Yoxas/statistical_literacy huggingface dataset:
This paper introduces this special issue aiming to deepen and extend research on mathematics teachers' work, from a resource perspective, by taking language and culture into account, and exploring two questions: How are teachers' interactions with resources interpreted and modeled across contexts? And, What challenges and insights emerge through recent efforts to engage these models in cross-cultural (and linguistic) research? The fields of resources, language and culture in mathematics education are each extensive, and we do not attempt to survey comprehensively across them. We have chosen instead to propose three approaches on resources in mathematics teachers' work that developed somewhat contemporaneously from three different countries with differing linguistic, curricular, and social contexts, corresponding to the work of the three guest editors. The models developed through these approaches are driven by the educational, and so cultural and material conditions of the time and the location of each author, and allow us to propose preliminary answers to our two guiding questions. We then move to pull the threads from these models together, and discuss the contributions to this Special Issue. This results in more robust and nuanced responses to our questions, and in identifying two themes that emerge from research that sit at the convergence of studies of teachers' interactions with resources, languages, and cultures: an invisibility-visibility dialectic and a local-global tension. Finally, this study leads us to consider a new region of mathematics education research.
This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.
This paper investigates the effects of the economic shock produced by the COVID-19 outbreak and diffusion on households'. Through a survey administered to Italian households, without loss of generality, we investigate changes in financial and economic decisions and the households' ability to cope with daily purchases, repay their debt obligations and face unexpected expenses. The paper also applies a statistical learning model through a synthetic indicator for the financial vulnerability of households, integrating the relevant information on the financial literacy and education of the surveyed individuals.
This paper is a search for the origins and nature of human consciousness. This portion, the first part of two, takes up a signal work of Jung and one of Freud, both published in 1911 and both deriving thinking as the first embodiment of higher mental functions. Thinking and consciousness (and subsequently spirit) are elaborated in terms of the nature and vicissitudes of libido. Jung's less well understood theory regarding a quantitative libido is particularly addressed as are his non-Cartesian ways of reasoning. Also addressed is Jung's turning away in the final analysis from a frankly metaphysical and transcendent position as compared to the pre-Socratic philosophers with whom focused thinking began in the western world. Differentiation is made between mechanistic and energetic field dynamics in how reality is viewed.
This paper is one in a series of rational analyses of the Dutch Simonshaven case, each using a different theoretical perspective. The theoretical perspectives discussed in the literature typically use arguments, scenarios, and probabilities, in various combinations. The theoretical perspective on evidential reasoning used in this paper has been designed to connect arguments, scenarios, and probabilities in a single formal modeling approach, in an attempt to investigate bridges between qualitative and quantitative analytic styles. The theoretical perspective uses the recently proposed logical formalism of case models, where cases represent possible combinations of evidence and events, ordered by an ordering relation. In the context of evidential reasoning, the ordering relation can be represented by a probability function. As an ordering relation is qualitative in nature, the theoretical perspective is in a formally precise sense simultaneously with and without probabilities.
This paper outlines the development, deployment and use, and testing of a tool for measuring and improving healthcare researcher embeddedness - i.e., being connected to and engaged with key leverage points and stakeholders in a health system. Despite the widely acknowledged importance of embeddedness for learning health systems and late-stage translational research, we were not aware of useful tools for addressing and improving embeddedness in scholar training programs. We developed the MN-LHS Embeddedness Tool covering connections to committees, working groups, leadership, and other points of contact across four domains: patients and caregivers; local practice (e.g., operations and workflows); local institutional research (e.g., research committees and agenda- or initiative-setting groups); and national (strategic connections within professional groups, conferences, etc.). We used qualitative patterns and narrative findings from 11 learning health system training program scholars to explore variation in scholar trajectories and the embeddedness tool's usefulness in scholar professional development. Tool characteristics showed moderate evidence of construct validity; secondarily, we found significant differences in embeddedness, as a score, from baseline through program completion. The tool has demonstrated simple, practical utility in making embeddedness an explicit (rather than hidden) part of applied and learning health system researcher training, alongside emerging evidence for validity.
