Categories Of Expertise Research Articles

A two-stage adaptive consensus reaching process with improved automatic strategy for multi-attribute large group emergency decision-making

The multi-attribute large group emergency decision-making (MALGEDM) problem has gained increasing attention and becomes an important topic in the field of decision-making. The existing consensus models, however, rarely consider the effective balance between experts’ willingness and time consumption. To address this problem, we design a two-stage adaptive consensus reaching process that combines two models of the minimum adjustment amount and the minimum iteration number in the MALGEDM context. The process can achieve consensus with an improved automatic strategy that considers experts’ willingness. Firstly, the self-organizing map algorithm is employed to classify large group heterogeneous experts into multiple subgroups for the dual decision of clustering and expert categories. Secondly, we classify different degrees of consensus into four scenarios (low, medium, medium-high, and high). On this basis, we have constructed a two-stage adaptive consensus model that incorporates the novel possibility degree. The minimum adjustment amount of the adaptive feedback process can be obtained by the optimization model in the first stage, which is used as a constraint for considering the experts’ willingness in the second stage. Finally, the practicality and timeliness of the proposed model is verified through an illustrative example of emergency decision-making. Moreover, comparative analyses are conducted to highlight the superiority of the proposed model.

Modeling and significance assessment of road construction participant and user benefits using expert evaluation methods

Human activities are related to obtain economic, technical, social and environmental benefits. The road construction process participants and road users have direct or indirect benefits from elaboration of new infrastructure. The number of the benefits received by individual entities is publicly discussed by politicians, lobbyists, experts and other decision-makers without having quantitative estimates of benefits, i.e., often relying on intuition or considering theoretical reasoning. The paper suggests a system of 19 benefit entities (criteria) assigned the ranks given by experts. The study involved three categories of experts, including 35 road engineers, 36 transport engineers and 61 road users. The values of the concordance coefficients obtained as a result of the conducted research were found significantly higher than critical values and showed that the opinions of the experts in each category were consistent (not contradictory). This made it possible to consider the average of the opinions of the expert group as a reliable result of solving the problem. Rank averages were replaced by the normalized weights of criteria using Average Rank Transformation into Weight-Linear (ARTIW-L) and – Nonlinear (ARTIW-N) methods. The global averages of criterion weights were used employing the Inverse Hierarchy for Assessment Main Criteria Importance (IHAMCI) method thus calculating the normalized weights of the road-related classified entities (three main criteria). The findings prove that road users benefit the most (weight 0.3485), the road construction contractor (weight 0.3325) is in the next position and the road owner (investor) takes the weight equal to 0.3190. The generated research data can be used for justifying the rationality of road investment.

The impact of visual working memory constraints on object recognition

ABSTRACT We explored the impact of visual working memory (VWM) constraints on the processing of complex objects, with a VWM task where participants (N = 75) adjusted the orientation of a bar to match a previously viewed one. Additionally, they performed a delayed match-to-sample task involving faces, houses, and pseudowords, where individual features or feature configurations were manipulated. Results showed a robust link between VWM precision for simple stimuli and memory for configural information in houses, beyond faces and pseudowords. Expert categories (faces, words) reduce VWM demands, while non-expert categories (houses) impose greater demands, especially for configural processing. Featural processing of non-expert categories places less demand on VWM. Extensive experience with an object category allows creating integrated chunks, facilitating rapid recognition and processing. Overall, configural processing of non-expert categories may place more demands on visual working memory than both featural processing of such categories and featural and configural processing of expert categories.

Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware.

Typology of solutions addressing diagnostic disparities: gaps and opportunities.

