USCOTS 2015 - Breakout Sessions


Friday, May 29th

11:15 a.m. - 12:30 p.m. Breakout Sessions #1

Session 1A: Room 206
Mapping the conceptual domain of statistics
Jim Stigler (UCLA)

Session 1B*: Room 205
Leo Breiman's two cultures versus our measly 1.25±?
George Cobb (Mount Holyoke College), Amy Wagaman (Amherst College), Patrick McDonald (New College of Florida)
Come explore the connection between algorithmic data analysis and the traditional core of the Liberal Arts: critical inquiry in service of deep understanding, and learn some methods for how you can make topics such as classification, clustering, and analysis of “Big Data” accessible to your students. Discuss how and whether to include these topics in an introductory statistics course. We believe that we teachers of statistics should insist that our students do better than “just wind it up and let it go”. That requires us to connect the learning of algorithms to the Liberal Arts. Otherwise, our teaching and our students’ learning will remain in that category which machine learners call “unsupervised.” (Stretching the metaphor: throughout the history of education, the Liberal Arts have provided the principles that “supervise” all learning.)

Session 1C*: Room 204
Developing data science curricula
Andrew Bray (Mount Holyoke College), Rob Gould (UCLA)
What is Data Science? Data Science is, perhaps, different things to different people, but for the sake of beginning a conversation, we’ll consider it to be a discipline that seeks understanding and insight through data, and requires computational skills beyond what has traditionally been included in a statistical inference curriculum. We will report on two projects that are developing data science curricula. Mobilize is an NSF-funded grant that is a partnership between UCLA and the Los Angeles Unified School District that is piloting a data science course for high school math students. Data Science Initiative at MassMutual, Mount Holyoke, and Smith Colleges is collaboration between academia and industry to build a rich educational environment for learning data science.

Session 1D*: Room 104
Statistics education for teachers (SET) report
Anna Bargagliotti (Loyola Marymount University), Chris Franklin (University of Georgia), Catherine Case (University of Florida)
Although statistics has been included as an important branch of K–12 mathematics education, there is a great need for preparing and supporting teachers trying to integrate statistics learning into the classroom. The American Statistical Association commissioned the “Statistical Education of Teachers” (SET) report to clarify the statistics teachers must know to effectively address current K–12 needs. This session will present the recommendations of the SET report. It will focus on having participants work through several examples in the report, examine teacher work on the examples, and discuss difficulties and potential “roadblocks” that occur. Participants will be familiarized with the SET report and partake in a rich discussion on how statistics should be incorporated into teacher preparation.

Session 1E*: Room 109
Connecting simulation-based inference with traditional methods
Kari Lock Morgan (Pennsylvania State University), Patti Frazer Lock, Robin Lock (St. Lawrence University)
Many introductory courses now use simulation-based methods to introduce inference, while continuing to include the more traditional methods that rely on formulas and approximations with theoretical distributions. This session will discuss ways to smoothly transition between simulation-based and traditional methods, emphasizing connections between the two approaches. The session will also address ways to help students make connections across different parameters, using ideas learned via simulation to help emphasize the common structure underlying traditional inference.

Session 1F: Room 207
Connections with language to help statistics students make content connections
Amy Wagler, Larry Lesser (University of Texas at El Paso)
Accessibility of written text is one factor that affects how effectively students make connections with a learning resource (e.g., see our paper in the January 2015 Journal of Technical Writing and Communication). We will give attendees an experiential motivation and then demonstrate and facilitate interaction with readily available applications which explore the readability of any written instructional resource. We will present and provide a framework for small group discussion about how instructors can make use of research findings to improve student connections to learning resources. The session offers an opportunity for participants to practice (so bring sample self-authored written material and a laptop if you like) using readily-available tools such as LexTutor, Coh-Metrix, and readability statistics in MicrosoftWord. A handout with key resources and references will be provided.

