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Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's academic environment and the professional application of their skills in an employment and internship setting. This study examined the design of a data analytics practicum course. The main objectives included (1) the identification of topics and skills employers look for in new hires in data analytics-related internships and entry-level positions, (2) the development and implementation of a Data Analytics practicum course and (3) reflection on the first-time offering of the course and suggested improvements for the next iteration. As part of this study, industry and organization survey responses drove the design of the course and development of key student learning gains for five learning modules throughout the semester. Faculty within the departments of information technology (IT), mathematics, and statistics collaborated in the construction, development, and implementation of team-teaching instructional practices of the Data Analytics Practicum in Spring 2023. This study applies an interdisciplinary approach to data analytics practicum development and instruction.

Assessment of attitudes towards statistics [ATS] is needed to support the success of statistics education in tertiary institutions, so measuring instruments with high accuracy is required. However, existing instruments to measure ATS have not considered the use of technology as an essential variable affecting success in statistics education. The current study sought to fill this gap by developing a standardized instrument to measure ATS and considering aspects of technology use as a necessity for statistics education in the modern era. The study involved 367 students from various study programs spread across several universities in Indonesia as participants. To examine the quality of the instrument, we performed factor analysis, reliability estimation, and item calibration. We calibrated items based on classical test theory [CTT] and item response theory [IRT] using the graded response model [GRM]. Exploratory factor analysis [EFA] indicated three main factors (i.e., interest, difficulty, and value) for measuring attitudes toward statistics. Factor loading of each factor component > 0.45, indicating that all items contributed to the main factor. Cronbach's alpha coefficient of the three factors ranged from 0.784 to 0.929, indicating that the instrument was reliable. Item calibration based on CTT and IRT-GRM indicated that item performance was satisfactory regarding item endorsement and discrimination. In addition, the information function indicated that the instrument accurately measures attitudes from very low to very high levels. Overall, the psychometric properties of the instrument indicated that the instrument was valid, reliable, and feasible for use in practice and research in the field of education.

Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.

High-impact practices include many options to help increase student learning, and project-based learning (PBL) is one such method. In this study, we look at the effect of PBL activities embedded in the content for introductory level mathematics and statistics courses across a semester. A pre-test and post-test are used to measure student learning, while student reflections and satisfaction is measured by using a survey. Additionally, these sections with intertwined PBL are compared with sections of the same course without PBL on final grades. Our results indicate that students perform better on the post-test after intertwined project-based learning throughout the semester, and most of the students are satisfied with their learning through the projects and making connections with the content. The comparison of final grades for courses with and without PBL shows similar achievement levels, and student performance is not affected by the reassignment of instructional time to group work in lieu of traditional lectures. With this study we recommend intertwined PBL with milestone projects throughout the semester to improve student learning gains and satisfaction with introductory level mathematics and statistics courses.

This study aimed to validate a modified Statistics Anxiety Scale for students in the United States taking university courses. Modifications were made by changing the wording of several items to be consistent with American English, and to accommodate students taking statistics courses in various formats. Items were added to investigate anxiety toward the use of statistical packages, and peer mentoring. Data from 352 participants and exploratory factor analyses were used to analyze the original 24-item SAS (SAS-O) and a version of the SAS with six additional items (SAS-M). The three-factor structure for SAS-O was consistent with the original validation study, explaining about 64% of the items' variance. The factor structure for SAS-M contained an additional two factors, that explained a total of 68 % of the items' variance. The factors were internally consistent, correlated with one another, and negatively correlated with Wise's Attitude Toward Statistics scale. Male students generally had lower application anxiety and examination anxiety than female students, and lower asking for help anxiety than non-traditional students.

Learning statistics can be challenging for many students, due to their inability to engage in statistical reasoning and application of techniques. This challenge becomes compounded in online learning contexts where students are spatially and temporally separated from the teacher. This paper describes and explains a case of theory-driven interventions designed to enhance the learning experiences of students enrolled in two similar business statistics units, one for undergraduate and the other for postgraduate programs. The paper based its claims primarily on the analysis of data from a student evaluation of teaching survey. This study affirmed the importance of a pedagogy-first approach. It argued that the interventions, which were effective in enhancing the student learning experience, were underpinned by a robust pedagogical analysis of the teaching and learning issues using both constructive alignment and transactional distance theory lenses.

