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Histories of statistics and quantification have demonstrated that systems of statistical knowledge participate in the construction of the objects that are measured. However, the pace, purpose, and scope of quantification in state bureaucracy have expanded greatly over the past decades, fuelled by (neoliberal) societal trends that have given the social phenomenon of quantification a central place in political discussions and in the public sphere. This is particularly the case in the field of education. In this article, we ask what is at stake in state bureaucracy, professional practice, and individual pupils as quantification increasingly permeates the education field. We call for a theoretical renewal in order to understand quantification as a social phenomenon in education. We propose a sociology-of-knowledge approach to the phenomenon, drawing on different theoretical traditions in the sociology of knowledge in France (Alain Desrosières and Laurent Thévenot), England (Barry Barnes and Donald MacKenzie), and Canada (Ian Hacking), and argue that the ongoing quantification practice at different levels of the education system can be understood as cultural processes of self-fulfilling prophecies.

This paper introduces a course design for a post-Calculus undergraduate course on mathematical modeling for the life sciences. The course includes differential equations, discrete, and cellular automata approaches (among others) to modeling questions in the life sciences, as well as a variety of biological topics. The course focuses on model set-up and revision for these different scenarios and approaches as well as contemporary science approaches. Changes in student ranking of biology and math as well as student feedback are included. This paper may be of interest to any faculty member designing or revising a similar course or inserting modules on these topics into any related course.

The dramatic changes brought on by the COVID-19 pandemic have changed the way in which mathematics and statistics support is offered. Students and staff have been presented with new opportunities and challenges. One-on-one interviews were conducted late in 2020 with 23 students and staff who had experience with fully online mathematics and statistics support. The interviewees were from University College Dublin, Ireland, and Western Sydney University, Australia. Utilising thematic analysis, five themes around online mathematics and statistics support common to both universities were identified. In this paper the three themes related to "connection" are explored; they are pedagogical changes, social interaction, and appreciation of mathematics and statistics support. These themes highlight the need felt by both students and staff for mutual connection. The paper concludes with a discussion on the repercussions of this study for future considerations of effective online mathematics and statistics support.

Finite Mathematics has become an enormously rich and productive area of contemporary mathematical biology. Fortunately, educators have developed educational modules based upon many of the models that have used Finite Mathematics in mathematical biology research. A sufficient variety of computer modules that employ graph theory (phylogenetic trees, food webs, networks), cellular automata (pattern formation, diffusion limited aggregation), fractals (both measurement and generation of self-similar structures), finite difference equations and deterministic chaos (logistic growth, predator-prey, SIR epidemiology), combinatorics and probability (genetics and evolution), information theory (biodiversity, sequence logos), and Boolean logic (operons) are available to adopt, adapt, and implement. An emphasis has been placed on modules that are freely available, that have been educationally vetted, and that run on a variety of operating systems. Most modules are easy to use, graphically visual, and amenable to modification. In this paper, two different approaches are stressed: (1) "glass box models" that allow students to see equations associated with each cell in a spreadsheet and to modify/extend those models with minimal effort; and (2) agent-based models that emphasize "bottom-up" modeling and that instantiate the power of massively parallel simulation and address the misconceptions of a "centralized mind-set."

This article provides conceptual ideas, data, and exercises, for integrating original sources of recent, state of the art, world-class life science research in the undergraduate mathematics curriculum and classroom. To this end, this article shows how one of the main goals of calculus in the life sciences, fitting parameters to data and assessing the reliability of the fitted parameters, fits in existing mathematics courses at levels from basic to multivariable calculus. The material for multivariable calculus explains methods of computation of quantitative criteria of "significance" that are currently the subject of scientific controversy. This article thus provides resources to strengthen students' mastery of calculus before, or in, mathematical modeling, probability, and statistics.

