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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.

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.

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.

As data are 'numbers with context' (Cobb & Moore, 1997), contextual knowledge plays a prominent role in dealing with statistics. While insights about a specific context can further the depth of interpreting and evaluating outcomes of data analysis, research shows how it can also hinder relying on data especially if results differ from expectations. In this article, the aim is to investigate how young students informally deal with empirical evidence, which differs from their initial expectations in a specific context. We present a case study with three pairs of students at the age of 9 to 10 who compare groups in survey datasets. The interpretative analysis shows how conjectures of varying degrees of confidence shape the students' statistical expectations and can play different roles in interpreting results from data analysis.

This report focuses on the ways in which 36 Grade 4 students recognized, explained, described, and employed variation during interviews conducted 1 month after participating in STEM-based activities in which they tested, adjusted, and re-tested catapults. An inductive thematic methodology was used for analysis of the interview transcripts to capture the ways in which students discussed their analyses and justified their conclusions from the activity. The results were based on 1080 instances of variation in student responses to the interview questions, which evidenced three ways students characterized variation: contextual variation, specific variation, and general variation. Findings point to the essential nature of context in building statistical understanding in relation to both specific and general aspects of variation as well as decision-making in that context.

Teaching practical skills is of particular interest in the study of human medicine. With regard to medical statistics this means the use of statistical software, which may be effectively taught by a flipped classroom approach. As a pilot study, we designed and implemented an elective course on medical statistics that focused on hands-on data analysis in SAS Studio. Students independently prepared for class using materials such as pretaped asynchronous lectures, and worked on exercises during synchronous class sessions. On course evaluations completed by n 15 students out of 26, students rated their satisfaction with the course a mean of 1.3 (SD 0.6) on a scale where 1 best and 6 worst. Twelve (80%) indicated that they processed all materials provided, and 11 students (73%) rated the frequency of direct contact with the instructor as sufficient. Nearly all (14 out of 15) viewed the course as an adequate substitute for a full face-to-face course. Our results suggest that the proposed course design is well-accepted. The flipped classroom format offers high flexibility and can be implemented easily online. Our pilot data are encouraging regarding the aim of designing a prospective follow-up study which compares the flipped classroom approach to a teaching format based on attendance.

Responsible citizenship and sound decision-making in today's information age necessitate an appreciation of the role of uncertainty in the process of generating data-based scientific knowledge. The latter calls for coordinating between different types of uncertainties, related to three types of relevant reasoning: statistical, scientific, and nature of science uncertainties. This article examines separately the uncertainties that young students articulate as they engage in activities designed to concurrently foster all three types of reasoning, and also explores how these different types can interrelate. The context of Citizen Science is particularly suited for this goal, providing a unique pedagogical opportunity for learning scientific content by engaging learners in authentic scientific practices, including data analysis. Based on literature from the three fields of statistics, science and nature of science education, we offer an integrative framework, Deterministic Relativistic and Middle ground (DReaM), which consists of nine sub-categories of uncertainty articulations. We utilize it to analyze an instrumental case study of a pair of middle school students' (ages 13 and 14) participation in a pilot study of an interdisciplinary extended learning sequence, as part of the Radon Citizen Science Project. The results of an interpretative microgenetic analysis identified all nine DReaM uncertainty articulations sub-categories. These are illustrated in the Findings section with key scenes from the pair's participation. The discussion depicts how these sub-categories manifested in this particular case study and suggests interrelations between them in a more extended depiction of the DReaM framework. We conclude with the pedagogical implications of the extended framework.

Students typically find research methods and statistics classes intimidating. In particular, learning different types of data measurement and operationalization can take significant time and practice to understand. Utilizing a pedagogical approach that turns a party game into a fun exercise, students collaboratively learn to operationalize variables and determine levels of measurement. Outcomes and feedback from students are discussed.

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

Since the early 2000s, we have seen an increase in the need for graduates in various STEM fields. The Professional Science Master's (PSM) program was created in 2001 to address this increased demand. While research has shown the benefits these programs may provide, there is limited research examining the current state of PSM programs. The current research evaluates the perspective of students, alumni, faculty and program directors concerning the benefits from the PSM. Results suggest these programs still create competitive graduate students with concepts from the PSM being implemented in non-PSM programs.

