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A large-scale (n = 1323) survey of mnemonic recall, self-reported familiarity, cued explanation, and application by introductory statistics students was conducted at a large research university in the southeastern United States. The students were presented 14 mnemonics during the fall 2017 term. Different nonoverlapping cohorts of students were asked at different time points to complete a survey about mnemonic use. At each time point, the students were asked to recall any mnemonic that they remembered, explain the mnemonic when cued, self-report their degree of familiarity, and apply the mnemonic. Of the 14 mnemonics, acronym-type mnemonics were recalled more frequently, but longer phrase-type mnemonics were explained and applied more often. These findings suggest that instructors should provide scaffolding to move a student from recalling a mnemonic to using a mnemonic toward successful completion of the statistics problem at hand. [ABSTRACT FROM AUTHOR]

This study contributes to the call for influencing practice by increasing attention to how learning environments can be designed to support learning in statistical inference. We report on a design experiment in secondary school (students 14–16 years old), that resulted in a set of lessons with the learning goal of teaching students how to apply concepts and principles of hypothesis testing for making an inference as to whether or not students in secondary school can taste the difference between two brands of cola soda. The design experiment resulted in four design principles for a simulation-based approach for learning hypothesis testing in secondary school. The design principles highlight the combination of practical and digital simulations of samplings. They stress the need for using random generators that allow for high reliability in collecting sample data and introduce a simulation-based method for determining p-values, i.e. to quantify how likely or surprising a sample result, or a result more extreme, is under a null hypothesis. [ABSTRACT FROM AUTHOR]

We tested the educational effectiveness of an inquiry-guided approach (Lee et al., 2004; Prince & Felder in Journal of Engineering Education, 95, 123–138, 2006) to teaching a possible outcome of statistical hypothesis testing known as type 1 error. The approach consisted of showing participants an animated space exploration video. In the video, the commander of a spaceship set out to determine whether the concentration of carbon dioxide on a distant planet exceeded the concentration found on Earth. To make this determination, the commander conducted a series of hypothesis tests. One of the tests suggested that the concentration of carbon dioxide was significantly higher than that on Earth. However, when juxtaposed with the results of the other hypothesis tests, the lone decision to reject the null hypothesis appeared to be a type 1 error. The video's educational effectiveness was evaluated in two ways. First, participants responded to a set of multiple-choice questions on type 1 error, both before and after watching the video. Participants' scores on the multiple-choice test increased significantly from pre-test to post-test. Second, participants rated the video on a set of semantic differential items that tapped its educational effectiveness. On average, participants found the video to be educationally effective. The article concludes with an evaluation of the video's features based on established best practices in statistics education (GAISE College Report ASA Revision Committee, 2016). [ABSTRACT FROM AUTHOR]

Tutors play an important role in effective Mathematics Learning Support, and ensuring good initial training and development is key. Typically, in the UK and Ireland, tutors do not gain any formal recognition for the training they receive. This paper details a four competences model of training for tutors and presents the analysis of feedback from 11 tutors and two staff involved in piloting the model at two institutions. The model, which is informed by professional development guidelines, consultation across the mathematical sciences community, and enabled by a national Mathematics Learning Support Network, provides immediate recognition of the professional development undertaken and is amenable to formal accreditation. A structure of four digital badges is used to assign immediate value to the training and development of tutors. To add further value for tutors, a capstone tutor badge has been established for those completing all four competences. Thematic analysis of feedback from the piloting of the model identifies three overarching themes: the importance of community, developing as a professional, and motivating and enabling professional development. Although primarily of interest to those involved in Mathematics Learning Support, the innovative structure of the model and the insights gained from its piloting are such that it offers potential for genuine transferability across other subject disciplines. [ABSTRACT FROM AUTHOR]

Nontraditional grading methods have recently become more common, and as with any large pedagogical shift, there are a number of questions to consider when applying a new grading scheme to a course. This article summarizes four types of nontraditional grading and shares experiences from the authors who have applied them to a variety of courses in statistics. This article is structured as a set of questions and answers, seeking to address many of the concerns and considerations that one may face as they transition a course's grading structure. for this article are available online. [ABSTRACT FROM AUTHOR]

In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics education we develop the idea of culturally relevant data. We see this development as a key ingredient to implementing culturally relevant pedagogy in teaching data-intensive courses leveraging the centrality of context through data in both statistics and data science to engage students particularly from historically marginalized groups in STEM. We provide suggestions as to ways of finding or creating culturally relevant data and using them in implementing culturally relevant pedagogy to support the learning and flourishing of students in statistics and data science courses. We also present findings from pilot work we have done in implementing these data in statistics courses. Finally, we discuss lingering questions and possible next steps for research in this area. for this article are available online. [ABSTRACT FROM AUTHOR]