Diagnostic disparities are preventable differences in diagnostic errors or opportunities to achieve diagnostic excellence. There is a need to summarize solutions with explicit considerations for addressing diagnostic disparities. We aimed to describe potential solutions to diagnostic disparities, organize them into an action-oriented typology with illustrative examples, and characterize these solutions to identify gaps for their further development. During four human-centered design workshops composed of diverse expertise, participants ideated and clarified potential solutions to diagnostic disparities and were supported by environmental literature scan inputs. Nineteen individual semi-structured interviews with workshop participants validated identified solution examples and solution type characterizations, refining the typology. Our typology organizes 21 various types of potential diagnostic disparities solutions into four primary expertise categories needed for implementation: healthcare systems' internal expertise, educator-, multidisciplinary patient safety researcher-, and health IT-expertise. We provide descriptions of potential solution types ideated as focused on disparities and compare those to existing examples. Six types were characterized as having diagnostic-disparity-focused examples, five as having diagnostic-focused examples, and 10 as only having general healthcare examples. Only three solution types had widespread implementation. Twelve had implementation on limited scope, and six were mostly hypothetical. We describe gaps that inform the progress needed for each of the suggested solution types to specifically address diagnostic disparities and be suitable for the implementation in routine practice. Numerous opportunities exist to tailor existing solutions and promote their implementation. Likely enablers include new perspectives, more evidence, multidisciplinary collaborations, system redesign, meaningful patient engagement, and action-oriented coalitions.

Historical and biographical essay. Memories of life of Dovlet Pozadovich Achegu, a surgeon of a superior expert category (1919–1963)

The relevance of the research is due to the growing interest of researchers and readers in the experience of the Soviet period of history, through life paths, biographies, and the results of the activities of ordinary people. The problem of the research is to analyze sociocultural processes through sources of personal origin (documents, photos, memories, etc.). The purpose of the research is using the biography of Achegu D.P. to understand social, cultural, professional and psychological aspects of the Soviet period. The historical and biographical method became the basis of the research methodology for examining the life and work of a superior expert category Dovlet Pozadovich Achegu (1919-1963), the first head of the surgical department of the Adyghe Regional Hospital. The methods of scientific analysis and generalization were also used. The research results. The description and analysis of the life story of Dovlet Pozadovich allows us to generalize and understand social, cultural and psychological phenomena of a certain historical period. The fate of Achegu included the main milestones of the Soviet period. He experienced all the joys and hardships of difficult military-political and ideological processes. All significant historical events of those years run like a red thread through his life. Undoubtedly, Dovlet was not unique; he was formed as a highly qualified specialist and an interesting person under the influence of the system of Soviet culture, education, norms of behavior and attitude towards the profession. Key findings. The younger generation of doctors and medical students need to know such stories and biographies as an example of service to the profession and duty of a doctor. The life and work of Achegu D.P. can serve as an example for the formation of personal meanings and value guidelines for modern youth. Such “microhistories” related to the humanistic direction of the study of history, analyze the cultural, professional, and family values of people. They describe small elements that are important for knowledge and understanding of a certain era, which help answer big questions and build the continuity of generations.

ECMEE: Expert Constrained Multi-Expert Ensembles with Category Entropy Minimization for Long-tailed Visual Recognition

When the training dataset follows a long-tail distribution, models tend to prioritize the majority of the data, thus resulting in lower predictive accuracy for the minority data. Among existing methods, integrating multiple experts with different logit distributions has yielded promising results. However, the current state-of-the-art (SOTA) ensemble method, i.e., Self-supervised Aggregation of Diverse Experts, trains three expert models separately to favor the head, middle, and tail data, respectively, without imposing mutual constraints. Failure to constrain the magnitude of logits among experts may result in higher category entropy, making it difficult to achieve an optimal ensemble solution. To address this issue, we propose the Expert Constrained Multi-Expert Ensembles with Category Entropy Minimization method, which consists of two new strategies: (1) Confidence Enhancement Loss to constrain the expert models based on maximizing target and non-target logit margins, thereby minimizing category entropy; (2) Shot-aware Weights associated with expert models to accommodate the shot-headed characteristic of the experts. Experiments demonstrate that our method effectively reduces expert category entropy, improves integration effectiveness, and achieves SOTA results on three datasets in diverse test distributions.

