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This study proposes and examines an analytical method with the aim of improving the quality of education and learning by situating the answers to full descriptive questions in probability and statistics to make variables of learners' comprehension of learned content as answer characteristics, based on actual student mistakes. First, we proposed and examined a method for extracting answer characteristics from the answers to the questions in probability and statistics as variables. Second, we proposed a method for obtaining answer characteristics to accurately describe learners' comprehension of each problem and indicate learning and educational policies for learners to improve learning by using regression trees. In addition, the relationship between learners' general ability and answer characteristics was visualized in an item characteristic chart to indicate the general comprehension of the learners. Further, the relationship between learners' learning strategy and answer characteristics was structuralized using Bayesian network models, and effective learning strategies for both learners as a whole and individual learners were extracted and evaluated towards the qualitative improvement of their comprehension using probabilistic reasoning. Our findings showed that the effectiveness of a learning strategy varies with each concept treated in a given problem; with the degree of basical or applied answer characteristics, indicating that the required learning strategy varies according to a given learner's stage of learning. Moreover, the improvement of hours studying dispersion for both mid-term and final examinations was revealed as effective for a wide range of subjects.

Based on empirical data, student learning outcomes on statistical material are not in accordance with the expectations of teachers so it is necessary to apply the Contextual Teaching and Learning (CTL) approach. A CTL implementation can help students to understand concepts and skilled resolve issues. The research purpose is to see whether the students learning outcomes on statistics materials that are taught using the CLT approach are better than conventional approaches. The methods used are experimental studies and the data will be obtained from the pre-test and post-test learning outcomes. The population are the entire class IX student while samples taken by random sampling techniques, two classes, namely as an experimental class and control. Research design is a Non-equivalent Control Group Design. Based on the results with two average difference tests on T-Test statistics were obtained that student learning outcomes in experimental class is higher than the controls. This means learning by applying the CTL approach can improve learning outcomes and the benefits for mathematics teachers gained insight into the CTL learning process.

The problem of the research is urgent due to the need to develop a special style of thinking. This type is based on problem cognitive activity, focused on lean production, resource conservation, and supported by an automated system that provides resources for solving socioeconomic problems in the conditions of the fourth industrial revolution and the digital economy formation. The purpose of the research is to theoretically prove and experimentally verify the need to change methods, tools, organizational forms of teaching robotics for the purposeful development of environmental thinking and lean manufacturing as the demanded competencies of future specialists in the context of digital transformation. The research methodology is the analysis of psychological and pedagogical work, development strategies, concepts of education; methods of mathematical statistics, methods of psychodiagnostics and survey. The pedagogical experiment is based on the example of assessing the ecological thinking formation and the energy conservation skill in teaching robotics to train engineering, technical and management specialists. The research results. The study clarifies the concepts "environmental thinking", "lean production", and "environmental competence" in the context of training specialists for the digital economy, in particular, engineering and technology. The study substantiates the didactic potential of a robotics course for the formation of environmental thinking as a demanded skill of future engineers. The authors describe didactic principles, a model for teaching robotics in a personalized environment for the formation of environmental thinking as the basis for the introduction of innovations and the challenges of automation, globalization and competitiveness. The authors conclude that a robotics course with specially organized interdisciplinary design forms of activity supported by appropriate means and training methods is necessary. It will create additional conditions for the formation of environmental thinking and lean production skills as key professional competencies in the training of demanded specialists in the digital economy.

Variation and variability are key concepts in K-16 statistics education. Prior research has investigated students' reasoning about variability in different contexts. However, there is a lack of research on students' development of understanding of variability when comparing distributions in bar graphs, dot plots, and histograms as they took an introductory college-level statistics course. This exploratory case study conducted three interviews with each of the ten participants through a four-month period, at the beginning, middle, and end of the course. The Structure of Observed Learning Outcomes (SOLO) taxonomy was used to analyze participants' responses. Results indicated that overall the group of participants demonstrated a stable understanding of variability over the semester (i.e. lack of improvement). However, when examining each student's reasoning, four types of reasoning development paths were found: improvement, lack of change, decline, and inconsistent. This study provides implications in teaching college introductory statistics course and recommendations for future research.