Session 1G: Room 108
Connecting data, analysis, and results using a reproducible framework
Mine Cetinkaya-Rundel (Duke University), Nick Horton (Amherst College)
The issue of reproducibility often comes up in the context of published research and the need to accompany such research with the complete data and analyses, including software/code. However, as statistics educators who teach data analysis, we should be instilling best practices in students before they set out to do research. The tools and techniques presented in the session will enable training of new researchers/practitioners with a scientifically sound and fully reproducible data analysis workflow. While the tool we will feature most prominently is R/RStudio/RMarkdown, we will also open the floor for discussion of other tools participants find valuable for making connections with client disciplines, practicing statisticians, and other professionals who use statistical ideas and methods and ideas for building a reproducible workflows for these tools. The session will feature with team-based activities that highlight the importance of doing data analysis in a reproducible framework. We will also demonstrate the use of tools like R/RStudio/RMarkdown for teaching reproducible data analysis, even at the introductory statistics level, as well as higher-level tools such as GitHub.

Session 1H: Room 203
Connecting students through peer-facilitated learning communities
Erin Curran, Dayius Turvold Celotta, Kerri Carlson (University of St. Thomas)
In 2010, a multidisciplinary committee at a liberal arts university initiated a STEM Learning Community Program modeled after peer-led team learning. This program provides weekly, semi-structured opportunities for small groups of students taking introductory courses in applied statistics, biology, chemistry, and calculus to collaborate on course-related activities. Program evaluation data suggest significant impacts on student learning as well as positive impacts on students’ problem solving and teamwork abilities, sense of community, and overall study habits. The implementation of peer-lead team learning in introductory statistics courses, as well as its impact on students’ abilities to connect with each other, the course, and problems within the discipline will be discussed.

3:00 p.m. - 4:15 p.m. Breakout Sessions #2

Session 2A: Room 206
Professor 2.0: Alternative approaches to navigating the academic universe
Roger Peng (Johns Hopkins University)

Session 2B*: Room 204
Leo Breiman’s two cultures versus our measly 1.25 ± ?
George Cobb (Mount Holyoke College), Amy Wagaman (Amherst College), Patrick McDonald (New College of Florida)
Come explore the connection between algorithmic data analysis and the traditional core of the Liberal Arts: critical inquiry in service of deep understanding, and learn some methods for how you can make topics such as classification, clustering, and analysis of “Big Data” accessible to your students. Discuss how and whether to include these topics in an introductory statistics course. We believe that we teachers of statistics should insist that our students do better than “just wind it up and let it go”. That requires us to connect the learning of algorithms to the Liberal Arts. Otherwise, our teaching and our students’ learning will remain in that category which machine learners call “unsupervised.” (Stretching the metaphor: throughout the history of education, the Liberal Arts have provided the principles that “supervise” all learning.)

Session 2C*: Room 104
Connecting the curriculum guidelines for undergraduate programs to your program
Chris Malone (Winona State University), Beth Chance (Cal Poly – San Luis Obispo)
The American Statistical Association has recently updated their Curriculum Guidelines for Undergraduate Programs in Statistical Science. These guidelines consist of the following main sections -- Background and Guiding Principles, Skills Needed, and Curriculum for Majors and Minors. This session will begin with a brief overview of the content in these three sections. Audience members will then gather in small groups to discuss specifics and implications of the guidelines. The main goal of this session is to better understand the details of these guidelines and to develop strategies for implementation into your own undergraduate curriculum.

Session 2D*: Room 207
Preparing to teach statistics: Connecting subject matter and pedagogical content knowledge
Stephanie Casey, Rita Zejnullahi (Eastern Michigan University), Nick Wasserman (Teachers College, Columbia University), Joe Champion (Boise State University)
The goal of our session is to assist those working with preservice and inservice teachers, including instructors of statistics courses, to help teachers make connections between content and pedagogy for teaching statistics. Teachers need new kinds of statistics courses that address Statistical Knowledge for Teaching, integrating their learning of subject matter knowledge with their learning of pedagogical content knowledge. We have written and piloted curriculum materials that connect the learning of subject matter knowledge with pedagogical content knowledge for the topic of bivariate categorical association. This session will center around participants engaging with selected activities from the curriculum materials in small groups, facilitated by authors and piloters of the material. Discussion of ways to support the integration of subject matter knowledge with pedagogical content knowledge in instruction of teachers will conclude the session.

