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Real-world applications of statistics are rarely 'off the top of your head'; however, statistics and research methods courses default to closed-book exams that only test rote learning. Trending research supports open-book exams testing the application of student knowledge rather than memory, however statistics courses in psychology are lagging amidst fears of cheating in online open-book multiple-choice exams. The aim of this study was twofold; first, to develop an online open-book multiple-choice exam that tests the application of psychology statistics and research methods knowledge, and second, to demonstrate that it is just as reliable a source of final grades as traditional closed-book exams. We compared results from a new Applied Exam (N = 104 undergraduate third-year psychology statistics students) with the previous year's Traditional Exam (N = 81), correlating these with Research Report grades (the best course-assessment indicator of real-world performance). Similarly strong positive correlations were observed between the written assessments and the Traditional Exam (0.59**) or Applied Exam (0.54**), and both exams display comparable bell curves for grade differentiation, suggesting we can depend on the new Applied Exam for final course grade data. It also reflects a better alignment with course objectives and graduate qualities for effective problem solving in novel situations. Automated assessment of applied knowledge benefits psychology instructors and organisations in reducing administration, and psychology students by alleviating the anxiety in closed-book invigilated exams. Together this presents an opportunity to improve student outcomes by encouraging the development of real-world skills, preparing them for competitive job markets that value critical thinking.

Data literacy has become a much sought after skill by organizations as the importance of data driven decision making continues to rise in importance. This study explores the teaching of Tableau as a supplement to Excel to enhance the data literacy skills of students. Twenty-eight students taking an introductory course in Business Analytics and Information Systems completed a series of five tasks in both Excel and Tableau and answered a two question survey on their perceptions of the relative ease of use and ability to produce correct and visually pleasing results for Tableau versus Excel. An assessment of the results showed that Tableau submissions produced higher scores in terms of correctness and more professionally formatted visualizations. Student survey responses also supported a significant preference for Tableau over Excel in terms of both ease of use and ability to produce correct results.

Understanding the educational aspirations of diverse groups among statistics majors provides insight into the discipline of statistics. This study utilizes multi-institution data from the 2019 and 2020 administrations of the National Survey of Student Engagement (NSSE) to explore educational aspirations for statistics majors through comparisons to other major types as well as among gender identity and race/ethnicity in a sample of 225,892 seniors, including 521 majoring in statistics. Preliminary results from a series of chi-squared analyses suggest that while other STEM majors are more likely to aspire for doctoral or professional degrees, statistics majors are more likely to aspire for a master's degree. However, among the statistics majors, there were no statistically significant differences in educational aspiration by gender identity and race/ethnicity.

The purpose of this article is to understand how educators may support students from different backgrounds (both relatively privileged and marginalized) to participate equitably and meaningfully in ethical data science discussions. To do this, we draw on the literature regarding STEM identity formation, use Cobb and Yackel's (1996) framework for analyzing social norms for discourse in inquiry-based classrooms, and draw on Hodge and Cobb's Cultural Participation Orientation towards developing an inclusive classroom environment. Finally, we describe the course elements (task structures, participation structures, and discursive moves) from a designed Ethical Data Science course that supported students' equitable participation in ethical data science discussions (Sandoval, 2004). [Note: The issue number (1) shown in the header on the PDF is incorrect. The correct issue number is 2. The page range (32-67) shown on the PDF is incorrect. The correct page range is 31-67.] ]

Learner engagement is well-established as critical for learning online. Professional development for online instructors emphasizes techniques for engaging students, and learning technology products tout features intended to promote engagement (e.g., adaptive content, video, gamification). But the influence of particular instructor practices and of particular learning technology features on theory-based aspects of student engagement is infrequently tested empirically, and even more rarely with Black, Latine, and low-income students, who are more likely to face barriers to learning online. This paper first provides a research-based theoretical model of affective engagement developed in conjunction with ongoing studies of blended learning implementations of courseware designed to enhance learning and engagement among historically and systemically marginalized students. Next, the paper describes development of survey-based measures of four components of affective engagement and the use of responses from over 850 students in introductory statistics courses to evaluate the reliability and factor structure of those measures. We conclude with implications for use of the engagement measures in future improvement-oriented research and practice.

