Tango Stat Ed ProjectTraining a New Generation of Statistics Educators

A Participant Perspective

Lisa Grossbauer

Tango Stat Ed: A Participant Perspective

• Project funded by the National Science Foundation to “Train A New Generation Of Statistics Educators.

• TANGO Stat Ed Researchers: Michael Posner from Villanova University, Villanova PA. and Monica Dabos from College of the Canyons, Santa Clarita, CA

• Leaders in the statistical community (including ASA presidents) identified critical need to “educate educators” on best practices for teaching statistics.

• Recognize the skills needed to succeed in today’s world depends largely on ability to make good decisions based upon synthesizing and analyzing large amounts of data

• TANGO Stat Ed “addresses these imperatives by creating mentorship and professional learning community (PLC) programs in regional hubs around the country”

• Pair TANGO participants with statistics educator mentors.

• Member of the second group of TANGO participants– TANGO mentor is from Franklin & Marshall, Iwan Praton. – TANGO partner is Lara Rosenberger also under mentorship with Iwan

Praton

Tango Stat Ed: A Participant Perspective

• Attended TANGO pre-workshop prior to the US Conference on Teaching Statistics (USCOTS) 2015.

• Grant provides funding for the mentorship program (2 hours per month) and attendance at USCOTS 2015 and 2017.

• Attend monthly professional learning community (PLC) meetings for the hub.

• Most TANGO participants are Community College Instructors and Adjuncts; not the typical TANGO member.

Tango Stat Ed: A Participant Perspective

• Statistics from CBMS 2010 – “60% of statistics courses are taught by part-time instructors”.

• Many colleges and universities have difficulty staffing their introductory statistics courses. Fill positions with faculty skilled in mathematics but not necessarily statistics.

• Researchers recognize the need to reach out to “typical” introductory statistics teacher.

• At Ursinus College – 60% or more of our sections are taught by visiting or adjunct faculty.

Tango Stat Ed: A Participant Perspective

• Access to experts in the field

• Tango Workshop Presentations:– Promoting Conceptual Understanding of Statistics by Kari Lock Morgan– Stat Ed Literature on Student Misconceptions by Bob delMas– Birth of an Activity by Beth Chance– Our Six Favorite Activities by Michael Posner and Monica Dabos

• Underlying theme conceptual understanding with simulation based activities to help students grasp concepts and vocabulary.

Tango Stat Ed: A Participant Perspective

Helpful Content Websites:• CATALST

– Bob delMas University of Minnesota “Change Agents for Teaching and Learning STatistics”

– Source for Helper vs. Hinderer Shell Lesson

• ARTIST– Robert delMas, University of Minnesota, Ann Ooms, University of Minnesota,

Joan Garfield, University of Minnesota, and Beth Chance, California Polytechnic State University

– “Assessment Resource Tools for Improving Statistical Thinking”

• AIMS− Adapting and Implementing Innovative Material in Statistics (University of

Minnesota Group)

• CHAOS, STEW, TUVALabs, JSE, DASL.

Tango Stat Ed: A Participant Perspective

Statistics at Ursinus: – Each section of STAT141 taught pedagogically different with students

experience varying quite a bit. – Designated “Q” course for the core – Quantitative Reasoning

Requirement.– Continuum of abilities in STAT141 argues for a more conceptual based

approach and increase rigor for those continuing on with STAT242(3).– Statistics Summit help to bridge differences and “educate educators” on

current best practices in keeping with GAISE guidelines.– Math Placement Update: Statistics Placement should be included to

better serve our students.– Reach out to all disciplines and establish relationships for cross

classwork with real data.– Eliminated “Math for the Liberal Arts” and advising students to take

STAT140 – Conceptual Statistics Course as attempt to better position students with a more “applicable math skill for professional careers”.

Introductory Statistics at Ursinus

• GAISE: Guidelines for Assessment and Instruction in Statistics Education

• Goals for Students in an Introductory Course: What it Means to be Statistically Educated.

http://www.amstat.org/education/gaise/GAISECollege_Goals.pdf

• Ursinus Math Department (Kari Hart) redesigned statistics minor in keeping with GAISE guidelines implemented 2014-2015

Introductory Statistics at Ursinus

Modified Course Goals in my Stat141 Syllabus:

• Students will appreciate that mathematics touches everything in our lives. • Students will use statistics as a way of thinking and of looking at the world.

• Students will gain a conceptual understanding of statistics in order to assess real life data in their major courses and future careers.

• Students will appreciate their social and moral responsibility to use their quantitative reasoning skills and critical thinking ability to contribute to society and thereby fulfill the mission of Ursinus College.

• Students will enhance their technical writing skills to effectively communicate their work.

• Students will become comfortable presenting problems to their peers as this is a skill that will be used heavily in your careers.

General Take-Aways:

Simulation Based Movement: physical model vs. simulation model

Introductory Statistics courses contain too much material so be more efficient in delivery by introducing vocabulary through simulation activities that are concept driven.

Build more intuitive feel for p-value early in the course Stress variability throughout the class – “signal and noise

around the signal” (Example 1). Build activities to allow students to struggle with raw data

giving special attention to data collection process Primarily use technology to calculate descriptors, example

correlation (Example 2).