Governmental and educational organizations have pointed out that students coming into the biological sciences require stronger skills in statistics and data modeling that are not usually addressed in typical engineering-based calculus courses. Our work here documents how faculty in biology and mathematics addressed this issue at a large urban university in the southwestern USA. The Calculus for Life Sciences course was redesigned to integrate data analysis and engage students in activities connecting the foundational and practical aspects of statistics-based calculus for professionals in life sciences. The goal was to obtain a better-prepared cohort of students with a positive perception of the use of mathematics and statistics in their careers. Over the course of four semesters, the course doubled the average enrollment of students, the failure rate reduced by more than 50%, and students reported positive attitudinal responses regarding the application of math to biological studies. The perception of mastery reported by students throughout the semester, however, did not correlate with the mastery of skills demonstrated on graded activities. This article provides a case study of the successes and pitfalls that we encountered as we attempted to shift all aspects of the course, including pedagogy, grading, and curriculum.

Courses: Research methods for undergraduates or graduates. Objectives: The aims of this activity are: (1) to clarify the basics of experimental design; (2) to illustrate the concept of levels of measurement; (3) to demonstrate in-person/hands-on data collection procedures; (4) to understand and practice the steps in null hypothesis testing; and (5) to engage with different kinds of inferential statistics.

This article discusses the development of an interactive online textbook for undergraduate teaching, in response to moving teaching online due to COVID-19. The development took place initially for a university with less than 5% of students studying online in 2019. The online textbook was incorporated into a large, diverse, multidisciplinary and predominantly first-year undergraduate research design and statistics course. Digital tools were embedded into the textbook to facilitate student engagement in lieu of face-to-face contact, including online crosswords for low-level statistical literacy, drop-and-drag activities, screencasts, exercises, animations, and videos. The textbook was prepared in markdown format. This approach allows for instant updating of material and provides focussed URLs that educators can use to point students directly to specific learning outcomes. Despite the textbook being developed in response to restricted teaching conditions, it has provided a cost-effective resource, targeted at teaching research design and basic statistics concepts, interpretation, and communication. Having returned to face-to-face teaching, the text is now fully integrated into the course. Importantly, the online textbook is freely available for other educators to use, either in its existing form, or by forking (on Github) the textbook sources to create a bespoke online textbook for other courses.

Statistical knowledge of a target's location may benefit visual search, and rapidly understanding the changes in regularity would increase the adaptability in visual search situations where fast and accurate performance is required. The current study tested the sources of statistical knowledge--explicitly-given instruction or experience-driven learning--and whether they affect the speed and location spatial attention is guided. Participants performed a visual search task with a statistical regularity to bias one quadrant ("old-rich" condition) in the training phase, followed by another quadrant ("new-rich" condition) in the switching phase. The "instruction" group was explicitly instructed on the regularity, whereas the "no-instruction" group was not. It was expected that the instruction group would rely on goal-driven attention (using regularities with explicit top-down knowledge), and the no-instruction group would rely on habit-like attention (learning regularities through repetitive experiences) in visual search. Compared with the no-instruction group, the instruction group readjusted spatial attention following the regularity switch more rapidly. The instruction group showed greater attentional bias toward the new-rich quadrant than the old-rich quadrant; however, the no-instruction group showed a similar extent of attentional bias to two rich quadrants. The current study suggests that the source of statistical knowledge can affect attentional allocation. Moreover, habit-like attention, a different type of attentional source than goal-driven attention, is relatively implicit and inflexible.

Introduction: This study involved a systematic literature review to document the sources behind publications and citations that comprise knowledge about rates of braille usage/literacy among people with visual impairments in the United States. Methods: Predefined search criteria were used to extract publications that potentially mentioned claims about braille literacy and usage rates, dating back as far as the early 20th century. Systematic analyses narrowed the set of publications to a collection of 95 articles and manuscripts that made specific statements about the prevalence of braille readership. Results: The analysis revealed that even in peer-reviewed publications, many claims about braille literacy are made without citations to dependable sources. All identified citations used as the basis for braille literacy rates since the 1970s can be traced back to two primary sources: a National Library Service report from 1979 and the American Printing House for the Blind (APH) Federal Quota data (which should not be used as a source for braille literacy rates). Discussion: This research explains the source of the often-cited "10%" statistic. The findings also challenge the field to question the bases for statements about braille literacy rates, including asking, "What do we mean by braille literacy?" Implications for Practitioners: There is no current source of data that succinctly measures braille literacy rates in the United States, and there are no data that explain whether braille literacy rates are rising or declining. Making effective, data-based decisions requires asking the right questions and being informed readers of research in the field of visual impairment.

