Yoxas/statistical_literacy huggingface dataset:
With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants' cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.
With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry. This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.
With the advancing digitization in medicine, digital medical data is playing an increasingly important role in health care and research, which is why data literacy must already be taught in medical education. To this end, a 28-hour online elective for medical students - following a constructivist approach - has been implemented. It teaches learners different aspects of data literacy for a critical collection and use of sensitive medical data. The assessment of the learners' reflections on the course topics shows, on the one hand, the importance of data literacy from learners' perspective and, on the other hand, the importance of taking an overarching and coherent view of medical data. In further curricular courses, such as medical ethics, and statistics, special themes are to be deepened in an application-oriented manner.
With the advent of the era of artificial intelligence, speech recognition engine technology has a profound impact on social production, life, education, and other fields. Voice interaction is the most basic and practical type of human-computer interaction. To build an intelligent and automatic physical education teaching mode, this paper combines human-computer interaction based on speech recognition technology with physical education teaching. Students input through voice signals, and the system receives signals, analyzes signals, recognizes signals, and feeds back information to students in multiple forms. For the system to process the external speech signal, this paper uses the Mel cepstral coefficient algorithm to extract the speech information. By comparing the speech recognition rate and antinoise rate of Hidden Markov Model, Probabilistic Statistics Neural Network, and Hybrid Model (Hidden Markov and Rate Statistical Neural Network combination), the speech recognition engine uses the hybrid model, and its speech recognition rate is 98.3%, and the average antinoise rate can reach 85%. By comparing the human-computer interaction physical education teaching method with the traditional teaching method, the human-computer interaction method is superior to the traditional teaching method in the acquisition of physical knowledge, the acquisition of physical skills, the satisfaction of physical education courses and the ability of active learning. It effectively solves the drawbacks of traditional physical education and rationally uses human-computer interaction technology. On the basis of not violating physical education, realize the diversification of physical education, improve the quality of teaching, improve students' individual development and students' autonomous learning ability. Therefore, the combination of human-computer interaction and physical education based on recognition engine technology is the trend of today's physical education development.
With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles. Due to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets-specifically, dataset I and BCI contests III-using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database. Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection. The simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.
With the appearance and aggravation of the aging society, cancer has become one of the major problems that threaten the life and health of Chinese residents seriously. To explore the cancer epidemiological characteristics among the elderly in China from 2005 to 2016, and to provide strategies for cancer prevention and treatment. Stratified analysis was conducted on the cancer mortality data of the elderly aged ≥60 years in China, which were selected from the Chinese Cancer Registry Annual Report. Joinpoint regression model was used to calculted average annual percentage change (AAPC) to estimate the time trends. Age-period-cohort (APC) model was used to explore the age, period and birth cohort effect on the risk of cancer death. Bayesian age-period-cohort (BAPC) model was used to predict trends in cancer mortality among elderly by gender and region to 2035. 2005-2016, cancer mortality in the elderly in China showed a decreasing trend (AAPC = -1.2%, P<0.001). Cancer mortality in rural areas was higher than that in urban areas, but the urban-rural difference gradually narrowed (t = 6.1, P<0.01). The APC model showed that cancer mortality increased with age. The relative risk (RR) for the period effect decreased. RR was lower for the later- born cohort than that for the earlier-born cohort in rural areas. Lung cancer mortality ranked first in both male and female, and showed an increasing trend among female in the 60-64, 80-84 and ≥85 age groups (AAPC60-64 = 1.0%, AAPC80-84 = 0.8%, AAPC≥85 = 2.0%, all P<0.05). By 2035, cancer mortality for the elderly was predicted to decline nationally, by sex and in rural areas, while rising in urban areas. Cancer mortality in the elderly in China showed a decreasing trend from 2005 to 2016, but it was still higher than the world average. Early cancer screening is important, especially in the elderly male and in rural areas.
With the construction and development of the "the Belt and Road", the international community has an increasing demand for high-quality technical and skilled talents who both understand Chinese and have an international perspective. Exploring a Chinese curriculum system suitable for the development of international students is conducive to promoting the international development of vocational education, improving the quality of international student training, providing talent support for Chinese and foreign enterprises, and promoting the "going out" of Chinese enterprises and international production capacity cooperation. The training of international students in higher vocational colleges starts late, has little experience, lacks theoretical innovation, and lacks the development characteristics of vocational education. The teaching quality evaluation of international Chinese course in higher vocational colleges is multiple-attributes group decision-making (MAGDM). Recently, the TODIM and EDAS technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the teaching quality evaluation of international Chinese course in higher vocational colleges. In this paper, the probabilistic hesitant fuzzy TODIM-EDAS (PHF-TODIM-EDAS) technique is built to manage the MAGDM under PHFSs. Finally, the numerical example for teaching quality evaluation of international Chinese course in higher vocational colleges is employed to show the PHF-TODIM-EDAS technique. The main contribution of this paper is outlined: (1) the TODIM technique based on EDAS technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight numbers under PHFSs. (3) the PHF-TODIM-EDAS technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for teaching quality evaluation of international Chinese course in higher vocational colleges and some comparative analysis is supplied to validate the proposed PHF-TODIM-EDAS technique.