General Take-Aways: Introduce sampling during data collection 80-90% of your time in the “real world” is spent preparing

data have students “collect data” Single analysis is rarely appropriate have students “play

with the data” ex. Remove outliers, explore missing data Talk more about Bias: Introduce them to the concept of

“bias towards the null” then a significant result becomes even more significant but bias towards the alternative doesn’t really help us.

Of course, the GAISE standards…

Put Inference at the beginning of an Introductory Statistics Course:

• Helper or Hinderer (source: CATALST Project University of Minnesota, Bob delMas and Rossman/Chance activity) http://www.tc.umn.edu/~catalst/

• Started with the Lesson Plan from CATALST but modified it to accommodate our textbook. (Handout)

• Followed six step investigational approach in Tintle, Chance, Cobb, Rossman, Roy Swanson, VaderStoep: Introduction to Statistical Investigations

• CATALST uses TinkerPlots replaced with RossmanChance applets.

• Adopted Ruth Carver’s (Germantown Academy) Buzz and Doris Activity Format (Introduction to Statistical Investigations).

Activity Example 1 Early Intro to Statistical Inference & P-Value

• Six Step Spiral Approach to Statistical Process: Tintle, Chance, Cobb, Rossman, Roy, Swanson, VanderStoep: “Introduction to Statistical Investigations”

• Reminded me of Polya’s Problem Solving Techniques

Activity Example 1 Early Intro to Statistical Inference & P-Value

CATALST Helper or Hinderer Background• You all recognize the difference between naughty and nice, right? What about

children less than a year old–do they recognize the difference and show a preference for nice over naughty? In a study reported in the November 2007 issue of Nature, researchers investigated whether infants take into account an individual’s actions towards others in evaluating that individual as appealing or aversive, perhaps laying for the foundation for social interaction. In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “google” eyes glued onto it) that could not make it up a hill in two tries. Then they were alternately shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”) and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the child was presented with both pieces of wood (the helper and the hinderer) and asked to pick one to play with. The researchers found that the 14 of the 16 infants chose the helper over the hinderer.

• Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557-559.

Activity Example 1 Early Intro to Statistical Inference & P-Value

Six Step Investigational Approach:

1. Address a research question: “Are infants able to notice and react to helpful or hindering behavior observed in others?”

2. Design a study and collect data: Give background information on the study reported in November 2007 issue of Nature. Discuss and have students define vocabulary

3. Explore the data: Group Work

4. Draw inferences beyond the data: How strong is the evidence? Chance Modeling using “rossmanchance applets” Discuss statistical significance Introduce p-value

5. Formulate Conclusions

6. Look back and ahead Polya’s Problem Solving Technique

Activity Example 1 Early Intro to Statistical Inference & P-Value

• Why do we consider an alpha level of 5%?• Cookies/Card Demo• About 5 person tolerance before we start to doubt this is

happening by random chance, ( about 3%• Behavioral Psychology Data

• P-Value – probability that your event happened by random chance.

• P-Value – “probability, computed assuming the null hypothesis is true, that the test statistic will take a value at least as extreme as that actually observed.”

Activity Example 1 Early Intro to Statistical Inference & P-Value

• Conceptually develop the idea of correlation avoid starting with formulas that overwhelm many students who are not algebraically strong. Correlation Activity: Build intuitive feel for the numerical values of correlation coefficients instead of simply defining the range

• Send an e-mail to correlations@stat.istics.net to establish a class

• Students login and specify the class for summary results to be displayed.

Activity Example 2 Correlation

• Assessment: Capture a screen shot to include on a quiz or test

http://www.istics.net/Correlations/

• Need to incorporate simulation based work

Activity Example 2 Correlation

Suggested websites for data from Michael Posner’s TANGO talk on “Real-World Statistics”• JSE Data Archive

http://www.amstat.org/publications/jse/jse_data_archive.htm• STEW Data includes lesson plans K-12:

http://www.amstat.org/education/stew/• TUVA

https://tuvalabs.com/datasets/• Qandl data: financial, societal data

https://www.quandl.com/• Census at schools:

http://www.amstat.org/censusatschool/

• Open Philly Data:

https://www.opendataphilly.org/• ICPSR Consortium for political and social research

http://www.icpsr.umich.edu/icpsrweb/ICPSR/access/index.jsp• General Social Survey

http://sda.berkeley.edu/GSS/• Publicly available Health-related datasets

– National Center for Health Statistics

http://www.cdc.gov/nchs/data_access/ftp_data.htm– NHANES (National Health and Nutrition Evaluation Survey)

http://www.cdc.gov/nchs/nhanes.htm– HHS (Dept of Health and Human Services)

statdata.hhs.gov

• NAEP (National Assessment of Educational Progress)

://nces.ed.gov/nationsreportcard/• Free Data from the National Climatic Data Center

https://www.ncdc.noaa.gov/data-access/quick-links#FREE• DASL - Data and Storage Library

http://www.stat.cmu.edu/DASL/• Futurity.org Research news from top universities;Sign up

for daily email

http://www.futurity.org/

Some Suggested Discussion Questions:

• What worked well in your classroom?• What topics do your students struggle with? Would an

activity help your students gain better understanding?• Ordering of topics in your course? GAISE standards and

the PA core standards?• How could you redesign labs to incorporate real life data?• Data collection for your students?• Dealing with missing data? “Messy Data”

Group Discussion

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• Add either a google form or qualtrics survey to get feedback from group discussions.

Questions & Feedback

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THANK YOU!