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.

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.

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.

Visual imagery has been an effective tool to communicate ideas connected with basic mathematics concepts since the dawn of mankind. The development of educational visualisation technology allows these ideas to be demonstrated with the help of some educational software. In this paper, we specifically consider the use of GeoGebra, a free, open-source educational application developed by an international consortium of mathematics and statistics educators, but other educational software could also be used for the same visualisation tasks. In this study, we present Torricelli's method for measuring the area under arc of cycloid as an example of using GeoGebra to visualise he area of planar figures. This kind of introduction is suitable for secondary schools and for training pre-service teachers. We will also show how GeoGebra can be used to develop students' understanding of representing data (i.e. the topic from statistics education). While students explore the visualisation of data, GeoGebra allows them to create and explore representations while building the understanding that is required for analysing data and drawing figural conclusions from graphical representations.

Most literature related to Benford's Law discusses what the law can be used for and how it works but fails to address effective methods and procedures for teaching the law to students. This article examines existing information resources to determine the most effective methods and procedures used to explain this Law to those who have no experience with it. A contribution to knowledge is made by providing step by step instructional approaches for teaching Benford's Law to students that are tied to existing literature. Benford's Law is a fascinating lesson for students who have been exposed to statistical and mathematical concepts for as long as they can remember yet know nothing of the law's existence. This lesson is suitable for any introductory statistics or mathematics course where students are learning about probability. The Law has a practical application in the field of business and can also be taught as part of a fraud examination, data analytics, or auditing course.

Education researchers are increasingly interested in applying data mining approaches, but to date, there has been no overarching exposition of their methodological advantages and disadvantages to the field. This is partly because the use of data mining in education research is relatively new, so its value and consequences are not yet well understood. Yet statisticians, sociologists and those who study computer-based education have discussed the methodological merits of data mining in education research. This article brings together their perspectives, providing an interdisciplinary overview of potential benefits and drawbacks. The benefits, regardless of scholar background, largely emphasize the speed and ease with which data mining approaches can help explore very large datasets. Perceived drawbacks, however, differ based on disciplinary expertise. For example, statisticians question data mining's exploratory nature and non-reliance on sampling theory, while sociologists raise concerns about an excessive reliance on data in research designs and in understandings of education.

Single-period inventory models with uncertain demand are very well known in the business analytics community. Typically, such models are rule-based functions, or sets of functions, of one decision variable (order quantity) and one random variable (demand). In academics, the models are taught selectively and usually not completely. Students are exposed to applications of selected models and solution aspects usually within the Inventory Control or similar topics. This learning-case oriented paper attempts to provide a fuller coverage of the solution process, starting with mathematical model building, following with calculus-based solution development and ending with an alternative approach through simulation. A special emphasis is dedicated to the learning processes, providing students with a variety of tools selected from Algebra, Calculus, Probability, Statistics and Spreadsheet Technology. The students are expected to benefit from studying this paper to better understand business solving processes. All computational operations are implemented in a spreadsheet program.

The SQL-Explore Learning Module detailed in this teaching tip provides an opportunity for students to apply database course knowledge beyond solving traditional pre-determined Structured Query Language (SQL) coding questions. In this unique constructivist activity using the apropos 5E Instructional Model, students explore tables to locate data anomalies, trends, and other key findings in a 100,000-invoice dataset. Detailed instructions and the source code needed to facilitate this innovative learning experience are included. Based on student feedback, 100% of study participants strongly agree or somewhat agree that exploring datasets through the SQL-Explore Activity enhances their knowledge of SQL.

This publication seeks to consider statistics education in Japan while referencing a student paper. The paper to be considered is as follows: "Gakusei no Koudou ga Gakusei Seikatsu Manzokudo ni Ataeru Eikyou" (The Influence of Student Behavior on the Degree of Satisfaction Perceived in Student Life).