Many educators and professional organizations recommend Quantitative Reasoning as the best entrylevel postsecondary mathematics course for non-STEM majors. However, novice and veteran instructors who have no prior experience in teaching a QR course often express their ignorance of the content to choose for this course, the instruction to offer students, and the assessments to measure student learning. We conducted a case study to investigate the initial implementation of an entry-level university quantitative reasoning course during fall semester, 2018. The participants were the course instructor and students. We examined the instructor's motives and actions and the students' responses to the course. The instructor had no prior experience teaching a QR course but did have 15 years of experience teaching student-centered mathematics. Data included course artifacts, class observations, an instructor interview, and students' written reflections. Because this was a new course--and to adapt to student needs--the instructor employed his instructional autonomy and remained flexible in designing and enacting the course content, instruction, and assessment. His instructional decision making and flexible approach helped the instructor tailor the learning activities and teaching practices to the needs and interests of the students. The students generally appreciated and benefited from this approach, enjoyed the course, and provided positive remarks about the instructors' practices.

Teaching and learning undergraduate statistics has been a most challenging task for undergraduate psychology majors (Salkind, 2017). A seasoned statistics instructor consulted with a seasoned instructional designer on a method to improve a particularly demanding course using a performance improvement approach to address learning difficulties she had noted in previous semesters. The Decoding the Disciplines methodology identified the most challenging concepts and provided a methodology to improve student learning performance. The methodology focused on five core concepts in psychological statistics: probability, variability, central limit theorem, independent/dependent variables, and degrees of freedom. The Decoding the Disciplines curriculum was used for three semesters. In these three semesters, performance was compared pre and post on these concepts within the semester. Repeated measures t-tests found a significant change in the percentage of correct answers between the pretest and the final exam on the five core concepts.

Since it has become necessary for each individual to be statistically literate, statistical education research has taken teachers' competencies into its agenda. The knowledge needed to teach statistics differs from the knowledge needed to teach mathematics since statistics is different from mathematics. Teachers and researchers need to consider these differences and be aware of the challenges of statistics teaching. This article focuses on the nature of statistical knowledge for teaching (SKT). Models of conceptualizing and measuring SKT from research literature were reviewed critically. The strengths and weaknesses of models were discussed. The article concludes with some implications for teacher education and research.

Students believe mathematics is best learned by memorization; however, endorsing memorization as a study strategy is associated with a decrease in learning (Schoenfeld, 1989). When the world changed with the onset of the COVID-19 global pandemic, instruction transitioned to fully remote instruction where many assignments and examinations became open textbook, open note, and even open Internet. In this new world, did students change their beliefs about the role of memorization in learning? Did academic performance change? And did the relationship between memorization beliefs and academic performance change? The current study takes advantage of data collected in an online interactive statistics textbook used by courses before (in-person) and after (remote) the declaration of the COVID-19 pandemic at three institutions, each representing a part of the California Master Plan for Higher Education (e.g., University of California, California State University, and California Community Colleges). Results from 2668 students who used the textbook showed that the UC institution had lower memorization belief scores compared to both the CSU and CCC institutions. Even when controlling for institution and chapter of the textbook, lower memorization belief scores were related to higher performance. Surprisingly, there were no significant differences in either memorization beliefs nor performance before and after transitioning to online remote instruction due to the pandemic. Although much of educational research is conducted in one institution, this kind of research can identify differences across institutional contexts to understand how learning can be affected by different teaching formats, including in-person and online/distance, brought on by disruptive social changes such as a global pandemic.

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants' motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

Owing to an exponential increase in the number of courses offered online, it is crucial to understand this mode of delivery on a deeper level. In this study, associations among course performance, the use of online resources (i.e., online homework assistance, practice questions and practice tests), mode of delivery (online versus in-person), and session length (Fall/Winter for 8 months versus Spring/Summer for 10 weeks) were examined. Archival data were used from an educational website for an introductory statistics course at a medium-sized Canadian university. Anonymized data were retrieved from 738 students enrolled in the course between 2018 and 2021. Course performance was measured by final course grades and use of resources was assessed in terms of the number of site visits and downloads. It was found that use of online resources was significantly and positively correlated with course performance. However, session length and mode of delivery did not yield significant differences in terms of final course grades. Future studies could examine potential moderators in the relationships between the use of resources with the session length, the delivery method, and course performance to see the effectiveness of the resources in various course delivery models (in-person, hybrid, synchronous online, asynchronous online, etc.).