This study examined the influence of the jigsaw co-operative learning strategy integrated with GeoGebra (JCLGS), on Ethiopian undergraduate statistics and chemistry learners' conceptual knowledge development and attitudinal change towards calculus. The post-positivism quantitative methods approach employed in a non-equivalent pre-and post-test comparison group quasi-experimental design. The samples had drawn using two-stage random sampling techniques. The sample size was 150 in both the experimental and comparison groups. Data were collected by using the calculus classroom achievement test and the five points Likert-scale attitude questionnaire. Data were analyzed using descriptive analysis, an independent-samples t-test and Two-Way ANOVA for repeated measures using SPSS 23.0. The results showed a statistically significant difference between the two groups of pre-post test scores on the Two-Way ANOVA, F(1,148)=106.913; n[superscript 2] = 0.419; p<0.01. The finding also implies that the blended learning strategy grounded in Vygotsky's social constructivism cognitive development learning theory had big practical significance on learners' conceptual knowledge development. Learners viewed the JCLGS as enjoyable and interesting. It was also a socially interactive and collaborative environment that allows learners' to be reflective, share prior experience and knowledge and independent learners. It encourages them to have a positive attitude towards calculus and GeoGebra. Because of this finding, mathematics and science educators are advised to model a similar blended learning strategy in a classroom instructional setting. It will benefit their learners to adequately construct conceptual knowledge and positively change their attitude towards mathematics.

Recent studies report that students' attitudes toward statistics play an important role in their statistics achievement. The goal of this study was to test the psychometric properties of the "Survey of Attitudes Towards Statistics" (SATS-28) among undergraduate health science students enrolled in an introductory statistics course. Most students held positive attitudes towards statistics. The average SATS scores were above neutral. Confirmatory factor analysis validated the four-factor structure of the questionnaire (Affect, Cognitive Competence, Value, and Difficulty), albeit with a few modifications. The RMSEA value of 0.065 was below the suggested cutoff of 0.07. Cronbach's alpha for the entire scale was .907, which was well above the recommended cutoff value of 0.7. The study provided evidence for the appropriate metric properties of the SATS-28 as a valid instrument for measuring the statistics attitudes of a sample of first year students at the University of Minnesota Rochester.

This mixed research aims to analysis and design the Web Game On Descriptive Statistics (WGODS) through the ADDIE model, data science and machine learning. The sample consists of 61 students from a university in Mexico. WGODS is a technological tool (quiz game) that presents various questions and answers about statistics (quantitative and qualitative data). The results of the linear regression (machine learning) indicate that the content and aesthetics of WGODS have a positive influence on the educational process. The ADDIE model allows the organization of WGODS considering the needs of the students. Also, data science identifies 4 predictive models on the use of WGODS in the field of statistics through the decision tree technique. Finally, teachers can transform the organization and development of school activities through the ADDIE model and technology. In particular, WGODS improves the educational process on the quantitative and qualitative data through a pleasant, attractive, simple, easy and useful web interface.

Statistics and methodology play a crucial role in psychological research, and university psychology courses teach students the theory and application of various statistical methods. However, students who conduct their own research projects oftentimes face problems which are specific to their data and cannot be solved using standard curricular procedures. Instead, these issues need to be solved on a case-by-case basis. This article presents the organisation and evaluation of a statistical consulting service for students who face methodological issues in applied research. We describe the scope, contents, workload, and administrative framework of the service and evaluate its effectiveness and helpfulness as reported by the consultees over a two-semester period. Results show that the service is frequently used and well-received, and that consultants are adequately compensated for their work. We further provide recommendations for teachers and consultants regarding the organisation of the service and the consulting process from a practical perspective.

Statistics play an important role in psychology, but statistics modules are notoriously unpopular amongst psychology students. We examined attitudes toward statistics and attitudes toward the statistical software package R in both undergraduate and postgraduate students across the duration of a statistics module. Participants' responses were analysed using both quantitative and qualitative techniques. Results demonstrated that, on average, students in introductory level modules held neutral (not negative) attitudes, but students at higher study levels held somewhat positive attitudes towards R and statistics. While not all students enjoyed learning R, our findings demonstrate that many students enjoyed statistics or R, most students found statistics/R valuable and they generally reported feeling competent using software by the end of their module. These results challenge the argument that R is not suitable for undergraduate psychology students. Consequently, benefits, challenges, and implications of teaching R to psychology students are discussed.

The following action research depicts how two researchers from two universities merged their courses and goals of instruction to impact change in future teachers. Two currently practicing middle school teachers from one university worked with researchers to develop a social justice lesson that had relevance to seventeen K-8 pre-service teachers at another university to promote changes in beliefs about equitable teaching practices and policy. Findings from pre- and post-surveys, field notes during enactment, and reflections teachers found teaching statistics for social justice (TS4SJ) in this setting provided an increased responsiveness to the needs of students and statistical connections while also attending to excuses by those facing dilemmas in belief and racial dominance.