This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is "data." The word rate for "data" is more than twice the next highest word rate, which is for "model." This contrasts sharply with how often the word "data" appears in most Mathematics books. Overall, we observed substantial agreement on important concepts in Data Analysis and Data Science. Eighteen of the 25 most frequent concepts are shared by both fields. One difference is that the words "problem" and "solution" had Top 25 word rates for Data Science, but not for Data Analytics. A close look at Statistics concepts suggests that Data Analytics is more focused on "exploratory" concerns, such as searching for patterns in data. Data Science retains more of the classical inferential activities that use sample data to draw conclusions about populations. Both fields deal with Big Data situations, but Data Scientists must continue to be prepared for traditional small sample applications.

Statistics is one of the important branches of mathematics taught in schools, colleges and universities. It is also an important tool in public policy discussions. This paper focuses on the use of statistics in the latter context, rather than its use in adult mathematics education research. I review the key characteristics of the statistical approach to constructing public knowledge, and give a very brief history of key points in its development. I discuss how what I call the "overt crisis of statistics," the apparent disenchantment of large sections of the public with the "expert" statistical methods, outputs and pronouncements, leads to dilemmas both for citizens and for democratic governments. Recently "Big Data" and data analytics seem to many to offer new solutions to problems resulting from the essential lack of certainty surrounding efforts to understand society, and from the need to make quick decisions in a rapidly changing world. These approaches have potential, but also limitations. This leads me to consider a second, "covert" crisis of statistics, resulting from a struggle between proponents of freely available public information and public argument, and those aiming to profit from the appropriation and sequestering of information for private ends. I finish by considering what can be done by ourselves, as citizens, as adult mathematics teachers, and as researchers.

Two-stage exams have gained traction in education as a means of creating collaborative active-learning experiences in the classroom in a manner that advances learning, positively increases student engagement, and reduces test anxiety. Published analyses have focused almost exclusively on the increase in student scores from the first individual stage to the second collaboration stage and have shown clear positive effects on gains in student scores. Missing from these analyses is a comprehensive evaluation of the effects of individual preparation, the characteristics of questions, and small group composition on the outcomes two-stage exams. I developed a simple quantitative framework that provides a flexible approach for estimating and evaluating the effects of individuals, questions, and groups on student performance. Additionally, the framework yields statistics appropriate for making inferences about productive collaboration, consensus-building, and counter-productive interaction that happens within small groups. Analyses of 12 exams across two courses and 2 years using the quantitative framework revealed considerable variation for all three of these effects within and among exams. Overall, the results highlight the value of quantitative estimation of two-stage exams for gaining perspective on the effects of individuals, questions, and groups on student performance, and facilitates data-driven revision of assessments, curricula, and teaching strategies towards achieving gains in students' collaborative skills.

The purpose is to improve insights and educational results by applying analytic methods. The focus is on the mathematics applied to learn from the kind of data available to most classes such as final examination marks or homework grades. The sample is 249 students learning introductory college statistics. The result is a predictive model for student success. final grade = 36.8 + 2.26 (complete). The effect ratio is 0.43 at a 95% level of significance. Difficulty ratios were calculated from homework practice questions so professors can see, prior to the first test, what topics and levels of difficulty within a topic need review. The type of over-practice needed to deliver the most improvement is identified. Dashboards to drill down on findings are discussed.

Here we demonstrate an inexpensive and straightforward electronic circuit that can be used to generate data in the form of counts, with the same statistical properties as those found in gamma-ray counting experiments. The circuit holds an avalanche Zener diode just below its Zener voltage, which allows for thermal electrons to traverse the in the Zener's PN junction, causing sporadic conduction events lasting ~10 ns. If counted in a manner similar to gamma-ray arrivals, we show the conduction events will follow identical statistical distributions as the gamma-rays.

Background: Upward trends in data-oriented careers threaten to further increase the underrepresentation of both females and individuals from racial minority groups in programs focused on data analysis and applied statistics. To begin to develop the necessary skills for a data-oriented career, project-based learning seems the most promising given its focus on real-world activities that are aimed at engaging student interest and enthusiasm. Method: Using pre and post survey data, the present study examines student background characteristics, learning experiences and course outcomes for a cohort of 33 rising high school seniors involved in a two-week, accelerated version of a project-based data analysis and applied statistics curriculum. Results: On average, students rated the experience as rewarding and the vast majority (78.1%) felt that they had accomplished more than they had expected. Based on responses to both the pre and post course surveys, roughly half of the students reported increases in confidence in applied skills (i.e. developing a research question, managing data, choosing the correct statistical test, effectively presenting research results, and conducting a statistical analysis of data), while more than 80% reported increased confidence in writing code to run statistical analyses. Fully 84.4% of students reported interest in one or more follow-up courses with interest in computer programming being endorsed by the largest number of students (53.1%). Conclusions: These findings support previous research showing that real-world, project-based experiences afford the best hope for achieving the kind of analytic and statistical literacy necessary for meaningful engagement in research, problem solving and professional development.