The aim of the present exploratory study was to examine students' situational engagement and motivation in the statistics classroom at Zayed University, in Dubai, United Arab Emirates (UAE). Two instruments were used for this purpose: a) experience sampling method (ESM), and b) the validated Mathematics Motivation Questionnaire (MMQ). This study employed two samples, at undergraduate level (2nd and 4th Semesters). Participants consisted of 100 students enrolled in Statistics I and Statistics II (Probability and Structure of Randomness). The results indicate that, apart from challenge and effort, emotional engagement is not significantly different across different activities. The results also indicate increases in intrinsic value and utility value and decreases in test anxiety. Finally, results indicate higher engagement and effort when social interaction is purposely planned and fostered, such as in small groups. On the contrary, individual class activities seem to generate slightly lower levels of engagement and effort. These findings have significant implications for educators and researchers who seek to enhance students' engagement and motivation in their statistics courses.

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).

Sampling distributions are fundamental for statistical inference, yet their abstract nature poses challenges for students. This research investigates the development of high school students' conceptions of sampling distribution through informal significance tests with the aid of digital technology. The study focuses on how technological tools contribute to forming these conceptions, guided by an emerging theory that describes this process. A workshop for high school students was organized, involving 36 participants working in pairs across four sessions, each with access to a computer. These sessions involved problem-solving activities, with the teacher introducing key concepts in the initial three sessions. The analysis, employing grounded theory, aimed to characterize the nature of students' conceptions of sampling distribution as evident in their responses. The findings reveal a transition from empirical to informal conceptions of sampling distribution among students, facilitated by computational mediation. This transition is marked by an abstraction process that includes mathematization, processing, uncertainty/randomness, and conditional reasoning. The study underscores the role of digital simulations in teaching statistical concepts, facilitating students' conceptual shift critical for grasping statistical inference.

Background: College students often do not retain what they learn in Statistics in order to apply it in Experimental Psychology. Self-explanation, that is, elaborating on what one is trying to learn by asking questions, making inferences, etc., improves learning and may improve retention. Objective: The purpose of this study was to determine whether self-explanation was superior to students' usual study methods specifically for learning some basic concepts in statistics, and, if so, if it was similarly useful for retention a semester after the initial learning. Method: We used 199 college students as participants in a randomized, between participant, two-part experiment examining the effects of training by prompting self-explanations as a potential solution to this applied problem. Results: The self-explanations that we elicited improved initial learning and were superior to students' usual study methods, but did not benefit retention. Conclusions: Future research on improving the quality of the self-explanations and training with spaced retrieval practice, in order to benefit retention, is suggested. Teaching Implication: Self-explanation should be implemented for teaching statistics in order to benefit initial learning. However, teachers should explore other methods to accomplish retention.

University students often learn statistics in large classes, and in such learning environments, students face an exceptionally high risk of failure. One reason for this is students' frequent statistics anxiety. This study shows how students can be supported using e-learning exercises with automated knowledge of correct response feedback, supplementing a face-to-face lecture. To this end, we surveyed 67 undergraduate social science students at a German university and observed their weekly e-learning exercises. We aggregated students' exercise behavior throughout the semester to explain their exam performance. To control for participation bias, we included essential predictors of educational success, such as prior achievement, motivation, personality traits, time preferences, and goals. We applied a double selection procedure based on the machine learning method Elastic Net to include an optimal but sparse set of control variables. The e-learning exercises indirectly promoted the self-regulated learning techniques of retrieval practice and spacing and provided corrective feedback. Working on the e-learning exercises increased students' performance on the final exam, even after controlling for the rich set of control variables. Two-thirds of students used our designed e-learning exercises; however, only a fraction of students spaced out the exercises, although students who completed the exercises during the semester and were not cramming at the end benefited additionally. Finally, we discuss how the results of our study inform the literature on retrieval practice, spacing, feedback, and e-learning in higher education.