Due to the emotional and social burden on Americans stemming from firearm injury and death, school psychologists (SPs) must be equipped with an understanding of risk factors that could lead to students engaging in gun violence. SPs are ideally situated due to their training and expertise in promoting healthy development in children and youth, as well as their unique position within school systems to advocate for the psychological well-being of students (APA, 2020). This article examines the rates of gun violence in the United States, providing insight into the different causes of gun-related deaths.

Analyzing the test scores of more than 10,000,000 students who participated in the Advanced Placement (AP) math exams from 1997 to 2019, this study examined the direction and magnitude of the trend in gender disparity by race in participation in and top achievement on AP Calculus AB, Calculus BC, and Statistics exams. The results of this study indicated that, in general, females' representation in all three AP exams increased significantly. Although the findings indicated that the female-to-male ratios (FMRs) in participation in the AP math exams increased significantly from 1997 to 2019 and favored females for all races, the gender disparities among top achievers for all math exams are still substantial. The relationships between the FMRs in participation and top achievement for all AP math exams were also analyzed within races, and the possible impacts of these findings within the context of the underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields were also discussed.

We evidence English teacher and student perspectives on the learning of pre-university mathematics 'A Level' courses through the pandemic period to July 2021. Data are drawn from a 2017-21 classroom-close study of enactment of such courses in 13 fairly representative centres, using an institutional ethnographic approach. The pandemic picture was generally one of the significant and sustained negative impacts, though over the course of the study, respondents reported progress in addressing early limitations in the harnessing of digital platforms for learning. A small number of participating students reported home-based study beneficial for their mathematics learning, and a bigger group identified some wider benefits that partly offset the challenges. Most participating 16-18-year-old students, though, reported finding remote learning of mathematics both demanding and limiting. Pandemic constraints impacted most strongly on opportunities to engage with newer emphases within A Level courses: problem solving, reasoning, modelling, statistics and mechanics. Receiving academics reported that mathematical preparedness, and confidence, for mathematics-intense university courses has also been widely affected, with a bigger range of preparedness and confidence than usual. The study draws attention to the importance of studying subject-specific impact and drawing on student as well as teacher perceptions. It exposes a range of consequences of the cancellation of examinations and a need to develop and share effective pedagogies for working remotely with pre-university students.

From March 2020, the Mathematics Support Centre at University College Dublin, Ireland, and the Mathematics Education Support Hub at Western Sydney University, Australia, moved wholly online and have largely remained so to the point of writing (August 2021). The dramatic and swift changes brought on by COVID-19, in particular to fully online modes of teaching and learning including mathematics and statistics support (MSS), have presented students and tutors with a host of new opportunities for thinking and working. This study aims to gain insight both from students and tutors about their experience of wholly online learning and tutoring in the COVID-19 era. In this sense, it represents a 'perspectives' study, the idea being that before we examine specific aspects of this experience, it would be best to know what the issues are. Employing a qualitative analysis framework of 23 one-on-one interview transcripts with tutors and students from both institutions in Australia and Ireland, we identified five key themes as central to the shared experiences and perspectives of tutors and students. In this study, we discuss three of these themes in relation to the new normal with the intention of supporting MSS practitioners, researchers and students going forward. The themes describe the usage of online support, how mathematics is different and the future of online MSS.

Six 7th-grade students engaged with an instructional sequence involving the use of the "TinkerPlots" software to organize data sets in ways intended to help them construe two attributes: the location of quarters of the data within a sub-range of the entire set, and the spread of those portions. We present evidence of students' thinking about these attributes drawn from their responses to questions posed in the instructional sequence. Comparison of findings from a pre-test and a culminating task suggest that the students enriched their ability to imagine and create hypothetical data from a given representative box plot, and that they became oriented to the spread of portions of a data set as indicated by the length of its quarters.