Nowadays, the rapid spread of the interdisciplinary approach contributes to the development of disciplines and scientific developments in many ways. Therefore, how the interdisciplinary approach is addressed in the studies carried out is important in terms of guiding other studies. For this purpose, an attempt to determine how 155 doctorate theses which were written in science departments at a university located in Turkey's south between 2010-2016 addressed the study discipline, subject of study and interdisciplinary approach process was made in this study. Furthermore, the opinions of instructors working in these departments on the interdisciplinary approach were also examined. As a result of the study, it was seen that 140 theses were written with the disciplinary approach, 15 theses were written with the interdisciplinary approach, and they were also associated with economics, sociology, psychology and geography among social science disciplines. The facts that the concept of interdisciplinary is not exactly known and that the necessary conditions for studying have not been provided, the lack of time, the presence of gaps in disciplines and failure to ensure the adequate cooperation are important reasons for the limited number of studies carried out with the interdisciplinary approach. Therefore, it is important to promote interdisciplinary studies and to provide suitable conditions for studying.

The Chicago Hardship Index is a proposed starting point for introducing students to structural urban inequities. ASA's mission statement to use statistics to enhance human welfare serves as a motivation for social justice projects. This article contains an application of ASA's ethical guidelines to such projects, background information about the history and landscape of Chicago community areas, and practical ideas for how to combine hardship index data with learning statistical and data science tools. Supplementary materials for this article are available online.

This is the fourth in a series of statistical articles for mathematics teachers. In this article, the authors discuss topics in General Mathematics in Unit 2 Topic 1 (Univariate data analysis and the statistical investigation process) and topics in Essential Mathematics, Unit 2 Topic 1 (Representing and comparing data).

This paper describes the evolution of "recognition of need for data" and "strategical thinking", two types of thinking identified by Wild and Pfannkuch in their Framework for Statistical Thinking in Empirical Enquiry, as well as its relevance for math teacher professional development. The research was carried out with ten in-service secondary-school math teachers during an educational experience (at Bogotá, Colombia), who, in the beginning, showed high performance in procedural knowledge of data analysis. The experience was founded on project-based learning that participants proposed and conducted via a survey concerning implications of a Bill, relevant for their job context. These teachers made mistakes and showed difficulties during the two first stages of the investigative cycle, problem formulation, and research planning, due possibly to their inexperience in designing an empirical inquiry. Teacher educator's guidance, to turn mistakes into learning opportunities and teachers' active participation, promoted the development of statistical thinking, especially linked to the types aforementioned.

Research students in social science disciplines frequently struggle to master statistical analysis. A contributing factor may be the statistical software that is used, as the design of such software may not address the needs of non-statisticians or non-computer programming students. Hence, decisions about which statistical software tools are most suitable for such end-users need to be made at the introductory level. This paper first identifies key human-computer interaction (HCI) factors that may directly influence students' statistical analysis performance. Factors include technical properties such as user interface design, statistical features available, visualization, data handling, preparation, and manipulation, and usage properties such as speed/number of steps, ease of command/use, and efficiency. Four popular software systems (ie, SPSS, R within RStudio Desktop, R Commander & jamovi) were evaluated. Findings suggest that HCI usage factors from an interaction perspective are likely to be especially important for students gaining an introductory knowledge of statistics.

Teaching introductory statistics is a service requirement for many mathematics faculty, and the audience is often anxious about the subject and not always excited about the material. Making class fun, relevant, and visually stimulating is key to keeping students engaged in the material and also in promoting student learning. Collecting data from the students, be it demographic information or experimental data, can achieve this, and immediately invites the students to be interested in the data and motivated to answer-related research questions. To facilitate and speed up this process, we developed the free data collection and analysis platform Classroom Stats which is designed to make class more fun and to improve student learning through straightforward visual analysis of the data. In this paper, we will discuss the importance of using student generated data in class and how to use Classroom Stats to make the process easy and efficient.