The majority of students from elementary to tertiary levels have misunderstandings and challenges acquiring various statistical concepts and skills. However, the existing statistics assessment frameworks challenge practice in a classroom setting. The purpose of this research is to develop and validate a statistical thinking assessment tool involving form one (Grade 7) students' statistical thinking. The SOLO model was applied to develop five testlet tasks. Each testlet task involved four components. This study employed the survey methodology to assess the statistical thinking of 356 form one students. Content validity was determined using the Content Validity Index (CVI). The construct validity was determined using Rasch analysis. The results demonstrated that the instrument for assessing the statistical thinking of the form one students was valid and trustworthy. This finding of the study also revealed new evidence that the instrument allowed the teachers to identify the students' progress effectively based on the hierarchical manner of item levels in the testlet format. The instrument was useful in identifying students' statistical thinking levels. The students' ability to respond appropriately to a task at a particular level reveals their degree of cognitive development. Testlet task was also easy to diagnose the strengths and weaknesses in learning statistics topics.

This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: "Interdisciplinary Mathematics and Science Learning" (https://imslearning.org/). The project developed learning sequences for 12 inquiry-based investigations involving 35 teachers and cohorts of between 25 and 70 students across years 1 through 6. The research used a design-based methodology to develop, implement, and refine a 4-stage pedagogical cycle based on students' problem posing, data generation, organisation, interpretation, and reasoning about data. Across the stages of the IMS cycle, students generated increasingly sophisticated representations of data and made decisions about whether these supported their explanations, claims about, and solutions to scientific problems. The teacher's role in supporting students' statistical reasoning was analysed across two learning sequences: "Ecology" in year 1 and "Paper Helicopters" in year 2 involving the same cohort of students. An explicit focus on data modelling and meta-representational practices enabled the year 1 students to form statistical ideas, such as distribution, sampling, and aggregation, and to construct a range of data representations. In year 2, students engaged in tasks that focused on ordering and aggregating data, measures of central tendency, inferential reasoning, and, in some cases, informal ideas of variability. The study explores how a representation-focused interdisciplinary pedagogy can support the development of data modelling and statistical thinking from an early age.

There are challenges making connections between language use and mathematics in mathematics education. A lack of connections between the two domains can have negative consequences on student learning and performance. The challenges in statistics classrooms, where language and contexts are important as a medium of instruction, have received little scrutiny. This article reports on collaborative research carried out in three largely Pasifika-dominated year 12 classes. Specifically, we explored the language resources and strategies that appear to enhance the statistical understanding of Pasifika students. Findings from the teacher reflection aspect of the study indicated that some strategies to incorporate student language and communicative resources in their learning worked better than others. Teachers may need to re-evaluate their teaching practices, especially if part of their population is learning English as a second language.

A persistent problem in teaching introductory statistics has been helping students overcome their fears and the abstract nature of what they need to learn. Students' own contextualised stories are argued to present an opportunity for humanising the abstract, helping reduce student fears to complement traditional teaching approaches. This paper applies Bruner (Actual minds, possible worlds, Harvard University Press, 1986) theoretical perspectives on narrative mode of thought to understand how students' own contextualised stories might support them in making sense of university introductory statistics. An exploratory, design research study was undertaken where 31 student participants were interviewed across a two-year period. All participants had completed an introductory statistics course where they wrote contextualised children's stories about normal distributions and sampling distributions of the mean. Using an assumption-based, conjecture-driven, reflective analysis, participant interview data was analysed to generate preliminary research findings. Two preliminary findings are detailed in this paper. One revealed that participants initially don't seem to naturally make connections with statistics using their own stories, while another showed that once they did so, their stories helped initiate pathways of access for making sense of their statistical learning. To test the preliminary findings, Bruner (Actual minds, possible worlds, Harvard University Press, 1986) theoretical perspectives on narrative mode of thought--presupposition, subjectification, and multiple perspectives--were used to develop an analytical tool. The methodology in the study provides new insights for understanding how students' own contextualised stories might help them make sense of their learning. The implications of the study are relevant for statistics education, particularly in the areas of statistical thinking processes.

Identifying wordlike units in language is typically done by applying a battery of criteria, though how to weight these criteria with respect to one another is currently unknown. We address this question by investigating whether certain criteria are also used as cues for learning an artificial language--if they are, then perhaps they can be relied on more as trustworthy top-down diagnostics. The two criteria for grammatical wordhood that we consider are a unit's free mobility and its internal immutability. These criteria also map to two cognitive mechanisms that could underlie successful statistical learning: learners might orient themselves around the low transitional probabilities at unit boundaries, or they might seek chunks with high internal transitional probabilities. We find that each criterion has its own facilitatory effect, and learning is best where they both align. This supports the battery-of-criteria approach to diagnosing wordhood, and also suggests that the mechanism behind statistical learning may not be a question of either/or; perhaps the two mechanisms do not compete, but mutually reinforce one another.