Recent standards reform documents across the globe have called for statistical literacy by the end of high school. Thus, it is important for teachers to develop a deep understanding of the content. Despite the relatively thick literature base connecting students' content knowledge and reasoning, research on these connections among practicing teachers is in critical need. Our study contributes to this lack of research by examining the knowledge structures and statistical reasoning--and possible ways knowledge structures supported reasoning--of a stratified purposeful sample of nine middle and secondary mathematics teachers as they responded to released items from the Levels of Conceptual Understanding in Statistics (LOCUS) assessment during two 60-90 minute task-based clinical interviews. Knowledge structures were categorized as compatible-connected, incompatible-connected, and incompatible-disconnected. Teachers with less incompatible knowledge elements in their structures engaged more frequently in reasoning coded as sound. However, teachers frequently engaged in both sound and unsound forms of reasoning on the same task item. Implications are offered for teacher education and future research.

Middle school mathematics teachers often do not emphasise statistics and geometry in their lesson plans for a variety of reasons. However, these are integral topics in mathematics, and using direct lecture or hands-on activities may have different effects on teacher confidence when approaching these subjects. Using qualitative and quantitative methods, anxiety was found to improve with lecture-based workshops, while confidence and effectance was found to improve with manipulatives-based workshops. The results showed some surprising and unsurprising reasons for these conclusions.

This paper reports some experiments on probabilistic reasoning designed to investigate the impact of the probabilistic problem presentation format (verbal-numerical and graphical-pictorial) on subjects' confidence in the correctness of their performance, other than the calibration between confidence and accuracy. To understand the potential effect of the format, these dimensions were assessed by monitoring contextual and individual variables: time pressure, numerical and visuospatial abilities, statistical anxiety and attitudes towards statistics. The participants included 257 Psychology students without statistical knowledge, recruited from Italian and Spanish universities, who fulfilled self-report validated measures. The students expressed their retrospective judgments of confidence item-by-item in relation to each probabilistic problem. This approach enabled the computation of two measures of calibration (the Bias Index - the Confidence-Judgment Accuracy Quotient). The results indicated that the problem presentation format did not exert a significant main effect on confidence, with the exception of when the interaction between the format and one subscale of the attitudes towards the statistics test was considered. The Bias Index, however, was significantly related to the interaction between format and time pressure. The study offers a point of reflection in relation to the potential effect exerted by the problem format and time constraint in calibration.

For decades, self-report instruments -- which rely heavily on students' perceptions and beliefs -- have been the dominant way of measuring motivation and strategy use. Event-based measures based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance. The results also reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace data, likely because of its covert nature. In that respect, it is of importance to recognize the crucial role of self-reports in capturing student learning holistically. This manuscript is 'frontline' in the sense that event-based measurement methodologies with online trace data are relatively unexplored. The comparison with self-report data made in this manuscript sheds new light on the added values of innovative and traditional methods of measuring motivation and strategy use.

Large-scale language testing uses statistical information to account for the quality of an assessment system. In this reflection article, I explain how basic statistics can be used meaningfully in the context of classroom language assessment. The paper explores a series of statistical calculations that can be used to examine test scores and assessment decisions in the language classroom. Therefore, interpretations for criterion-referenced assessment underlie the paper. Finally, I discuss limitations and include recommendations for teachers to use statistics.

Affect dynamics, the investigation of how student affect transitions from one state to another, is a popular area of research in adaptive learning environments. Recently, the commonly used transition metric "L" has come under critical examination when applied to data that exclude self-transitions (i.e., transitions where a student remains in the same affective state on consecutive observations); in this situation, recent work has shown that "L" potentially overestimates the significance of certain transitions. In order to deal with the unintended side effects of removing self-transitions, a solution was proposed that shifts the chance value of "L" from zero to a positive value that varies based on the number of affective states in the study. Although this treatment compensates for the aforementioned issues, it could lead to misinterpretation of the results due to the counterintuitive nonzero chance value. Motivated by these issues, in this work we study a modified version of the "L" statistic, which we refer to as "L*" , with an aim to shift the chance value back to zero when self-transitions are removed. We begin by studying the mathematical and statistical properties of "L*," and we also compare these properties to those of the "L" statistic. After analyzing the theoretical attributes of "L*," we then evaluate its performance when applied to data. In our first evaluation, we compute "L*" values on simulated sequences of affective states, where the base rates of the states vary from being uniform to highly non-uniform. The results provide evidence that the "L*" values at chance are not sensitive to unequal base rates, as the computed values from our experiments are centered closely around zero. Our second evaluation applies "L*" to actual student data generated from students working in a digital learning environment; in this case, the use of "L*" seemingly gives a more coherent picture in comparison to the values returned by the "L" statistic. Finally, along with these analyses, we also outline a recommended procedure for applying "L*" to sequences of affective states.