Session 2E*: Room 109
Fun in statistics class: A vehicle for students to make connections
John Weber (Georgia Perimeter College), Dennis Pearl (Pennsylvania State University), Larry Lesser (University of Texas at El Paso
This session offers practical methods and experience on the active use of fun items (e.g., songs, jokes, cartoons) in teaching and learning statistics. We'll discuss instructors’ motivations and hesitations about using fun in the classroom, focusing on overcoming the barriers/hesitations while connecting fun to sound pedagogy. We'll discuss how using fun to connect students with each other as a classroom community and how fun can help students connect with the subject and specific content. Teams of participants will work (with special artistic consultants) to create or enhance specific fun items (e.g., arising from the A-mu-sing competition) suitable for achieving a specific pedagogical objective in teaching statistics. Participants will make connections to resource collections (CAUSEweb.org/resources/fun/) and learn to generate a mini-lesson with a fun item. Finally, participants will be connected to an interdisciplinary research base showing the value of fun in teaching and learning and how its effective use requires the planned active engagement of students. Participants will learn direct connections to tools and resources that break down these barriers and make using fun items in their teaching simple to implement.

Session 2F: Room 205
Community-based learning and statistics: A real-world connection
Gina Reed (University of North Georgia), Kelly McConville (Swarthmore College)
In this session, participants will consider introducing community-based learning into the courses they teach. Community-based learning connects students with community organizations by helping them meet the organization’s need for data collection and/or analysis. Also, community-based learning creates a concrete connection between the course content and the interests of the students. This session will begin with a presentation about community-based learning and some examples of integrating community-based learning into courses. Then the participants will break into small groups and will discuss and plan how to incorporate a community-based learning project into a course of their choosing.

Session 2G:Room 203
Six sigmas of separation: Strategies for making inter—disciplinary connections
Eric Reyes, Diane Evans (Rose-Hulman Institute of Technology)
We have seen a large number of non-majors taking our statistics courses. Making connections with other departments and their disciplines can help us better engage and serve these students. In this session, we will discuss three strategies for interacting and connecting with disciplines outside of statistics and describe the effect these have had on our curriculum. The first strategy is what has been termed “attacking the labs,” which means engaging other campus departments in the classes they teach and in the labs they conduct. The second strategy, interdisciplinary discussions, will be introduced by examining a series of course projects that required students to work in teams to address issues related to student life on campus. We will also describe discussion questions we use in upper-level courses to get students interacting with statistics in their discipline-specific contexts. The third strategy, reformulating in-class activities using external knowledge, encourages instructors to revise common problems into active learning scenarios with a context that fits students’ backgrounds and interests. Upon leaving this session, participants will have begun developing ways to make connections on their own campuses.

Session 2H:Room 108
Statistical consulting: Making connections across the curriculum
Aimee Schwab (University of Nebraska – Lincoln), Pamela Fellers (Grinnell College)
Exposing students to statistical consulting has numerous benefits and can be integrated into all levels of the curriculum. During this session participants will learn about ways to incorporate consulting experiences into a variety of statistics courses. Activities were originally designed to work between courses, assigning an advanced statistics student to “consult” an introductory student, however they may be modified for use within a single introductory or advanced course. Participants will discuss benefits and drawbacks to including consulting opportunities, possible activities for each course level, designing clear learning objectives and student preparation assignments, and assessing student learning.

  • 4:15 p.m. - 4:30 p.m. Break
  • 4:30 p.m. - 5:30 p.m. - Technology Demonstrations
  • "A Visual Way to Master Statistics" - Room 109
    Presented by: Kelsey Poor - Hawkes Learning, Mount Pleasant, SC
    With a 30+ year foundation in statistics, Hawkes Learning has established the highest standards for excellence in courseware with interactive instruction, error-specific feedback, and mastery-based homework assignments. Hawkes’ innovative browser-based platform revolutionizes the learning experience, motivating your students to succeed while improving their understanding of statistical concepts and problem-solving skills through visualization. Join us to learn how Hawkes Learning is adapting to the needs of students across the country by offering courseware that is accessible with or without an internet connection – plus see a demonstration of our comprehensive course management system and new tablet-friendly learning platform. The presentation will conclude with a little fun: a raffle for a $100 Amazon Gift Card!