Statistics anxiety is a significant problem for students' learning experience in the psychological sciences. This article explores a novel teaching methodological approach during the global pandemic to improve students' performance by reducing their statistics anxiety. The subject design and teaching methodological approach were developed based on twenty years of experience taking into consideration the student feedback, while its implementation was aligned with the 5-E's Victorian Teaching and Learning pedagogical Model (VTLM). Moreover, to reduce the statistics anxiety, two sections were embedded in the Learning Management System of the subject: (1) an interactive web-based mindfulness intervention and (2) a head-start 'Ready, Set, Go' information pack. The implementation of these approaches resulted in a better understanding of statistical concepts and utilization of the SPSS software by the majority of the students (80% pass-rate). Consequently, a 6% increase in pass rates and an average of 32% increase in satisfaction scores for each item in the university central survey were observed. While the uniqueness of the first year of COVID-period makes comparisons difficult, it is matched by the urgency of the need for effective interventions to support student success. [ABSTRACT FROM AUTHOR]

The use of group work projects is common in introductory statistics courses, including projects where students collect their own data. However, the COVID-induced lockdown at the start of 2020 meant that data collection was compromised. In this study, we examine a situation where students were permitted to use artificial (made-up) data for their project during the COVID lockdown. We examine features of these project reports and the data the students used before, during and after the lockdown. We find evidence that during-COVID projects (based on artificial data) were more likely to study people but less likely to involve interaction with people, and were less likely to require t-tests for analysis. We also found that the artificial data were less likely to include outliers, but no evidence that the artificial data were contrived to produce 'small' p values. [ABSTRACT FROM AUTHOR]

There is currently a lack of diversity in the discipline of statistics. In order to provide opportunities within the discipline of statistics to diverse groups, the present study presents a detailed description and evaluation of a one-credit workshop and mentoring program for undergraduate women interested in exploring opportunities within statistics. The key features of this program include career education, mentoring, and student–faculty interaction. A description of the program, along with key artifacts, is provided as a model for researchers and educators interested in creating similar experiences. The main components of this program were weekly class meetings; weekly reading assignments and written reflection; weekly near-peer mentoring meetings; and a final career portfolio. Initial evaluation of the program is conducted based on student and mentor feedback and results indicate that the program was generally well received. Areas for future work, including additional recruitment efforts and extending the program to additional populations, are explored. [ABSTRACT FROM AUTHOR]

This paper is based on a study that explored a pedagogical approach consisting of interactive story-based tasks used in a first-year undergraduate business statistics course. It addresses the types of understanding students developed for selected statistics topics. Participants were undergraduate business majors required to take the course. Data consisted of the students’ work with the four story-based tasks. Findings suggest that interactive story-based tasks have the potential to be a worthwhile pedagogical approach to support students’ understanding of several statistics concepts at various levels of understanding, but further research is needed to better understand the pedagogical approach needed for adult learners to better implement these tasks to support the deepest level of understanding for all concepts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the formative and evaluative activities involving statistical graphs in the new textbooks for Chilean rural multigrade education. The methodology is qualitative, at a descriptive level and uses the content analysis technique. The sample is made up of the six primary education textbooks distributed by the Ministry of Education for rural multigrade schools. The results show the predominance of the bar chart, semiotic level 3, the task of calculating and the personal context in both types of activities, although with respect to the reading level, it is evident that level 4 predominates in the formative activities and level 2 in the evaluative ones. According to the results, it is recommended to incorporate graphs proposed by the curricular guidelines of the Ministry of Education, which are absent in textbooks as well as to include evaluative activities that require reflection on the nature of the data, context, representation and conclusions obtained from them. [ABSTRACT FROM AUTHOR]

Many approaches exist in teaching statistics, however, learning statistics is frequently perceived by students as challenging. While evidence-based teaching approaches like case discussions and flipped-classroom models have been successfully incorporated into multiple disciplines, these methods can have inadequate success when students are affectively detached or sometimes paralyzed by the subject. The sudden need to switch to virtual teaching in Spring 2020 created unprecedented challenges that were overwhelming for instructors and students. This article explores the challenges of teaching a statistics course, and the importance of purposeful use of technology and supportive feedback to effectively engage students and promote learning. Furthermore, this article discusses the results of implementing a Technology-enhanced Supportive Instruction (TSI) model (used since Fall 2019) in teaching a business statistics course. The TSI model uses Microsoft Excel in conjunction with intentional interactions between the student and teacher to help students stay engaged in face-to-face and remote learning. The analysis of Fall 2020 course evaluations, when the course was taught in a dual mode, shows positive students' perception of instruction and engagement, and indicates that Fall 2020 ratings during dual-mode instruction were not different from Fall 2019 ratings during face-to-face instruction, which supports the potential utility of the model. for this article are available online. [ABSTRACT FROM AUTHOR]