Mathematics curricula have traditionally focused on content knowledge, often in the form of a scope and sequence of increasingly difficult mathematics. The importance of using and applying mathematics is recognised in the current Australian Curriculum Mathematics (AC: M) as 'proficiencies' that are intended to be integrated with the content. There is little support for teachers to develop these proficiencies -- reasoning, understanding, problem solving and fluency. Learning progressions are sequences of learning that focus on cognitive processes, and thus provide a useful basis for curriculum development. Using an empirical Statistical Reasoning Learning Progression as an exemplar, a new approach to curriculum development is suggested that links content knowledge with the proficiencies. The outcome is a zone-based, rather than year level based, curriculum that allows teachers to target their teaching, so that students develop increasingly sophisticated understanding of statistics and probability.

The aim of this study is to explore Israeli high school graduates' mathematical explanations for the spread of the coronavirus, given that the mathematics required to do so was part of their school curriculum. An online questionnaire consisting of two sections provided a variety of potential framings for explaining the phenomenon. The first section invited the participants to explain the spread of the coronavirus in terms of their school majors in general, with no specific reference to mathematics. The second section asked explicitly to explain the mathematical context underlying the phenomenon. In this section, the participants were asked to discuss the Prime Minister's speech given in the media a few weeks earlier, in which he described the spread of the coronavirus as a geometric series. Data analysis of 87 participants' responses to the questionnaire revealed 11 different mathematical ideas used to explain the spread of the coronavirus. These ideas included are as follows: doubling, sequence, exponential growth, using powers, tree diagram, recursion, fast-growing rate with covariation, probability, parabola and quadratic function, acceleration, and factorial. It was also found that the second section of the questionnaire elicited a wider range of mathematical ideas than the first one. We suggest possible explanations for the emergence of the mathematical ideas, which seem to reflect the graduates' intuitive knowledge, influenced not only by their mathematics track level but also by their chosen high school majors. Possible implications are discussed.

Maturity and citizenship in a democracy require that laypersons are able to critically evaluate experts' use of mathematics. Learning to critically reflect on the use of mathematics, including the acquisition of the mathematical knowledge and skills required to that end, has been repeatedly postulated as an indispensable goal of compulsory education in mathematics. However, it remained unclear in how far such reflection is possible, even for the well-educated layperson in mathematics. We use different discussions in German mass media on the pandemic policy in the SARS-CoV-2 crisis in 2020 as examples with far-reaching individual and social consequences. The selected discussions build heavily on mathematical concepts such as mortality rates, casualty numbers, reproduction numbers, and exponential growth. We identify the concepts and discuss how far they can be understood by laypersons. On the one hand, we found that some mathematical models are inappropriate, which can also be determined by laypersons. On the other hand, we found uses of mathematics where ideal concepts are intermingled with complex statistical concepts. While only the ideal concepts can be understood by laypersons, only the statistical concepts lead to actual data. The identification of both types of concepts leads to a situation where the use of mathematics evades social control and opens spaces for misconceptions and manipulation. We conclude that the evaluation of experts' use of mathematics by laypersons is not possible in all relevant cases, and we discuss possible implications of this result.

Proper sampling techniques are of the utmost importance in obtaining accurate and precise results in chemical analysis. A Python language computer simulation providing fast feedback on sampling concepts and statistics was delivered to students through Google Colab. Students used the simulation to aid in their exploration of sampling and sampling statistics and to answer questions in a guided learning activity. This activity was conducted in a virtual environment, but it could easily be used in an in-person setting. Students reported that the simulation was easy-to-use, effectively illustrated the concepts of sampling, and supported the stated learning objectives.

