Friday, May 29
F1. Speeding up dissemination of innovative projects and practices
Facilitator: Lisa Dierker
Table sign: Innovation
Description: With the proliferation of MOOC’s and other social/crowd sourcing technologies, there may be new opportunities to disseminate innovative statistics education practices that go beyond making materials available and move toward sharing, spreading and demonstrating innovation directly to students and instructors by virtually linking classrooms and/or centralizing student and instructor support. Come share your ideas and hear ideas from others that express new ways to imagine and reimagine our classrooms.
F2. Graduate level biostatistics education in health sciences
Facilitator: Matt Hayat
Table sign: Biostatistics
Description: One or more semesters of biostatistics coursework is usually required for graduate training in the health sciences (e.g., biomedical sciences, public health, nursing, dentistry, pharmacy). Discussion will touch on the many considerations in developing and teaching a first and/or second graduate level course in biostatistics; we can discuss what to teach, how much of it to teach, how to teach it, what level to teach it at, and ideas for delivery, student engagement, and assessment.
F3. MASTER: Motivations and affectives in statistics education research
Facilitator: Marjorie Bond
Table sign: MASTER
Description: MASTER is a research cluster whose mission is to improve students’ cognitive learning by studying their affectives and motivation and to facilitate the collaboration of researchers who study affectives and motivation. Affectives include attitude components as well as other student characteristics which influence learning.
F4. Maintaining academic integrity in institutions
Facilitator: Grace Esimai
Table sign: Integrity
Description: With the application of modern technology on the rise, its use in the classroom is no exception. Many students use technology to transgress the academic integrity and honor codes of their institutions. Examples include taking pictures of exams, texting exam passwords to friends outside an authorized venue, the “Buddy System” of exchanging tests, and illegal “Test Bank” websites started and/or actively maintained by students. Degrees are awarded for knowledge and integrity. The question is: How can we overcome these problems in the classroom? Participants will not only learn how students cheat but will learn effective strategies to combat academic dishonesty.
F5. Statistics in K-12: Preparing teachers and students
Facilitator: Anna Bargagliatti
Table sign: K-12 Statistics
Description: With the implementation of the Common Core in 43 states, statistics is now being taught in 6th-12th grades. This discussion will discuss these questions: Is the statistics content as envisioned in the Common Core actually being taught? What are the challenges and how can we overcome them? How can we advance teacher content knowledge in statistics? What are the challenges and how can we work to overcome them? What are people doing regarding K-12 teacher and student learning in statistics? How can information on success stories be gathered in systematic ways?
F6. Data science and analytics programs and courses: Similarities and differences
Facilitator: John McKenzie
Table sign: Data Science
Description: There are many names for the multidimensional discipline of gaining knowledge from data, much of which are impossible to manage and analyze using traditional tools due to the data’s size and/or complexity. It is called data science in the ASA Guidelines for Undergraduate Statistics Programs, but Monster.com indicates that business analytics is its most marketable designation. Statistics, computer science, and operations management have introduced courses and programs to deal with this interdisciplinary field. This discussion will compare such courses and programs. It will also discuss whether these courses should be placed in separate departments or in an academic center.
F7. Using simulation-based methods in intro stat
Facilitator: Kari Lock Morgan
Table sign: Simulation
Description: When using simulation-based methods in introductory statistics, several questions may arise. Should I use them only to reinforce concepts, or do I want students to actually use them? Should I use bootstrapping, and if so, which type(s) of bootstrap intervals should I cover? Should I also cover the traditional methods? If so, should I do them before, after, or simultaneously? When doing a randomization test, how should I handle data that doesn’t come from a randomized experiment? What technology options support this approach? We will discuss these questions, and any others that participants would like to raise.
F8. Teaching a statistical literacy course
Facilitator: Milo Schield
Table sign: Stat Literacy
Description: An increasing number of colleges are offering a course in Statistical Literacy for students in non-quantitative majors. What topics should this course include? Is “Concepts & Controversies” adequate?
F9. Raft debate: Statistics, computer science or data science
Facilitator: Kay Endriss
Table sign: Raft Debate
Description: Castaways representing Statistics, Computer Science, and Data Science (whatever that is) are stranded on a desolate island with only a one-person life raft for escape to civilization. Who should survive for the sake of humanity? Based on the volume of applause, the audience chooses the sole survivor as the three cajole, plead, pontificate, and resort shamelessly to props and costumes.