An investigative project can engage the learner in all aspects of a statistical investigation, including developing a question or issue of interest, gathering needed information, exploring the data, and communicating the results. This article summarizes the available literature regarding the implementation of investigative projects, including the potential benefits of projects, how projects have been used in a variety of settings, advice for those looking to implement projects, and future research avenues. [ABSTRACT FROM AUTHOR]

This article aims to make a comparative analysis of the active methodology Problem-Based Learning with the Traditional Method of Teaching, as an alternative for teaching statistics. This theoretical essay was conducted to discuss the characteristics of Problem-Based Learning for Statistics teaching, as well as the advantages in relation to the Traditional Method of teaching. The use of active methodologies, particularly the one adopted here, can achieve educational goals and empower students for the demands and changes of contemporary society. This is due to the fact that by investigating and solving problems of the Problem-Based Learning teaching methodology, students can develop skills and social skills, becoming active agents of their learning. Thus, the PBL teaching methodology can offer significant benefits to Statistical Education. By promoting active learning, developing cognitive skills, integrating theory and practice, empowering for citizenship and offering practical guidelines, the PBL teaching methodology can transform how statistics is taught and learned, preparing students for the challenges of contemporary society. [ABSTRACT FROM AUTHOR]

Infographics are visual storytelling techniques used to communicate complex information. However, infographics can be misleading if they are not created ethically. When universities teach how to create infographics, they often do so without emphasizing the ethical issues underlying infographics. To address this gap, we designed a study to educate statistics and data science students about the ethics of infographics by using Rest model's three stages: awareness, orientation, and intention. Students' awareness of the ethical issues underlying infographics was captured before and after sensitizing them to five ethical principles derived from the AI4People's framework applied to a data science context. The students were then exposed to scenarios with ethical dilemmas. Their identification of the ethical principles in these scenarios was measured. The results showed a significant increase in students' awareness of the ethical issues underpinning the interpretation of infographics, suggesting that ethical training of current users and future designers would be beneficial. [ABSTRACT FROM AUTHOR]

This paper explores the specialized knowledge mobilized by a mathematics teacher in the selection and use of examples for teaching sequences. Taking an experimental case study approach, we analyse the examples deployed in a series of third-year secondary level lessons on sequences and identify the different knowledge subdomains activated according to the mathematics teachers' specialized knowledge analytical model. We will analyse active and passive examples, pointing out the mathematical entity that is being exemplified and the aspect of this entity which is being emphasized by the example. The results identify the different subdomains and categories which are drawn on in the selection and use of examples, along with the various interconnections across knowledge subdomains which interact in the process. [ABSTRACT FROM AUTHOR]

Understanding the margin of error (MoE) as a part of statistical literacy which is useful for the public to select credible information from various surveys and polls. The study aims to reveal the levels of statistical literacy of mathematics education students, especially in understanding MoE, and compare them based on four variables: gender, enrollment in a statistics course, year in the program, and type of university. The online survey research involved undergraduate students of the mathematics education study program from 21 universities in Indonesia’s western, central, and eastern regions as the sample (n = 970). Descriptive statistics was used to describe the literacy levels and inferential statistics (t-test and F-test) to compare them based on the four variables. The results of the study reveal that: (1) student literacy in understanding the MoE concept is dominant at the non-literate level; and (2) there are significant differences in students’ literacy levels in terms of gender, enrollment in a statistics course, year in the program, and type of university. The study indicates that the literacy of mathematics education students is still low, so the statistics course is expected to focus more on developing statistical literacy. [ABSTRACT FROM AUTHOR]

Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at‐risk for underachievement via digital learning platforms. Objectives: We sought to understand how early in the academic year students' end‐of‐year learning outcomes could be predicted by their performance and feedback‐seeking behaviours within a digital learning platform. We analysed data collected at different time points in the academic year and across different cohorts of students within the context of high school advanced placement (AP) Statistics courses. Methods: High school students enrolled in AP Statistics spanning three academic years between 2017 and 2020 (N = 726; Mage = 16.72 years) completed 3 or 4 homework assignments, each 2 and 3 months apart. Results and conclusions: Across the three cohorts, and even as early as the first assignment, a model consisting of demographic variables (gender, race/ethnicity, parental education), assignment performance, and interaction with the digital score report explained significant variation in students' final course grades (R2 = 0.314–0.412) and AP exam scores (κ = 0.583–0.689). Students' assignment performance was positively associated with end‐of‐year learning outcomes. Students who more frequently checked their digital score reports tended to receive better learning outcomes, though not consistently across cohorts. Implications: These findings further an understanding of how students' early performance and feedback‐seeking behaviours within a digital learning platform predict end‐of‐year learning outcomes. Lay Description: What is already known about this topic: Students' early academic performance predicts end‐of‐year success.Students' self‐regulated learning behaviours are another predictor of academic success.Clickstream data have been used as an indicator of the application of self‐regulated learning strategies, particularly behaviours associated with seeking feedback.Indicators of feedback‐seeking behaviours reflected via clickstream data have been shown to predict student learning.Yet, how soon associations between early performance and such indicators of self‐regulated learning as feedback‐seeking are robust across different groups of learners, different learning circumstances, and different types of learning outcomes has yet to be more fully understood. What this paper adds: We investigated whether performance and feedback‐seeking behaviours predict end‐of‐year learning outcomes at different points during the academic year across multiple cohorts of learners (2017–2018, 2018–2019, and 2019–2020) and using two different learning outcomes (class grades, AP exam scores).One cohort was affected by COVID‐19 during the 2019–2020 academic year, allowing us to examine whether differences emerge amidst unprecedented circumstances.We used assignment scores and online feedback‐seeking to predict end‐of‐year learning outcomes.Assignment scores were positively associated with end‐of‐year grades.First assignment performance predicted AP exam scores.Use of feedback (an indication of self‐regulated learning behaviours) tended to predict learning outcomes, though not consistently across cohorts. Implications for practice and/or policy: The findings from this study could improve understanding of how digital learning platform measurements predict end‐of‐year learning outcomes though also highlight the importance of context differences.Such findings hold implications for early identification of at‐risk students and appreciation for differences between learners and contexts.These findings also contribute to a growing understanding of the impact of the COVID‐19 pandemic on student engagement and learning. [ABSTRACT FROM AUTHOR]

The flipped classroom (FC) is a pedagogical model with an active learning concept and a hybrid course design that reverses the typical lecture process. Given the widespread use of online delivery methods, there is a need to explore the FC in delivering statistics courses. This study employs a prospective quasi-experimental study design to evaluate the effectiveness of the flipped method and to compare it with the traditional method of teaching statistical data analysis software. Each of the 71 students enrolled in a medicine program was allocated to either a traditional classroom or a FC. The difference between the two teaching methods is evaluated using overall assessment performance as the main outcome measure. Our results show that the teaching method had a large and significant effect on assessment performance, with the FC method exhibiting a higher student overall performance than the traditional classroom. The data suggest integrating the FC in teaching statistical data analysis software is a helpful alternative to the traditional classroom. This study may serve as a guide and inspiration for educators in statistics courses to incorporate the concept of flipped learning, so as to engage students in a more active learning experience. [ABSTRACT FROM AUTHOR]

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. for this article are available online. [ABSTRACT FROM AUTHOR]

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. for this article are available online. [ABSTRACT FROM AUTHOR]

Over four semesters of a large introductory statistics course the authors found students were engaging in contract cheating on Chegg.com during multiple choice examinations. In this paper we describe our methodology for identifying, addressing and eventually eliminating cheating. We successfully identified 23 out of 25 students using a combination of unique academic and digital fingerprints, and identified students who used virtual private networks (VPNs) to protect their online identity. There were two forms of cheating – posting questions and waiting for responses from tutors, and looking for questions that had already been solved. We found that 165 questions from these examinations were posted by 10 different students, but that the most common form of cheating was searching for answers that had already been posted. This paper discusses these patterns of Chegg usage, the consequences of not catching cheating early on, and how students reacted to being caught. Also provided are R and Python code that readers may use to identify cheating students in their own courses. [ABSTRACT FROM AUTHOR]

The aim of this note is to give a general theorem related to the divergence of series. A direct consequence of this theorem is the divergence of harmonic series. Furthermore, we show that this theorem can imply the divergence of some other familiar divergent series. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports presented at the Fourteenth Southern Hemisphere Conference on the Teaching and Learning of Undergraduate Mathematics and Statistics, held in Cape Town, South Africa in November 2023.

