involve an object in play or pretend play, or in a fantasy story. This demonstrates that early creative development is strongly influenced by the cultural context and substantiates the cultural nature of human cognitive development.

Assessment of Postoperative Cognitive Dysfunction: Results From a Survey of Turkish Anaesthesiologists.

ObjectivePostoperative cognitive dysfunction (POCD) is one of the most common complications in elderly surgical patients. We conducted a survey study to evaluate the perspectives of Turkish anaesthesiologists on postoperative cognitive disorders.MethodsWe conducted a prospective online survey with questions and answers were recorded either in a Likert scale from 1 to 5 (completely disagree to completely agree) or as yes/no/do not know types of answers. Potential participants were contacted through an e-mail that included a brief introductory note, instructions, a link to the survey and the authors’ contact information.ResultsWe analysed 129 surveys (9.9% of total potential respondents). The majority believed that the risk of cognitive side effects should be considered when choosing the type of anaesthesia (87.6%) and that preoperative cognitive function should be routinely assessed (74.4%). When caring for an agitated and confused patient postoperatively, 56.6%, protocols to screen and manage postoperative cognitive disorders were rarely used. Nearly all respondents believe that postoperative delirium and POCD are neglected areas in anaesthesiology.ConclusionOverall, participants perceive postoperative cognitive disorders as important adverse outcomes following surgery and anaesthesia. They are aware of the main risk factors for their development but may lack information on the prevention and management postoperatively.

Best Practices of Convolutional Neural Networks for Question Classification

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.

Medico-political metaphors of counter-terrorism: The case of Turkey

ABSTRACT Medico-political metaphors can be defined as the organic imagining of a society (re)creating a normative distinction between identity and difference and mobilising specific types of political answers in which threats are constructed through organic language. Accordingly, society is made to resemble a body, thus creating a sense of unity, integrity and finitude, while terrorism is made to resemble a “pathology” that “infects”, weakens and ultimately destroys the healthy social body. In this narrative, “terrorists” are rendered as abnormal and external, and thus terrorism is depoliticised. It is fictionalised as a “technical” issue necessitating expert intervention, in a manner resembling the doctor-patient relationship. To date, there has been little research on the interaction between this organic understanding of society and the Turkish experience of counter-terrorism practices. Therefore, taking as its context the Syrian civil war, this article aims to analyse how medico-political metaphors in the counter-terrorism discourse of the Turkish government function as boundary-producing practices. The article critically assesses how medico-political metaphors in terrorism discourse (re)constitute a power relationship through abnormalisation, externalisation and depoliticisation, and thus contribute to Critical Terrorism Studies by highlighting how policy makers use medico-political metaphors to constitute a reality about terrorism in order to mobilise certain political responses.

Question Classification and Answer Extraction for Developing a Cooking QA System

In an automated Question Answering (QA) system, Question Classification (QC) is an essential module. The aim of QC is to identify the type of questions and classify them based on the expected answer type. Although the machine-learning approach overcomes the limitation of rules as is the case with the conventional rule-based approach but is restricted to the predefined class of questions. The existing approaches are too specific for the users. To address this challenge, we have developed a cooking QA system in which a recipe question is contextually classified into a particular category using deep learning techniques. The question class is then used to extract the requisite details from the recipe obtained via the rule-based approach to provide a precise answer. The main contribution of this paper is the description of the QC module of the cooking QA system. The obtained intermediate classification accuracy over the unseen data is 90% and the human evaluation accuracy of the final system output is 39.33%.

