Teaching Statistics最新文献

{"title":"Using a computer‐aided personalized system of instruction to enhance the mastery of statistics in online learning","authors":"Robert C. Butler, Christopher D. Blair, Rae Ette Newman, Leah L Batchelor","doi":"10.1111/test.12346","DOIUrl":"https://doi.org/10.1111/test.12346","url":null,"abstract":"This study compared the effectiveness of teaching a distance education statistics course using a computer‐aided personalized system of instruction (CAPSI) in comparison to a distance education course that used video lectures. Data were collected between 2017 and 2022. Two‐hundred and sixty‐eight students were included in the sample. Results supported that students enrolled in the CAPSI statistics course were less likely to drop out of the course and mastered significantly more material than students enrolled in the lecture‐based distance education course. It is recommended that instructors teaching statistics in distance education settings consider using CAPSI to improve student outcomes.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63468934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics","authors":"J. Noll, Maria Tackett","doi":"10.1111/test.12345","DOIUrl":"https://doi.org/10.1111/test.12345","url":null,"abstract":"As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63468886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Announcement of Special Issue 2023 in Teaching Statistics","authors":"H. MacGillivray","doi":"10.1111/test.12326","DOIUrl":"https://doi.org/10.1111/test.12326","url":null,"abstract":"This Special Issue will showcase work that was presented at SRTL-12. Many ubiquitous forms of data do not clearly fit the sample-population assumptions that underpin the statistical reasoning that has been the focus of much in statistical education. For example, data collected in real time (GPS, live traffic, tweets), image-based (photographs, drawings, facial recognition), semi-structured (scraped from social media posts), repurposed (school testing data to estimate housing prices) and big data (open access internet data, civic databases) are all examples of non-traditional data. While non-traditional forms of data have been with us for some time, the digital age has led to a pervasive culture of data in all aspects of life, including those of our students. Widespread availability and access to myriad of non-conventional, repurposed, massive or messy data sets necessitate broadening educational knowledge to better understand how learners make sense of and interrogate data as well as how they model, analyze and make predictions from these forms of data. This special issue focuses on empirical studies that investigate or nurture learners' understanding and reasoning with non-traditional, messy and/or complex data and models. Papers will focus on practical advice and implications for good practice in teaching statistics using non-traditional data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43283940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Information","authors":"","doi":"10.1111/test.12309","DOIUrl":"https://doi.org/10.1111/test.12309","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48089431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C Oswald George Prize Announcement 2022","authors":"H. MacGillivray","doi":"10.1111/test.12327","DOIUrl":"https://doi.org/10.1111/test.12327","url":null,"abstract":"The article entitled “Characteristics of statistical literacy skills from the perspective of critical thinking” by Shunya Koga has been awarded the C. Oswald George prize for 2022 This paper describes the development of a framework to illustrate statistical literacy skills in terms of critical thinking, through investigating existing research on critical thinking, and examining and aligning its characteristics in the domain of statistical literacy across the range of its descriptions in statistical education research. The critical thinking concept is wide and diverse, and this study organizes the characteristics of critical thinking skills that are representative studies in philosophical research, by identifying their similarities and differences. The study examines how those skills are demonstrated in the context of statistical literacy as described in considerable existing research, for example in situations such as interpreting and critically evaluating statistical information. The critical thinking skills presented in this study are intended for adults or high school students and above. The article acknowledges the challenges in the many possible ways of investigating critical thinking skills in the teaching and assessing of statistical literacy, that is, in the implementation of the research descriptions in the practice of teaching and assessment. One difficulty is that curricula are not necessarily focused only on the characteristics of statistical literacy common across the various research descriptions of it. Here just one application of the developed framework of characterizations of critical thinking in the context of statistical literacy is considered, namely a course explicitly on statistical literacy. Assessment could be analyzed, but here some teaching materials, namely textbooks written for the course, are considered to illustrate the framework. By investigating, identifying, analysing, aligning and bringing together wide-ranging research work on statistical literacy and critical thinking skills, this paper provides thoughtful insight and a framework for investigating critical thinking skills in the teaching and assessing of statistical literacy, that is, in the implementation of the research descriptions in actual teaching and assessment. In doing so, the paper also implicitly indicates that investigation of critical thinking skills is needed into wider aspects of statistical thinking skills. Congratulations to the author for a thoughtful and challenging analysis and development.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43121966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The how of data","authors":"H. MacGillivray","doi":"10.1111/test.12329","DOIUrl":"https://doi.org/10.1111/test.12329","url":null,"abstract":"For many decades, professional statisticians and statistics educators have emphasized the central importance of identifying, taking account of, and reporting the 5 W's of data—What, Why, When, Where, and by Whom. If data are to be collected or accessed, we can add How—how can we obtain the data we need or want. The word “How” used broadly, can also encompass much of the 5 W's, as the What and Why are needed to understand How the necessary or desired data can be obtained, or were obtained. That these are all integral to statistics and statistics investigations has also been emphasized but it can never be highlighted enough that they should be at the heart of teaching statistics, no matter to whom or at what level. It can be a delight for teachers to discover this; I will always remember the excitement of senior school teachers learning this 30 years ago in hands-on professional development workshops— “You mean this is all part of statistics, not just preliminaries to statistics? Wow!”