In recent years, numeracy has had an increasing focus in the Australian educational system, with policies and assessments in place for both students and teachers. In order to address the requirements of their careers, teachers need to have sufficient numeracy capabilities. In our study, we explored the numeracy capabilities of post-graduate pre-service teachers enrolled in a numeracy unit at an Australian university. Specifically, we investigated participants' statistical literacy capabilities by examining responses to a multi-part question involving the analysis of Australian National Assessment Program--Literacy and Numeracy (NAPLAN) data presented graphically. Participants' multiple-choice answers were analysed quantitatively. To assess the depth of participants' statistical literacy reasoning, the explanations for their responses were analysed qualitatively using an adaptation of the Structure of Learning Outcomes (SOLO) taxonomy levels. Although the vast majority of participants exhibited strong basic statistical literacy skills, few participants demonstrated high-level statistical reasoning. Surprisingly, there were few differences in the response patterns of participants who had or had not studied university mathematics in their undergraduate studies.

There is widespread misuse of statistics in research, particularly in the life sciences, which is one of the contributing factors to reproducibility concerns in research. However, the formal quantitative training that life sciences research trainees receive is often quite limited. Our survey of statistics requirements in undergraduate life sciences programmes offered by the top research-intensive universities in Canada, the U15 Group of Canadian Research Universities, confirmed that training in statistics tends to be limited and more general in nature. To help raise awareness and address these limitations, this paper shares findings of this statistics requirements survey and describes the evidence-based framework for a second-year undergraduate course at the University of Toronto, which was introduced to integrate statistics instruction with the life sciences research process. We hope these insights will inform future quantitative course offerings, and ultimately, better prepare students to effectively engage with statistics in life sciences research.

Research suggests teachers have positive motivational and emotional orientations regarding statistics but little statistical knowledge. How does this fit together? Since teachers' professional competence in statistics has not been well explored, we asked 88 in-service mathematics teachers about their orientations regarding teaching statistics and tested their statistical content knowledge. First, we investigated how "positive" their orientations were by comparing them to their orientations regarding teaching fractions. Then, we analyzed relationships between teachers' orientations and content knowledge in statistics using mixed-effects logistic regression models. The results showed that teachers' orientations regarding teaching statistics were: (1) poorer than those regarding teaching fractions and (2) related to their statistical knowledge. Teachers with high self-efficacy showed higher knowledge than teachers with low self-efficacy, and anxious female teachers had higher knowledge than less anxious female teachers. We also found that knowledge decreased with increasing age of the teachers. The findings underscore the need to strengthen statistics in teacher education, including both content knowledge and the development of positive orientations.

The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report advocates for use of real data with context and purpose. This work contributes to the growing literature on assessing statistical literacy by investigating the influence of context as it relates to assessment performance among post-secondary introductory statistics students. We discuss the development of an isomorphic form of an existing assessment instrument, and report results which concluded that test takers demonstrated lower statistical literacy scores when assessment tasks incorporated real data from published studies as context when compared with functionally similar tasks such as those with a contrived data set and a realistic context.

We explore ways in which statistics can be used to understand disease spread and support decision-making by governments. "Past performance does not guarantee future results"--we hope. We discuss and show examples from the National Science Foundation (NSF)-funded COVID-Inspired Data Science Education through Epidemiology (CIDSEE) project. Throughout, the emphasis is on the relationships between evidence, modeling and theorizing, and appropriate action. Statistics should be an essential element in all these aspects. We point to some "big statistical ideas" that underpin the whole process of modeling, which can be illustrated vividly in the context of pandemics. We argue that statistics education should emphasize the application of statistics in practical situations, and that many curricula do not equip students to use their understandings of statistics outside the classroom. We offer a framework for curriculum analysis and point to some rich teaching resources.

The biggest difference in statistical training from previous decades is the increased use of software. However, little research examines how software impacts learning statistics. Assessing the value of software to statistical learning demands appropriate, valid, and reliable measures. The present study expands the arsenal of tools by reporting on the psychometric properties of the Value of Software to Statistical Learning (VSSL) scale in an undergraduate student sample. We propose a brief measure with strong psychometric support to assess students' perceived value of software in an educational setting. We provide data from a course using SPSS, given its wide use and popularity in the social sciences. However, the VSSL is adaptable to any statistical software, and we provide instructions for customizing it to suit alternative packages. Recommendations for administering, scoring, and interpreting the VSSL are provided to aid statistics instructors and education researchers understand how software influences students' statistical learning.

