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Setting the Stage for Students’ Conceptual Change in Learning Statistics. CAUSE Webinar June, 2008. Bob DelMas University of Minnesota. Marsha Lovett Carnegie Mellon University. Main Premise. Much of student learning is driven by relatively few basic learning mechanisms

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### Setting the Stage for Students’ Conceptual Change in Learning Statistics

CAUSE Webinar

June, 2008

Bob DelMas

University of Minnesota

Marsha Lovett

Carnegie Mellon University

College of Education and Human Development

University of Minnesota

Main Premise Learning Statistics

• Much of student learning is driven by relatively few basic learning mechanisms

• An effective course/lesson creates the conditions in which these learning mechanisms work together to support the learning goals we have set for our students

College of Education and Human Development

University of Minnesota

Learning Principle #1 Learning Statistics

• New knowledge is acquired through the lens of prior knowledge

• Students see things differently from the way we do

• What we intuitively feel will foster learning may not even be understood by students (This is called the expert blindspot)

College of Education and Human Development

University of Minnesota

Implications Learning Statistics

• Students often do not know:

• What features are important to attend to?

• How to find what is important in a problem, situation, question?

• Which situations are similar to each other in important ways?

• What ideas or concepts should be distinguished?

College of Education and Human Development

University of Minnesota

Okay, get this thing to look Learning Statistics

something like x = 4.

Find the set of values which

may be substituted for x and which

make the statement true.

Huh????

There’s no

Good, he’s shown that thestatement is true no matter

the value of x.

Illustration: Two Sides of the Elephant

Students don’t always see things the way we do

Solve for

x

College of Education and Human Development

University of Minnesota

Illustration: Statistics Problems Learning Statistics

• Data-analysis problems involve lots of details and real-world issues

• Experts know what to attend to, e.g., variables measured, study design, possible confounds, etc.

• Students may attend to other aspects, e.g., cover story, how the question is phrased, number of variables presented

College of Education and Human Development

University of Minnesota

Instructional Strategies Learning Statistics

• Give students explicit direction about what features are important and what they should attend to

• Give students practice identifying (and explaining) what is important

• Gradually build up the complexity of problems so students are not overwhelmed with too much information at once

College of Education and Human Development

University of Minnesota

Learning Principle #2 Learning Statistics

• The way students organize knowledge determines how they use it

• Just as prior knowledge influences how new knowledge is interpreted, the organization of new knowledge influences how it is used

• Instructional strategies:

• Helping students see the connections and relationships – both in new knowledge and between old and new - will create more links for effective retrieval

College of Education and Human Development

University of Minnesota

Learning Principle #3 Learning Statistics

• Learners refine their knowledge and skills with timely feedback and subsequent opportunities to practice

• Without feedback, students often do not know their own gaps and inaccuracies

• Without additional opportunities to practice, they cannot strengthen their refined knowledge and skill

College of Education and Human Development

University of Minnesota

Illustration: StatTutor Feedback Learning Statistics

• As compared to a traditional statistics lab assignment, where feedback comes days after the error was made, StatTutor alerts students when they have made an error and offers multiple levels of feedback

College of Education and Human Development

University of Minnesota

StatTutor Learning Statistics

College of Education and Human Development

University of Minnesota

Instructional Strategies Learning Statistics

• Look for where you can give students feedback on key skills they are practicing

• Look for how to make the feedback timely

• Look for opportunities for students to get extra practice on the skills where they received feedback

College of Education and Human Development

University of Minnesota

Learning Principle #4 Learning Statistics

• Meaningful engagement is necessary for deeper learning

• Applying what they have learned is one way to get students actively engaged with the material

• Authentic practice motivates students and focuses their effort on important aspects of the task

• Statistics examples and strategies

• Students work on projects (often in groups)

• Students do activities in class (e.g., collecting data, running physical simulations)

College of Education and Human Development

University of Minnesota

Main Premise Learning Statistics

• Much of student learning is driven by relatively few basic learning mechanisms

• An effective course/lesson creates the conditions in which these learning mechanisms work together to support the learning goals we have set for our students

College of Education and Human Development

University of Minnesota

Adapting and Implementing Innovative Materials in Statistics:The AIMS Curriculum

• Transform an introductory statistics course into one that implements the Guidelines for Assessment and Instruction in Statistics Education (GAISE) (http://www.amstat.org/education/gaise/)

• Use research-based design principles to adapt innovative instructional materials (Cobb & McClain, 2004).