. Unfortunately, learning from discipline and/or teaching frontlines does not necessarily penetrate the citadel of educational authority. The question of the Who, the What, the How, and the How much of teaching statistics in education faculties, whether for future teachers or future research (where the multiple t-test tyranny appears to continue unchecked), is open for a different discussion. As the eras of big data and data science gradually grew and then exploded, the 5 W's and the How of data in teaching have “of course” become even more important and have received renewed attention, as commented by many authors, including in the 2021 special issue of Teaching Statistics. But as Shatz [6] reminds us in this issue, we should avoid saying “of course” and be ever mindful of the perpetual need to both explain and illuminate what statistics is, including that the central roles of the 5 W's and the How of data are of critical importance in real data science. In this issue, Lasater et al [2] highlight that “two critical learning elements now are working with complex publically-available datasets and choice and use of appropriate visualization in investigating multivariable data.” In [2], “These are the focus of the lab activity described here, set in an important social context.” Expansion to complex, large publically-available datasets and technologically intensive procedures does not mean relegation of other types of datasets or data collections. It just means the big tent of statistics and statistics teaching got even bigger. Collecting data, observing data, experimental design, and surveys still have major roles to play across all of statistics and its applications, and in teaching. But no matter what type or size of dataset, and no matter what the teaching context, without knowing, taking account of, and reporting on the 5 W's and the How of the data, analysis and interpretation may be compromised. Three articles in this issue provide excellent illustrations of this ","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48270929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning statistics with interactive pictures using R Shiny: Generally preferred, but not generally advantageous","authors":"F. Zhao, Lena Schützler, O. Christ, R. Gaschler","doi":"10.1111/test.12324","DOIUrl":"https://doi.org/10.1111/test.12324","url":null,"abstract":"Constructing interactive web apps has become more accessible for instructors, for example, by using the R package Shiny. Here we explored learners' preferences and the efficiency of interactive simulations versus static pictures in acquiring statistics knowledge of Cohen's d and standard normal distribution. Results revealed that students' spontaneous interaction with pictures was infrequent (pilot study, N = 26). While prompts (Exp. 1, N = 152) effectively ensured the manipulation of simulations, student exposure to interactive simulations led to longer learning times though similar test performance compared with student exposure to static pictures. Multiple interactive representations led to lower test performance than single interactive and static representations (Exp. 2, N = 117). Though no advantage was gained regarding learning outcomes, participants preferred the interactive variant (Exp. 3, N = 119). Taken together, this study demonstrates that the superiority of interactive pictures cannot be assumed to hold in general. Further work should evaluate how mental model construction can be effectively scaffolded by interactive simulations.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42731779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical edutainment: An experimental way to teach for good measure","authors":"Dennis K. Pearl, L. Lesser","doi":"10.1111/test.12323","DOIUrl":"https://doi.org/10.1111/test.12323","url":null,"abstract":"Concepts of experimentation and measurement are explored using statistics educational fun items and illustrated by sharing our process in conducting an experiment on cartoon captions.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42891811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistics and clinical trials: It's all about the design","authors":"Sheuli Porkess","doi":"10.1111/test.12325","DOIUrl":"https://doi.org/10.1111/test.12325","url":null,"abstract":"This article explains the basic ideas and practical challenges in clinical trials of new medicines to show the practical application of statistics in the real‐world. The article explores the key considerations for the objectives and design of clinical trials and how these relate to the statistical investigation process. The article also includes examples of practical exercises for students.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41304976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bare bones, or a rich feast? Taking care with context in a data rich world","authors":"S. Finch, I. Gordon","doi":"10.1111/test.12322","DOIUrl":"https://doi.org/10.1111/test.12322","url":null,"abstract":"Providing a rich context has become a sine qua non of principled teaching of applied statistical thinking. With increasing opportunities to access secondary data, there should be increasing opportunity to work with rich context. We review the contextual information provided in 41 data sets suitable for introductory tertiary statistics teaching, available in the R “datasets” package, and investigate the source information for four data sets. We find failure to describe and retain important contextual information, including aspects that raise questions about the credibility of the data for statistical inference. The sanitization of data reduces the opportunities for learning meaningful lessons in statistical thinking and the real‐world application of statistics. We advocate for teachers and users of such data to be curious about the provenance and context, and for the curators and distributors to examine, where possible, the primary sources, to accurately preserve the context and optimize pedagogical opportunities.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41845798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}