Search

The present paper investigates one aspect of questionable research practices relating to Arabic L1 learners of foreign languages, namely the use of statistics. The objective of the paper is to argue that reproducible research requires adopting wise practices in linguistics and that the excessive focus on quantification does not seem to serve this purpose. Statistical significance tests in quantitative research are routinely used in linguistic inquiry as well as language teaching and learning studies with a view to supporting the relevant explanatory insights in linguistics. In this article, I will expose the misuse of statistics by doctoral students in English departments of Morocco working on Arabic L1 learners' data, by highlighting some practices that are at odds with international good practices in academic research in linguistics. I will take stock of the current questionable practices in this regard to dispel some of the misunderstanding about the use of statistics which is now gaining grounds lest this becomes an orthodoxy. I will argue that research on Arabic L1 learners' data should be focused more on exploration and discovery, as well as the validation of epistemological insights than on mere descriptive quantification geared to hypothesis verification. These areas of focus constitute the crux of academic research in linguistics, but they seem to be lost in statistics in doctoral students' theses. Recommendations and solutions are provided for enhancing transparency and improving reproducibility of doctoral research outcomes to advance theory building and the delivery of new research lines in linguistics as well as to avoid the risk of research waste, in line with the requirements of open science.

This study investigates the impact of Project-Based Learning (PBL) with an emphasis on statistics on 26 Kinesiology students from a prominent Chilean university. A mixed-methodological approach was employed for the qualitative and quantitative analysis of data collected through surveys, supplementary interviews, and performance evaluations of these students. Furthermore, group grades during the project execution were examined. The correlation between academic performance and the perception of learning through this method was explored. The results indicate a generally favorable assessment of PBL, emphasizing its contribution to the development of statistical and scientific skills, as well as improvement in academic performance, with the option to incorporate additional methods to cater to different student needs. It is concluded that PBL is a potential pedagogical strategy that promotes active engagement in learning and the development of practical skills relevant to health sciences students in Chile.

This paper argues that engaging students in informal statistical reasoning from early school years is essential for the development of statistical understanding. We investigated if and how children aged six-seven years old identified variation in a table of data and made predictions through the design of a teaching experiment. The classroom teaching experiment was comprised of four 45 minutes lessons addressing the understanding and interpretation of data sets. In order to describe students' informal predictive reasoning, we used the framework of "data lenses". More specifically, we analyzed the different types of answers the students produced as they engaged in predictive reasoning during an interview given before and after the teaching experiment. The participation of students in (classroom) and out-of-school (family) communities of practice was also taken into consideration. Our results demonstrate that the students benefited from their learning experience and developed data understanding.

The traditional points system and subsequent Grade Point Average (GPA) in education perpetuates an evaluation of academic performance which reflects arbitrary weighting of assignments and/or assessments. As such, GPAs which are calculated using a traditional points system are not unidimensional in their design. The baseball batting and slugging percentage, which serves as established metrics for performance evaluations among baseball players, better reflects unidimensionality. In essence, this paper puts forth an analysis and discussion which posits that baseball batting average and slugging percentage can serve as an example for how unidimensionality can become more prevalent in educational assessments, especially as it relates to the traditional points system and GPA.

The study compared two survey datasets from higher education students in Germany and Ghana regarding access to digital devices; perceived value of digital media, tools, and services used for learning; gap analysis of the actual and desired use of digital teaching and learning formats; and types of media usage profiles among students. The findings underscored commonalities between the two groups, revealing that students in both contexts are equipped with mobile devices, and are highly utilized for their learning. Similarly, both student groups exhibit a preference for utilizing external media, tools and services not owned nor administered by their respective universities. However, a stark contrast emerged in terms of the provision of, and expressed demand for digital teaching and learning formats, attributable to significant disparities in the underlying internet infrastructure and service provision between the two countries. The high intensity in the use of videos, social networks and messaging applications means majority of the students in both contexts were classified as entertainment users of media by means of a latent class analysis. While students in Germany showed differentiation between non-traditional and traditional students in terms of their media usage patterns, there was little differentiation among Ghanaian students. The study concludes by offering suggestions for enhancing support for non-traditional learning and improving digital education in Ghana and similar contexts.