Statistical literacy has a large and important role in the teaching of statistics. Most mathematics and statistics courses are hierarchical, and the earlier material forms the foundation for later material. We construct a hierarchical structure for an introductory statistics course using Rasch analysis of the student scripts for the final examination. This forms the basis of a statistical literacy construct that has wider implications than just an undergraduate course. The world is overwhelmed with data from an exponentially increasing number of sources. Such data is of various types and contexts, ranging from social, commercial, scientific, survey and human. Statistical literacy has therefore assumed a larger and more important role. [ABSTRACT FROM AUTHOR]

The design and analysis of experiments (DOE) has historically been an important part of an education in statistics, and with the increasing complexity of modern production processes and the advent of large-scale online experiments, it continues to be highly relevant. In this article, we provide an extensive review of the literature on DOE pedagogy, and provide five perspectives on the subject: one from each of the authors as well as a composite profile derived from a survey of DOE instructors. Our work provides a snapshot of current DOE pedagogy that showcases both the similarities and variety in how the subject is taught, as well as a look ahead at how its instruction may evolve. for this article are available online. [ABSTRACT FROM AUTHOR]

In recent years, big data has become ubiquitous in our day‐to‐day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data—the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP. [ABSTRACT FROM AUTHOR]

Digital Learning Resources (DLRs) have the function of facilitating learning; however, the evaluation regarding their functionality is limited since there are few tools for this purpose. Of these, the instrument that stands out is "Perception of the use of Digital Learning Resources in Statistics" (IPDLR-S), which allows for evaluating the perception of undergraduate university students about the functionality of the LDRs. With the objective of providing a tool with scientific quality, the IPDLR-S instrument underwent to an analysis of construct validity and reliability. The instrument was applied to 1,148 university students, the construct validity analysis was performed using an exploratory (EFA) and confirmatory (CFA) factor analysis, and the reliability was determined by ordinal Cronbach's alpha. According to the EFA, it was found that all the items are represented in the factorial model; however, differences were found for the theoretical model since a fourth dimension emerges (Cooperative Learning). On the other hand, using the CFA, the resulting factorial model showed a good fit (GFI = 0.999, RMSEA = 0.064, CFI = 0.999, and TLI = 0.999). Overall reliability, as well as by factor showed an optimal value (ordinal Cronbach's alpha .98). The IPDLR-S instrument has adequate psychometric properties. [ABSTRACT FROM AUTHOR]

In recent years, the adjunct faculty phenomenon has grown steadily. This research focused on adjunct instructors teaching introductory statistics courses. The purpose of the study was to give a voice to adjunct instructors by allowing them to describe their experiences teaching statistics. We conducted a qualitative study with 15 adjunct instructors of introductory statistics through semi-structured interviews. The participants came from several fields:psychology, nursing, and business, among others. Thematic analysis was used to find themes of statistical anxiety, use of technology in the classroom, lack of curriculum flexibility, and connection to the host institution. Our findings can inform institutions and interdisciplinary departments hosting introductory statistics courses. Similarly, educational statisticians can gain knowledge on the practices and barriers of adjunct instructors. [ABSTRACT FROM AUTHOR]

In recent years, big data has become ubiquitous in our day‐to‐day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data—the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP. [ABSTRACT FROM AUTHOR]

Statistical tables associated with named probability distributions and their families, such as the standard normal, Student's t, and chi-square tables, among others, have been utilized for years and are still widely used today, especially for mathematics and statistics education. While such tables can be found in many statistics textbooks and even on the internet, various shortcomings exist regarding the process of obtaining and distributing tables, as well as the content and appearance of some. In this article, a description is provided of a free, web-based Shiny application created by the author to address these shortcomings and which enables users to generate common statistical tables that are simple and quick to make, customizable, and that users may download and distribute without fear of committing copyright infringement. [ABSTRACT FROM AUTHOR]

ChatGPT is one of many generative artificial intelligence (AI) tools that has emerged recently, creating controversy in the education community with concerns about its potential to be used for plagiarism and to undermine students' ability to think independently. Recent publications have criticized the use of ChatGPT and other generative AI tools in the classroom, with little focus on the potential benefits. This article focuses on the potential of ChatGPT as an educational tool for statistics and data science. It encourages readers to consider the history of trepidation surrounding introducing new technology in the classroom, such as the calculator. We explore the possibility of leveraging ChatGPT's capabilities in statistics and data science education, providing examples of how ChatGPT can aid in developing course materials and suggestions for how educators can prompt students to interact with ChatGPT responsibly. As educators, we can guide the use of generative AI tools in statistics and data science classrooms so that students and educators can leverage the benefits of this technology. [ABSTRACT FROM AUTHOR]

Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics graduate students. We also present results of a survey administered prior to the first workshop and after the last workshop to assess changes in attitudes towards writing and obtain student feedback to improve the workshops. Lastly, we discuss potential modifications of these workshops including ways to use these workshops or elements from them for undergraduates or in statistics and data science courses. for this article are available online. [ABSTRACT FROM AUTHOR]

Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students' names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size leads to isolation. At Utah State University, we offer introductory statistics classes for various audiences using a large lecture format. The authors have collectively led these large lectures dozens of times and found that, despite its shortcomings, the large lecture format can be an asset when teaching statistics. With an active learning approach such as recommended by the revised GAISE College report, large class sizes permit realistic sampling, facilitate student-driven simulation, and provide bountiful opportunities for experimentation. In this article, we discuss the benefits of large classes for statistics teaching and present examples of using large class sizes to create an engaging environment that where students are involved in active learning and collecting real data to foster statistical thinking. [ABSTRACT FROM AUTHOR]

Understanding statistics is essential for engineers. However, statistics courses remain challenging for many students, as they find them rigid, abstract, and demanding. Prior research has indicated that using project-based learning (PjBL) to demonstrate the relevance of statistics to students can have a significant effect on learning in these courses. Consequently, this study sought to explore the impact of a PjBL intervention on student perceptions of the relevance of engineering and statistics. The purpose of the intervention was to help students understand the connection between statistics and their academic majors, lives, and future careers. Four mini-projects connecting statistics to students' experiences and future careers were designed and implemented during a 16-week course and students' perceptions were compared to those of students who took a traditional statistics course. Students enrolled in the experimental group (a synchronous learning experience) and the control group (an online learning experience) were sent the same survey at the end of the semester. The survey results suggest that the PjBL intervention could potentially increase students' understanding of the usefulness of statistics and effectively enhance their perceptions of belonging to the engineering community. This study summarizes the results of this PjBL intervention, the limitations of the research design, and suggests implications for improving future statistics courses in the context of engineering. [ABSTRACT FROM AUTHOR]

Doctoral students in education-related fields are required to take graduate level statistics courses and often face anxiety and negative attitudes about taking these courses. Using a mixed methods survey research design (N = 95), this study explored students' experiences with statistics anxiety and how course instructors can support them to mitigate statistics anxiety and improve attitudes. Analyses of quantitative survey data found that students who had never taken a statistics course before beginning their doctoral program, and students that had completed less doctoral coursework had higher negative attitudes towards learning statistics; and older students had higher statistics anxiety. Plans to use research in the future predicted more positive attitudes and lower statistics anxiety. Analysis of qualitative survey data found that students: (1) expressed that their attitude towards learning statistics was very important and played a big role in how they approached their coursework; (2) considered their plans to use research skills in the future as motivation to learn statistics; and (3) believed that their instructors' attitudes and instructional practices supported learning and decreased statistics anxiety. This study has implications for how statistics and research methods courses are taught in higher education, and how the experiences of graduate students in education may have lasting implications for research use in Prek-12 education settings. [ABSTRACT FROM AUTHOR]

Employing a statistical modeling inspired pedagogy is becoming a widespread practice in the statistics education community. Many have incorporated the practice of formulating conjectures in their modeling-enhanced educational designs and have reported on its benefits. We further elucidate the mechanism through which students' conjecturing may be beneficial, in particular to their emergent reasoning with informal statistical models and modeling, as well as examine what challenges it may entail – the double-edged sword of conjecturing. We introduce a framework to describe young learners' reasoning with informal statistical models and modeling (RISM), in which students' conjecturing is represented as one of two parallel planes of model creation and refinement. We offer a case study of a pair of students' participation in an integrated modeling learning sequence, including both real-world modeling tasks and probability-world modeling tasks. The pair was chosen as both students held strong, opposing real-world conjectures. Our goal is to elucidate the roles these conjectures can play, for better or for worse, to fully harvest the pedagogical potential of conjecturing. [ABSTRACT FROM AUTHOR]