This paper presents a computer vision algorithm for extraction of image-based metrics for suturing skill assessment and the corresponding results from an experimental study of resident and attending surgeons. A suturing simulator that adapts the radial suturing task from the Fundamentals of Vascular Surgery (FVS) skills assessment is used to collect data. The simulator includes a camera positioned under the suturing membrane, which records needle and thread movement during the suturing task. A computer vision algorithm processes the video data and extracts objective metrics inspired by expert surgeons' recommended best practice, to "follow the curvature of the needle." Experimental data from a study involving subjects with various levels of suturing expertise (attending surgeons and surgery residents) are presented. Analysis shows that attendings and residents had statistically different performance on 6 of 9 image-based metrics, including the four new metrics introduced in this paper: Needle Tip Path Length, Needle Swept Area, Needle Tip Area and Needle Sway Length. These image-based process metrics may be represented graphically in a manner conducive to training. The results demonstrate the potential of image-based metrics for assessment and training of suturing skill in open surgery.
This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.
This paper presents a normalized standard error-based statistical data binning method, termed "bin size index" (BSI), which yields an optimized, objective bin size for constructing a rational histogram to facilitate subsequent deconvolution of multimodal datasets from materials characterization and hence the determination of the underlying probability density functions. Totally ten datasets, including four normally-distributed synthetic ones, three normally-distributed ones on the elasticity of rocks obtained by statistical nanoindentation, and three lognormally-distributed ones on the particle size distributions of flocculated clay suspensions, were used to illustrate the BSI's concepts and algorithms. While results from the synthetic datasets prove the method's accuracy and effectiveness, analyses of other real datasets from materials characterization and measurement further demonstrate its rationale, performance, and applicability to practical problems. The BSI method also enables determination of the number of modes via the comparative evaluation of the errors returned from different trial bin sizes. The accuracy and performance of the BSI method are further compared with other widely used binning methods, and the former yields the highest BSI and smallest normalized standard errors. This new method particularly penalizes the overfitting that tends to yield too many pseudo-modes via normalizing the errors by the number of modes hidden in the datasets, and also eliminates the difficulty in specifying criteria for acceptable values of the fitting errors. The advantages and disadvantages of the new method are also discussed.
This paper presents a novel metaheuristic algorithm inspired by the actions of stadium spectators affecting behavior of players during a match which will be called stadium spectators optimizer (SSO) algorithm. The mathematical model of the SSO algorithm is presented and the performance and efficiency of the presented method is tested on some of the well-known mathematical test functions and also CEC-BC-2017 functions. The SSO algorithm is a parameter-free optimization method since it doesn't require any additional parameter setup at any point throughout the optimization process. It seems urgently necessary to design a novel metaheuristic algorithm that is parameter-free and capable of solving any optimization problem without taking into account extra parameters, as the majority of metaheuristic algorithms rely on the configuration of extra parameters to solve different problems efficiently. A positive point for the SSO algorithm can be seen in the results of the suggested technique, which indicate a partial improvement in performance. The results are compared with those of golf optimization algorithm (GOA), Tiki taka optimization algorithm (TTA), Harris Hawks optimization algorithm (HHO), the arithmetic optimization algorithm (AOA), CMA-ES and EBOwithCMAR algorithms. The statistical tests are carried out for the obtained results and the tests reveal the capability of the presented method in solving different optimization problems with different dimensions. SSO algorithm performs comparably and robustly with the state-of-the-art optimization techniques in 14 of the mathematical test functions. For CEC-BC-2017 functions with ten dimensions, EBOwithCMAR performs better than the proposed method. However, for most functions of CEC-BC-2017 with ten dimensions, the SSO algorithm ranks second after EBOwithCMAR, which is an advantage of the SSO since the proposed method performs better than the well-known CMA-ES optimization algorithm. The overall performance of the SSO algorithm in CEC-BC-2017 functions with 10 dimensions was acceptable, in dimension of 30, 50 and 100, the performance of the proposed method in some functions decreased.
This paper presents a study that examines the effect of a graduate course titled "Selected topics in interpersonal communication skills" on the students' interpersonal communication skills as part of their 21st century skills. Subject to the COVID-19 constraints, the course was taught online in the winter semester of 2021 to 46 students, who practiced in four groups. The students, who were studying at the Technion a science and technology research university for a research-oriented graduate degree in a science, technology, engineering, or mathematics (STEM) subject, attended synchronous bi-weekly 1-h lectures and 1-h practice sessions. The two research questions were as follows: (1) Did the interpersonal communication skills of the participants change following their participation in the course, and if so, how? (2) What was the effect of online learning on the students' interpersonal communication skills of (a) written and oral communication, (b) peer evaluation and feedback, and (c) self-reflection? Research tools included students' self-presentations, questionnaires, peer assessments, and reflections during the course. Analyzing the data quantitatively and qualitatively, we found that the graduate students improved their interpersonal communication skills and benefited from exposure to a variety of knowledge and research fields, contributing to a sense of pride in their university affiliation. The students suggested adding a practical component on providing constructive feedback and rendering the course mandatory to all the graduate students in the university. The contribution of this research is the creation and favorable assessment of an online course that develops interpersonal communication skills among graduate students from a variety of STEM faculties.