This article presents the results of applying an educational sequence implemented with technological support on an LMS and focused on peer assessment that was designed specifically to address key concepts in statistics with first-year undergraduate students. Individualized information is available for a total of n=232 students to support the empirical conclusions that are drawn. Based on the comparison of the peer assessments and the academic performance obtained in the two previous academic years in which a different methodology was applied, differential effects are found in the quality of the assignments presented. This, together with the perception of the learning by the students, suggests the incorporation of peer assessment processes in future curricular design.

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.

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.

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.

Most higher education institutions in the UK now offer some form of additional individual support for mathematics and statistics. Whilst numerous studies have shown mathematics support can improve grades and reduce failure rates, there is a lack of research on other outcomes of interest such as anxiety or confidence, and very little research relating specifically to statistics support. This study uses quantitative and qualitative results from student questionnaires to evaluate the effectiveness of support in reducing anxiety and increasing confidence immediately after the first statistics support session and in the longer term. Comparisons of and preferences for online or face-to-face sessions and other aspects relating to anxiety were also explored. Key quantitative findings include a significant reduction in statistics anxiety after only one session of statistics support and a long-term increase in confidence with statistics. When asked how support impacts on anxiety or confidence, key themes emerging included feeling comfortable asking questions in statistics support, tailoring to individual needs and confirmation of understanding. The majority of students preferred face-to-face sessions over online particularly those with higher levels of statistics anxiety. Although differences were generally not significant, higher levels of anxiety were observed before online sessions and greater changes in anxiety occurred during face-to-face sessions.

As the key to STEM education, teachers' level of STEM literacy plays a crucial role in teaching and learning. The purpose of this study is to assess the current STEM literacy of science teachers in China. This is a survey study using a questionnaire instrument to gain both quantitative and qualitative data. The quantitative data were analyzed by Statistical Package for the Social Science (SPSS) to evaluate the status of STEM literacy among China's school science teachers. Interpretive methods were applied for qualitative data analysis to support the quantitative data. The researcher found that current science teachers generally have a poor understanding of STEM education, lack STEM knowledge and teaching skills, but have the awareness to improve their own literacy. It is hoped that by investigating the STEM literacy of science teachers, the research will have a positive impact on improving science education and is expected to contribute to the development of science education in China.

Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6-12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL tasks (syllable and letter), but showed intact learning for the nonlinguistic SL tasks (tone and image). In the ASD group, better linguistic SL was associated with higher language skills measured by parental report and sentence recall. Therefore, the atypicality of SL in autism is not domain-general but tied to specific processing constraints related to verbal stimuli. Our findings provide a novel perspective for understanding language heterogeneity in autism.

The current study presents a systematic review of teachers' data literacy, arising from a synthesis of 83 empirical studies published between 1990 to 2021. Our review identified 95 distinct indicators across five dimensions: (a) knowledge about data, (b) skills in using data, (c) dispositions towards data use, (d) data application for various purposes, and (e) data-related behaviors. Our findings indicate that teachers' data literacy goes beyond addressing the needs of supporting student learning and includes elements such as teacher reflection, collaboration, communication, and participation in professional development. Considering these findings, future policies should acknowledge the significance of teacher dispositions and behaviors in relation to data, recognizing that they are as important as knowledge and skills acquisition. Additionally, prioritizing the provision of system-level support to foster teacher collaboration within in-school professional development programs may prove useful in enhancing teachers' data literacy.

Generative artificial intelligence (AI) has shown the potential to reshape the world and redefine daily workflows. One specific instance of generative AI, ChatGPT, specializes in understanding natural language and generating human-like conversational text. Its free access, user-friendly interface, and instant feedback have propelled its popularity within and beyond education. Given its extensive knowledge of traditional statistics and contemporary data science, it can be considered for integration into modern statistics education. However, there have been ongoing questions and serious concerns regarding the accuracy and accountability of the responses generated by ChatGPT. This study explores ChatGPT's capabilities in addressing conceptual problems, implementing analytical techniques, and facilitating teaching while considering its disadvantages and ongoing development. With continued practice and deeper insights into this novel technology, its benefits can be cautiously leveraged in teaching and learning activities.