This research investigates the role of reinforcement sensitivity theory (RST) in statistics education among two distinct samples of undergraduate psychology students. In Study 1, 318 students in a third-year statistics course completed self-report measures of RST, anxiety, attitudes, and self-efficacy concerning the study of statistics. In Study 2, 577 students from first-, second-, and third-year statistics courses participated. Controlling for age and gender, both studies found students who were high in goal-drive persistence reported lower statistics anxiety, higher statistics self-efficacy and more favourable attitudes toward learning statistics, while students who were high in the tendency to avoid threat reported higher statistics anxiety. Those with a more sensitive behavioural inhibition system reported greater statistics anxiety and less favourable attitudes, in Study 2 but not Study 1. Results indicate that RST dimensions account for as much as 23% of additional variance in statistics anxiety, 18% in statistics self-efficacy, and 11% in attitudes, after controlling for age and gender. These studies highlight the utility of RST dimensions, notably goal-drive persistence, threat sensitivity, and behavioural inhibition, in providing critical information as to personality differences among students that need to be considered when developing programs targeting statistics anxiety, attitudes, and self-efficacy.

This paper presents the results of a systematic scoping literature review of higher education mathematics and statistics support (MSS) evaluation focusing on its impact on students. MSS is defined as any additional organised mathematical and/or statistical aid offered to higher education students outside of their regular programme of teaching by parties within the students' institution specifically assigned to give mathematical and/or statistical support. The objective of this review is to establish how MSS researchers investigate the effect of MSS on students and what that impact is. Based on a predefined protocol, five databases, the proceedings of eight conferences, two previous MSS literature reviews' reference lists, and six mathematics education or MSS networks' websites and reports were searched for publications in English since 2000. A two-round screening process resulted in 148 publications being included in the review which featured research from 12 countries. Ten formats of MSS, seven data sources (e.g., surveys), and 14 types of data (e.g., institution attainment, usage data) were identified with a range of analysis methods. Potential biases in MSS research were also considered. The synthesised results and discussion of this review include the mostly positive impact of MSS, issues in MSS evaluation research thus far, and rich opportunities for collaboration. The role MSS has and can play in mathematics education research is highlighted, looking towards the future of MSS evaluation research. Future directions suggested include more targeted systematic reviews, rigorous study design development, and greater cross-disciplinary and international collaboration.

This study argues that the works of philosopher Jürgen Habermas can provide useful directions for mathematics education research on statistical literacy. Recent studies on the critical demands posed by statistical information in media highlight the importance of the communicative component of statistical literacy, which involves students' ability to react to statistical information. By adapting Habermas' construct of communicative rationality into a framework for statistical literacy, a novel analytical tool is presented that can provide theoretical insights as well as in-depth empirical insights into students' communication about statistical information. Central to the framework are the four validity claims of comprehensibility, truth, truthfulness, and rightness which interlocutors need to address to engage in statistical communication. The empirical usefulness of the framework is shown by presenting the results of a study that examined Grade 5 students' responses to fictional arguments about the decline of Arctic sea ice. The Habermas-based framework not only reveals that complex evaluations of statistical arguments can take place even in Grade 5 but also shows that students' evaluations vary greatly. Empirical results include a content-specific differentiation of validity claims through inductively identified sub-categories as well as a description of differences in the students' uses of validity claims.

Great effort has been invested in increasing STEM achievement among students, but feelings of low status among underrepresented or otherwise vulnerable students may be creating additional challenges. The present study assessed how perceptions of social status within the classroom--termed subjective social status--aligned with objective course performance and differed by sex, first-generation status, work status, and race/ethnicity among 713 students enrolled across three introductory statistics classes. Findings indicated that final exam score was moderately related to ratings of subjective social status, suggesting that factors besides objective course performance may influence classroom subjective social status. When asked to explain how they evaluated their standing in the course, students reported five main themes, including both academic achievement with respect to exam scores and their understanding of course content. When examining differences by status-based identities in subjective social status, we found that female and first-generation students had lower subjective social status compared to their male and continuing-generation peers, although results were less robust for first-generation status. Likewise, working students reported lower subjective social status relative to non-working students, despite showing no difference in final exam score. In contrast, although Asian/Asian American students outperformed Latine students, there were no differences in reports of subjective social status between Asian/Asian American, Latine, and white students. Taken together, results suggest that factors beyond course performance may relate to students' subjective social status, and subjective social status may contribute to disparities in academic performance, especially by sex and work status. [Page range listed on publisher's website incorrect, correct page range is p1921-1946.]