    Just Added! "Teaching statistics with JMP is as easy as 1, 2, 3" - Room 108
    Presented by: Julian Parris
    In this hands-on workshop, learn how to easily introduce students to data analysis and visualization with JMP. We will explore univariate, bivariate, and multivariate methods, and we will also introduce several new tools in JMP to teach fundamental statistical concepts.

    Saturday, May 30

    11:15 a.m. - 12:30 p.m. Breakout Sessions #3

    Session 3A: Room 206
    Engaging students with data
    Shonda Kuiper (Grinnell College), Nick Horton (Amherst College)
    Technology is quickly changing the discipline of statistics. Today's students often struggle to connect the material from their statistics courses to the analyses they see in daily media and news outlets. Simply teaching students how to conduct statistical analysis on carefully vetted and simplistic textbook datasets does not provide the statistical thinking needed for students to properly make decisions with today's increasingly big data. Gould challenged us (ISR, 2010) to bring more modern examples and data into the classroom. In this session, we will discuss activities that bridge the gap from smaller, focused textbook problems to modern settings and questions, such as data relevance, visualization, and simple multivariate analysis. These activities are designed to be easily incorporated into traditional statistics courses and to help prepare the next generation to think critically with data. By engaging students in interactive data visualizations and multivariate thinking, we demonstrate to students how statistics is relevant in their own lives.

    Session 3B*:Room 107
    Making connections with “clips,” collaboration, and community
    James Bush (Waynesburg University), Jennifer Bready (Mount Saint Mary College)
    This fun, entertaining, and informative session will first show participants how to use examples from film, television, literature, and both print and digital media to not only entertain students, but to motivate several major topics in an introductory statistics course. Using activities related to media helps to engage students that are typically non-mathematics majors to see how the mathematics around them can be applied. Participants will take part in classroom-ready activities involving probability, sampling (and sampling variability), correlation, and inference. In the second part, participants will learn how to encourage students and/or faculty to use statistics beyond the classroom to connect with real-world situations in their community.
    All activities presented during the first part are ready for immediate use in the classroom. Participants will be provided with the materials and access to all media clips. The small group discussion in the second part will provide the participants with ways in which they can extend statistical learning to connect with both the school community (through communication and collaboration with faculty and students in other disciplines) and with professionals in the larger community, for either research projects or service-learning.

    Session 3C*: Room 204
    Developing data science curricula
    Andrew Bray (Mount Holyoke College), Rob Gould (UCLA)
    What is Data Science? Data Science is, perhaps, different things to different people, but for the sake of beginning a conversation, we’ll consider it to be a discipline that seeks understanding and insight through data, and requires computational skills beyond what has traditionally been included in a statistical inference curriculum. We will report on two projects that are developing data science curricula. Mobilize is an NSF-funded grant that is a partnership between UCLA and the Los Angeles Unified School District that is piloting a data science course for high school math students. Data Science Initiative at MassMutual, Mount Holyoke, and Smith Colleges is a collaboration between academia and industry to build a rich educational environment for learning data science.
    We hope to engage in a lively discussion about what a data science curriculum should look like and how (and whether) it should influence what is taught in “introductory” statistics. Participants will engage in activities and lessons from these courses and will participate in a data-science curriculum-building activity in which they will examine “traditional” course outlines and discuss how they might be changed or supplemented. Required: Participants will need laptops with internet connections in order to access the Rstudio labs.

    Session 3D*: Room 108
    Statistics education for teachers (SET) report
    Anna Bargagliotti (Loyola Marymount University), Chris Franklin (University of Georgia), Catherine Case (University of Florida)
    Although statistics has been included as an important branch of K–12 mathematics education, there is a great need for preparing and supporting teachers trying to integrate statistics learning into the classroom. The American Statistical Association commissioned the “Statistical Education of Teachers” (SET) report to clarify the statistics teachers must know to effectively address current K–12 needs. This session will present the recommendations of the SET report. It will focus on having participants work through several examples in the report, examine teacher work on the examples, and discuss difficulties and potential “roadblocks” that occur. Participants will be familiarized with the SET report and partake in a rich discussion on how statistics should be incorporated into teacher preparation.