Implementation of Vocational College Lecturers Teaching Practice in Construction Technology Program

The research was conducted to identify practical teaching practices among lecturers of Construction Technology in Vocational College Malaysia. This research uses quantitative methods (questionnaires) and is supported by interview methods and observations for data collection and subsequently determines the answers to the identified research problems. There are three teaching practice components, namely pre-teaching practice, practice during teaching, and post-practice practice. The elements contained in the pre-teaching component of the lesson are the preparation of lesson plans, the provision of teaching aids and the preparation of lesson content. The current practice element of teaching is the teaching method and the element of post-practice practice is training and evaluation. The respondents involved were 46 Construction Technology lecturers at six Vocational College. The data obtained through pilot studies have been tested and demonstrated the reliability of Alpha Cronbach which is 0.819 high. An overview of the differences in pre-teaching practice, during teaching and post-lessons according to the experience and expertise category uses the T test with a significant level of 0.05. In overall, the findings show that all three components of pre-teaching practice, during teaching and post-teaching are at a high level. The findings also showed that there were no significant differences in pre-teaching practices, teaching and post-lectures based on the experience and expertise categories of lecturers.

Digital teaching competence of higher education professors: self-perception study in an Ecuadorian university

Background Teaching professionalization aimed at the digital transformation of educational scenarios and training processes for students in contemporary higher education requires the mastery of digital competence by the teaching staff. The objectives of the study were to analyze the self-perceived level of digital teaching competence (DTC) of the faculty of the Technical University of Manabí (UTM), Ecuador, and to establish the relationship between age, sex, and academic profile variables with digital teaching competence. Methods A quantitative methodological approach was adopted to develop a descriptive-correlational field study with a non-experimental design. The participants were 277 professors, selected through non-probabilistic and voluntary sampling, who completed the DigCompEdu Check-In questionnaire sent by e-mail. Results The data showed that the integrator and expert categories obtained high levels in all competencies. There is also a difference in the pedagogy variable by the interaction of sex and academic profile. Conclusions It is concluded that the competences self-perceived by the professors are within the intermediate categories such as integrator and expert. Likewise, the age, sex, and academic profile variables differ in the digital pedagogy level, which produces an inconsistent relationship, with the exception of the variable evaluates and provides feedback, where it was significant.

Reaching Consensus on a core outcome set for patient safety in perioperative care: SAFEST Project

Abstract Background In the European Union, 8 to 12% of patients admitted to hospitals experience adverse events, imposing a considerable burden on the health systems. The lack of standardised indicators to comprehensively analyse perioperative patient safety limits the evaluation of interventions to tackle this public health problem. This study aims to prioritise the most relevant and feasible core indicators to assess patient safety in perioperative care to develop a Core Outcome Set. Methods A two-round eDelphi was conducted. Sixty-seven experts were invited to participate and asked to independently rate indicators in importance and feasibility on a 9-point Likert scale. The indicators were grouped by the perioperative period and subgrouped by Donabedian's quality of care conceptual model Structure - Process - Outcome. Consensus to include was defined as ≥75% of experts scoring an indicator as 7 to 9, and 15% or less scoring 1 to 3. Results Forty-eight experts completed the Round 1 eDelphi survey, and 8 started the survey but did not complete it. Round 1 survey was answered by a diverse range of targeted categories of experts, including healthcare professionals (76.8%) and patients (10.7%). From the 247 indicators included in Round 1, 15.8% reached a consensus to include regarding both importance and feasibility. Across the perioperative period and Donabedian's quality of care conceptual model subgroups, a higher consensus to include was achieved regarding importance than feasibility. The results of Round 2 have yet to be available, as the study is ongoing. Conclusions A consensus of a broad and heterogeneous panel of experts on core indicators for reporting perioperative patient safety was developed. While the experts highly punctuated the indicators' importance, the indicators' feasibility aspect needs to be further explored to implement effective and efficient monitoring and evaluation of surgical-related patient safety systems in clinical practice. Key messages • Prioritising indicators important for patients and the clinical practice is crucial to monitor and evaluate patient safety. • This consensus facilitates benchmarking across EU countries.