"Gamified" active learning has been shown to increase students' academic performance and engagement and help them make more social connections than standard course settings. However, the costs to use an educational game design with efficient delivery of the game/course plan can be problematic. Our first objective was to evaluate the effectiveness of gamification by using existing techniques (e.g., simple HTML-based games) and readily available collaborative tools (e.g., wikis) from a typical learning management system (LMS), such as Blackboard. Our second objective was to examine students' attitudes toward gamification (e.g., perceived usefulness). An online survey was given to 80 graduate students who took an entry-level biostatistics course from 2015 to 2017 at a midwestern university in the United States. Our study was conducted using an experimental group (class with implementation of gamification) and control group (class without implementation of gamified activities) that were randomly selected from graduate-level statistics courses. A Welch's independent "t"-test revealed a significant difference (p < 0.001) in the mean exam scores of the experimental and control groups. The difference favored classes with gamification. The findings suggest that using built-in LMS tools to design gamified learning activities can enhance students' academic performance and the competencies gained, as well as provide more diversified learning methods and motivation, and offer easy modifications for different learning needs.

This publication seeks to consider statistics education in Japan while referencing a student paper. The paper to be considered is as follows: "Gakusei no Koudou ga Gakusei Seikatsu Manzokudo ni Ataeru Eikyou" (The Influence of Student Behavior on the Degree of Satisfaction Perceived in Student Life).

The aim of this research is intended to examine the effect of mathematics and statistics perception towards students' statistical literacy. The research utilized a survey method with quantitative approach. The research population was undergraduate students from major informatics in one of university in Jakarta. The sample consisted of 80 students, 50 of students were male, while 30 of the students were female. All the participants belonged to similar grade level and enrolled in a statistics course. A survey instrument entitled "Mathematics and Statistics Perception Scale (MSPS)" was intended to examine undergraduate students' perceptions of self-efficacy and their attitudes toward the application of statistics in real world settings. The final version scale consists of 11 items. A multiplechoice survey with 22 questions measuring statistical literacy skills and consisted of items measuring skills on interpreting or critiquing the prevalence of statistical phrases and ideas in the media. The data collected were analyzed using linear regression and independent sample t-test. The result revealed that students' mathematics and statistics perception was found to be significantly and positively related to statistical literacy skills, while there is no significant differences between gender, perception, and statistical literacy. The linear regression analysis estimates the linear regression function to be y = 3.706 + 0.439x.

This is ex post facto research to find the relation between student's perceptions to the statistics lecturer in learning process with statistics achievement of students semester V of the Department of Mathematics Education of Universitas Muslim Maros. The population is all students of the Department of Mathematics Education Universitas Muslim Maros in academic year 2017/2018. And the sample is 30 respondents of students who taught by statistics lecturer. Data analyze by SPSS software. According to analyze get that student's perception to statistics lecturer generally very positive with average 138,57 and statistics achievement is very good with average 79,63. Besides that, inferential show that there is relation between student's perceptions to the statistics lecturer with coefficient is 0,594. From analyze, it can conclude that there are relation between student's perceptions to the statistics lecturer in learning process with statistics achievement of students semester V of Department of Mathematics Education Universitas Muslim Maros.

The purpose of this study is to describe Mathematical Literacy (ML) of Prospective Elementary School Teachers with attention to aspects of mathematical skills and gender. The type of research is qualitative with the research design of Case Study. Respondents are assigned 12 Prospective Elementary School Teachers, consisting of 6 men and 6 women. The instruments used to collect data on ML are tests, and interview guidelines. The collected data were analyzed descriptively. The results of this study are: (1) ML of Prospective Elementary School Teachers which can be seen from the aspect of mathematical skills and gender is categorized as low, respondents having strong mathematical skills indicate a better ML than respondents having weak mathematical skills, and female respondents indicate better ML than male respondents; (2) in terms of Content, the highest score is achieved on the Numbers Content, the weakest Content is in terms of Algebra and Function, while the strongest content is in the field of Probability and Statistics; (3) in terms of Context, the highest and lowest average Personal Context is in the Scientific Context and the maximum score achievement is obtained for Occupational and Scientific Contexts

Students in an online statistics course were prone to become increasingly disengaged as the semester progressed. In Spring 2015, we took a proactive measure to retain student engagement by introducing a cyber mentoring session. We describe the framework, operation and effectiveness of cyber mentoring in improving students' learning experience and performance. With the implementation of cyber mentoring, the percentage of online students passing the class increased from 76% to 94%, and the mean score of online students increased from 74.6 to 83.2 (out of 100), while the corresponding parameters did not change significantly for students in the face-to-face classes.