    Session 3E*: Room 205
    Fun in statistics class: A vehicle for students to make connections
    John Weber (Georgia Perimeter College), Dennis Pearl (Pennsylvania State University), Larry Lesser (University of Texas at El Paso)
    This session offers practical methods and experience on the active use of fun items (e.g., songs, jokes, cartoons) in teaching and learning statistics. We'll discuss instructors’ motivations and hesitations about using fun in the classroom, focusing on overcoming the barriers/hesitations while connecting fun to sound pedagogy. We'll discuss how using fun to connect students with each other as a classroom community and how fun can help students connect with the subject and specific content. Teams of participants will work (with special artistic consultants) to create or enhance specific fun items (e.g., arising from the A-mu-sing competition) suitable for achieving a specific pedagogical objective in teaching statistics. Participants will make connections to resource collections (CAUSEweb.org/resources/fun/) and learn to generate a mini-lesson with a fun item. Finally, participants will be connected to an interdisciplinary research base showing the value of fun in teaching and learning and how its effective use requires the planned active engagement of students. Participants will learn direct connections to tools and resources that break down these barriers and make using fun items in their teaching simple to implement.

    Session 3F: Room 109
    Encouraging cooperative learning in introductory statistics classes
    Camille Fairbourn (Utah State University), Pat Humphrey (Georgia Southern University), Georgette Nicolaides (Syracuse University)
    Life requires the ability to work effectively with others. While it's rarely greeted with enthusiasm, group work in a statistics class can help students make deeper connections to course material and to other students. Assigning, facilitating, and assessing group work with introductory students poses special challenges due to both the level of the course content and the skills of the students. Join us for a session that will use group work procedures to share, discuss, and solve issues about group assignments, grading, peer evaluation, and group facilitation in face-to-face, distance-delivered, and fully online classes.

    Session 3G: Room 207
    GAISE into your classroom
    Michelle Everson and Deb Rumsey (Ohio State University), John Gabrosek (Grand Valley State University), Megan Mocko (University of Florida)
    The original GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report was endorsed in 2005, and while the overarching goals and major guidelines for teaching and assessing statistics are certainly timeless, changes in how teaching is put into practice are natural over time, such as new focus on randomization, and the use of new software to integrate teaching and learning simultaneously. Because of all the exciting changes in statistics education tools and methods, ASA has made it a priority to revise GAISE accordingly, so it continues to be easily and clearly applicable to teachers of statistics. To accomplish this goal, ASA has recently charged a committee with revising and updating the College Report of the Guidelines for Assessment and Instruction in Statistics Education (GAISE). Four members of the new GAISE committee will be facilitating this session. The goal of the session is to bring together and connect instructors who share the goal of learning more about GAISE, reflecting on how the landscape has changed in Statistics Education in the last 10 years, and brainstorming ways the newer GAISE report can inform the teaching of their own introductory statistics courses.

    Session 3H: Room 208
    Connecting with basic knowledge
    Mary Parker, Colleen Hosking (Austin Community College)
    What prior knowledge and experiences are we assuming our students have that some of them don’t have? Can we find some quick, meaningful, memorable ways to address these? The goal of this session is to help you identify some of these concepts and topics you have noticed among your students and, in a small group, make a plan for addressing at least one of those. We will draw on our experiences with Statway and show how we have used that to enhance all of our statistics classes

    3:00 p.m. - 4:15 p.m. Breakout Sessions #4

    Session 4A: Room 208
    Developing a personal statistics education research agenda
    Michael Posner (Villanova University), Bob delMas (University of Minnesota)

    Session 4B*: Room 107
    Making connections with “clips,” collaboration, and community
    James Bush (Waynesburg University), Jennifer Bready (Mount Saint Mary College)
    This fun, entertaining, and informative session will first show participants how to use examples from film, television, literature, and both print and digital media to not only entertain students, but to motivate several major topics in an introductory statistics course. Using activities related to media helps to engage students that are typically non-mathematics majors to see how the mathematics around them can be applied. Participants will take part in classroom-ready activities involving probability, sampling (and sampling variability), correlation, and inference. In the second part, participants will learn how to encourage students and/or faculty to use statistics beyond the classroom to connect with real-world situations in their community.
    All activities presented during the first part are ready for immediate use in the classroom. Participants will be provided with the materials and access to all media clips. The small group discussion in the second part will provide the participants with ways in which they can extend statistical learning to connect with both the school community (through communication and collaboration with faculty and students in other disciplines) and with professionals in the larger community, for either research projects or service-learning.