The main goal of this study was to evaluate the impact of an animation and visualization of data structures (AVDS) tool on both perceptions and objective test performance. The study involved a rigorous experiment that assessed the usability, acceptability, and effectiveness of the AVDS tool in solving exercises. A total of 78 participants responded to questionnaires and were exposed to the AVDS tool, after which they completed a performance test, half (39) with the AVDS tool (the experimental group) and half (39) without the tool (the control group). Findings showed that the usability of AVDS was good; the experimental group even perceived AVDS usability as excellent. The results show that perceived usefulness, perceived ease of use, and attitudes toward usage jointly led to positive intention to use the AVDS tool. Furthermore, perceived ease of use was a key factor influencing participants' intention to use AVDS. In addition, the AVDS system improved test results and provided flexibility in use, enhancing learning experience and performance.

Innovative instructional methods can help improve student engagement and learning outcomes when teaching difficult subjects, such as statistics. This instructional innovation article illustrates how gamification can be applied in management education to improve students' learning experience, engagement, and acquisition of knowledge. Our purpose is to demonstrate how gamification is not only a powerful way to build on the use of games and game thinking in our field, but also a versatile application of education technology that could potentially enhance the way management knowledge is taught. Furthermore, it is a low-risk way for management educators to join and contribute to the larger virtual revolution. We document the process of combining the Technology, Pedagogy, and Content Knowledge (TPACK) competency framework and the Mechanics, Dynamics, and Aesthetics (MDA) design framework to create both theoretically and practically motivated gamification designs in a graduate-level statistics class. With student data and feedback, we demonstrate that gamification helped create a positive learning experience, facilitated interactions in the course, and assisted the learning of statistical knowledge. We offer suggestions and concrete examples for interested educators to implement gamification in their courses.

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre-service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment-based teaching approach (FVPA-based teaching approach) can (1) promote pre-service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre-test-post-test control group quasi-experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre-service teachers were included in the study. The pre-service teachers in the experimental group adopted the FVPA-based teaching approach, and those in the control group adopted the traditional peer assessment-based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre-service teachers critically interpret data, understand their teaching and learning issues, and improve self-reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre-service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA-based teaching approach in improving pre-service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed.

This article presents a case study that explores digital experiences in statistics teaching within Danish lower secondary school, focusing on the development of students' statistical concepts. The study tracks the progress of a student named Frida, who engages with the digital tool TinkerPlots over the span of a year. Frida developed a unique 'plot--stack--drag' technique that significantly influenced her conceptual development during this period. Her routines with the tool not only supported her in some instances, but also created conflicts due to their impact on her personal goals and anticipations. This article delves into the educational implications of the dialectical relationship between students' development of tool-based routines and their personal goals established during the process. The research findings highlight the profound impact of interactions between students and digital tools, such as TinkerPlots, on shaping students' understanding of statistical concepts. This underscores the importance of educators' heightened awareness of students' personal goals and anticipations influenced by digital tools, which, in turn, opens the door to innovative learning opportunities.

This article describes an activity and resource from The New York Times that can be used to help learners cultivate critical statistical literacy. Critical statistical literacy involves understanding, interpreting, and questioning statistical information to make informed decisions (Casey et al., 2023; Franklin et al., 2015; Weiland, 2017). It is a vital skill that needs to be learned and reinforced with students early and often. The authors find What's Going On in This Graph? (WGOITGraph?), a collaboration between the American Statistical Association (ASA) and The New York Times Learning Network (NYT LN), to be a useful, accessible, effective online resource and share an example implementation.

This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging due to the potential abstraction of multivariate datasets, and especially when students have a minimal background in statistics or data science. This method aims to help teachers bridge the gap between basic descriptive statistics and the more advanced concepts of PCA; this is done by disregarding mathematical optimization, while emphasizing the use of summary tables and the programming language R. The focus is on implementing this method in an introductory tertiary data science course; however, it may potentially be used in higher level courses, and across a variety of disciplines.

Statistical literacy is critical for all sociology students because it facilitates academic and professional success, high-paying jobs, and informed citizenship. Most students, however, lack adequate statistical literacy to engage with sociological research. Within that general deficit, there are gender, racial, and social-class differences, with students from historically marginalized groups starting and staying behind. In this conversation, we argue that to deepen statistical literacy and reduce inequalities, instructors must be willing to sacrifice breadth of content to attend to students' psychosocial needs throughout sociology curricula, especially in courses where quantitative methodology is not the core focus. We synthesize prior literature into a holistic psychosocial approach for teaching quantitative sociology content at all course levels: build interest and motivation, foster a growth mindset, develop statistical efficacy, encourage belonging, and challenge stereotypes.