USABILITY GUIDELINES FOR DESIGNING M-HEALTH CLINICAL CODING AND GROUPING APPLICATION

The rapid progression of technology has led to web-based e-Health systems migrating to mobile device systems known as m-Health apps. Past researchers gave guidelines on the general e-Health, but the usability guidelines for m-Health apps were lacking. General guidelines are challenging to refer to when developing mobile apps with specific domains and complex functionalities. However, the absence of specific m-Health guidelines has made it challenging to design a quality and suitable app if it follows the general guidelines. Hence, specific usability guidelines for specific domains and complex functionalities apps with quality and sound research were developed to ensure the users get the best usable app. This study aims to produce a m-Health usability guideline for the clinical-based app of clinical coding and grouping for healthcare users. A total of ten participants participated in this study, which were fell into three categories; namely four industrial practitioners in the field of UI/UX, four experts with more than 20 years of experience in UI/UX and usability expertise, and two end-users, which consist of doctors and lecturers in public health and clinical coding. The data were collected through semi-structured interviews, allowing the participants to share feedbacks and comments to improve the usability guidelines and prototype for clinical coding and grouping of the m-Health app. The four participants from the expert categories had undergone usability evaluations, namely heuristic evaluations, while the two end-users’ groups had engaged in cognitive walkthroughs to improve the usability guidelines and the app’s prototype. The transcript of the interviews and usability evaluations were transcribed for thematic analysis using Nvivo software. The emerging themes were categorised into legibility and readability consisting of sub-themes font, icon, label and logo, buttons, and visual design that includes the sub-theme, theme and style, illustration and animation and colour. The emerging themes were a few vital usability components that were taken into consideration during the development of the m-Health app. The study highlights some of the usability components that were the foundation of the usability guidelines and the prototypes developed may be used by researchers and developers to develop the clinical-based m-Health app in the future. The usability guidelines and the clinical coding and grouping m-Health app prototype can be further tested towards other m-Health apps with similar features and between more experts and other wider audiences, such as older age groups and colour-blind people to produce a more robust and holistic app that is universally suited for all people. Moreover, the expansion from a medium-fidelity prototype to a high-fidelity app was also encouraged for future researchers and developers.

Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment.

Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons' feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills.

Mapping the distribution of health equity research and practice across a university: a network analysis.

Health equity research spans various disciplines, crossing formal organizational and departmental barriers and forming invisible communities. This study aimed to map the nomination network of scholars at the University of Rochester Medical Center who were active in racial and ethnic health equity research, education, and social/administrative activities, to identify the predictors of peer recognition. We conducted a snowball survey of faculty members with experience and/or interest in racial and ethnic health equity, nominating peers with relevant expertise. Data from a total of 121 individuals (64% doing research on extent and outcomes of racial/ethnic disparities and racism, 48% research on interventions, 55% education, and 50% social/administrative activities) were gathered in six rounds of survey. The overlap between expertise categories was small with coincidence observed between education and social/administrative activities (kappa: 0.27; p < 0.001). Respondents were more likely to nominate someone if both were involved in research (OR: 3.1), if both were involved in education (OR: 1.7), and if both were affiliated with the same department (OR: 3.7). Being involved in health equity research significantly predicted the centrality of an individual in the nomination network, and the most central actors were involved in multiple expertise categories. Compared with equity researchers, those involved in racial equity social/administrative activities were less likely to be recognized by peers as equity experts.

Human Factors Analysis and Classification System - AHP Drone Model Assessment

Drone systems have become increasingly applied to various commercial, technical, agricultural, and military sectors. With the emerging significant effects on the frequency of drone accidents in many situations and areas, it has become increasingly necessary to form unmanned aircraft systems and usage limitations. The first step to do that is by highlighting the sources of accidents in the current process. The Human Factors Analysis and Classification System (HFACS) is the main technique for accident risk assessments in drone systems which clusters the accident key factors causes into 4 basic domains. In this study, 4 categories of drone experts participated in the study. The research utilized a new approach that integrates an analytical hierarchical decision-making model with the HFACS for drone accident causation investigation. The results of the study showed discrepancies among the different groups of drone operators and identified organizational factors and unsafe actions as being key issues in the evaluation.