Saturday, May 30
S1. Preparing to teach K-12 statistics
Facilitator: Stephanie Casey
Table sign: K-12 Teachers
Description: Join us to discuss grades K-12 teacher preparation and professional development for teaching statistics.
S2. Statistics education in nursing
Facilitator: Todd Schwartz
Table sign: Stat in Nursing
Description: Statistics (at some level) is often included as part of the curriculum for nursing students, particularly at the graduate level. Instructors of statistics in Schools/Colleges of Nursing may face unique issues. These include ‘math anxiety’ and heterogeneity of the mathematical/statistical backgrounds of these students, the challenge of selecting the most pertinent statistical topics at the appropriate depth and breadth, and promotion and reappointment criteria as faculty members functioning in a discipline other than (bio)statistics. This roundtable will allow a forum for discussion of these issues, as well as brainstorming for possible solutions to challenges we face.
S3. Study of fun
Facilitator: John Weber, Larry Lesser and Dennis Pearl
Table sign: Study of Fun
Description: “Fun-feathered friends” interested in the use of fun (e.g., cartoons, songs, games, jokes, magic) in teaching statistics are invited to gather for Saturday lunch to discuss possible collaborative opportunities for collection enhancement (www.causeweb.org/resources/fun), professional development (e.g., a virtual conference), or research. There are ways to be involved on different fronts at different levels of commitment, and the next steps will be informed by the interests of who attends. If you’d like some background on this topic beforehand, see this 2013 paper by the CAUSE-supported Study of Fun Cluster: www.amstat.org/publications/jse/v21n1/lesser.pdf.
S4. Developing an analytics curriculum
Facilitator: Amy Phelps, Diane Fisher
Table sign: Analytics
Description: Do you have an analytics major or minor? Are you considering such a program at your institution? Do you have experiences to share or questions about developing such a program at your institution? Big Data is a hot topic these days. Check out the appropriately named hit song “Dangerous” by the group, Big Data! This roundtable invites you to share ideas and concerns about developing an academic program in business analytics or data science. While the topic may be in the forefront, its definition is not entirely clear and comes with additional academic interpretations and problems given its multi-disciplinary nature.
S5. Structuring a statistics curriculum in a department of mathematics
Facilitator: Eric Reyes
Table sign: Stat in Math Dept
Description: Which courses should be offered? What prerequisites should be placed on various courses? There are several questions to ask when putting together a curriculum; these questions become more complicated when the goal is not to produce a Statistics major but offer service courses to other departments and to Math majors wishing to obtain a concentration in Statistics. Bring ideas for constructing a program that is appropriate for both Math majors and outside disciplines seeking a minor.
S6. Match Colleges: Making connections with two-year colleges
Facilitator: Monica Dabos
Table sign: Two-Year Colleges
Description: This roundtable discussion will help to create and deepen connections at the community-college level. There are opportunities to participate in existing mentorship programs, like TANGO Stat Ed (Training a New Generation of Statistics Educators), an NSF-funded program, or the ASA Member Initiative, both of which match instructors teaching statistics at the community college level with renowned statistics educators/researchers. The discussion will broaden to other ongoing endeavors for professional development targeted at two-year college statistics instructors.
S7. Big ideas in teaching big data
Facilitator: Milo Schield
Table sign: Big Data
Description: One definition of big data is any data in which the association between any two variables is statistically significant. So what statistical topics should we teach involving big data as part of a traditional statistical inference course?
S8. Revising GAISE recommendations for introductory statistics
Facilitator: Michelle Everson, John Gabrosek
Table sign: GAISE
Description: ASA has recently charged a committee with revising and updating the College Report of the Guidelines for Assessment and Instruction in Statistics Education (GAISE). The goal of this discussion 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 over the past decade, and brainstorm about ways the new GAISE report can help to support effective teaching of introductory statistics courses.
S9. Teaching simulation-based inference
Facilitator: Beth Chance, Soma Roy
Table sign: Simulation
Description: This discussion will consider issues involved with reorganizing an introductory statistics course to focus on the entire process of statistical investigations and use simulation-based inference from the very beginning of the course. Specific topics to be discussed include ordering of topics, use of software and activities, potential benefits to students and faculty, and issues to consider before making such a change. We will also discuss a listserv and blog that CAUSE has created for teachers interested in simulation-based inference.