1. An Inquiry-Based Introductory Statistics Course Lisa Dierker
Inquiry based currilculum --
BRING IN TALK THAT INCLUDES ALL OF THE SPEAKERS – Nick, Deveaux, George!!
Here at Wesleyan we face similar challenges that you face
TAKE FROM THEIR TALKS
14% complete stats
Dear Gizem, The applicants thus far are a mix and that is how we intended it. We will be presenting a completely inquiry based approach to teaching that both statisticians and non-statisticians will find novel, I believe. In addition, we will be discussing broader issues where input from presenters and participants will be the focus. In short, for those who want to come and learn, it should be a great experience. That said, we encourage and welcome participants from whom we can also learn. One attendee for example will be a statistician from Smith who has written books that translate SAS and R code. In fact, for those participants who have thought a LOT of about teaching intro stat, we will likely ask them to facilitate one of the afternoon break out sessions.
Note taker – writing it down so all on same page….
An Inquiry-Based Introductory Statistics Course Lisa Dierker
Design thinking….
Project MOSAIC M-CAST
October 21, 2011

2. Making Innovation Work….
flat/shrinking resources
“boxes”
“priority fear”
Institution
feasible/sustainable
affordable
policy
Small numbers
Diverse backgrounds
Competing pressures
Instructors
easy
attractive
inspirational
Students
feasible
attractive
inspirational
Large numbers
Diverse backgrounds
Competing pressures
WE NEED TO BE INSPIRED (LOOK TO OUR STUDENTS FOR THAT)
MAJOR CHALLENGES
(do better with what we have got)
Diversity of students (how do we make a course that serves the Math majors and the math phobic and the molecular biology
Other responsibilities – research agenda’s. Like to get out of the office nad get some exercise once in awhile.
SLIDE Alpha Rather than the Omega
Much effort put into thinking about what students need (in introductory statistics) assuming this is all that they have. Reasonable since not all students get stats and many only take it at introductory level. Fostered different approaches fro m the traditional (consumer based), not work with data, but think about data presented in news papers and elsewhere. Traditional courses in statistics are of course focused on the math and logic behind common statistical tools.
Being a consumer of data is important, content knowledge is important (we are highly in favor of both), but in designing an alternative model for statistics education here at Wesleyan, we have set our sites on an outcome that we hope will give us both and so much more.
Excitement, Self Efficacy surrounding the application of statistics and the deep Desire to Learn more (which we hope translates into more exposure). And the confidence to go out and learn more, not only through formal course work, but through the myriad of opportunities that we all encounter in terms of using data to make decisions.
Inquiry-based Projects (start with the guts) what we think makes the difference in terms of our ability to inspire students.
Useful! Want to take further courses in statistics. (model meant to serve 100+ students per semester)
The course is completely designed around student interest (not what they are interested on average, but what excites and interests each student individually). Complete an independent research project while they are learning instroductoy statistics.
Organized around steps in research (and topics focused on answering students questions) – cover ANOVA and Chisq in fairly rapid succession despite the fact that these can be many chapters away from each other in a text book.
Data multi-disciplinary (see that skills they are learning translates to any topic – hormones or height or tree rings or
Syntax (for far more than running analyses, decision making in research)
OUTLINE FROM KARL WAS USELESS….
EVERYONE BRINGS THEIR BEST STUFF – NOT REINVENTIING THE WHEEL…

3. Our Definition of Inquiry-Based Statistics
Web prep. \Work on Workshop talk – benefits, just need
to work flexibly with data… great lecturer, grading,
assignments, activity based opportunities, etc. –
give the students more responsibility…..
A course that allows students to “decompose their topic, identify key components; abstract and formulate different strategies for addressing it; connect the original question to the statistical framework; choose and apply methods; reconcile the limitations of the solution; and communicate findings” (Nolan and Temple Lang 2009).