With the continuous development of the times, music education in primary and secondary schools is constantly innovating in terms of teaching concepts, teaching methods, and implementation methods. The reform of basic music education has aroused the reflection of music teacher education in colleges and universities, and the talent training model of music teachers has become a hot issue that has been widely concerned in the new era. To explore the educational significance and influencing factors of entrepreneurship education in college music education and examine the mediating role of self-efficacy and gender adjustment in entrepreneurial education, based on the analysis of music education and entrepreneurial talent training in colleges and universities, a questionnaire survey, statistics, and data processing were adopted in this work. In this work, a path model was established based on the mediating effect and the moderating effect, and the equation model was tested. The results show that music education has a positive correlation with entrepreneurial self-efficacy and a negative correlation with entrepreneurial intention, which has not been proved to have a certain effect on entrepreneurial intention. Entrepreneurial self-efficacy has a positive correlation with entrepreneurial intention, and the mediating effect between music professional education and entrepreneurial intention has also been proved. Since there are gender differences and certain stereotypes that cannot be ignored in music education learning, gender adjustment also has a certain influence on entrepreneurial self-efficacy based on music professional education, thereby having a moderating effect on entrepreneurial intentions. Entrepreneurship education can improve the comprehensive competitiveness of students. As a result, entrepreneurship education can improve the innovation and entrepreneurship education model and the music education system in colleges and universities by combining with the relationship between entrepreneurial efficacy and intention, and it contributes to the overall cultivation of talent in colleges and universities.
With the continuous improvement in the strength of medium-thick plate materials, the hot straightening of plates at high temperatures is increasingly influencing the final defect characteristics of products. In the high-temperature hot straightening process, the temperature and straightening speed of the plate significantly influence its intrinsic material properties, which, in turn, affect the straightening characteristics of the plate. However, most current material models used in the straightening process do not consider the relationship between temperature and strain rate, which leads to an inaccurate characterization of the actual material structure. Additionally, the continuous reverse bending mechanics model for straightening does not account for the impact of different bending strain rates on the bending characteristics of the plate in the thickness direction. In this study, a numerical calculation method was employed to investigate the evolution process of stress and curvature in the roll-type hot straightening process of medium-thick plates. Experimental data and mathematical methods were utilized to develop a viscous plastic material model that accounted for temperature and strain rate. Furthermore, a cross-sectional continuous reverse bending model was established, taking into account the temperature and straightening speed, enabling a reasonable interpretation of the mechanical parameter behaviors of medium-thick plates during high-temperature straightening.
With the coronavirus disease 2019 (COVID-19) pandemic, rates of in-hospital antimicrobial use increased due to perceived bacterial and fungal coinfections along with COVID-19. We describe the incidence of these coinfections and antimicrobial use in patients hospitalized with COVID-19 to help guide effective antimicrobial use in this population. This study was conducted in 3 tertiary-care referral university teaching hospitals in New York City. This multicenter retrospective observational cohort study involved all patients admitted with COVID-19 from January 1, 2020, to February 1, 2021. Variables of interest were extracted from a de-identified data set of all COVID-19 infections across the health system. Population statistics are presented as median with interquartile range (IQR) or proportions with 95% confidence intervals (CIs) as indicated. Among 7,209 of patients admitted with COVID-19, 663 (9.2%) had a positive culture from the respiratory tract or blood sometime during their initial hospital admission. Positive respiratory cultures occurred found in 449 (6.2%) patients, and 20% were collected within 48 hours of admission. Blood culture positivity occurred in 334 patients (4.6%), with 33.5% identified within 48 hours of admission. A higher proportion of patients received antimicrobials in the first wave than in the later pandemic period (82.4% vs 52.0%). Antimicrobials were prescribed to 70.1% of inpatients, with a median of 6 antimicrobial days per patient. Infection-free survival decreased over the course of hospitalization. We detected a very low incidence of coinfection with COVID-19 at admission. A longer duration of hospitalization was associated with an increased risk of coinfection. Antimicrobial use far exceeded the true incidence and detection of coinfections in these patients.