In statistics classes, the central limit theorem has been demonstrated using simulation-based illustrations. Known population distributions such as a uniform or exponential distribution are often used to consider the behavior of the sample mean in simulated samples. Unlike such simulations, a number of real-data-based simulations are here implemented in which the populations are empirical distributions of data selected from Japanese firms. The dataset chosen contains 38 variables familiar to business students, such as sales and assets. The maximum population size of the variables is 2243. One thousand samples with replacement are selected for specific variable--sample size combinations. Hypothesis testing results indicate that the normality hypothesis for the sample mean is rejected for 31 variables at the 0.1% level even with a sample size of 500. It is emphasized that the data for these variables indicate that this should not be a surprise, and emphasize the importance of looking at data.

Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks--"dual processes" and "grounded cognition"--in the context of people's conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building "statistical habits of mind." However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students' meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education.

This study is a case study within a three-year-long design research study. The purposes of this study are twofold. The first purpose includes investigating whether there is a significant difference in seventh-grade students' proportional reasoning skills before and after a teaching experiment conducted in line with the perspectives of Hypothetical Learning Trajectories and the theory of Realistic Mathematics Education and assess retention of those skills by means of quantitative analysis. The second purpose includes describing the changes and patterns in students' levels of ratio understandings and related uses of informal, pre-formal, and formal representations through the analysis of students' written work in the achievement tests and task-based interview results. Findings of the study showed that the intervention proved successful in enhancing students' proportional reasoning skills and supporting the shift in students' levels of ratio understanding and representation use from informal ones to more formal ones. Students' levels of ratio understanding and representation use are described in detail with several sample student responses from the pre-, post- and retention assessments, which also delineate the shift in students' proportional reasoning skills from informal to formal ones. Several discussions are made to discuss the findings and their potential implications.

Data Structures and Algorithms (DS) is a basic computer science course that is a prerequisite for taking advanced information systems (IS) curriculum courses. The course aims to teach students how to analyze a problem, design a solution, and implement it using pseudocode to construct knowledge and develop the necessary skills for algorithmic problem solving and abstract thinking. While the literature acknowledges the difficulty of this course, few references were found that examine the process students undergo while solving DS algorithmic problems. The study's objective is to explore and describe IS students' problem-solving processes and challenges requiring a high level of abstract thinking in a "black box" approach. During the study, 13 students were observed while solving a complex problem, using "think aloud" (TA) techniques. Each observation was recorded, transcribed, and iteratively analyzed using principles of provisional coding in qualitative data analysis. The findings suggest that the quality and correctness of the solutions depend on three main factors: abstract thinking, flexibility applied during the solution process, and an absence of misconceptions related to concepts and the basic understanding of the problem. The students' levels of abstract thinking also influenced the quality of visualization used while trying to solve the problem. This study's findings may raise the awareness of DS course designers and instructors regarding the importance of the role of abstract thinking, possible misconceptions, and strategies used in problem solving as factors influencing students' ability to solve complex problems.

In marketing, the traditional approach for teaching quantitative concepts uses individual courses, such as marketing research and business statistics. While this pedagogy is common, research indicates it is difficult for students to master quantitative concepts in a single course. Instead, quantitative reasoning (QR) requires repeated practice, so students develop a quantitative habit of mind. While the integration of analytics across the marketing curriculum could help students deepen their understanding of quantitative concepts, program-wide curricular change tends to be slow because it requires the coordination of multiple instructors across several courses. The current study investigated an innovative alternative -- spiral pedagogy -- that integrated quantitative concepts into a marketing management course. The study analyzed 384 QR assessments from 40 students before, during, and after the course. The time of the assessment had a large effect on students' use of QR ([eta][subscript p][superscript 2] = 0.93) and quantitative confidence ([eta][subscript p][superscript 2] = 0.89). Qualitative feedback also indicated the spiraled lessons fostered a quantitative habit of mind that helped students beyond the classroom.

This study investigated daily motivation regulation as a multilevel mediator of undergraduate students' intrinsic and extrinsic motivation and academic functioning. Undergraduate students (N = 124) completed measures on motivation, motivation regulation, and study time for 10 consecutive days leading up to a statistics exam. Bayesian multilevel mediation models were used to examine motivation regulation as a mediator between motivation and daily study time at the within-person level and exam performance at the between-person level. Within-person findings revealed motivation regulation mediated the relation between both intrinsic and extrinsic motivation and study time although the indirect effect was substantially stronger for extrinsic motivation. Between-person findings did not support mediation between intrinsic and extrinsic motivation and exam performance. Specifically, none of the motivation or motivation regulation factors predicted students' exam performance. It appears that motivation regulation typically stems from extrinsic motivation and is more closely associated with process-oriented rather than product-oriented academic functioning.

