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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.

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.

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.

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.

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.

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.

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.

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.

This paper explores the potential of using Chat Generative PreTrained Transformer (ChatGPT), a powerful language model, as an innovative tool to enhance education in the classroom. Drawing from firsthand experience, this paper demonstrates how ChatGPT can be integrated into the teaching and learning process of statistics and data analytics classes. We provided concrete examples of how ChatGPT can be used to clarify concepts such as p-values and confidence intervals, facilitate data analytics by providing step-by-step guidance, and provide explanations for analytical outputs. The benefits of using ChatGPT help improve student engagement, foster critical thinking, and provide personalized assistance. However, the limitations of using ChatGPT, including its potential for bias in responses, should also be addressed.

Self-efficacy is defined as people's perceptions of their abilities to organize cognitive, social, emotional, and behavioral skills and their decisions on how much effort to use to attempt the action. This exploratory sequential, mixed-methods study examined nontraditional doctoral students' perceptions on how instructional strategies helped with their self-efficacy in online statistics learning as aligned with four sources of self-efficacy (i.e., mastery experiences, vicarious experiences, verbal persuasion, and physiological reactions). The relationship between the instructional strategy used and the students' statistics self-efficacy was examined. The effective instructional strategies are discussed and recommendations provided for online statistics instructors and course designers.

This study examines Cho & Demmans Epp's short-form adaptation of Rovai's well-known Classroom Community Scale (CCS-SF) as a measure of classroom community among introductory undergraduate math and statistics students. A series of statistical analyses were conducted to investigate the validity of the CCS-SF for this new population. Data were collected from 351 students enrolled in 21 online classes, offered for credit in Fall 2020 and Spring 2021 at a private university in the United States. Further confirmatory analysis was conducted with data from 128 undergraduates enrolled in 13 in-person and hybrid classes, offered for credit in Fall 2021 at the same institution. Following Rovai's original 20-item CCS, the 8-item CCS-SF yields two interpretable factors, connectedness and learning. This study confirms the two-factor structure of the CCS-SF, and concludes that it is a valid measure of classroom community among undergraduate students enrolled in remote, hybrid, and in-person introductory mathematics and statistics courses.

This study aimed to improve students' literacy skills in carrying out distance learning and examine the effect of problem-based learning in reverse online classes on statistical literacy skills in distance learning. This research was conducted during the COVID-19 pandemic at Prisma University and Manado State University with 198 students taking statistical methods courses. A quantitative approach to quasi-experimental research was used. The instruments used are written tests and video-based projects with relevant entrepreneurship questions. Data was collected through pre- and post-tests, then analyzed using a two-way Analysis of Variance (ANOVA) and an independent sample t-test. The results showed that problem-based learning in reverse online classes positively affected statistical literacy skills in distance learning. In addition, positive responses from students working on statistical projects in entrepreneurship were found. This may be observed from the perceptions of students who show high satisfaction with the assignment in the field of entrepreneurship in this study. Furthermore, the results of this study confirm that student involvement is a critical element in implementing distance learning.

A review of studies analyzing the statistical knowledge of primary schoolchildren (6-12 years old) is carried out. Based on a review in JCR/SSCI, Scopus, Eric, Google Scholar, Science Direct, World Scientific, Springer, and Wiley Online library, 18 articles (2003-2021) have been identified and analyzed based on two objectives: (1) to identify the different study approaches and (2) to analyze the elements of statistical knowledge. The results show that almost half of the investigations were carried out based on one of the following approaches: the Toulmin approach (TM), the statistical mathematical working space (SMWS), the structure of observed learning outcomes (SOLO) taxonomy and Curcio's graph reading levels (CGRL). It is concluded that CGRL is the most common approach and statistical graphs are the most analyzed statistical objects.

During the last two years, the COVID-19 pandemic had a secondary effect of increased media content loaded with mathematical, often probabilistic information (and misinformation). Our exploratory study investigates the probabilistic intuitions, misconceptions, biases, and fallacies in conditional probability reasoning of mathematics teacher candidates in the context of the pandemic. The pre-service mathematics teachers who participated in our study were given a questionnaire with five contextual conditional probability problems, all formulated similarly to media statements often encountered when discussing the COVID-19 pandemic. Our findings confirm the previous findings on biases and fallacies related to conditional probability problems with a social context. They were also indicative of several types of errors (both numerical and logical) as more common than expected. Our results also reveal that pre-service mathematics teachers apparently separate the content learned in the classroom from the application of the knowledge in critical examination of the information to which they are daily exposed by the media.