The purpose of the research paper was to investigate the effect of eclectic learning approach on the academic achievement and retention of students in English at elementary level. A sample of forty students of 8th grade randomly selected from Government Boys High School Khurram District Karak was used. It was an experimental study and that's why sample subjects were classified into two equal groups on the basis of pre-test scores. For data collection, pre-test post-test equivalent groups designed was used. Descriptive statistics i.e., mean, standard deviation and inferential statistics i.e., t-test were employed for analyzing the data. After analyzing the data, it was come to light that eclectic learning approach has a positive effect on students' academic achievement and retention. Eclectic learning approach was found more productive, effective and successful in teaching of English as compared to traditional learning approach at elementary level. So, eclectic learning approach should be adopted by the teachers for improving students' performance in English at elementary level.

Past research have pointed out that excellent strategies have been written but extremely small have been accomplished in their implementation. It has additionally been proposed that only 10% of formulated strategies are successfully implemented. However, crafting the best strategy is not the end in itself but the ultimate result will only be realized once the plan is successfully implemented. A strategic plan is a management tool that is used to transform organizational objectives into actions. The purpose of this study was to establish the effects of strategic plan implementation on organizational performance: a case study of public Secondary Schools in Kenya. The country in the recent past has experienced challenges like student declining performance in national examinations and increased student enrolments this implies that student cannot be able to join higher learning institutions which mean their future is blurred. The specific objectives were to analyze the effects of leadership style and communication in successful implementation of the strategic plans. A descriptive survey design using stratified sampling was used. The study population comprised of the school managers including teachers, principals and deputy principals serving in selected schools. Primary data was collected through questionnaires that were administered through "drop and pick later" method while secondary data was gathered from relevant Ministry of Education, Teachers Service Commission (TSC) and Government of Kenya publications like the Strategic plan, TSC Act, Ministry of Education (MoE) records. Quantitative data was analyzed though descriptive statistics and multiple regression analysis. Quantitative data was presented using bar graphs, pie charts and frequency distribution tables.

Random forests are presented as an analytics foundation for educational data mining tasks. The focus is on course- and program-level analytics including evaluating pedagogical approaches and interventions and identifying and characterizing at-risk students. As part of this development, the concept of individualized treatment effects (ITE) is introduced as a method to provide personalized feedback to students. The ITE quantifies the effectiveness of intervention and/or instructional regimes for a particular student based on institutional student information and performance data. The proposed random forest framework and methods are illustrated in the context of a study of the efficacy of a supplemental, weekly, one-unit problem-solving session in a large enrollment, bottleneck introductory statistics course. The analytics tools are used to identify factors for student success, characterize the benefits of a supplemental instruction section, and suggest intervention initiatives for at-risk groups in the course. In particular, we develop an objective criterion to determine which students should be encouraged, at the beginning of the semester, to join a supplemental instruction section.

Educational data science (EDS) is an emerging, interdisciplinary research domain that seeks to improve educational assessment, teaching, and student learning through data analytics. Teachers have been portrayed in the EDS literature as users of pre-constructed data dashboards in educational technologies, with little consideration given to them as active producers of data analytics. This article presents the case study results of an EDS program at a large university in Midwestern U.S.A. in which faculty and instructors were provided with access to institutional data and data analytics technologies in order to explore questions related to their classroom and departmental environments. Semi-structured interviews of program participants were conducted to examine the participants' experiences as practitioner researchers in EDS. The analysis showed that participants were motivated to participate to improve their learning and educational environments through data analytics, as opposed to developing a research agenda in EDS; that participants experienced a range of barriers related to data literacy; and that participant community support in addition to administrative support are vital to teacher-focused EDS programs. This study adds to a small but growing body of research in EDS that considers teachers as producers and not just consumers of data analytics.