It is well-known that Statistics is a very old science and was devised because of the need for it in dealing with a variety of issues related to life. It has developed and continues to develop steadily within itself whilst concomitantly developing other sciences . Most of the governments in the world are establishing different statistical centers/organizations in order to collect data in an efficient manner for preparing development plans for the different sectors of their countries. These development plans require accurate information so that decisions can be based upon the reliable data acquired. Research is one of the most effective and indeed essential tools utilized in higher education. The minimization of research misconduct, the reinforcement of the validity of the methodology, the achievement of trust in the findings, constitutes a trio of standards required of each and every researcher to adhere to as they constitute the recognized research ethics and research integrity as a means of ensuring that the science of Statistics may be recognized as a valid element in providing the realization of dealing effectively with the issues related to life This paper includes a quick glance at the history of Statistics, and the role of Statistics in higher education as related to institutions, students and academic staff; and we will notice how the majority of basic and traditional works of higher education depend mainly on Statistics. The role of Statistics in data analysis, data science era, research, internal and external auditing of HEIs, the conclusions and the research directions are also discussed. This paper shows that the role of statistics in higher education is not only important but also comprehensive and significant. [ABSTRACT FROM AUTHOR]

The aim of this paper is first to examine, through a qualitative analysis of statistics syllabi, the current state of statistical education in a sample of universities in Colombia. The focus is on statistics teaching in degrees for economics and business administration students. The results from the qualitative analysis reflect a preponderance of traditional and didactic teaching methods centered on the teacher, not on the student. The second aim is to present findings from a case study that has developed an innovative pedagogical intervention, called a data fellows program, from the University of Manchester, United Kingdom, which evidences opportunities for how statistics can be taught effectively to non-STEM majors. Further, the data fellows model has also been explored in the context of developing statistical and data skills capacities in Latin America. We reflect on how the lessons from the UK case study could open up opportunities for rethinking the teaching of statistics in Colombia through developing data projects and experiential learning to practice statistics in the real world. [ABSTRACT FROM AUTHOR]

Learners' efficacy beliefs are an important determinant of their performance and future study in an academic domain, and recent work has highlighted the complexities associated with promoting learners' statistics efficacy. The current study tested the effectiveness of a course-embedded, activity-driven intervention, grounded in principles of self-regulated learning, in promoting graduate learners' statistics efficacy and concept knowledge. The intervention was designed to elicit and scaffold students' analysis-specific efficacy beliefs and facilitate their monitoring of and critical reflection on their statistics understanding across engagement in an intermediate, graduate-level statistics course. Students' pre- and post-course statistics efficacy was assessed; students' statistics concept knowledge was assessed at the completion of the course. Engagement in the intervention explained a moderate amount of variance in learners' post-course statistics efficacy and concept knowledge; efficacy and concept knowledge scores were higher for students in the intervention condition compared with students in the comparison condition. The findings suggest the use of these activities as a method of prompting students to engage more strategically with learning material in statistics. Implications for future research and pedagogy are discussed. [ABSTRACT FROM AUTHOR]

In this experiment, we recruited 261 psychology faculty to determine the extent to which they were able to visually estimate the overlap of two distributions given a Cohen's d effect size; and vice-versa estimate d given two distributions of varying overlap. In a pre-test, participants in both conditions over-estimated effect sizes by half a standard deviation on average. No significant differences in estimation accuracy by psychology sub-field were found, but having taught statistics coursework was a significant predictor of better performance. After a short training session, participants improved substantially on both tasks on the post-test, with 63% reduction in absolute error and negligible overall bias (98% bias reduction). Furthermore, post-test performance indicated that learning transferred across answering modes. Teachers of statistics might find it beneficial to include a short exercise (less than 10 minutes) requiring the visual estimation of effect sizes in statistics coursework to better train future psychology researchers. [ABSTRACT FROM AUTHOR]

This paper discusses a first teaching experience in which a Basic Education teacher investigates her own practice, and it aims at investigating learnings that made her reflections evident, which are shared with a research group in which she participates, while performing and analyzing her practice based on exploratory statistics teaching. Data analyzed consider audio recording transcription of this first teaching experience, and video recording of interactions with the research group from episodes and comments intentionally selected by the research-teacher. The episodes analyzed make evident learnings which emerge from the implementation of lessons based on the exploratory statistics teaching, which revels this complex path marked by the importance of individual and collective reflection in sharing and contraposition context, expanding the teacher-researcher perceptions and professional strengthening based on a previously carried out planning. Investigation of the own practice, despite requiring courage and public exposition, when performed in an honest and responsible way, demonstrates its relevance for the teacher’s professional learning. The experience also has allowed noticing out the exploratory teaching perspective as able to promote students’ reasoning in complex situations, such problematization in this practice, revealing itself as possible and promising for teaching in Basic Education. [ABSTRACT FROM AUTHOR]

The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges. [ABSTRACT FROM AUTHOR]