Purpose: This study aims to explore the findings related to data literacy skills for students to succeed in the digital age labor market and the role of university-industry collaborations (UICs) in the co-design and co-delivery of curriculum for the development of students' data literacy. Design/methodology/approach: The study uses an interview-based research methodology to gather insights from industry partners and stakeholders. The interviews focus on identifying key data literacy skills, understanding the significance of these skills and exploring the role of UICs in enhancing students' data literacy. Findings: The findings reveal several important data literacy skills for students. The most commonly mentioned skills include data evaluation/analysis, identifying the relevance of data and data protection in a sensitive manner. Participants also emphasized the importance of recognizing the interrelationships among data, adapting data across different contexts and strategically combining diverse data. The study emphasizes the role of universities in providing a well-rounded educational setting that fosters the development of data literacy skills. Additionally, it highlights the value of practical collaborations between universities and industries, enabling students to apply theoretical knowledge in real-world contexts. Originality/value: The study highlights the interconnected nature of various data skills and emphasizes the significance of data literacy in navigating the complexities of the digital age labor market. It also sheds light on the role of UICs in codesigning and codelivering curricula to enhance students' data literacy. The findings provide valuable insights into the practical implications for UICs in preparing students for the data-driven job market.

The purpose of this study was twofold. First, to explore middle and high school students' intuitively-based (mis)conceptions in probability, particularly availability and representativeness heuristics. Second, to investigate teachers' awareness of these intuitively-based (mis)conceptions and the effectiveness of their instructional practices to support students' understanding of probability beyond heuristics. The participants were two middle school mathematics teachers, three high school mathematics teachers and their students. Data were collected through a diagnostic test administered to students as a pretest and posttest, interviews with the teachers and two students from each class and classroom observations. The findings indicated the existence of intuitively-based (mis)conceptions regarding availability and representativeness heuristics among middle and high school students. In general, the teachers did not consider the students' intuitions and difficulties in probability in their instructions. Not only did they rarely guide students to analyze and solve the tasks coherently and deliberately, but they also did not discuss students' intuitively-based (mis)conceptions. The results highlighted that if teachers do not attend to student thinking and change their instructional practices accordingly, their knowledge about students' difficulties will not necessarily help students overcome their intuitions and attain a probabilistic way of thinking.

Previous research has identified an unfilled gap between, on the one hand, mathematical prerequisites needed for a formal treatment of least squares and, on the other hand, only teaching procedural aspects of curve fitting. As a response to this, the present study explores students' suggestions of how they think a computer or calculator does curve fitting. The data evolve through observations of mathematics classroom group work and interviews during a physics laboratory group work of year 10 secondary school students in Sweden. The results show that these students seemed well equipped to, on their own, suggest relevant and mathematically founded strategies for formal curve fitting though not least squares. Specifically, the students were inventive in their group work in the sense that several of their suggestions for how curve fitting could work are the same as those that appear in mathematics history from early 1700 until contemporary methods, covering both algebra and statistics. As such, the mathematical content in the results contrasts previous research on informal fit, which often results in the students counting the number of points over and under the fitted line but with less articulated justifications for their fit.

A project involving data related to socioeconomic indicators was administered in an introductory statistics course. As part of this project, student groups investigated a data set from the United States Department of Agriculture Food and Nutrition Service website with information on recipients of Supplemental Nutrition Assistance Program (SNAP) benefits. Pre/post data from the Miville-Guzman Universality-Diversity Scale (M-GUDS) short form were collected to measure changes in diversity awareness. The survey administered also included demographic information and open-ended questions. Based on study results (n = 48), there was moderate evidence of a change in the "diversity of contact" subscale of the M-GUDS short form. Written feedback indicated that students demonstrated empathy for individuals with potentially different lived experiences. The focus of this paper is to describe the study results and provide information on the importance of incorporating elements of diversity into curriculum.

It is becoming increasingly clear that not all students require the same education, and the requirement of personalized education is increasingly in demand. The incorporation of adaptive learning (AL) has increased in recent years. However, research on this subject is still evolving at the university level. In this study, we investigated the impact of integrating an AL tool (CogBooks[R]) in a university course (statistics for decision making) taught in an innovative online modality called FIT (flexible, interactive, and with technology), in which the course is designed in the CANVAS[R] platform and uses Zoom[R] as a means of communication with students. Learning outcomes were compared between the FIT courses with or without AL and between AL strategies in online and face-to-face courses. It was clear that AL improved the students' achievement regardless of the modality. In addition, we conclude that students achieve better in AL courses in the classroom than in distance courses. Satisfaction surveys favor a preference for FIT courses with AL over classroom classes with AL. Our results suggest that AL is a solid strategy for teaching undergraduate courses.