This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is "data." The word rate for "data" is more than twice the next highest word rate, which is for "model." This contrasts sharply with how often the word "data" appears in most Mathematics books. Overall, we observed substantial agreement on important concepts in Data Analysis and Data Science. Eighteen of the 25 most frequent concepts are shared by both fields. One difference is that the words "problem" and "solution" had Top 25 word rates for Data Science, but not for Data Analytics. A close look at Statistics concepts suggests that Data Analytics is more focused on "exploratory" concerns, such as searching for patterns in data. Data Science retains more of the classical inferential activities that use sample data to draw conclusions about populations. Both fields deal with Big Data situations, but Data Scientists must continue to be prepared for traditional small sample applications.

Many studies have argued the negative effects of external rewards on internal motivation while others assert that external motivation does not necessarily undermine intrinsic motivation. At a private university, students were given the option to earn bonus points for achieving mastery in the online homework systems associated with Statistics and Pre-Calculus courses. The results showed a significant difference in online homework grades and final exam scores, dependent upon when the incentive was given. The findings of this research suggest that college students thrive when incentivized. When compared to the students who were not incentivized, the incentivized group had a statistically significantly higher mean for both online homework scores and final exam scores. Many of the incentivized students chose to take the opportunity to earn the bonus points to increase the final semester grade, which apparently also helped to increase the content knowledge necessary for the final exam.

The impact of a complex of interactive computer algebra system (CAS) based methods with usage of supplementary education materials (e.g., interactive presentations, computer modeling patterns and supplementary home tasks prepared in CAS Mathematica) on the education outcomes in the subject "Probability theory and mathematical statistics" was estimated. A comparative analysis of education outcomes for two groups of third-year students of engineering specialization taught in Bauman Moscow State Technical University was carried out. Group I of 72 students was taught in the year 2016-17 using teaching methods including only lectures and seminars, without usage of supplementary interactive materials. A written control work was chosen as a tool of examination; its results were used as a means of assessment of education outcomes achieved by the students in the first module "Probability theory" of the semester course. The results demonstrated by Group I proved not to be satisfactory for achieving the aims of education: successful continuation of learning the next modules, and further usage in professional practice. After detailed analysis of the students' works and interviewing them, the basic difficulties in understanding and interpretation of the tasks were clarified. Consequently, Group II of 70 students taught in the year 2017-18 in the same specialization as Group I, was provided with new variants of home tasks and supplementary sets of interactive materials to study on lectures and seminars, as well as for independent study at home. The students of Group II have demonstrated significantly higher outcomes as compared to the students of Group I.

The aim of this paper was to analyze attitude towards statistics in high-school students using the SATS scale designed by Auzmendi (1992). The sample was 200 students from the sixth semester of the afternoon shift, who were enrolled in technical careers from the Technological Study Center of the Sea (Centro de Estudios Tecnológicos del Mar 07 CETMAR 07, for its acronym in Spanish) in Veracruz, México. The participants were selected using a non-random sampling by convenience. The procedure followed was the statistical exploratory factorial analysis by García-Santillán, Venegas-Martínez and Escalera-Chávez (2013), while the data analysis was made using the SPSS Statistics 19 software to analysis the information. The results provide significant evidence to ascertain that there is a component which explains the attitude towards Statistics of high-school students and it has three dimensions: liking, confidence and usefulness. The former leads us to think that there is a factor with an affective, a behavioral and a cognitive part. Hence, the affective component is present when they like working with Statistics; confidence is a behavioral component associated with the disposition and security a subject has when facing Statistics and finally, usefulness as a cognitive component involving beliefs, that is to say, the value a student places on the knowledge of Statistics.