Robust evidence links physical activity to positive cognitive and academic performance outcomes, and engaging students in movement within the classroom has the potential to benefit learners at every age. Offering college students an opportunity to be active in the classroom can enhance learner engagement, promote critical thinking, and increase content understanding and retention. Designed to bridge the gap between research and practice, this article shares specific strategies for movement integration at the college level. These strategies have been used successfully across content areas and class formats and can be modified to match student need and classroom environment. Increasing awareness of active pedagogy among educators has the potential to positively impact adoption and implementation of the practice with the ultimate goal of influencing students' learning experiences and academic success. The purpose of this article is to provide college instructors with practical examples to incorporate movement into the college classroom.

The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through descriptive quantitative. The results of data analysis showed that 32% of students had level 1 statistical reasoning (the lowest level), 20% were at level 2, 28% at level 3, 12% at level 4 and 8% at level 5 (highest level). Based on the result, it can conclude that students' statistical reasoning ability in learning statistical method is not satisfactory, students are still very lacking in reasoning.

This article discusses the basics of the "4 scales of measurement" and how they are applicable to research or everyday tools of life. To do this you will be able to list and describe the four types of scales of measurement used in quantitative research; provide examples of uses of the four scales of measurement; and determine the appropriate measurement scale for a research problem. The article is designed to present an overview of statistical methods in order to better understand research results. Formulas and mathematical computations will not be presented, as the goal for this article is to merely provide a basic understanding of statistical measurement.

With COVID-19 pandemic, which was started in Wuhan, China, and is still affecting the whole world as well as our country, education offered distantly. This research aims to determine the students' opinions and their behaviors towards teaching Numerical Analysis in the form of distance education. The research was carried out with a group of students studying at the Faculty of Engineering in a private university in Northern Cyprus during the 2019-2020 academic year. In the research, an interview form was applied to get students' opinions about distance education and to understand whether it is appropriate to give a numerical analysis course over distance education or not. At the end of the research, students' positive and negative views about distance education emerged. Most of the students argued the idea that distance education platform is not able to provide education as effectively as the traditional classroom environment. It is supported that it is more accurate to give the form of traditional education because of the application of a Numerical Analysis course is challenging.

A compelling demonstration of implicit learning is the human ability to unconsciously detect and internalize statistical patterns of complex environmental input. This ability, called statistical learning, has been investigated in people with dyslexia using various tasks in different orthographies. However, conclusions regarding impaired or intact statistical learning in dyslexia remain mixed. This study conducted a systematic literature search of published and unpublished studies that compared statistical learning between people with and without dyslexia using different learning paradigms in different orthographies. We identified 49 papers consisting of 59 empirical studies, representing the data from 1,259 participants with dyslexia and 1,459 typically developing controls. The results showed that, on average, individuals with dyslexia performed worse in statistical learning than age-matched controls, regardless of the learning paradigm or orthography (average weighted effect size d = 0.47, 95% confidence interval [0.36, 0.59], p < 0.001). Meta-regression analyses further revealed that the heterogeneity of effect sizes between studies was significantly explained by one reader characteristic (i.e., verbal IQ) but no task characteristics (i.e., task paradigm, task modality, and stimulus type). These findings suggest domain-general statistical learning weakness in dyslexia across languages, and support the need for a new theoretical model of statistical learning and reading, that is, the SLR model, which elucidates how reader and task characteristics are regulated by a multicomponent memory system when establishing statistically optimal representations for deep learning and reading.