Many students persistently misinterpret histograms. This calls for closer inspection of students’ strategies when interpreting histograms and case-value plots (which look similar but are different). Using students’ gaze data, we ask: How and how well do upper secondary pre-university school students estimate and compare arithmetic means of histograms and case-value plots? We designed four item types: two requiring mean estimation and two requiring means comparison. Analysis of gaze data of 50 students (15–19 years old) solving these items was triangulated with data from cued recall. We found five strategies. Two hypothesized most common strategies for estimating means were confirmed: a strategy associated with horizontal gazes and a strategy associated with vertical gazes. A third, new, count-and-compute strategy was found. Two more strategies emerged for comparing means that take specific features of the distribution into account. In about half of the histogram tasks, students used correct strategies. Surprisingly, when comparing two case-value plots, some students used distribution features that are only relevant for histograms, such as symmetry. As several incorrect strategies related to how and where the data and the distribution of these data are depicted in histograms, future interventions should aim at supporting students in understanding these concepts in histograms. A methodological advantage of eye-tracking data collection is that it reveals more details about students’ problem-solving processes than thinking-aloud protocols. We speculate that spatial gaze data can be re-used to substantiate ideas about the sensorimotor origin of learning mathematics. [ABSTRACT FROM AUTHOR]

The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center, spread, and shape (so-called global view). However, the latter is challenging for students to acquire. This study, involving 68 students in grades 4, 6, and 8, had two main objectives. First, we aimed to examine the potential impact of grade level on how students perceive and compare distributions presented in dot plots. Despite variations in specific pre-knowledge, our findings showed no significant grade-related differences in students’ gaze behavior or their ability to process global features when comparing distributions. This finding suggests that there is no significant developmental trend across grade levels regarding students’ capacity to perceive distributions globally, a critical aim in statistics education. Our second objective was to extend the findings of our previous work regarding the identification of specific eye-tracking measures as indicators of students’ local vs. global views. In line with our hypotheses, differences in gaze behavior between students with a local and global view were determined regardless of their grade level. This robustness across different educational stages underscores the validity of specific eye-tracking measures as reliable indicators for students local vs. global view of data. Our research highlights the potential of eye-tracking for understanding students’ data interpretation approaches in statistics education. [ABSTRACT FROM AUTHOR]

Inferential reasoning is an integral part of science and civic society, but research shows that it is a problematic domain for many people. One possibility for a more accessible approach to inferential reasoning is to use randomization tests via computer simulations. A case study was conducted with primary preservice teachers after they had passed a course module that introduced inferential reasoning with randomization tests. The study examines how three pairs of primary preservice teachers construct models in a randomization test task with both hands-on and computer-supported components. Our findings suggest that there is a common sequence in the model building process: planning, modeling the random assignment, setting sample size and replacement, and running the simulation. Our findings show in depth how TinkerPlots can be a supportive and constraining medium in the modeling process, building on experiences with physical simulations of a randomization test. These insights can be used for redesigning technology-supported learning trajectories for randomization tests. Together with other case studies our findings provide a good sense of university students' modeling processes. [ABSTRACT FROM AUTHOR]

As the field of K‐12 data science education continues to take form, humanistic approaches to teaching and learning about data are needed. Data feminism is an approach that draws on feminist scholarship and action to humanize data and contend with the relationships between data and power. In this review paper, we draw on principles from data feminism to review 42 different educational research and design approaches that engage youth with data, many of which are educational technology intensive and bear on future data‐intensive educational technology research and design projects. We describe how the projects engage students with examining power, challenging power, elevating emotion and lived experience, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labour visible. In doing so, we articulate ways that current data education initiatives involve youth in thinking about issues of justice and inclusion. These projects may offer examples of varying complexity for future work to contend with and, ideally, extend in order to further realize data feminism in K‐12 data science education. Practitioner notesWhat is already known about this topic Data feminism is an emergent framework for changing data practices and discourse in service of equity and justice.Data science education is rapidly growing as a topic of interest in the educational technology research and design communities.Many educational technology and design projects have been launched and shared in publications that preceded the widespread distribution of the data feminism framework.What this paper adds Data feminism is partially re‐articulated in terms familiar to educational technology research communities.Prior and recent projects are organized with respect to how they illustrate potential connections to core data feminism principles.This paper identifies specific strategies that recent projects have used that have potential for realizing data feminism principles.Implications for practice and/or policy Educational technologists can use the re‐articulated principles of data feminism for education to inform their future design work.Tractable steps to achieve data justice that are attainable within existing educational systems can be pursued.Communities can and should bring together multiple ways of knowing to support new educational practices and futures with data. [ABSTRACT FROM AUTHOR]