This pilot qualitative study explored K-8 pre-service teachers' (PSTs') learning experience in a Statistics and Probability course and their ability to make connections between mathematics and their everyday lives when given opportunities to investigate mathematics through a social issue. Data from student surveys, assessments, and written reflections will be reported, analyzed, and discussed. The researchers also describe approaches and reflect upon experiences for PSTs while studying school choice voucher programs.

This study examines factorial structure and the gender invariance of the Turkish version of the Survey of Attitudes toward Statistics (SATS-36). The SATS-36 has 36 items measuring six components: affect, cognitive competence, value, difficulty, effort, and interest. Data were collected from 347 university students. Results showed that the Turkish version of the SATS-36 had a six-factor structure and it was gender invariant. Internal consistency coefficients supported good score reliability for the subscales of the SATS-36.

The ability to bridge multiple disciplines is critical to the successful practice of collaborative statistics, yet the literature on statistical education devotes relatively little attention to how this skill can be taught. Our goal here is to describe a general conceptual framework within which a curriculum on communication and leadership could ultimately be organized. The primary research question pertains to whether an actionable model of cross-disciplinary communication for collaborative statisticians can be developed, and our task here is to describe such a model and also to illustrate its use. Within this model most communications either share or request information. For example, statisticians might provide information about statistics (e.g., specific statistical approaches, general statistical principles), comment on the clinician's understanding of statistics, share their understanding of clinical content, and request information (e.g., about clinical content, the design and execution of the study being discussed, etc.). Clinical investigators contribute an analogous set of components. In addition, a critical element to the interaction is the higher-level task of developing a mutually understood agreement about the work to be performed: in essence, proposing and negotiating such an agreement. The model is illustrated using a case study, and general qualitative feedback from investigators who performed the case study was obtained, commenting on both successful and unsuccessful interactions with statisticians. Implications for curriculum development are discussed.

The flipped classroom aims to improve learning by engaging students in educational activities outside of traditional lessons. Flipped classrooms have steadily gained popularity in the last decade and are a topic of discussion in teaching and learning forums. However, its adoption in mathematics and statistics has been subdued. Most higher education mathematics and statistics are still delivered through traditional lectures where students are passive participants. In this study, experiences of flipping a large first-level statistics class are presented. The implementation included a combination of peer learning and tutor-assistance in lectures. Student performance, in the form of final examination and overall marks over four semesters (two with traditional delivery and two flipped), were analysed for differences with respect to the two teaching modes after adjusting for demographic differences. In addition, student survey data were analysed with a view to revealing any relationship between attitude towards a flipped classroom and performance. The results showed that students' performance improved and an increased understanding of concepts was achieved through the flipped classroom approach. Evidence also indicated an increase in learner engagement. Student feedback indicated a higher preference for a flipped mode overall and in particular for ages 20 and below.

Statistics is one of the important branches of mathematics taught in schools, colleges and universities. It is also an important tool in public policy discussions. This paper focuses on the use of statistics in the latter context, rather than its use in adult mathematics education research. I review the key characteristics of the statistical approach to constructing public knowledge, and give a very brief history of key points in its development. I discuss how what I call the "overt crisis of statistics," the apparent disenchantment of large sections of the public with the "expert" statistical methods, outputs and pronouncements, leads to dilemmas both for citizens and for democratic governments. Recently "Big Data" and data analytics seem to many to offer new solutions to problems resulting from the essential lack of certainty surrounding efforts to understand society, and from the need to make quick decisions in a rapidly changing world. These approaches have potential, but also limitations. This leads me to consider a second, "covert" crisis of statistics, resulting from a struggle between proponents of freely available public information and public argument, and those aiming to profit from the appropriation and sequestering of information for private ends. I finish by considering what can be done by ourselves, as citizens, as adult mathematics teachers, and as researchers.