Background: Processing and comprehending information from multiple sources have been a primary means of learning and are essential 21st-century skills to construct knowledge for a deeper understanding. Objectives This study examined students' individual differences in search strategies and internet epistemic beliefs as well as the effect of distinct note-taking formats on students' online multiple-document reading about measurement scales and statistical graphs, considering students' gender and prior knowledge. Methods: Participants were 124 Taiwanese university students (79% female) taking the Educational Statistics course in three different classes. I used a quasi-experimental design to test the effect of different note-taking formats on online multiple document reading. Three classes of students were randomly assigned to one of the experimental conditions with sourcing cues: the individual note taking with multiple tabs, the collaborative note taking with multiple tabs, or the individual note taking in a single tab. Results and conclusion: The study showed that the uncertainty belief about Internet-based information and note-taking formats predicted students' online multiple document reading. A stronger belief that the internet information is uncertain and may change with time positively predict online multiple document reading comprehension. Moreover, individuals taking notes in a single tab outperformed those taking notes individually in separate tabs. Major takeaways from the study: Implications for instruction were made to foster students' uncertainty belief and teach them how to construct pre-outlined tables with sourcing cues to collect the contextual information and reflect on the source information to streamline learners' online multiple document reading comprehension.

The reality is that textbooks and state standards have more content than can reasonably be covered for proficiency in most classrooms. In years past, this typically meant cutting the last unit or two or moving along regardless of who was keeping up, in many cases resulting in cutting statistics from the curriculum. This article provides five reasons statistics should be featured in mathematics curriculum: (1) Students need relevant curriculum; (2) They will not "lose out" on mathematics; (3) Teachers can combine algebra and statistics through modeling; (4) Students can experience real-world murkiness; and (5) Statistics is invaluable to teaching for social justice. The author also includes a project to feature statistics from her own instruction, further detailed in the supplemental material (link online).

Research courses are part of many higher education curricula. However, students' attitudes towards statistics and research courses tend to be negative. One way to measure students' attitude is with the Revised-Attitudes Towards Research scale (R-ATR). The current study examined: (1) the internal reliability of the R-ATR, (2) the attitude of Dutch second-year architecture students towards research courses, and (3) whether attitude is related to age and gender. It was found that the R-ATR has good internal reliability and that Dutch second-year architecture students' attitude towards research courses is reasonably favorable. Students generally acknowledge the usefulness of research courses and do not feel anxious, but find them stressful and difficult to some extent and do not enjoy them. Further analyses showed three types of students: relaxed students, virtuous students and worried students, who each require a different approach to improving their attitude towards research courses. No relationship emerged between attitude and age or gender, but female students considered research courses somewhat more useful. Providing a research-friendly, enjoyable, and supportive environment might improve students' attitude towards research courses.

Community colleges have an opportunity to promote achievement of more science, technology, engineering, and mathematics (STEM) students and meet larger goals of scientific advancement and educational equity. Understanding community college students' needs and backgrounds is key to increasing students' success in STEM fields and realizing this potential. The objective of this paper is to use data from the U.S. Department of Education's National Center for Education Statistics and other sources to characterize community college students and their academic achievement and to offer equity-based approaches to increase success, particularly in STEM. Here, I document that community college students, who constitute approximately one-third of U.S. undergraduates, are a unique population with greater proportions of underrepresented STEM minorities, parents, and students requiring developmental education. They are also more likely to be older, working, part-time, low-income, and first-generation students and more likely to differ demographically from faculty. I also found lower rates of academic achievement among community college students, including lower rates of retention and STEM degree attainment with evidence of even lower achievement for STEM underrepresented groups. The data point to the need for equity-based strategies to address achievement disparities for STEM community college students, including increasing community college faculty diversity and sensitivity to diverse students' needs and experiences; adopting inclusive, active-learning pedagogies; and reforming developmental education.

Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181-214, 2000). We investigated self-explanation quality as mediator in a worked examples--problem-solving paradigm. We developed a coding scheme to assess self-explanation quality in the context of ill-defined statistics problems and analyzed self-explanation data of a study by Schwaighofer et al. (J Educ Psychol 108: 982-1000, 2016). Schwaighofer et al. (J Educ Psychol 108: 982-1000, 2016) investigated whether the worked example effect depends on prior knowledge, working memory capacity, shifting ability, and fluid intelligence. In our study, we included these variables to jointly explore mediating and moderating factors when individuals learn with worked examples versus through problem-solving. Seventy-four university students (mean age = 23.83, SD = 5.78) completed an open item pretest, self-explained while either studying worked examples or solving problems, and then completed a post-test. We used conditional process analysis to test whether the effect of worked examples on learning gains is mediated by self-explanation quality and whether any effect in the mediation model depends on the suggested moderators. We reproduced the interaction effects reported by Schwaighofer et al. (J Educ Psychol 108: 982-1000, 2016) but did not detect a mediation effect. This might indicate that worked examples are directly effective because they convey a solution strategy, which might be particularly important when learning to solve problems that have no algorithmic solution procedure.

