Journal Article

  • The purpose of this paper is to describe and analyze the first steps of a pair of 7th grade students working through an especially designed curriculum on Exploratory Data Analysis (EDA)ina technological environment. Theverbal abilitiesof these students allowed us to follow, at a very fine level of detail, the ways in which they begin to make sense of data, data representations, and the ‘culture’ of data handling and analysis. We describe in detail the process of learning skills, procedures and concepts, as well as the process of adopting and exercising the habits and points of view that are common among experts. We concentrate on the issue of the development of a global view of data and their representations on the basis of students’ previous knowledge and different kinds of local observations. In the light of the analysis, we propose a description of what it may mean to learn EDA, and draw educational and curricular implications.

  • This article begins with some context setting on new views of statistics and statistical education. These views are reflected, in particular, in the introduction of exploratory data analysis (EDA) into the statistics curriculum. Then, a detailed example of EDA learning activity in the middle school is introduced, which makes use of the power of the spreadsheet to mediate students’ construction of meanings for statistical conceptions.Throughthisexample,Iendeavortoillustratehowanattemptatseriousintegrationofcomputersinteachingandlearningstatisticsbringsaboutacascadeofchanges incurriculummaterials,classroompraxis,andstudents’waysoflearning.Atheoretical discussion follows that underpins the impact of technological tools on teaching and learning statistics by emphasizing how the computer lends itself to supporting cognitive and sociocultural processes. Subsequently, I present a sample of educational technologies, which represents the sorts of software that have typically been used in statistics instruction: statistical packages (tools), microworlds, tutorials, resources (including Internet resources), and teachers’ metatools. Finally, certain implicationsandrecommendationsfortheuseofcomputersinthestatisticaleducational milieu are suggested.

  • In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common in recent years. Existing literature has documented multiple success stories of teaching statistics with R, bolstered by the capabilities of R Markdown. In this article, we present an in-class data visualization activity intended to expose students to R and R Markdown during the first week of an introductory statistics class. The activity begins with a brief lecture on exploratory data analysis in R. Students are then placed in small groups tasked with exploring a new dataset to produce three visualizations that describe particular insights that are not immediately obvious from the data. Upon completion, students will have produced a series of univariate and multivariate visualizations on a real dataset and practiced describing them.

  • In reaction to misuses and misinterpretations of p-values and confidence
    intervals, a social science journal editor banned p-values from its pages. This study
    aimed to show that education could address misuse and abuse. This study examines
    inference-related learning outcomes for social science students in an introductory
    course supplemented with randomization and simulation content. Learning gains
    were measured across a suggested taxonomy of inference learning outcomes using
    the Reasoning about P-values and Statistical Significance (RPASS-10) scale. Three
    graphical comparisons of students’ Pretest and Posttest proportions were encoded by
    learning gain or loss, an inference learning outcome taxonomy, or if a correct
    concept or misconception was assessed. What students learned and the difficulties
    that persisted shape recommendations for teaching and future research.


  • The connection between mathematics and statistics is an important aspect in
    understanding college students’ learning of statistics because studies have shown
    relationships among mathematics attitudes and performance and statistics attitudes.
    Statistics attitudes, in turn, are related to performance in statistics courses. Little
    research has been done on college students’ perceptions of their mathematics and
    statistics experiences. To fill this gap, a phenomenographical study of 12 college
    students with self-identified negative attitudes about statistics was conducted to
    understand their perceptions of their previous mathematics and statistics classes. An
    integrated approach to data analysis was conducted in two phases. First, themes
    emerged from an inductive analysis. Second, the six recommendations from the
    Guidelines for Assessment and Instruction in Statistics Education (GAISE) College
    Report (2005) were used as a priori categories as an organizing framework for
    coding the data. Themes that emerged from the researchers’ analysis of the data were
    changing attitudes about statistics, defining the nature of statistics, seeking help, and
    blaming the teacher. The GAISE recommendations did not appear to be realized in
    the statistics classes taken by these students in various programs of study.
    Implications of these findings are discussed and recommendations for further
    research are suggested. In understanding students’ experiences from their
    perspectives, statistics educators can improve pedagogy and student learning.

  • The statistics education community continues to explore the differences in
    performance outcomes and in student attitudes between online and face-to-face
    delivery methods of statistics courses. In this quasi-experimental study student
    persistence, exam, quiz, and homework scores were compared between delivery
    methods, class status, and programs of study for students enrolled in an
    undergraduate introductory statistics course. Student persistence and homework
    grades were significantly different for course delivery method. Anxiety levels,
    measured using the Statistical Anxiety Rating Scale (STARS), were compared
    between delivery methods, programs of study, and gender. One anxiety
    subscale—Test & Class Anxiety—was significantly different between delivery
    methods and genders. Implications and suggestions for further study are offered
    based on the study results.

  • Graduate teaching assistants (GTAs) are responsible for the instruction of many statistics
    courses offered at the university level, yet little is known about these students’ preparation for
    teaching, their beliefs about how introductory statistics should be taught, or the pedagogical
    practices of the courses they teach. An online survey to examine these characteristics was
    developed and administered as part of an NSF-funded project. The results, based on
    responses from 213 GTAs representing 38 Ph.D.–granting statistics departments in the
    United States, suggest that many GTAs have not experienced the types of professional
    development related to teaching supported in the literature. Evidence was also found to
    suggest that, in general, GTAs teach in ways that are not aligned with their own beliefs.
    Furthermore, their teaching practices are not aligned with professionally-endorsed
    recommendations for teaching and learning statistics.

  • This study reports on a classroom activity for Grade 5 students investigating their
    reaction times. The investigation was part of a 3-year research project introducing
    students to informal inference and giving them experience carrying out the practice of
    statistics. For this activity the focus within the practice of statistics was on
    introducing two different ways of collecting data to answer a statistical question, in
    this case, “What is the typical reaction time of Grade 5 students?” Workbook entries
    were used to assess students’ capacities to engage in the investigation. Results
    indicated that although the students were proficient with the procedures and
    measures introduced, they were less able to explain and apply the underlying
    concepts. The activity provides a suggestion and benchmarks for others wishing to
    follow student development of concepts related to the practice of statistics.