Principal Component Analysis (PCA) is a powerful statistical technique for reducing the complexity of data and making patterns and relationships within the data more easily understandable. By using PCA, students can learn to identify the most important features of a data set, visualize relationships between variables, and make informed decisions based on the data. As such, PCA can be an effective tool to increase students data literacy by providing a visual and intuitive way to understand and work with data. This article outlines a teaching strategy to introduce and explain PCA using basic mathematics and statistics together with visual demonstrations.

This study investigated first-year undergraduate statistics students' statistical problem-solving skills on the probability of the union of two events, conditional probability, binomial probability distribution, probabilities for x-limits using the z-distribution, x-limit associated with a given probability for a normal distribution, estimating the y-value using a regression equation, and hypothesis testing for a single population mean when a population standard deviation is unknown. The study was a descriptive case study and employed a mixed-method research approach. Data were collected through content analysis of a statistics course examination script of 120 first-year undergraduate students of statistics in an open distance-learning university in South Africa. Polya's Model of Problem Solving was used as the framework of analysis. The study revealed that the students, in general, had poor statistical problem-solving skills.

It is important for young people to be aware of job profiles and activities in the professional world. Bringing the education system closer to the professional world is vital for them to make decisions about their academic and professional futures. Programs developed to connect 15-year-old students who in Spain are in year 4 of their Compulsory Secondary Education, and Research Support Units within the Health Research Institutes of the Hospitals and the Clinical Research Support Platforms of the Carlos III Institute of Health are a good opportunity to highlight the role of biostatistics in clinical trials. The aim of this article is to share the outcomes of and learnings from an interactive workshop for 15-year-old students on biostatistics and clinical trials conducted within the 4°ESO + Empresa program and directed by the Scientific Support Unit of the Health Research Institute of Hospital 12 de Octubre in Madrid, Spain.

Statistical software is commonly used in undergraduate social sciences statistics courses. Due to the increase in online/hybrid courses and the cost of SPSS, instructors may wish to switch to another statistical software. We cover seven programs: Excel, Google Sheets, jamovi, JASP, PSPP, R, and SOFA. We compare programs using the following criteria: ease of download, quality of online instructions, availability of instructor resources, sophistication of analyses available, ease of use, operating system requirements, whether it uses point-and-click or code, and whether a VPAT is available. Adopting new course materials is a valuable part of instruction but time-consuming. Therefore, this review provides information about commonly available or free open-source programs so instructors can choose based on the needs of their students and/or institutions.

The rapidly increasing capacity of technology to collect, organize, and manage data has spurred changes in the practice of statistics: new methods of collecting data, large data sets, new forms of data, different ways to visualize and represent data, and recognition of the importance of being able to understand and to communicate data-based arguments and findings from the perspective of data consumers and data producers. Using a narrative review based on Delphi methods, we asked leading members of the statistics education community to describe trends they have observed in the field and to identify interesting and relevant papers related to those trends. We received 24 responses and over 200 suggestions for papers. Our analysis included papers published in journals, book chapters, conference proceedings, handbooks, and curricular documents. We focused on future directions for statistics education research, and thus included articles based on opinion or principles if the arguments made a strong case supported by evidence as to why the idea was needed. From our analysis of 50 papers in this review, we suggest four emerging themes in statistics education research, challenging what should be taught and suggesting new ways of thinking about the teaching and learning of statistics: Data Science, Visibilizing Statistical Concepts, Social Statistics, and New Contexts for Learning. The review focuses on articles from 2017-mid 2022 and highlights the relevance and importance of each theme. Our choice of a particularly important paper for each theme is annotated in the references.

In this paper, we develop a personal synthesis of the most outstanding research on the teaching and learning of probability in the past years. We conducted a systematic search to examine publications on this topic in mathematics education, statistics education, education, and psychology journals. This exploration was complemented by additional studies published in conference proceedings or books. We classified these papers to highlight the main recent research tendencies in the field, according to the theme studied and considering the research objectives. Epistemological analyses suggest that informal inference based on simulation diminishes the topic abstraction but reduces probability to its frequentist view. Topics receiving particular attention include children's probabilistic knowledge, the effect of visualizations on solving conditional probability problems, teachers' education and probability modelling. In the final section, we recommend relevant points in which more investigation is needed to complete our knowledge about teaching and learning. In particular, we miss research on teachers' mathematical knowledge of many probability concepts and on their didactic knowledge.