Curriculum change and the ready access to school level appropriate statistical software has seen the focus of statistical practice for novice statisticians move from primarily constructing graphs and calculating statistics to describing and reasoning from distributions. Many multi-faceted concepts and statistical ideas underpin distributions, which students find difficult to navigate and cognitively coordinate. Limited research, however, exists on how to enhance students' communication when describing distributions. This paper explores the actions of a teacher as she supported 14-15-year-old students to develop notions of distribution and to describe distributions. The findings indicated that the teacher, through knowing, modelling, and listening, supported the development of student statistical language and communication. Students, through engaging in specifically designed instructional activities to engineer learning around the concept of distribution, seem to be able to transition from using their own language to using statistical language to describe distributions that communicated the concepts and features that were identified in the distribution description framework.

The teaching and learning of statistical thinking begins at a young age in Australia, with a focus on data representation and interpretation from Foundation Year (age 5), and the collection, sorting and categorising of items from the natural environment starting even earlier. The intangible concept of "data," as part of statistical literacy, can be complex for children to grasp, especially when applying the notion of data to the everyday world or when data are explored in isolation to an investigation process. Authentic data modelling experiences present meaningful opportunities to apply statistical thinking although expert STEM knowledge is not always accessible to primary classroom teachers, nor is it always obvious how to implement such authentic problems within a classroom context. In this exploratory case study, we present data from a Year 4 classroom (age 9) statistical investigation addressing, 'How big is a leaf?' linking data to the real-life STEM context they represented. The authors were interested in how the teacher's communication processes supported her students' emerging understandings about data. Wit's (2018) cognitive tuning framework offered a way to capture how the communication processes in a group build to a commonly shared frame of reference. Findings revealed a pattern of communication between the teacher and students, supporting students' changing conceptions of data and related statistical thinking processes, throughout the investigation.

The introduction of statistical concepts into school curricula in Australia and New Zealand in the early 1990s initiated an ongoing research program into the learning and teaching of statistics and probability in both countries. This paper reviews the contribution of Australian and New Zealand researchers to building statistical literacy at school, alongside international developments. From recognising how students develop understanding of specific statistical and probabilistic concepts, through teacher knowledge and beliefs for teaching statistics, to intervention studies and targeted teaching, the field of statistics education has grown and changed. Statistics and probability are now well established as part of the mathematics curriculum. The importance of linking statistical literacy and statistical understanding across the curriculum, as well as in STEM, has also begun to receive attention as other subjects have recognised the importance of data in their fields. Following a comprehensive review of the field in Australia and New Zealand, this paper then considers emerging areas of interest, such as new approaches to data visualisation, and suggests future research.

The capacity to interrogate data with critical thinking is a strong predictor of statistical literacy (SL). This data interrogation, from the data consumers' perspective, incorporates four complex response skills: "interpreting," "communicating," "evaluating," and "decision-making," and those skills are strongly supported by students' appreciation of three interrelated knowledge components ("text and context," "representation," and "statistical-mathematical knowledge"). Due to the need to be critical data-information readers, students' SL should develop during their formal schooling. The aim of this paper was to investigate differences in SL between Indonesian year 9 and year 12 students and between female and male students. The same test was administered to 48 year 9 students (50% females) and 48 year 12 students (50% females) from 16 different schools in Indonesia. Findings revealed that the highest percentage of year 9 and 12 students demonstrated evidence of "consistent but non-critical thinking" (level 4), suggesting that they exhibited their statistical knowledge but not in critical ways. There were 42% of year 9 students showing limited statistical thinking (levels 1 to 3) compared to 17% of year 12 students. Furthermore, while there were no significant gender differences in students' SL and its all skills, the study shows significant grade level differences in overall SL as well as in its skills except "interpreting." Implications of this study include the development of a framework that provides a coherent assessment of students' SL from a data consumers' perspective, along with suggestions for classroom teaching.

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

In 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.