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest. In this paper, according to the pest picture data obtained through the pest detection lamp in the complex natural background and the marking categories of agricultural experts, the pest data set pest rotation detection (PRD21) in different natural environments is constructed. A comparative study of image recognition is carried out through different target detection algorithms. The final experiment proves that the best algorithm for rotation detection improves mean Average Precision by 18.5% compared to the best algorithm for horizontal detection, reaching 78.5%. Regarding Recall, the best rotation detection algorithm runs 94.7%, which is 7.4% higher than horizontal detection. In terms of detection speed, the rotation detection time of a picture is only 0.163s, and the model size is 66.54MB, which can be embedded in mobile devices for fast detection. This experiment proves that rotation detection has a good effect on pests' detection and recognition rate, which can bring new application value and ideas, provide new methods for plant protection, and improve grain yield.

Corporate criminal liability in Islamic banks in Malaysia

PurposeThis paper aims to highlight provisions that may attract corporate criminal liability (CCL) in legislation and regulations enacted in Malaysia. Further, this paper identifies gaps or obstacles in the implementation of CCL in Islamic banks (IBs) in Malaysia.Design/methodology/approachThis research adopts the qualitative methodology. More specifically, it uses normative legal research by focusing on primary and secondary data obtained from legislation, regulations, decided case laws, guidelines, law textbooks and bank annual reports in relation to CCL provisions. It also conducts semi-structured interviews with different categories of experts, including legal practitioners (lawyers), regulators from Bank Negara Malaysia (BNM) and Securities Commission Malaysia, officers of the Attorney General's Chambers and officers from legal departments in IBs.FindingsThe results conclude that IBs should implement the law on CCL because they are considered corporations. It is also found that not all IBs complied with CCL provisions brought corporate offenders before the court.Research limitations/implicationsThis research is restricted by its specialisation in CCL in IBs in Malaysia.Practical implicationsThe CCL provision has to be implemented effectively by IBs to achieve the benefit. However, not all IBs implement CCL provision properly. The understanding created by the interview data illuminates the challenges in implementing CCL provisions. Thus, this paper seeks to change the approach in the implementation of CCL provisions by IBs in Malaysia.Originality/valueThe paper touches upon a new area, notably CCL in IBs, which is not well researched in past literature. Although there is a vast research on CCL, corporate crime in IBs in Malaysia is still an unexplored area. This study gives light on the implementation of CCL provisions in IBs.

The deviation-from-familiarity effect: Expertise increases uncanniness of deviating exemplars.

Humanlike entities deviating from the norm of human appearance are perceived as strange or uncanny. Explanations for the eeriness of deviating humanlike entities include ideas specific to human or animal stimuli like mate selection, avoidance of threat or disease, or dehumanization; however, deviation from highly familiar categories may provide a better explanation. Here it is tested whether experts and novices in a novel (greeble) category show different patterns of abnormality, attractiveness, and uncanniness responses to distorted and averaged greebles. Greeble-trained participants assessed the abnormality, attractiveness, uncanniness of normal, averaged, and distorted greebles and their responses were compared to participants who had not previously seen greebles. The data show that distorted greebles were more uncanny than normal greebles only in the training condition, and distorted greebles were more uncanny in the training compared to the control condition. In addition, averaged greebles were not more attractive than normal greebles regardless of condition. The results suggest uncanniness is elicited by deviations from stimulus categories of expertise rather than being a purely biological human- or animal-specific response.

