"Using Your Laptop to Gain iNZight and VITality: Intuitive, Free Software for Analysis and Conceptual Development"
Chris Wild (University of Auckland)
This is a "bring-a-laptop" breakout. In the abstract to my plenary I talk about how the explosive growth in the world of data makes it imperative to find ways to get students much further, much faster. iNZight and VIT are twin systems that attempt to address this challenge by facilitating visually-based understandings of both patterns in data and inferential concepts.
iNZight is a simple data analysis system intended to facilitate rapid exploration of multivariate data using simple forms of graphics without the distractions of driving complex software and minimising the need for the user to remember "the names of things". Graphics always appear instantly whereas summaries and formal inferential information have to be summonsed up (with a button click).
VIT (Visual Inference Tools) provides teachers and students with an integrated suite of dynamic visualisations of aspects of statistical inference covering: sampling variation, bootstrap confidence intervals, confidence interval coverage, randomisation variation and randomisation tests applied to data features: proportions, means, medians, quartiles, interquartile ranges, group differences in these features (including analysis of variance) and regression slopes. This is user-driven software using user-input data (population or sample as applicable). The visualisations are designed for "connecting all the dots" in the processes involved. The software is designed to facilitate both teacher demonstration and student activities.
This "bring-your-laptop" session will be a blend of demonstration and hands-on experience with the software. Memory sticks containing Windows and Mac versions of the software will be distributed at the breakout but if you want to play with them ahead of time you can download the current version of iNZightVIT from www.stat.auckland.ac.nz/~wild/iNZight.
"Preparing to Teach K-12 Statistics: Using Digital Tools for Teaching and Learning"
Hollylynne Stohl Lee and Tyler Pulis (North Carolina State University) and Stephanie Casey (Eastern Michigan University)
Many K-12 schools are changing towards classroom environments that have a plethora of digital tools available. Teachers need to be prepared to teach in these environments in ways that harness the power of digital tools to advance statistical thinking, reasoning, and literacy. The goal of our session is to assist those working with pre-service and in-service teachers, including instructors of statistics courses, to consider how to integrate appropriate technology and pedagogy to develop teachers' technological pedagogical statistical knowledge. You are encouraged to bring your laptop or mobile device to be engaged in collaborative discussions about essential aspects of understanding how to teach statistics with technology and how teachers may develop such understandings. During small group work we will be engaged with tasks using technologies such as TinkerPlots and Fathom. Free demos can be downloaded and installed at go.keycurriculum.com/fathom_trial.html and go.keycurriculum.com/tinkerplots_trial.html.
"Evaluating the Impact of Change in Curriculum and Teaching"
Bob delMas, Elizabeth Fry, Laura Le, and Anelise Sabbag (University of Minnesota)
This session will share approaches for evaluating the impact of curricular and pedagogical changes made in teaching introductory college statistics courses. Specifically, we will share methods and instruments developed for this purpose as part of recent NSF-funded grants. Three instruments developed and used to evaluate change, the GOALS (Goals and Outcomes Associated with Learning Statistics), MOST(Measure of Statistical Thinking) and Affect instruments, will be shared. These instruments are used to assess students' statistical reasoning, statistical thinking, and their attitudes/beliefs about statistics. Participants in the session will be invited to examine the instruments and determine how well they are aligned with desired outcomes in teaching statistics. Working in small groups, they will discuss how these instruments might be used to indicate potential effects on student learning that changes made in their teaching or curriculum have had, and also how they may inform future pedagogical decisions. Participants will also be given examples of data collected using these instruments from students who took CATALST and non-CATALST courses. They will discuss which results are most striking and how these results reveal the impact of changes made in the CATALST curriculum and pedagogy. Finally, participants will be asked to plan their own use of these instruments in evaluating an array of possible changes they might make in their own classes.
"Tossing the Tables: Using RStudio to Adapt to Student Learning Styles through Evolving Technology"
Jennifer Broatch and Jenifer Boshes (Arizona State University)
Students supplement so many aspects of their lives via technology, so why not supplement conceptual understanding this way too? As educators we must be able to adapt traditional methods into the world in which our students thrive. To make introductory statistics more relevant to students and their unique way of processing information, we propose introduction of software into the classroom right from the beginning. Too often in current classroom settings we take time away from course content and comprehension of the big picture in order to explain how to use antiquated tables and techniques. Being able to read statistical tables was a way to circumvent having to do difficult, if not impossible integrations. Now we have the technology that allows us to explain and process information with extreme precision and will provide students with a myriad of visual representations to help them on this path to understanding.