Learning to interpret visual representations of data is an important step towards becoming an informed consumer of research. The current study assesses the effectiveness of two versions of a scaffolded online module in improving students' ability to identify main effects and interactions in 2 × 2 factorial designs. Across two experiments (N = 313), we compared exam performance between sections of a lower-division conceptual statistics course that completed the module (in addition to other course activities) to a section that did not complete the module (n = 91). The first iteration of the module (used in Experiment 1, n = 96) was completed once individually and once with peers and did not enhance students' individual performance on conceptually-related exam questions. However, performance on the module was low, indicating that students may have needed more support to benefit from this experience. In Experiment 2 (n = 126), we made three empirically-driven changes to better scaffold student learning: we added a worked example, offered a greater variety of examples, and instructed students to complete the whole activity with peers. Under these conditions, performance increased on related exam questions. We conclude that this freely available module is a promising intervention to strengthen students' ability to understand factorial designs.

During the COVID- 19 pandemic, when her school shifted to distance learning, and anti- Asian hate crimes reached alarming heights of racially targeted attacks, Susie Sujin Min found herself suffering in silence. The distressing news and video footage of horrendous hate crimes were having their intended impact, leaving her as an Asian American feeling upset, isolated, vulnerable, and silenced. Although her students--who are primarily of Asian descent and over 90% students of color--did not explicitly bring up this issue during remote learning sessions in spring 2021, she sensed that they, too, were affected and suffering in silence. She decided she could no longer pretend that everything was alright behind the computer screen. While she had been incorporating activities and projects centered around social justice and equity issues for years, this was different--it was deeply personal. As their teacher and an Asian American adult, she recognized the importance of providing guidance and hope by demonstrating how classroom lessons could empower them in understanding and navigating through these challenges. In this article, she will highlight some examples from her Statistics and Probability and AP Statistics classes, both of which she taught in the fully remote learning setting during the 2020-2021 school year, illustrating the positive impact these changes--of redefining the purpose of teaching and learning mathematics--had on her students' learning and identity development.

Educators are tasked with continually updating course objectives, content, assignments, and assessment to meet model curriculum guidelines. IS2020 proposes program level outcomes for required and elective areas. Two elective areas in IS2020 are Data and Business Analytics and Data and Information Visualization. IS2020 details 14 program level competencies (organized within knowledge elements and skills) that are then integrated into individual course-level design. This work presents a set of laboratory exercises to fulfill the competencies of both elective areas. The set of exercises have been taught in the classroom over several years and have been refined to evaluate coverage of the 14 program competencies. The exercises begin with step-by-step tutorials that build student capabilities with software. Advanced exercises propose open challenges to solve. These resources provide IS programs with a draft of potential exercises to include in courses and a framework for covering program-level objectives.

There has been a trend of increased statistical expectations for students and calls for increased statistical preparation for their teachers in recent years, but preparation has not yet reached recommended levels. A similar preparation gap existed at the inception of the Advanced Placement Statistics program, and this study examines a group of statistics teachers identified as exemplary by experts in the field to determine what challenges they faced and how they overcame them. Semi-structured interviews using a Communities of Practice framework (Wenger, 1998) were conducted. The challenges and responses to those challenges are identified, and these have implications for supporting new and established teachers of statistics at the K-12 level.

In this new era drenched with data, statistical literacy becomes more essential for individuals to be able to read, communicate, and make informed decisions. Moreover, statistical literacy is highly essential for education policy makers who are highly accountable for all policy outcomes including school improvement, resource allocation, curriculum planning and intervention. Hence, there is a need to understand their perceptions and beliefs. The aim of this study is to explore whether attitude towards statistics and statistical anxiety are related to the education policy makers' statistical literacy. Considering that statistics coursework is the basis and major contributor to a statistically literate society, real problems with statistics are likely due to non-cognitive factors, which include attitudes or beliefs towards statistics. There is a global increase in literature exploring beliefs and attitudes of teachers towards statistics, indicating that studies on attitudes towards statistics do not stop at the students' level but should also be extended to education personnel who uses statistics in their workplace. While pre-service teachers in college claimed that statistics anxiety is the main obstacle to get their teaching degree. This is alarming as they are the future teachers and education policy makers with anxiety may develop avoidance to read educational diagnoses containing statistical information. Participants self-reported their statistical literacy with 20 multiple choice items tailor made to the work of education policy makers. Data were drawn from a survey elicited using a cross-sectional method on 328 education personnel working at different levels in Ministry of Education. The findings show that attitude towards statistics is not significantly related to statistical literacy while statistics anxiety has a significant negative relationship with statistical literacy. Statistical anxiety also has a negative significant relationship with attitudes towards statistics. These findings help strengthen Model of Statistical Literacy, where dispositional element including beliefs and attitude was addressed while confirming Anxiety Expectation Model. Future studies to explore other potential predictors of statistical literacy and suggested to investigate possible difference in attitude towards statistics between adult workers and students.