Much has been done on assessment literacy (AL) components and job demand-resources (JD-R). However, an interdisciplinary look at AL components as the predictors of JD-R and its possible consequences for the engagement and burnout of teachers' assessment performance has been neglected. To fill this gap, the present study explored this issue in the context of Iran. To this end, through convenience sampling, 146 Iranian EFL teachers were selected to answer questionnaires on AL, JD-R, burnout, and engagement. A series of multiple regression analyses were run to analyze the collected data. The results showed that some components of AL such as 'test construction', 'administering, rating, and interpreting test', 'psychometric properties of a test', 'using and interpreting statistics', and 'authenticity' were significant predictors of job demand. Moreover, the results revealed that alternative and digital-based assessment, recognizing test type, distinction and function, and authenticity were significant predictors of job resources. Furthermore, test construction, administering, rating, and interpreting test, psychometric properties of a test, and using and interpreting statistics could significantly predict teachers' burnout. In addition, alternative and digital-based assessment, giving feedback in assessment, and ethical and cultural considerations in assessment turned out to significantly predict teachers' engagement. These findings can have theoretical and practical implications for stakeholders.

This study investigates the correlates of statistics anxiety. Considering that statistics anxiety and spatial anxiety have been separately correlated with related constructs (e.g., mathematics anxiety, academic performance, etc.), the possibility that spatial anxiety plays a role in statistics anxiety is explored. When facing statistics or mathematics operations, people may imagine or visualize the task operations they must do to obtain the result. To examine this hypothesis, 778 students in a Social or Health Sciences program, enrolled in a--often mandatory--statistics course from Canadian, French and Belgian universities completed an online survey. The results show moderate to strong positive correlations between all three types of anxiety (spatial, mathematics, and statistics). In addition, a mediation analysis reveals the intermediate role played by mathematics anxiety in the relationship between spatial and statistics anxieties. Nonetheless, the direct link from spatial anxiety to statistics anxiety is non-negligible in the model. Finally, the results also indicate that women report higher levels of statistics anxiety, which may be partly explained by their higher level of spatial anxiety.

It is important to note that the development of pre-service teachers' noticing abilities does not happen spontaneously; hence, assistance programs are crucial. This qualitative study aimed to examine pre-service teachers' noticing of student thinking within the context of lesson study. Three pre-service teachers conducted three lesson study cycles. Lesson plans, voice and video recordings of lesson study meetings and implementations, observations, field notes, and reflective writings are used as data collection techniques. The findings indicated that the pre-service teachers' early levels of noticing were constrained. Their noticing levels increased as the lesson study progressed. Hence, the improvement of pre-service teachers' noticing abilities can be assisted by lesson study. Activities such as planning, reflection and implementation helped pre-service teachers develop their noticing levels. To enhance the development of noticing skills, it can be proposed that lesson study should be integrated into teacher training programs.

The goal of this research was to provide a general assessment framework of students' statistical reasoning in medicine, then build three scales to assess students' statistical reasoning ability in solving practical medical problems, including Description, Interpretation and Prediction. On that basis, the study designed a set of tools to assess students' statistical reasoning ability in solving practical medical problems. Through the analysis of the students' performance, assessments of the students' statistical reasoning in medicine were done. These research results were suitable for student assessment in medical statistics courses, useful for faculty and students in teaching and learning medical statistics.

J.D. Brown (JD) served as the author of an instructional column titled "Statistics Corner" in the "Shiken": JALT "Testing & Evaluation SIG Newsletter" for an extensive period spanning over two decades, from 1997 to 2019. This publication was under the auspices of the Testing and Evaluation Special Interest Group, a subdivision of the Japanese Association of Language Teaching. The audience for the columns was diverse, and included language teachers, graduate students, and language practitioners encountering real-world issues with language assessment. Throughout his tenure, JD addressed over 40 reader-submitted inquiries related to testing and quantitative research. The subjects explored in these columns can be broadly classified into two thematic areas: Second Language Testing and Second Language Research. This present article aims to provide an exhaustive examination of the diverse range of topics covered within each of these thematic categories and to trace the evolution of these subjects over the course of JD's twenty-year involvement with the newsletter. Its goal is to help situate JD's abiding concern with connecting theory and practice.