This meta-analysis study aimed to examine the effect of data literacy education, which affects databased decision processes, on data use knowledge and skills of school administrators and teachers. Therefore, theses on data literacy education for school administrators and teachers and relevant studies in peer-reviewed journals were examined through several databases. The study was conducted using the Comprehensive Meta-Analysis (CMA) software, using a total of eight studies published between 2006-2021. The results revealed that the selected studies were heterogeneous. Therefore, a random effects model was applied in the study. The overall effect size value of data literacy education was calculated as 2.16 according to Cohen d, suggesting that data literacy education makes a positive contribution to data use knowledge and skills of school administrators and teachers. The subgroup analyses conducted to determine the source of heterogeneity in results have shown that data literacy education did not differ by the type or the country of publications but varied by the type of participants, where studies conducted with mixed participants had high effect values.

Students' knowledge of data analysis determines the quality of the research they report but they tend to perceive courses on statistics or data analysis as challenging and unappealing. The present study aimed to investigate the correlation between biology education students' attitudes toward statistics and their proficiency in determining various data analytical techniques. This study involved biology education students from the Universitas Muhammadiyah Malang who had completed a course in statistics, research methods, and data analysis. The Survey of Attitudes Toward Statistics (SATS) and the Statistics Assessment of Graduate Students (SAGS) were used sequentially as instruments for collecting data on student attitudes toward statistics and data analysis competencies. The results revealed that while most students had positive attitudes toward statistics, they perceived the subject as challenging to learn. Additionally, the students demonstrated low levels of competency in data analysis. The aspects of cognitive competence and difficulty from SATS correlate significantly with their SAGS score. These results suggest that there is a need for a revision of the biology education curriculum to better equip students with the skills and knowledge to increase their data analysis competencies.

Teacher knowledge is one of the main factors in the quality of mathematics learning. Many mathematics teachers have difficulty using proportional reasoning. Proportional reasoning is one of the essential aspects of the middle school mathematics curriculum to develop students' mathematical thinking. Teachers should realize that developing proportional reasoning is not an easy task. In this study, we investigated how teachers give proportional reasoning about the concept of proportional and nonproportional situations, especially in making sense of them. The research subjects were mathematics teachers who had taught proportional-related material. Data was collected using task-based interviews outside the teacher's working hours. Data analysis and interpretation were completed using a framework meaning-based approach. The results of the data analysis showed that the teacher is careful in understanding information, is aware of multiple meanings, and knows key information in understanding the contextual structure of proportional and non-proportional situations. Furthermore, they are also able to identify additive and multiplication relationships, have flexibility in understanding proportional and non-proportional situations separately or collectively, and understand problem-solving systematics in detail.

The purpose of this study was to determine K-8 preservice teacher (PST) candidates' statistical thinking when selecting the best center representation for the given data. Forty-four PSTs enrolled in a Statistics and Probability for K-8 Teachers course in a university located in the southeastern region of the United States were asked to complete a 2007 National Assessment of Educational Progress test item. All 44 PSTs' data were qualitatively analyzed for correctness and statistical thinking strategies used. Findings were that most PSTs either incorrectly selected the mean, rather than median, as the best measure of center for the given data or did not use appropriate statistical reasoning when explaining their answers. Future research includes modifying the explanation component so PSTs must better explain their statistical thinking for their choice of best measure of center using the context of the problem. Future research could also include implementing a pre- and post-test design with the post-test item embedded in the final exam. This design will provide additional understanding of how much knowledge PSTs bring to the course versus how much they learn in the course and provide incentive for giving thoughtful consideration for their answers.

Over the last decades, it has been shown that teaching and learning statistics is complex, regardless of the teaching methodology. This research presents the different learning profiles identified in a group of future Primary Education (PE) teachers during the study of the Statistics block depending on the methodology used and gender, where the sample consists of 132 students in the third year of the PE undergraduate degree in the University of the Basque Country (Universidad del País Vasco/Euskal Herriko Unibertsitatea, UPV/EHU). To determine the profiles, a cluster analysis technique has been used, where the main variables to determine them are, on the one hand, their statistical competence development and, on the other hand, the evolution of their attitude towards statistics. In order to better understand the nature of the profiles obtained, the type of teaching methodology used to work on the Statistics block has been taken into account. This comparison is based on the fact that the sample is divided into two groups: one has worked with a Project Based Learning (PBL) methodology, while the other has worked with a methodology in which theoretical explanations and typically decontextualized exercises predominate. Among the results obtained, three differentiated profiles are observed, highlighting the proportion of students with an advantageous profile in the group where PBL is included. With regard to gender, the results show that women's attitudes toward statistics evolved more positively than men's after the sessions devoted to statistics in the PBL group.

This paper analyzes the relationship between proportional reasoning and understanding fair games in Costa Rican students. We conducted a quantitative and qualitative analysis of the answers to six items on comparing ratios of increasing difficulty level and another item on prize estimation in a fair game. We describe the strategies employed and the semiotic conflicts detected in 292 Costa Rican students from Grades 6 to 10 (11-16-year-olds), comparing the findings with those established in previous research. The results show an increase in the level of proportional reasoning with the grade, although the age at which the higher levels are reached is lower than that assumed by Noelting. The percentage of students applying correct strategies in the fair game problem also increases with grade, and a relationship between the understanding of fair game and the level of proportional reasoning is observed.