This study aimed to determine the differences in science process skills in each class in the Physics Education Study Program. This research was a quantitative study with a comparison design. The sample was 108 students with details of 54 control class students who used conventional guidebooks, while the control class totaling 54 students used a science process skill guide. The data was obtained from observation activities using observation sheets of science process skills which were analyzed using descriptive statistics and inferential statistics. The results showed that there were significant differences in the mastery of science process skills of students seen from the value of the average mastery of science process skills of students using guidebooks based on science process skills. The experimental class has an average science process skill that is better than the control class.

In the past several decades, higher education has witnessed the exponential growth of online learning. New technology has dramatically transformed the way education is delivered compared to what takes place in the traditional classroom. It has enabled online delivery of course materials to students outside of face-to-face classroom in an asynchronous manner and provide them with self-paced flexibility. Given the abstract nature of statistics content, effectiveness of the instructional strategies and course design in online statistics instruction has become particularly important to students' learning success. In this qualitative study, the authors explored perceptions of the Educational Leadership doctoral students towards an online graduate level introductory statistic course in terms of whether the online course instructional strategies and course design helped them learn statistics. The authors assessed effectiveness of the instructional strategies and design of the online statistics course as well as students' needs, so more effective instructional strategies could be used for online statistics teaching. Students identified the PowerPoint presentations with recorded lectures to be the most useful strategy. This strategy, along with live Q&A sessions and guided practices and activities, helped to connect the textbook information and its application to the real world.

The flipped classroom has recently become a popular method used in both higher education and K-12 classrooms, yet research has not consistently demonstrated clear benefits of flipping a classroom. Also, any benefits seen might not be from the flipped design itself, but instead from the individual aspects of a flipped classroom (e.g., more active learning in class, more feedback on homework). This suggests that research focused on how to develop the activities and components of the flipped classroom, instead of simply flipping the traditional in-class and out-of-class activities, is critical to flipped pedagogy. These activities (both in and out of the classroom) should be designed based on a theory of learning. We draw upon the Practicing-Connections (PC) Hypothesis, supported by contemporary theories and research in the cognitive sciences, to design instructional activities to promote learning by having students practice making connections between concepts and situations. This paper examines a flipped class based on a theory-driven design versus a basic flipped classroom. The results of this work offer suggestions as to what dimensions of flipping may be important and how to design and evaluate flipped classrooms based on theories of learning.

Quantitative decision making (management science, business statistics) textbooks rarely address data cleansing issues, rather, these textbooks come with neat, clean, well-formatted data sets for the student to perform analysis on. However, with a majority of the data analyst's time spent on gathering, cleaning, and pre-conditioning data, students need to be trained on what to look for when generating or receiving data. A critical scan of the data needs to be performed (at a minimum) to look for errors in the data set before data analysis can be performed.

Virginia Highlands Community College (VHCC) implemented MTH 155 -- Statistical Reasoning Co-Requisite -- in the spring semester of 2018. MTH 155 replaced MTH 146 on VHCC's campus. Prerequisites for this course are the first five developmental MTE modules. The intended co-requisite audience for this new course consists of students having successfully completed at least three of the five MTE modules. These students co-enrolled in MCR 5 and received targeted assistance for the MTH 155 course. Successful completion of MTH 155 resulted in the prerequisite MTE modules 1-5 being satisfied. This paper will present the results of this study along with implications for future work with co-requisite mathematics courses.

The consequence of the assessment for the research statistics course at the department of physics Education in Universitas Jambi demonstrates the students' capacities to hold the idea of the mastery of research statistical concept is very low. It winds up one of the shortcomings for them to finish their final assignment. Therefore, the researcher examined the student's mastery of the research statistical concept by utilizing Problem-Based Learning as one of the methods that were chosen from the learning advancement. The research method is quantitative with 295 numbers of samples. Questionnaires and MCQs were used as the instruments of the research. The result had been investigated utilizing a simple regression analysis. PBL influences the mastery of research statistical concept. In 6th semester male students', the impact of PBL toward the mastery of the research statistical concept contributed 72% and 67% for female students. Meanwhile, for students at 8th semesters, the impact of PBL on the mastery of research statistical concept contributed 68% for male and 70% for female.