Clinical Screening Interview Using a Social Robot for Geriatric Care

Social robots are coming to our homes and have already been used to help humans in a number of ways in geriatric care. This article aims to develop a framework that enables social robots to conduct regular clinical screening interviews in geriatric care, such as cognitive evaluation, falls’ risk evaluation, and pain rating. We develop a social robot with essential features to enable clinical screening interviews, including a conversational interface, face tracking, an interaction handler, attention management, robot skills, and cloud service management. Besides, a general clinical screening interview management (GCSIM) model is proposed and implemented. The GCSIM enables social robots to handle various types of clinical questions and answers, evaluate and score responses, engage interviewees during conversations, and generate reports on their well-being. These reports can be used to evaluate the progression of cognitive impairment, risk of falls, pain level, and so on by caregivers or physicians. Such a clinical screening capability allows for early detection and treatment planning in geriatric care. The framework was developed and implemented on our 3-D-printed social robot. It was tested on 30 older adults with different ages, achieved satisfying results, and received their high confidence and trust in the use of this robot for human well-being assessment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the goal of using a social robot to perform geriatric well-being assessment through clinical screening interviews. In order to conduct clinical screening interviews, the social robot needs the following essential features: having a verbal conversational interface, adapting to different types of clinical screening interviews, scoring and evaluating answers, having nondirective listening responses, and enabling directive listening responses. The proposed general clinical screening interview management (GCSIM) model demonstrates these capabilities on the social robot. The robot can give structured clinical screening interviews with different question–answer sheets. This will help advance assistive technologies for use by geriatric physicians, nurses, and social service professionals to keep older adults healthy, safe, and independent at home. Robots will become more and more essential in working alongside geriatric practitioners to help monitor older adults at home and to provide early detection and warning of cognitive/mental health problems, falls’ risk, and so on. This early detection property can improve quality-of-care and help older adults remain living at home.

Questions Analysis in Mathematics Textbook from Competency-Based Curriculum up to Curriculum 2013

Mathematics textbooks have a significant role in mathematics teaching, and learning activities in schools and the questions contained in textbooks are usually used by students to practice independently. The purpose of this study is to analyze the types of problems in mathematics textbooks used from 2000 to 2017 in Indonesia, especially in the material of one-variable linear equations. The method used in this study is a six-dimensional analysis method, which consists of mathematical activities, the level of difficulty of the questions, the types of answers expected, contextual situations, the types of responses and the types of mathematical questions. Data collection techniques are done by analyzing and describing the types of questions in mathematics textbooks used from 2000 to 2017. The results of this study are the problems in mathematics textbooks for linear variable equations of one variable that do not have diverse types, the types of questions in textbooks are still many in the form of questions that calculate or use a variety of arithmetic operations, apply directly the basic knowledge or skills and without context in everyday life. Also, the existing questions are questions with closed answers, namely questions that only require answers without a reason and questions with a single procedure

An Analysis on ‘Magic Cross’ Problem Solving Process of Elementary Mathematics Gifted Students

The purpose of this study is to analyze the characteristics and types of correct answers in the magic cross problem solving process of elementary mathematics gifted students, and to examine their ability to understand problems in connection with mathematical justification. For this, we first looked at the characteristics and types of teacher question required in mathematical exploration, and the types and levels of mathematical justification. The subjects of the study were 20 students from the elementary mathematics class at P University s Institute of Science and Gifted Education, and they were interviewed individually and semi-structured. First, the basic questions were set up to present the problems by dividing them into four stages, and the changes in the types of correct answers suggested by students in each order were analyzed at each stage, and the characteristics of elementary mathematics gifted students found here were analyzed. Next, we looked at the two principles that make up the core concept of the magic cross problem: how the even-odd relationship and the idea of balance (symmetry) are explained through mathematical justification. As a result of the study, the correct answer to the magic cross problem presented by elementary mathematics gifted students tended to be determined according to the order of location, and the widespread thinking such as changing conditions did not appear easily. It was also possible to observe each level of mathematical justification, and it was not easy to interpret the magic cross problem from a perspective of a balance(symmetry). Based on these results, we expect that various discussions related to the problem solving process of elementary mathematics gifted students can be conducted.

Revisiting Preference Organization in Context: A Qualitative and Quantitative Examination of Responses to Information Seeking

ABSTRACTQuantitative studies applying conversation analysis to the study of the timing of answers to sequence-initiating actions expose anomalies in terms of what is known about preference organization. After briefly describing preference organization, anomalies in answer-timing research, and one explanation for such anomalies, this article presents one qualitative and one quantitative study of responses to one thickly contextualized action: positively formatted polar interrogatives implementing information seeking with a relatively ‘unknowing’ stance. Data include 249 questions gathered from videotapes of unstructured conversations. Qualitative results suggest that, rather than two basic answer types (i.e., affirmation/disaffirmation), there may be three: unconditional affirmation, unconditional disaffirmation, and conditional. Quantitative analyses of time to answer, eyeball shifting, and pre-beginning behavior suggest that unconditional disaffirmation may not be dispreferred relative to unconditional affirmation. Instead, conditional answers may be dispreferred. Results begin to reconcile anomalies and expand our current understanding of preference organization. Data are in American English.