Despite statistics learning becoming more important during this information explosion era, many students still deem the subject complex and challenging. Measures of dispersion, a critical component of statistical knowledge that students often struggle with, have received little attention in research on statistics education. The goal of this study was to uncover students' errors in solving problems involving measures of dispersion by examining students' response in the diagnostic test through the lens of APOS theory. The participants consisted of 85 grade 11 high school students and were then divided into three groups according to their performance to better understand the difficulties and errors made by students from different cognitive levels. The findings revealed that majority of low achievers operate at the action level, as indicated by the numerous conceptual errors discovered during the test. These students have limited conceptual understanding on the topic which required proper remedial from the educators. The study's results are discussed, as well as potential implications for education.

This article showcases an example of a large-scale open educational resource (OER) statistics course redesign at Guttman Community College from evaluation, creation, and development. It highlights ways I identified students' needs and responded to them, integrated equity into the OER design by describing how social justice principles were applied, as well as explicit examples of integration of culturally and locally relevant content in the design. This practical illustration of a course redesign is significant due to the lack of literature available on creating culturally and locally relevant and responsive OER. It is the hope that this example will encourage and inform the development of other such relevant and responsive OER projects to promote equity within open education.

We investigated how the transition to remote instruction during the COVID-19 pandemic affected students' engagement, self-appraisals, and learning in advanced placement (AP) Statistics courses. Participants included 681 (M[subscript age]=16.7 years, SD[subscript age]=0.90; %female=55.4) students enrolled in the course during 2017-2018 (N=266), 2018-2019 (N=200), and the pandemic-affected 2019-2020 (N=215) school years. Students enrolled during the pandemic-affected year reported a greater improvement in affective engagement but a decrease in cognitive engagement in the spring semester relative to a previous year. Females enrolled in the pandemic-affected year experienced a greater negative change in affective and behavioral engagement. Students enrolled during the pandemic-affected year reported a greater decrease in their anticipated AP exam scores and received lower scores on a practice exam aligned with the AP exam compared to a prior year. Although students were resilient in some respects, their self-appraisal and learning appear to have been negatively affected by pandemic circumstances.

With the emergence of data science as an inherently multidisciplinary subject, there is increasing demand for graduates with well-rounded competence in computing, analytics, and communication skills. However, in conventional education systems, computing & quantitative, and communication skills are often taught in different disciplines. Data storytelling is constructing and presenting data stories to highlight the analytical insights to achieve the communication goals to a specific audience. Digital data storytelling leverages digital storytelling techniques and best practices in communication to deliver stories that can be shared in digital formats to a wide audience. In this paper, we describe and reflect on a semester-long project-based learning pilot using Digital Storytelling as a framework to allow students to explore topics themed around human flourishing and sustainability with the end goal of constructing data stories delivered in digital or video format (i.e., Digital Data Storytelling). The pilot work was conducted in an introductory data science course at a 4-year Minority Serving Institution in collaboration with students studying non-STEM disciplines at a partner community college. Our pilot demonstrates the potential benefit of this sustainability-aware Project-Based Learning design in raising students' awareness of sustainability issues, increasing confidence in cross-disciplinary communication competency, and at the same time deepening their understanding of data science concepts. We further reflect on the significant role of an effective program model as well as challenges and opportunities for building transdisciplinary communication competency to prepare for a diverse data science workforce.

We analyze here the online and face-to-face teaching of mathematics and statistics at the university level and compare the results of students from two courses of applied mathematics. We examine the influence of online teaching on the performance of students with the help of grades from five consecutive years. A questionnaire on satisfaction with online teaching was also administered to the students. We show that online teaching has a positive impact on the successful results of students if we provide them with video recordings created for all lectures and seminars. We present some opinions of students on this type of teaching.

Statistics education is increasingly important to our society with enrolment increases of 16% in introductory statistics courses and 85% in upper-level statistics courses. Research has demonstrated many factors related to students' behaviors and outcomes in statistics courses such as past achievement, attitudes, and effort. We sought to model these factors together to better understand how introductory statistics students' attitudes were related to students' achievement behaviors and what student characteristics mediated such relationships. Structural equation modeling with data from N=301 students in an introductory statistics course for psychology majors revealed that majors with higher GPAs had more interest, enjoyment as well as utility value for statistics, and these variables were in turn related to expectations for success or achievement behaviors. Females had lower interest in statistics, and this was related to lower expectations of success. The findings highlight the need to increase interest and enjoyment and utility value for non-majors studying statistics. Recommendations for how to adapt the statistics classroom to that end are discussed.