In this session we will provide an interactive demonstration on how to use RStudio to actively engage students in the classroom. RStudio is open source and the ideas that we present during this session can be easily adapted to any introductory statistics textbook. We will provide examples of R Scripts to facilitate a classroom discussion, typical of an introductory statistics class. By looking at real world data in a classroom, students become more interested, more quickly, in the course content. In contrast with R, the GUI of RStudio provides a user-friendly interface. This open source add-on to R has many desirable features including easy data entry, accessibility of previously created plots, and a useful help menu. Such manageable features cause students to more readily accept the integration of software in the classroom, often times getting them more excited about working through real world problems.
"Flipping the Classroom: Changing the Classroom Model to Enhance Student-centered Learning and Optimize Use of Time"
Rob Carver (Stonehill College), Michelle Everson (University of Minnesota), Shonda Kuiper (Grinnell College), Michael Posner (Villanova University)
The concept of a flipped or inverted classroom includes a broad range of classroom innovations that change the structure of the course, student-teacher interactions, the nature of classroom activity, and the ownership of the learning. This model, which is designed to allow more time for deeper interactions with students by moving some of the learning that students do outside of the classroom, has recently received a significant amount of press. This breakout session will share an overview and perspectives from four novice "flippers" on what a flipped classroom is or could be. It will provide an opportunity for the exchange of ideas in small breakout groups, and it will encourage participants to identify at least one aspect, module, or topic within their current courses that can be usefully flipped or inverted. Some specific questions that we will address are: 1) How we can prepare students for the flipped classroom? 2) What is the most efficient use of class time? 3) How we can assess students and the success of the course? 4) What is the most effective use of the instructor's time? 5) What are the benefits to student engagement and learning? Along the way, we will also touch on strategies for overcoming challenges - or how to avoid flopping when flipping.
"How Little Changes Can Make a BIG Difference"
Deb Rumsey (Ohio State University)
Sometimes our best teaching experiences come when we least expect them, with the smallest of changes. One day I just didn't want to lecture on bar graphs and histograms. So I left my lecture notes on my desk, printed off a data set that'd I'd found that had many variables, made copies, walked into class and said "knock yourselves out". What they gave me I will never forget. In this session, we will share and discuss how small changes can make big differences in teaching and learning. Bring one or two of your own moments to share.
"A Wealth of Activities for Introductory Statistics from the Statway and New Math Pathways Projects"
Mary Parker, Gustavo Cepparo, and Colleen Hosking (Austin Community College)
Many of the original versions of the activities developed in 2010-2011 for the StatwayTM project and New Math Pathways project are publicly available now through a Creative Commons license. These are available from the Charles A. Dana Center website: www.utdanacenter.org/higher-education/new-mathways-project
Each activity has a student handout and an extensive instructor handout. Many begin with a "rich task" and scaffolding questions to engage the students and helps them learn in a way that transforms their thinking about statistical issues. This workshop is designed to help you explore these activities and the design principles underlying them, and to begin adapting activities for use in your own classroom.
"Changing Gears: Using Activities to Link Study Design, Data Analysis, and Inference"
Daren Starnes (The Lawrenceville School)
Which should come first: study design or data analysis? Instructors and textbooks have generally agreed to disagree about the best ordering of these two topics. Whatever the sequence, design principles are often presented in isolation from data analysis techniques. Wouldn't it be better if students learned how the design of a study guides the proper method of analysis? Or better still, what conclusions can be drawn from a particular study? That's a little tricky if we avoid discussing statistical inference until the second half of the course! A viable alternative is to intersperse activities that blend study design, data analysis, and inference throughout the term. This change can be made fairly easily in existing courses without impacting textbooks or syllabi.
In this session, participants will engage in three hands-on activities of the type described above. The first involves data from a lottery that was used to decide which employees received a promotion. This activity reveals the logic of inference for random sampling. The second activity centers on a completely randomized experiment involving distracted driving. Using simulation, we'll decide if the results are statistically significant. The final activity explores the logic of inference in a matched pairs experiment with quantitative data. Classroom-ready handouts and teacher notes will be provided.