    Session 4C*: Room 206
    Connecting the curriculum guidelines for undergraduate programs to your program
    Chris Malone (Winona State University), Beth Chance (Cal Poly – San Luis Obispo)
    The American Statistical Association has recently updated their Curriculum Guidelines for Undergraduate Programs in Statistical Science. These guidelines consist of the following main sections -- Background and Guiding Principles, Skills Needed, and Curriculum for Majors and Minors. This session will begin with a brief overview of the content in these three sections. Audience members will then gather in small groups to discuss specifics and implications of the guidelines. The main goal of this session is to better understand the details of these guidelines and to develop strategies for implementation into your own undergraduate curriculum.

    Session 4D*: Room 204
    Connecting simulation-based inference with traditional methods
    Kari Lock Morgan (Pennsylvania State University), Patti Frazer Lock, Robin Lock (St. Lawrence University)
    Many introductory courses now use simulation-based methods to introduce inference, while continuing to include the more traditional methods that rely on formulas and approximations with theoretical distributions. This session will discuss ways to smoothly transition between simulation-based and traditional methods, emphasizing connections between the two approaches. The session will also address ways to help students make connections across different parameters, using ideas learned via simulation to help emphasize the common structure underlying traditional inference.

    Session 4E*: Room108
    Preparing to teach statistics: Connecting subject matter and pedagogical content knowledge
    Stephanie Casey and Rrita Zejnullahi (Eastern Michigan University), Nick Wasserman (Teachers College, Columbia University) , Joe Champion (Boise State University)
    The goal of our session is to assist those working with preservice and inservice teachers, including instructors of statistics courses, to help teachers make connections between content and pedagogy for teaching statistics. Teachers need new kinds of statistics courses that address Statistical Knowledge for Teaching, integrating their learning of subject matter knowledge with their learning of pedagogical content knowledge. We have written and piloted curriculum materials that connect the learning of subject matter knowledge with pedagogical content knowledge for the topic of bivariate categorical association. This session will center around participants engaging with selected activities from the curriculum materials in small groups, facilitated by authors and piloters of the material. Discussion of ways to support the integration of subject matter knowledge with pedagogical content knowledge in instruction of teachers will conclude the session.

    Session 4F: Room 109
    Connecting statistics education with health sciences at the graduate level
    Matt Hayat (Georgia State University), Michael Jiroutek (Campbell University), MyoungJin Kim (Illinois State University), Todd Schwartz (University of North Carolina at Chapel Hill)
    Health topics are relevant to everyone, seen in every facet of everyday life, and made ubiquitous through media and news outlets bringing health topics into the public’s daily lives. We will introduce several approaches to making use of these real world phenomena by applying and extending the GAISE recommendations, and connecting statistics education with health science focused graduate training. The value for participants attending this breakout session will be in making connections among statistics education, graduate training for non-statisticians, the health sciences, and statistical software. This session will be highly interactive and connective on many levels, with respect to small group activity, exchange among small groups and all session participants, and session leader presentations. Participants will gain more in-depth appreciation and insight into the GAISE recommendations as applied to the health sciences at the graduate level, and will consider multiple ways to apply them.

    Session 4H: Room 205
    When am I ever going to use this?
    Jacqueline Wroughton (Northern Kentucky University), April Kerby (Winona State University)
    Many students come into a statistics class thinking of it as another box to check off of their list of requirements. They believe that the material covered is not something that they will ever use – in their career or life. As statistics educators, one of our goals is for students to see how the material is used in their career and/or life. How can we make this connection obvious to our students?
    This breakout session will include two different approaches that we have used in our statistics classes to help students make this connection. One is aimed for an introductory algebra-based statistics course which aspires to not increase teacher burden (either in class time or in grading). The other is aimed for a second algebra-based statistics course which focuses on students using technology to answer statistical questions. In addition, it includes having students write coherent reports on the topic at hand.
    Attendees of this session will get to participate in each approach, view student responses to the instruments, and walk out with extra examples to use in their classroom if they so desire.

    list