Self-efficacy is associated with a range of educational outcomes, including science and math degree attainment. Project-based statistics courses have the potential to increase students' math self-efficacy because projects may represent a mastery experience, but students enter courses with preexisting math self-efficacy. This study explored associations between pre-course math confidence and coding confidence with post-course statistical intentions and perceived achievement among students in a project-based statistics course at 28 private and public colleges and universities between fall 2018 and winter 2020 (n = 801) using multilevel mixed-effects multivariate linear regression within multiply imputed data with a cross-validation approach (testing n = 508 at 20 colleges/universities). We found that pre-course coding confidence was associated with, respectively, 9 points greater post-course statistical intentions and 10 points greater perceived achievement on a scale 0-100 (0.09, 95% confidence interval (0.02, 0.17), p = 0.02; 0.10, 95% CI (0.01, 0.19), p = 0.04), and that minoritized students have greater post-course statistical intentions than nonminoritized students. These results concur with past research showing the potential effectiveness of the project-based approach for increasing the interest of minoritized students in statistics. Pre-course interventions to increase coding confidence such as pre-college coding experiences may improve students' post-course motivations and perceived achievement in a project-based course. Supplementary materials for this article are available online.

An anonymous survey was given to n = 73 students in an asynchronous online statistical literacy course at a mid-sized Hispanic Serving Institution. Informed by teaching experience, literature on lexical ambiguity, and everyday usage of statistics words and phrases, the first author designed the survey to yield insight into how students view phrases such as "average person," "random person," and "outlier person," and to explore possible connections or patterns with such phrases. Findings suggest that students view phrases such as "random person" in a way much further from standard usage than they do phrases such as "anonymous person." Considerations such as diversity, agency, and variable identification are identified as possibly affecting usage and meriting further investigation. Recommendations for teaching and future research are provided. Supplementary materials for this article are available online.

R is becoming the standard for teaching statistics due to its flexibility, and open-source nature, replacing software programs like Minitab and SPSS. The main driver for reform within Scottish statistical undergraduate programs is the creation of the Scottish Qualification Authority's Higher Applications of Mathematics course which has statistics as a core topic. The course saw R introduced into the Scottish high school curriculum for the first time from August 2021. This facilitates the need for R to be introduced into Higher Education courses at an earlier stage. In academic year 2021/22 we introduced RStudio into our first year introductory statistics class. This class is taken by students studying Mathematics, and those studying Biomedical Sciences. Both cohorts were surveyed in order to assess their anxiety and enjoyment of learning how to use R, with a goal of assessing any differences between the groups. We found that there was no association in software anxiety at the start of the class. However, as the class progressed the Mathematics students reported lower levels of anxiety compared to the Biomedical Sciences students. The Mathematics students seemed to enjoy the class more than the Biomedical Sciences students, thus, needing further investigation into enjoyment versus anxiety.

As the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there remains a need for tools that assist faculty when making data-based examples and assignments. In this article, we describe a free application, available both online and through an R package, which we developed for instructors of courses that incorporate introductory statistical methods. The app treats the process of coming up with data for examples and assignments synergistically, beginning with either data creation (through random number generation) or adjustment (when the user has data) and ending with an assessment of the data's suitability for the desired analysis. The app enables the quick creation of many versions of variables for multiple versions of assignments and is compatible with file types associated with popular interdisciplinary software such as Excel, R, and SPSS. We highlight the app's features and discuss survey feedback provided by faculty that demonstrates the desire and need for and effectiveness of such an app. Supplementary materials for this article are available online.

In recent years, the discipline of statistics has begun reckoning with its difficult history. Institutions are reconsidering names that have honored key historical figures in statistics who have deep ties to eugenics movements and racial and class prejudice. These names, however, continue to appear in our classrooms, where we teach the methods created by these individuals, raising the question of how instructors should address their legacies. Three examples of famous statisticians and their work - Francis Galton's use of conditional probabilities to demonstrate "hereditary talent," Karl Pearson's attempt to quantify the intelligence of Jewish immigrant students, and Ronald A. Fisher's creation of the analysis of variance to de-emphasize environment in human development - highlight the intimate ties between statistics and eugenics. These examples, along with a discussion of the context of these men, eugenics movements, and the statisticians and scientists who opposed their eugenic programs, can humanize the field for students, teach them about the challenges in accurate and unbiased data collection and analysis, and connect historical mistakes to contemporary ethical issues. Confronting this history in the classroom can both improve the teaching of the statistical methodologies themselves and begin a broader conversation about the role of statistics in the world. Supplementary materials for this article are available online.