Based on the problems obtained, the ability of students to subjects of research statistics is still categorized as low until this causes them to be weak in completing their final assignments. Therefore, the researchers investigated how students' mastery of the research statistical concepts and their learning motivation using learning innovations, one of which was Problem Based Learning (PBL). This research was conducted at the Islamic Education School (PGMI) Islamic State Teacher Education Institute (IAIN) Bengkulu used was semester 8th students for a sample. The research method was quantitative with a sample of 145. The research instrument was using a motivation questionnaire and multiple choice questions. The results will then be analyzed using simple regression analysis which will be presented in the form of a data table.

Statistics for teacher evaluations in seated and online classes show a glaring difference in both quantitative and qualitative scores. The generalized analysis of the quantitative and qualitative elements of the same course, instructor, assignment as grouped by learning environment provide an opportunity to inform future practice. In this four-year study at a small, private Midwestern university (n = 316 students over 20 course sections), the seated students were much more engaged and satisfied as demonstrated by their response rates, Likert-scale ratings of course and professor, and associated comments. Reflection and best practices were employed in the online course for continuous improvement, but the numeric values for online evaluations remained consistently and significantly lower in mean value. This study concludes with a series of takeaways for enhancing connections with students and increasing their associated evaluation scores.

In recent years, online teaching has become extremely popular. Most institutions of higher learning are offering online courses in almost every field of study. Teaching any course online is challenging, but teaching quantitative courses, such as operations management, management science, statistics, and others, have added a more challenging dimension to online teaching. Publishers have been assisting professors of quantitative methods courses by developing various teaching and evaluation tools. This study explores one such publisher's tool, Quiz Me Mastery Points, of Pearson's MyOmLab. The performance of students on their examinations and the Mastery Points they earned through the Quiz Me feature were compared, and it was determined that there was a significant correlation between the two.

Malaysia Ministry of Education has put critical thinking skills elements into one of the six aspirations that pupils need to achieve. This was a turning point that teaching methods should be transformed. Therefore, innovation of teaching methods needs to be done. One of the strategies suggested is adventure-based learning (ABL). The main objective of this study is to examine the impact of ABL methods in the teaching of Basic Statistics on critical thinking skills. This study uses a quantitative approach with quasi experimental pre and post design. Only one group of students is formed. The study was based on the cluster sampling method and involving the semester two students of the Preparation Programme of Teaching Bachelor (PPTB). Instruments used are modules of adventure-based teaching methods and critical thinking skills tests. The findings of this study are to prove that this adventure-based learning can be used as an alternative method to assist the lecturers of the Teacher Education Institute to produce credible graduates. It helps lecturers apply the 21st century learning method in their teaching and learning.

Based on a cognitive--motivational modeling of construct relations, this study analyzed the role of prior statistics experiences within a framework of domain-specific self-concept and value variables to predict education science students' statistics anxiety. Data were analyzed from two independent samples comprising 113 and 87 participants--using the passing of a statistics exam as the experience measure in each case. In both samples, the results of three-way analyses of variance demonstrated students' statistics anxiety to be substantially explained by their negative utility value and self-concept but only to a minor extent by their prior statistics experiences. Students' statistics anxiety appeared to be dependent on value and self-concept scores across all experience levels. Though producing somewhat varying effect patterns, the findings from both samples led to similar effects, indicating the crucial role of self-belief variables. Results are discussed in terms of conceptual, methodological, and instructional implications.

Due to the emotional and social burden on Americans stemming from firearm injury and death, school psychologists (SPs) must be equipped with an understanding of risk factors that could lead to students engaging in gun violence. SPs are ideally situated due to their training and expertise in promoting healthy development in children and youth, as well as their unique position within school systems to advocate for the psychological well-being of students (APA, 2020). This article examines the rates of gun violence in the United States, providing insight into the different causes of gun-related deaths.