4. Goals INQUIRY-BASED CURRICULUM Maximize the following:
1. Excite the students (of all kinds)
2. Easy for instructors
3. Sustainable ($)
DOABLE. ALL NEED TO BRING OUR BEST STUFF-
COLLABORATIVE EFFORT
Know we are doing it right when we are doing all these things and more.
HAPPY IS NOT ENOUGH….Courses must inspire and produce a sense of self-efficacy
NO ONE OF THESE THINGS NEEDS TO BE PERFECT OR AT A CERTAIN LEVEL TO GET YOU TO THESE DISTAL OUTCOMES!!!!
GESTALT WILL DO IT (SOME OF ALL OF IT).
Proximal Outcomes
Quantitative reasoning skills
Content knowledge
Statistical programming skills
Cross-disciplinary thinking.
Improved attitudes toward statistics
Distal Outcomes
Inspired students with self-efficacy
Feelings that doors are open
Access to and interest in more!

5. Rich data that stimulates student curiosity
Public Access
Faculty
Caterpillar Ecology
NOT DASL AND OZDASL (not for canned exercises), but for actual research.
ALSO USE TERMINOLOGY ACROSS DISCIPLINES….
AddHealth
Community
CMT

6. Outline of Inquiry Weeks 1 and 2
Generate testable hypothesis from available data
Conduct a literature review
Write a research plan
Week 3 and 4
Statistical software basics
Formatting and managing data in the service of a question
Graphing
Week 5 through Week 10
Conduct analyses
Univariate, bivariate, testing for confounding, moderation
Week 11 through Week 14
Reconciling study limitations
Presenting and interpret results
GIVE SMOKING AND DEPRESSION EXAMPLE….

7. In Class and Out of Class Support
Lecture clips
Syntax
Peer Tutors
Instructor Office Hours
Open Learning Initiative
Monday
1-on-1
support in
small
lab
sections
Wednesday
1-on-1
support in
small
lab
sections
Friday
Drop in
hours
Lectures/
Project
support
WEEK
INSIDE VS. OUTSIDE THE CLASSROOM.
OLI
Orienting text
Question Forum
Data documentation
Statistics Writing Models

8. Syntax as a window into core statistical concepts
Exposed to several software packages and become skilled at one (SAS, Stata, SPSS, R)
Focus on the commonality and patterns
All syntax based
Simplified translational resources

9. Taking students outside of their comfort zone….
How do we have the time or energy? Because we are not doing all of the other stuff…. Lab instructors are not lecturing, prepping activities for class, grading and what unfortunately is about 80% of any traditional course.
Making the hard stuff easier on students – THAT IS NOT the same as avoiding the hard stuff.
LET THEM FAIL, BUT NOT LET THEM BE A FAILURE….
….and then loving them through the fall out!

10. Core Features The students set the topic and do the work
Didactic portions heavier out of class and support for active engagement heavier in class
As much one-on-one support as needed
Flexible content for all types of learners
Everyone involved in designing and running the course bringing their best stuff.

11. Approach to content knowledge (lectures/text)
Audience/institution adaptable features ….
Literature review
Research paper
Approach to content knowledge (lectures/text)
Amount of content knowledge
Number and type of data sets
Choice of software package (use of GUI)
Use of multivariate tools
Full poster presentation
AUGMENTING THE CENTER…
More difficult to sustain and without (still getting what we want).
TEACHING TO TEACHERS….. DATA REPORTS (context about the place, not generalizing to the world).
NICK HORTON – GREAT WORK WITH JOAN GARFIELD – when he talked about it (that is data management). FLIGHT SCHEDULES
MOVEMENT – we can’t do that because we have to do that. George was going to say what a course had to have. I was going to say that to do this model, you had to have certain things (DATA MANAGEMENT)

12. Poster presentations
SCIENCE FAIR (GRADE SCHOOL)

13. QAC201 (Applied data analysis) - enter as guest
Access is more than the availability of seats. It is about providing a welcoming place at the table.
Moodle.Wesleyan.edu
QAC201 (Applied data analysis) - enter as guest

14. Acknowledgements TUES0942246 from the National Science Foundation
Lauren B. Dachs Grant in Support of Interdisciplinary Research in the Social Impacts of Science.
Center Grant (NIDA DA010075) awarded to the Methodology Center, Penn State University
Numerous colleagues and advisors have generously contributed to the development of this course: Drs. George Cobb, Lisa Harlow, Daniel Long, Michael Singer, Wendy Rayack, Erika Fowler, John Kirn, Marc Eisner, Manolis Kaparakis and Jennifer Rose. 14