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

The ability of the brain to extract patterns from the environment and predict future events, known as statistical learning, has been proposed to interact in a competitive manner with prefrontal lobe-related networks and their characteristic cognitive or executive functions. However, it remains unclear whether these cognitive functions also possess a competitive relationship with implicit statistical learning across individuals and at the level of latent executive function components. In order to address this currently unknown aspect, we investigated, in two independent experiments (N[subscript Study1] = 186, N[subscript Study2] = 157), the relationship between implicit statistical learning, measured by the Alternating Serial Reaction Time task, and executive functions, measured by multiple neuropsychological tests. In both studies, a modest, but consistent negative correlation between implicit statistical learning and most executive function measures was observed. Factor analysis further revealed that a factor representing verbal fluency and complex working memory seemed to drive these negative correlations. Thus, the antagonistic relationship between implicit statistical learning and executive functions might specifically be mediated by the updating component of executive functions or/and long-term memory access.

The evidence-based decision-making movement often assumes that once evidence is available (e.g., via the What Works Clearinghouse), decision-makers will integrate it into their practice. Research-practice partnership studies have shown this is not always true. In this paper, we argue that instead of assuming research will be useful and used, we should directly study strategies for disseminating evidence and mobilizing knowledge. We present a framework for organizing knowledge mobilization research into three facets: (1) examining "norms" embedded in evidence we communicate, (2) "descriptively" understanding how decision-makers reason about this evidence as well as their varied decision-making needs, and (3) "prescriptively" developing and evaluating communication strategies that facilitate better use of evidence by decision-makers. We delineate this three-faceted framework--"normative," "descriptive," "prescriptive"--and demonstrate how it considers the perspectives and priorities of both researchers and decision-makers. Focusing on a case study--of how statistical evidence is conveyed by clearinghouses--we point to existing evidence in education and other fields such as data visualization and cognitive psychology that should inform our communication practices and identify areas where further knowledge mobilization research is needed.

Evidence suggests that Active Learning (AL) and Active Learning Spaces (ALSs) have the potential to improve student learning outcomes. In this study, the student experiences of AL and ALSs in four classes in mathematics and statistics at a Norwegian university are examined through classroom observations, surveys, and interviews. The student interviews are analysed using constant comparative method, which resulted in one main category and three sub-categories presenting what the students perceived to be significant factors that impacted their learning experiences in the ALSs and how these factors affected their motivation to engage with the subject. The main category is called Engagement and Comfort, and the three sub-categories are called Students' Varying Preferences, Student-student Communication, and Teachers' Communication. Students' motivation was looked at through an Approach-Avoidance Theory lens. Crucial to the student experiences was that some students felt deeply uncomfortable in the ALSs, which prevented them from engaging and benefitting from the ALSs. This research highlights how students' comfort in the ALSs is essential to ensure their engagement and suggests many aspects that can support students' comfort. Future research and professional development programmes built upon the reflections here can be used to support improved educational quality.

Purpose: This paper aims to describe the statistical arguments produced by third-grade students (8-9 years old) and to identify the teaching support for collective argumentation in a lesson based on data comparison. A Lesson Study Group researched and planned the lesson around a problem from the official mathematics textbook. Design/methodology/approach: From an interpretative perspective, we have analysed the arguments produced by students in a situation where they should give reasons to support or refute some claims based on data analysis. We looked at how some teaching support strategies promoted collective argumentation. Findings: The strategies outlined in the lesson plan enabled the teacher to foster collective argumentation, encouraging students to provide reasoning based on data analysis. The lesson plan served as a means of improving the context presented in the textbook, guiding the development of well-structured teaching, and promoting high-quality teaching practices. Research limitations/implications: One of the limitations, and future lines of research by the LSG is the deepening of teachers' understanding of the support required for CA in their classrooms so that they can distinguish the components of an argument and their role in and interpretation of the effectiveness of the arguments. In addition, the lesson plan did not consider in depth aspects such as the argumentative processes of the students, nor did it implement specific actions to promote argumentation. Addressing these limitations would be interesting and is necessary, considering that teachers still do not understand the key role of argumentation in learning and that the mastery of CA strategies is still a challenge for initial teacher training and for professional development programs for teachers. Practical implications: For research purposes, evidence is presented of the types of teacher support in collective argumentation in a comparative task of two dot plots. For teaching purposes, these types of support can be pointed out within Lesson Study Groups and included in lesson plans, allowing discussions and base-data argumentation. Social implications: The research has social implications in civic development, educational inclusion, and adaptation to technological and pandemic changes, with a focus on inferential statistical reasoning and the crucial role of the teacher in facilitating collective argumentation in the online school classroom. Originality/value: The study enriches knowledge about the potential of Lesson Study and the possibilities of planned online lessons to develop professional learning on collective argumentation with data, as exemplary teaching practices that should be widely shared.