الحماية القانونية الإجرائية للمساهمين في تحقيق العدالة الجنائية

Legal protection for those contributors to achieving criminal justice, including the category of witnesses, experts, detectives and victims, is one of the important rights that should be provided to them due to the importance of the role they play in the field of detecting and proving crimes against their perpetrators As it is possible that these individuals will be exposed to some influences and pressures that affect their performance, which leads them to refrain from telling the truth or changing it, hence the need to provide protection for them and their family members. The topic of the research deals with the procedural legal protection for these individuals by highlighting the Law on the Protection of Witnesses, Experts, Detectives and Victims No. (58) of 2017. The adoption of this protection represents a guarantee of human rights in the first place and also represents a strengthening of the criminal justice system, and that the law on the protection of witnesses, experts, informants and the victims of the above came in implementation of the global and regional agreements signed by the Republic of Iraq, which urges the states parties to include protection measures within their laws.

Consensus on the Objectives of an Educational Intervention for Patients with Oropharyngeal Dysphagia and Their Informal Caregivers: A Delphi Study.

PurposeIn the absence of literature allowing for an evidence-based approach to therapeutic patient education (TPE) in Oropharyngeal Dysphagia (OD), this study aims to reach a consensus of experts on the content of a competency framework of an educational program for OD patients and their informal caregivers (ICGs).MethodsWe used the Delphi consensus-building method. Four categories of experts were recruited: 12 patients, 17 ICGs, 46 healthcare professionals (HCP) (experienced in OD, not necessarily certified in TPE), and 19 experts in TPE (trained individuals to set up and run TPE programs not necessarily HCPs). The content of the questionnaire of the first round (R) was established according to the result of a scoping review and the opinion of an expert committee. We carried out three rounds. In R1 and R2, we collected the opinions on the relevance (7-point Likert-type scale) and on comprehensiveness (YES/No question and asking participants to propose additional content). Participants were also invited to leave comments on each objective. In R3, we asked the participants to give their opinion about the relevance of the objectives again and asked them to rank the themes from highest to lowest priority.ResultsObjectives were considered relevant for all participants if they reached consensus when the interquartile (IQR) ≤ 1, and if the median indicated agreement (Mdn ≥ 6) (6= appropriate, 7 = totally appropriate). Following three rounds, the final content of the educational program is composed of 23 educational objectives organized in 13 themes with an agreement about relevance amongst all participants (Mdn ≥ 6; IQR ≤ 1). The comprehensiveness criterion received also a consensus (IQR ≤ 1). The participants ranked the theme “normal swallowing vs difficulty swallowing” as the highest priority.ConclusionThis Delphi study resulted in a consensus, on the content of a competency framework of an educational program for OD patients and their ICGs. Further steps are needed to construct learning activities based on these objectives before testing their feasibility and efficacy.

What Geometry for teaching? Appropriations of Experts on pedagogical studies mission in Europe, late 19th century

Background: The training of teachers who teach mathematics is surrounded by knowledge that changes over time. These changes are for several moments mobilized by specialists, in this context, the official documents are possible historical sources for reading and analyzing changes. Objective: To analyze the systematizations of a “Geometry for teaching” put into circulation for the training of teachers, at the end of the 19th century, in Rio de Janeiro, at this time the Republic, by professors Amélia Fernandes da Costa, Luiz Augusto dos Reis and Manoel José Pereira Frazão, appointed by the republican power for the mission of pedagogical studies in Europe. Data collection and analysis: Data were collected from the travel records of commissioners, and also from publications in educational, analysed from the perspective of Cultural History. Design: Use for the study the official reports found by the documents and Revista Pedagogica, printed for teachers, these documents, acquired or accumulated by the specialists in question, were solved and problematized following the following investigation steps: recompilation of teaching experiences; comparative analysis of teachers' knowledge; analysis of systematization and use of knowledge as knowledge (Valente, 2018; 2020). Setting and Participants: It is a historiographical study in documents. Results: An analysis of the sources revealed that the performance of the documents, as well as their active participation in political issues and primary education, the recognition of peers, a call from the State for the pedagogical trip and the consequent responsibility to produce an official report, placed them in the category of Experts, thus allowing us to qualify them and put into circulation a proposal for a new “Geometry for teaching”, characteristic of intuitive times, and against the previous proposal. Conclusions: The “Geometry for teaching” put into circulation, should be mobilized by teachers in close relationship with Design and Handicrafts and was not structured as a subject in primary school.