In this article, we explore the drivers of students' success in a first-year university statistics course. Using a unique sample from Westminster International University in Tashkent, we discover that student engagement with their studies is reflected in their class attendance and in the use of online resources, which continue to play an important role in their academic performance. Moreover, we discovered that socio-demographic factors such as age, gender, marital status, employment, and language of instruction of prior schooling are significant in explaining student academic outcomes. These factors have important policy implications for both the university and the broader national level.

To meet the demands of industry, undergraduate business curricula must evolve to prepare analytics-enabled professionals in fields such as finance, accounting, human resource management, and marketing. In this article, we provide a case study of developing a rigorous, integrated finance and data analytics course that was delivered using a simultaneous team-teaching approach within a regional, teaching university. This case study describes developing the learning outcomes; defining the integrated teaching approach; identifying and developing course content and materials; and planning the course delivery. Next, we describe the course delivery, including student achievement of course learning outcomes, and a summary of student feedback from end-of-course evaluations is provided. Finally, we present lessons learned from delivering the course and provide considerations for future work such as scaling this course and replicating it in other business domains.

Data science is inherently collaborative as individuals across fields and sectors use quantitative data to answer relevant questions. As a result, there is a growing body of research regarding how to teach interdisciplinary collaboration skills. However, much of the work evaluating methods of teaching statistics and data science collaboration relies primarily on self-reflection data. Additionally, prior research lacks detailed methods for assessing the quality of collaboration skills. In this case study, we present a method for teaching statistics and data science collaboration, a framework for identifying elements of effective collaboration, and a comparative case study to evaluate the collaboration skills of both a team of students and an experienced collaborator on two components of effective data science collaboration: structuring a collaboration meeting and communicating with a domain expert. Results show that the students could facilitate meetings and communicate comparably well to the experienced collaborator, but that the experienced collaborator was better able to facilitate meetings and communicate to develop strong relationships, an important element for high-quality and long-term collaboration. Further work is needed to generalize these findings to a larger population, but these results begin to inform the field regarding effective ways to teach specific data science collaboration skills.

Using software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public health and more, it is crucial that we as instructors deliver the basic concepts of statistics effectively. In line with guidelines presented in the GAISE College Report, this article demonstrates intuitive approaches to teaching proportion and mean inference that take advantage of statistical software and emphasize conceptual understanding. The article recommends putting aside asymptotic-based methods for proportion inference and using the exact binomial method. Regarding mean inference, we propose a more contextualized and simplified process that uses the distribution of the sample mean directly and avoids standardized statistics such as z or t. In both the proportion and mean inference contexts, we discuss the benefits of the proposed approaches and provide detailed examples that demonstrate the methods using the Rguroo statistical software.

Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing data in recent doctoral dissertations and subsequent publications using NHANES data as a case study. We also explored missing data handling coverage in top-selling applied statistical textbooks. Thirty-seven doctoral dissertations (published from 2007 to 2017) and 17 subsequent journal articles were included in the analysis. Overall, 29 (78.4%) dissertations did not explicitly state whether they had missing data. Five (62.5%) dissertations reporting missing data did not report an assumed mechanism of missingness. Only one subsequent journal article reported the missing data percent for key variables. 28 (75.7%) dissertations and 16 (94.1%) journal articles reported the use of NHANES sample weights. Of the top-selling 20 applied statistics/biostatistics textbooks examined, 14 did not mention imputation. This sample reflects poor rigor in analysis, reporting, and handling of missing data found among recent graduates and poor coverage in textbooks. Checklist utilization and improvement in statistical training on missing data handling are needed. Supplementary materials for this article are available online.

Background: Statistics courses are a necessary component of undergraduate education, but psychology majors often have negative associations with statistics. One way to promote learning within classrooms is to promote classroom connectedness, but few studies have examined classroom connectedness using a social network paradigm. Objective: The purpose of this study was to examine the degree of connectivity within the lab component of a statistics course, and test if the networks were associated with psychological constructs related to academic success, specifically, grit and resilience. Method: Using descriptive network analysis and exponential random graph models (ERGMs), we examined the level of connectedness in statistics labs and whether or not students were likely to connect with each other based on sharing similar levels of grit and resilience. Results: Results suggested an overall low degree of connectivity within each lab. However, in the most well-connected lab, students were more likely to connect with other students who had a dissimilar level of grit, but a similar level of resilience. Conclusion: There is high variability between different student networks, although all networks were sparse. Future research could further examine the connection between student connectedness, grit and/or resilience, and how these could connect to student outcomes.