Over the last decade, there has been a 10-fold increase in the number of published systematic reviews of prevalence. In meta-analyses of prevalence, the summary estimate represents an average prevalence from included studies. This estimate is truly informative only if there is no substantial heterogeneity among the different contexts being pooled. In systematic reviews, heterogeneity is usually explored with "I"-squared statistic ("I"[superscript 2]), but this statistic does not directly inform us about the distribution of effects and frequently systematic reviewers and readers misinterpret this result. In a sample of 134 meta-analyses of prevalence, the median "I"[superscript 2] was 96.9% (IQR 90.5-98.7). We observed larger "I"[superscript 2] in meta-analysis with higher number of studies and extreme pooled estimates (defined as <10% or >90%). Studies with high "I"[superscript 2] values were more likely to have conducted a sensitivity analysis, including subgroup analysis but only three (2%) systematic reviews reported prediction intervals. We observed that meta-analyses of prevalence often present high "I"[superscript 2] values. However, the number of studies included in the meta-analysis and the point estimate can be associated with the "I"[superscript 2] value, and a high "I"[superscript 2] value is not always synonymous with high heterogeneity. In meta-analyses of prevalence, "I"[superscript 2] statistics may not be discriminative and should be interpreted with caution, avoiding arbitrary thresholds. To discuss heterogeneity, reviewers should focus on the description of the expected range of estimates, which can be done using prediction intervals and planned sensitivity analysis. [This report was written on behalf of the Prevalence Estimates Reviews--Systematic Review Methodology Group (PERSyst).]

The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students' drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students' images and students' demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research.

The "Journal of Statistics and Data Science Education" ("JSDSE") has a history of initiatives to expand its readership and impact. In this article, we reflect on "JSDSE"'s relevance to K-12 education and how it might increase its influence in this area. We characterize "JSDSE"'s K-12 impact partially in terms of its ability to foster statistical knowledge for teaching (SKT). We also introduce a new construct, statistical knowledge for the transformation of teaching (SKT[superscript 2], to more fully capture the K-12 contributions "JSDSE" can make. Our analysis draws upon the perspectives of an experienced high school teacher (the first author) and a statistics education researcher (the second author). The first author of the article surveyed recent "JSDSE" issues to identify and reflect upon articles with implications for his teaching practice. The second author framed the first author's article reflections in terms of their connections to SKT and SKT[superscript 2]. Drawing upon our collaborative analysis, we propose strategies to help JSDSE more fully realize its potential to contribute to SKT and SKT[superscript 2]. We explain how acting on the proposed strategies may help "JSDSE" readers, authors, and editors bridge the persistent historical gap between formal scholarship and practice in K-12 education.

Purpose: The purpose of this manuscript is to provide a summary of Measurement in Physical Education and Exercise Science (MPEES)-related activities in 2021. Manuscripts submitted: In 2021 original submissions (i.e., not counting revised manuscripts) increased by [approximately]20% as compared to 2020. Fifty-eight countries were represented across the 464 original manuscripts submitted to MPEES in 2021. MPEES continues to welcome high-quality submissions from around the world in 2022. Manuscripts submitted by section: The seven sections of MPEES are: (1) exercise science, (2) physical activity, (3) physical education pedagogy, (4) psychology, (5) research methodology and statistics, (6) sport management and administration, and (7) tutorial and teacher's toolbox. More than three-fourths of the manuscripts submitted to MPEES in 2021 were submitted to one of three sections: exercise science or physical activity or research methodology and statistics. MPEES continues to welcome high-quality submissions within each of the seven sections that comprise the journal in 2022. Review process: Each of the original manuscripts submitted to MPEES in 2021 had received an initial decision by April 4, 2022 (i.e., 464 of the 464 manuscripts received). When an immediate decision (e.g., desk-rejection) was rendered by the editor-in-chief it always (i.e., for 369 of 369 manuscripts) occurred within 10 days of receiving the manuscript. When a manuscript was assigned to a section editor by the editor-in-chief it typically (i.e., for 91 of 95 manuscripts) received an initial decision within 90 days of receiving the manuscript. Twenty-four countries were represented across reviewers for the original manuscripts sent out for external review by MPEES in 2021. We gratefully acknowledge, and list in the Appendix, the many scholars (N = 151) from around the world who served as a reviewer for MPEES in 2021. Manuscripts published: Volume 25 (i.e., the volume published in 2021) of MPEES published a total of 37 manuscripts. More than two-thirds of the published manuscripts were submitted to one of two sections: exercise science or research methodology and statistics. MPEES continues to strive for publishing high-quality manuscripts within each of the seven sections that comprise the journal in 2022. Fourteen countries were represented across the manuscripts published in Volume 25 of MPEES. MPEES continues to strive for publishing high-quality manuscripts written by one or more authors from around the world in 2022. Impact factor: The 2020 impact factor for MPEES was 2.30 representing an increase of [approximately]31% compared to 2019 when the journal's impact factor was 1.75. The value of the 2020 impact factor placed MPEES in the second quartile (i.e., ranked #131 out of 265 journals) within the Education & Educational Research category of Clarivate Analytics. The 2021 impact factor for MPEES is expected to be released in [approximately]June 2022.