The purpose of this study is to look closely at how aspects of instructional leadership, and organizational learning affect the quality of madrasah in improving the quality of graduate the state madrasah aliyah. The experiment was conducted using a quantitative approach with descriptive and inferential methods, in inferential methods used correlation analysis and regression analysis. The process is first conducted analysis of data validity and reliability of data as well as test for normality using the Kolmogorov-Smirnov Test. The study population is the overall teacher the State Madrasah Aliyah at Jakarta Capital Region. The study sample size of 150 teachers. The collecting data about the instrument with research using Likert scale, to obtain data on instructional leadership, organizational learning, quality of madrassah and the quality of graduates. The results of research known that headmaster instructional leadership has a strong and positive relationship with the quality of the madrasah, the quality of graduates, organizational Learnings have strong relationships and positive impact on the quality of madrasah. The quality of graduates and have a reciprocal relationship with a high instructional leadership. It can be concluded that an increasing in the quality of madrasah and the quality of graduates at the school can be done with an increase in instructional leadership and organization of learning in the madrasah. Thus improvement instructional leadership and organizational learning have a positive influence on the improvement of the quality of madrasah and the achievement of the quality of graduates.

Understanding and analyzing data is a crucial aspect of quantitative literacy in the 21st century, leading scholars and policy makers to push for more rigorous instruction in this area rooted in statistical investigation. In this study, we examine 144 upper-elementary mathematics lessons on data and statistics topics to assess the extent to which instruction in this sample meets reformers' goals. Analyses of videos and transcripts suggest that sample lessons emphasized the analysis of data through calculations and graph construction. However, they less often featured statistical investigation, with minimal emphasis on question formulation, data collection, and interpretation and with little to no attention paid to the concept of variability. We also note issues with the accuracy of statistics content presented to students. Results suggest focusing improvement efforts on all aspects of statistical investigation and considering ways to build elementary teachers' content knowledge of statistics.

Understanding and analyzing data is a crucial aspect of quantitative literacy in the 21st century, leading scholars and policy makers to push for more rigorous instruction in this area rooted in statistical investigation. In this study, we examine 144 upper-elementary mathematics lessons on data and statistics topics to assess the extent to which instruction in this sample meets reformers' goals. Analyses of videos and transcripts suggest that sample lessons emphasized the analysis of data through calculations and graph construction. However, they less often featured statistical investigation, with minimal emphasis on question formulation, data collection, and interpretation and with little to no attention paid to the concept of variability. We also note issues with the accuracy of statistics content presented to students. Results suggest focusing improvement efforts on all aspects of statistical investigation and considering ways to build elementary teachers' content knowledge of statistics.

Statistical thinking is essential to understanding the nature of scientific results as a consumer. Statistical thinking also facilitates thinking like a scientist. Instead of emphasizing a "correct" procedure for data analysis and its outcome, statistical thinking focuses on the process of data analysis. This article reviews frequentist and Bayesian approaches such that teachers can promote less well-known statistical perspectives to encourage statistical thinking. Within the frequentist and Bayesian approaches, we highlight important distinctions between statistical evaluation versus estimation using an example on the facial feedback hypothesis. We first introduce some elementary statistical concepts, which are then illustrated with simulated data. Finally, we demonstrate how these approaches are applied to empirical data obtained from a Registered Replication Report. Data and R code for the example are provided as supplementary teaching material. We conclude with a discussion of key learning outcomes centred on promoting statistical thinking.

The African Data Initiative started as a crowd-sourced campaign to improve the teaching of statistics in African universities. The analysis of climate data provides one suitable context to illustrate ideas that lead to a radical new form of teaching. The problem within the context comes first, the technicalities are largely reduced -- mathematics is supported by meta knowledge and backed up by modelling; calculations are reduced by user-friendly software that is also used by experts. The problems are treated similarly to research questions and the results are often easier to interpret, making sense as potential answers in their context. The criteria of this approach are compared to the framework proposed by W. G. Cobb to reform statistics education in the light of the latest developments in statistics, driven by the huge increase of data. Implementation details are presented around three components: case studies, data, and the required skills. Together, these three components describe an alternative education pathway centred around statistical problem solving. The focus on interpretations of results within a real context enables software, mathematical thinking and modelling to play a supportive role, which flattens the prerequisites of complex methods and encourages their use across all levels of education.