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

Mathematics education research has been slow to adopt inclusive practices concerning Queer students. In this study, two doctoral dissertations highlight responsible research approaches to advance inclusivity. Ambiguous use of the terms "sex" and "gender" and using only binary statistical methods can lead to erroneous assumptions about factors that might be biological, psychological, or social. Queer-related discussions in classrooms and research, both now largely non-existent, could support students in feeling visible and valued.

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

The internet, which is constantly advancing in technology, together with the rapidly changing internet communication technology terminals, has formed a new internet media, which has penetrated into all fields of human material life and spiritual life. This article proposes a design scheme for optimizing the impact of internet environment health on college pupils' ideological and moral education and the promotion path. It summarizes the influencing factors of contemporary college pupils' ideological and moral education through cluster analysis, optimizes the factors using Apriori arithmetic in data mining, and realizes the promotion of the path to solve problems. Finally, it carries out simulation testing and analysis. In order to promote the effective development of college pupils' ideological and political education, we should strengthen the internet management, purify the internet environment, strengthen the construction of "red websites," and enhance their attractiveness.

Purpose: Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of the rich statistical regularities in the writing system. Reading skills in developing first- and second-language readers in English have been shown to be predicted by individual differences in sensitivity to regularities in mappings from orthography to phonology (O-P) and semantics (O-S), with good readers relying more on O-P consistency, and less on O-S associations. However, SL and its relation to reading has been primarily studied in English readers in high-income Western countries. Method: We examine individual differences in sensitivity to regularities in emergent French readers in rural agricultural communities in Côte d'Ivoire (N = 134). Results: We show that, in contrast to previous studies, in this cohort better readers are leveraging semantic associations more strongly, while individual differences in sensitivity to orthographic consistency did not predict reading skill. Relatively little variance in reading skill was explained by sensitivity to regularities, and we discuss these findings in terms of literacy acquisition in low-literacy and low-exposure contexts. This showcases the importance of cross-linguistic and cross-cultural research to back up universal theories of literacy, and suggests that current SL accounts of reading must be updated to account for this variance in reading skills.

Two novel proofs show that the sum of a specific pair of normal random variables is not normal are established in this note. This is one of the most often misunderstood facts by first-year students in probability theory and statistics. The first proof is concise using the moment generating function. The second proof checks whether the moments of the sum have the property of normal distribution.

For the last decade or more, circulation numbers of physical materials have declined in academic libraries across the United States. In the spring of 2020, the COVID-19 pandemic drastically altered society and daily life, not to mention library functions. In particular, fears of contagion via physical surfaces and transmission by contact led many libraries to shutter their in-person services or temporarily close altogether. The circulation of physical materials was hit particularly hard, as the ability to browse shelves, check out items, access interlibrary loan, and a host of other similar services were curtailed. This article will examine the statistical markers of physical material handling (checkouts, renewals, item browses, etc.) for two years before and after the pandemic, to reach conclusions about how COVID-19 impacted usage, what we can tell about general patterns in circulation before and due to these changes, and ultimately what these numbers tell us about the future of traditional material use.