This paper presents an analysis of survey data to examine the association between supervised structured mentoring and students' intent to pursue a career in science. Data were collected from students in the 10 Building Infrastructure Leading to Diversity (BUILD) research training programs, developed through grants from the National Institutes of Health. Propensity score matching and multinomial logistic regression demonstrated that exposure to BUILD programs-meaning participation in undergraduate research, receipt of mentoring from a primary mentor, and/or participation as a funded scholar and/or associate of each BUILD site's training program-was associated with increased intent to pursue a science career. These findings have implications for STEM program evaluation and practice in higher education.
This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities.
This paper presents an international academic partnership in teaching and research with two case studies. The cases explore the integration of Strengths-Based Nursing and Healthcare (SBNH) and SBNH-Leadership (SBNH-L) in nursing science programs. SBNH values and foundations were integrated within an undergraduate-level community health course in Canada and SBNH-L was introduced into a graduate-level program in Brazil. Both cases comprise active learning activities promoting the uptake of the values and foundations of SBNH and the capacity to identify strengths and innate capacities. This paper synthesizes the issues and provides recommendations to enhance teaching-learning strategies to support SBNH adoption by students to support the humanization of healthcare. International partnerships in education and research and facilitating factors are discussed.
This paper presents findings from a pilot study focused on examining intergenerational violence in a three-generation sample, which included young children, in a rural area of South Africa. The aims of the pilot study were to investigate the feasibility of participant recruitment, consent, and interviewing; length and burden of the study questionnaires; appropriateness and acceptability of the measures used; and young children's (age 4-7) ability to comprehend the measures and participate meaningfully in interviews asking about violence. Data were collected for 4 months with three groups of participants, often within families (young adults, their children, and the young adults' former caregivers), using cognitive interviews, quantitative questionnaires, and qualitative in-depth interviews. All groups participated in arts-based methods and child interviews included visual and tactile aids. Pilot study findings demonstrated feasible recruitment within families for a three-generation study using comprehensive consent protocols and mandatory reporting information. Adults and young children were able to participate in the extensive interviews (2-3 h and 1 h, respectively) without significant burden. The employed measures were appropriate and acceptable to the setting, though minor revisions were made to improve comprehension of certain items. Young children were able to engage and participate meaningfully in the research, though they were not able to answer abstract reasoning items in cognitive interviews and children who were less developmentally advanced required more play- and arts-based accommodations to support their participation. Future research around sensitive topics, such as violence, appears feasible within families and including young children as participants even in resource-poor settings. The online version contains supplementary material available at 10.1007/s42448-023-00157-w.
This paper presents the findings of longitudinal research conducted in Ethiopia exploring the effects of COVID-19 school closures on children's holistic learning, including their socio-emotional and academic learning. It draws on data from over 2,000 pupils captured in 2019 and 2021 to compare primary school children's dropout and learning before and after school closures. The study adapts self-reporting scales used in similar contexts to measure grade 4-6 pupils' social skills and numeracy. Findings highlight the risk of widening inequality regarding educational access and outcomes, related to pupils' gender, age, wealth and location. They also highlight a decline in social skills following school closures and identify a positive and significant relationship between pupils' social skills and numeracy over time. In conclusion, we recommend a need for education systems to promote children's holistic learning, which is even more vital in the aftermath of the pandemic.
This paper presents the methodology of a PhD project on identity development in German dual track VET. The mixed methods approach comprises theory-driven longitudinal case studies using qualitative and quantitative data and a document analysis of curricula. The study was part of a larger design-based research project and aimed to evaluate a newly developed and implemented VET curriculum for the retail sector from the learners' point of view. The new curriculum contained a special competence dimension to foster vocational identity development and a major interest was to investigate the extent to which the curriculum succeeded to do so. The paper will start with a summary and assessment of limitations and advantages of research designs applied in different existing studies examining identity development. The author will then outline identity-relevant theories and approaches that proved to be useful for her research interest. Subsequently, she will describe the resulting design for collecting and analyzing her data. The core result consists of a theme-centered process analysis visualizing the individual state of developmental tasks. With the presentation of three exemplary cases she will illustrate how her approach allowed her to deeply understand the identity of the respective person and at the same time generate general insights about identity development of learners in VET. Finally, strengths and limitations of her approach will be discussed. This research approach is also suited to supporting pedagogical design, representing major added value.