With the deepening reform of ideological and political education, Medical Parasitology teaching needs to update the teaching concept, change the teaching ideas, as well as keep trying to combine ideological and political education with the curriculum content closely. In addition to teaching students' basic knowledge and practical skills, teachers are needed to cultivate their moral literacy and political awareness through course teaching, so as to provide the basis for students' subsequent adaptations to social environments and jobs. Currently, the study of ideological and political education in Medical Parasitology teaching is still in the exploratory stage. Therefore, colleges and universities need to carry out effective construction of ideological and political education in Medical Parasitology teaching, in order to achieve good teaching outcomes and provide insights into ideological and political education in teaching.
With the development of information technology, information has been an important resource in clinical medicine, particularly within the emergency department. Given its role in patient rescue, the emergency department demands a high level of information literacy from nurses to effectively collect, analyze, and apply information due to the urgency and complexity of emergency nursing work. Although prior studies have investigated the information literacy of nursing staff, little has been undertaken in examining the patterns of information literacy and their predictors among emergency department nurses. To clarify the subtypes of information literacy among nurses in the emergency department and explore the factors affecting profile membership. A cross-sectional study was conducted among a convenience sample of 2490 nurses in the emergency department from April to June 2023. The clinical nurses completed the online self-report questionnaires including the general demographic questionnaire, information literacy scale, self-efficacy scale and social support scale. Data analyses involved the latent profile analysis, variance analysis, Chi-square tests and multivariate logistic regression. Four latent profiles were identified: 'Low information literacy (Class 1)', 'Moderate information knowledge (Class 2)', 'High information knowledge and support (Class 3)' and 'High information literacy (Class 4)', accounting for 20.14%, 42.11%, 23.36% and 14.39%, respectively. Each profile displayed unique characteristics representative of different information literacy patterns. Age, years of work, place of residence, hospital grade, title, professional knowledge, using databases, reading medical literature, participating in information literacy training, self-efficacy, and social support significantly predicted information literacy profile membership. Information literacy exhibits different classification features among emergency department nurses, and over half of the nurses surveyed were at the lower or middle level. Identifying sociodemographic and internal-external predictors of profile membership can aid in developing targeted interventions tailored to the needs of emergency department nurses. Nursing managers should actively pay attention to nurses with low information literacy and provide support to improve their information literacy level. Insights from the current study of the latent profile analysis are beneficial to hospital managers in understanding the different types of emergency department nurses' information literacy. These insights serve as a reference for managers to enhance nurses' information literacy levels.
With the development of multimedia systems in wireless environments, the rising need for artificial intelligence is to design a system that can properly communicate with humans with a comprehensive understanding of various types of information in a human-like manner. Therefore, this paper addresses an audio-visual scene-aware dialog system that can communicate with users about audio-visual scenes. It is essential to understand not only visual and textual information but also audio information in a comprehensive way. Despite the substantial progress in multimodal representation learning with language and visual modalities, there are still two caveats: ineffective use of auditory information and the lack of interpretability of the deep learning systems' reasoning. To address these issues, we propose a novel audio-visual scene-aware dialog system that utilizes a set of explicit information from each modality as a form of natural language, which can be fused into a language model in a natural way. It leverages a transformer-based decoder to generate a coherent and correct response based on multimodal knowledge in a multitask learning setting. In addition, we also address the way of interpreting the model with a response-driven temporal moment localization method to verify how the system generates the response. The system itself provides the user with the evidence referred to in the system response process as a form of the timestamp of the scene. We show the superiority of the proposed model in all quantitative and qualitative measurements compared to the baseline. In particular, the proposed model achieved robust performance even in environments using all three modalities, including audio. We also conducted extensive experiments to investigate the proposed model. In addition, we obtained state-of-the-art performance in the system response reasoning task.
With the digital transformation of education, data and digital technologies are regarded as the driving forces for teaching innovation. Teachers' data literacy and digital teaching competence are becoming increasingly important for empowering students' digital capacity, ethically technology usage, and collaboration or communication skills in the classroom. Therefore, whether teachers' data literacy and digital teaching competence can empower students in the classroom needs to be explored. This study aims to reveal the relationship between teacher's information communication technology (ICT) attitude, ICT skills, data literacy, digital teaching competence and empowering students. The data were collected from an online self-assessment scale which included a total of 629 K-9 teachers who participated in this study. Using SPSS and AMOS, a model was built by using Structural Equation Models to explain and predict the relationships. The results indicated that: (a) ICT attitude had no significant impact on digital teaching competence, and ICT skills significantly predicted digital teaching competence, but neither ICT attitude nor skills had a significant direct impact on empowering students; (b) data literacy significantly predicted digital teaching competence and had a significant direct impact on empowering students; (c) digital teaching competence, as dominant mediator in ICT attitude, ICT skills and data literacy, strongly predicted empowering students. The findings provided valuable evidence for teachers, policymakers, administrators, teacher educators, and teachers to better reimagine the teachers' digital teaching competence. In the future, the teachers' digital teaching competence should become the top priority in teacher ICT training, which was the most direct influencing factor for empowering students.