Introductory undergraduate statistics courses widely focus on statistical concepts or software-based data analysis. Despite the fact that the analysis of real data has shown to enhance students' engagement, the step of data collection is often neglected. Once students know the challenges of data collection, they are more aware of potential imperfections, such as a lack of representativeness, during data analysis. In this paper, we present a course that closes the gap allowing Business and Economics students to conduct a full survey under realistic conditions including questionnaire design, sampling, and data analysis. It entangles theory and application by combining course-based research experiences with cooperative learning and a flipped classroom approach. Students do not only obtain competences in the field of statistics, they also gain experiences and self-confidence for future research projects because the lecturer acts as a mentor guiding the students throughout the project. Although statistics is usually an unpopular field for Business and Economics students, their motivation was high throughout the semester as they acted as researchers who analysed a specific research question. This is in agreement with student feedback, which highlights the promotion of research-related competences and self-efficacy within the course.

Despite the growing interest in mindfulness in higher education, the literature on its relation to decision-making under risk (i.e. academic misconduct) and statistics anxiety is scarce. The present research shall fill this gap. Based on the prospect theory, we assessed the mediating effect of mindfulness on the relationship between statistics anxiety and academic dishonesty moderated by risk aversion. Data were collected from 791 undergraduate students in six Israeli academic institutions studying for bachelor's degrees in social sciences. Questionnaires included the following measures: risk behaviour according to the prospect theory framework, Mindful Attention Awareness Scale, Statistics Anxiety Rating Scale, Academic Misconduct Scale and sociodemographic variables. Correlations among these variables were explored. The data was analysed using Structural Equation Modelling (SEM). The results indicate that the variance in academic dishonesty is explained by students' statistics anxiety with a mediation of Mindfulness moderated by Risk Aversion. Mindfulness negatively affects Academic Dishonesty, while Risk Aversion has a significant positive effect on Mindfulness. Finally, among individuals with high statistics anxiety, Risk Averse individuals show significantly higher Mindfulness than Risk Seekers. We conclude that mindfulness-based interventions might be a constructive tool to reduce risk-taking and promote ethical decision-making among individuals who experience high levels of statistics anxiety. Furthermore, developing mindful skills may help individuals with higher anxiety levels neutralize these unwanted feelings and get along with their learning tasks. Hence, avoid academic unethical behaviours.

The COVID-19 pandemic altered course delivery in higher education at many universities. This article evaluates the differences between student experiences in the fall 2019 semester (pre-pandemic) and those during the fall 2020 semester (pandemic) within a multidisciplinary, project-based introductory statistics course. Results indicated that there were minimal differences in student experiences of this course based on delivery mode (in person vs. online).

There is a need for public health undergraduates to acquire skills in data collection, statistical programming, and infectious diseases modeling. Public health officials and accreditation bodies underline the importance of a cumulative, "real-world" experience as part of a student's education. The Watermelon Meow Meow (WMM) outbreak is a cumulative experience that teaches upper-level undergraduate/graduate students about infectious disease dynamics by asking students to: participate in a fictitious outbreak; collect and analyze outbreak data. Innovative to our approach is the use of DataCamp as a technology to support learning statistical programming and framing WMM under principles of Universal Design Learning (UDL). We evaluated 27/32 student responses using a mixed-methods approach. We found WMM: augmented traditional lecture-style instruction and increased student awareness of heterogeneous risks associated with infectious diseases. We identified three student typologies: students who learn best from: (i) integrating traditional lecture plus WMM; (ii) participating in WMM data collection but not coding; and (iii) from lecture and classroom-based learning from peers. WMM is an example of a more general approach - which we call Slate, Operate, Translate - that instructors can follow to combine technology and a hands-on experiment to satisfy both UDL principles and increasing demands of public health education in a mathematics/statistics class.

Teaching statistics to undergraduate business students is an enduring challenge, often due to student apprehension and low understanding of relevance. The Data to Decision Project is an approach designed to address this challenge. At the onset of an introductory statistics course, fifty-two students received instruction on the Define, Collect, Organize, Visualize and Analyze (DCOVA) framework to integrate data with business-related decision-making. Concurrent with this instruction, students selected and completed individual projects, using the DCOVA framework to explore their individual decisions. Quantitative and qualitative methods indicated that this approach positively impacted the students' ability to apply statistics, as well as their attitudes regarding their statistics abilities.