An online authentic learning environment (OnALE) is proposed in this study to facilitate students' learning of inferential statistics in a real-life context. The efficacy of the OnALE, in comparison to the conventional approach relative to the students' performance, was explored. Respondents from the experimental group were purposively selected to complete the Perception Questionnaire regarding the features of the OnALE. The Analysis of Covariance (ANCOVA) on the post-test scores using prior knowledge scores as covariate disclosed a significant variance in the post-test scores between control and experimental groups (F (1, 74) = 10.924, p < 0.05, partial [eta-squared] = 0.129), with the experimental group displaying a higher mean score. Outcomes from the Perception Questionnaire revealed that all respondents at least agreed that each authentic learning characteristic in the OnALE facilitated their learning. The highest and the lowest rated characteristics were "Collaboration" and "Multiple Roles and Perspectives," respectively. The framework of the OnALE characteristics for varying levels of students' performance unveiled the combinations of authentic learning characteristics beneficial to students from different performing groups. This framework functions as a guideline for statistics educators and learning designers to provide an effective online learning environment.

Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test 40 participants' ability to learn simple first-order and complex second-order relations between uni-modal visual and cross-modal audio-visual stimuli. Using the difference in reaction times between sequenced and random stimuli as the index of domain-general statistical learning, a significant difference and dissociation of learning occurred between the initial and final learning phases. Furthermore, we used a negative and positive occurrence-frequency-and-reaction-time correlation to indicate implicit and explicit learning, respectively, and found that learning simple uni-modal patterns involved an implicit-to-explicit segue, while acquiring complex cross-modal patterns required an explicit-to-implicit segue, resulting in a X-shape crossing of regularity learning. Thus, we propose an X-way hypothesis to elucidate the dynamic interplay between the implicit and explicit systems at two distinct stages when acquiring various regularities in a multidimensional probability space.

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.

The COVID-19 pandemic has brought the rapid transition to online quantitative education, leading to unique challenges and opportunities in assessments for both students and instructors. Focusing on undergraduate students and teaching staff at a Canadian university, this study investigates and compares their experiences and perspectives regarding assessment in online mathematics and statistics courses during the pandemic. Framed by the theory of community of inquiry, our analysis highlights how assessment affected the cognitive, social, and teaching aspects of their learning and teaching experiences. The analysis was based on interview data from the focal student and teaching staff participants. Findings indicated that students and teaching staff had complex experiences with the assessments designed and administered in the online courses during the pandemic. Such complexity was attributed to varied factors, such as technology integration, uncertainty during the pandemic, assessment structure, alignment with course content, and connection with real-life applications. Implications for transforming the assessment practices to meet the demands of students and instructors in online mathematics and statistics education were also provided.

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.

Software installation is an early barrier to success for graduate students in our online statistics class. To help them overcome this potential barrier, our instructional team created the SPSS Early Adopter Program (SEAP). The SEAP gamified the installation task with a discussion badge and bonus point; these external motivators encouraged students to avoid procrastinating on their SPSS installation. To document student experiences with the SEAP, we conducted a nonexperimental, descriptive study of 475 students enrolled in an online statistics course. Data collection and analysis consisted of descriptive statistics of the SEAP completion rate and a reflexive thematic analysis of students' spontaneous reactions to the installation process. Results showed that 205 of 475 students (43%) completed the SEAP challenge. In their unsolicited responses, 14 SEAP completers (7%) spontaneously expressed confusion and uncertainty; we coded this as a theme of academic vulnerability. Another 27 SEAP completers (13%) spontaneously expressed comments like, "I finally figured it out!" We coded these responses as a theme of academic resilience. For academically resilient students, the SPSS Early Adopter Program transformed a success barrier into a rewarding challenge.