College of Education and Human Development

University of Minnesota

Research Basis for Lesson Statistics:

• Use of simulation throughout course

• Revisit concepts throughout course

• Informal to formal ideas of sampling

• Making and testing conjectures

• Simulation of Samples (SOS) Model: Organizational scheme to support abstraction of important concepts across simulations

College of Education and Human Development

University of Minnesota

Outline of a Lesson Statistics:

• Statement of a Research Question

• Whole class discussion

• Activity 1

• Students work in small groups, make conjectures

• Generate or Simulate data

• Small group discussion of results

• Whole class discussion

• Activity 2: Repeat cycle

• Wrap Up: Discussion and Summary of Main Ideas

College of Education and Human Development

University of Minnesota

Sample Lesson: Reese’s Pieces Statistics:

• Part of Unit on Sampling and Sampling Variability

• Adapted from Rossman and Chance Workshop Statistics

• Initial whole class discussion :

• If I get only five orange Reese’s Pieces in a cup of 25 candies, should I be surprised?

• Out of 100, how many Yellow, Orange, Blue?

• Conjecture: Expected count for Orange for each of 10 random samples, n = 25

College of Education and Human Development

University of Minnesota

Separates and counts each color

Then calculates and records proportion of Orange

College of Education and Human Development

University of Minnesota

Students work in small groups to answer questions

• Did everyone have the same proportion of orange candies?

• Describe the variability of this distribution of sample proportions in terms of shape, center, and spread.

• Do you know the proportion of orange candies in the population? In the sample?

• Which one can we always calculate? Which one do we have to estimate?

• Based on the distribution, what would you ESTIMATE to be the population parameter, the proportion of orange Reese’s Pieces candies produced by Hershey's Company?

• What if everyone in the class only took 10 candies? What if everyone in the class each took 100 candies? Would the distribution change?

College of Education and Human Development

University of Minnesota

Activity with Reese’s Pieces Applet Statistics:

http://www.rossmanchance.com/applets/Reeses/ReesesPieces.html

Students work in groups of 3 to 4 to run the simulation, answer questions, and make and test conjectures:

How does this compare to the dot plot on the board?

Where does 0.2 fall? Where does 0.7 fall? [Informal idea of p-value]

Conjecture what will happen if we change to n = 10? n = 100?

Run the simulations to check your conjectures.

College of Education and Human Development

University of Minnesota

Three dotplots Statistics:

• For each sample size (n=10, n=25, n=100), how close is the mean sample statistic (mean proportion), to the population parameter?

• As the sample size increases, what happens to the distance the sample statistics are from the population parameter?

• Describe the effect of sample size on the distribution of sample statistics in terms of shape, center and spread.

College of Education and Human Development

University of Minnesota

POPULATION

Each time we do a simulation, we want to make sure we know what each part of the simulation represents.

Can you identify:

Distribution of Sample Statistics

The Population?

The Population Parameter?

SAMPLE

The Sample?

STATISTIC

The Sample Statistic?

PARAMETER

The Distribution of Sample Statistics?

College of Education and Human Development

University of Minnesota

Simulation of Samples (SOS) Model Statistics:

College of Education and Human Development

University of Minnesota

More Practice with Follow Up Activities Statistics:

• Next day: simulations of sampling coins, words

• Students discover the predictable pattern

• Third day: Students “Discover” the central limit theorem” using stickers and Sampling SIM software

College of Education and Human Development

University of Minnesota

Remember that . . . Statistics:

It’s not teaching that causes learning. Attempts by the learner to perform cause learning, dependent upon the quality of feedback and opportunities to use it(Grant Wiggins, 1993).

College of Education and Human Development

University of Minnesota

Reference Statistics:

AIMS Lessons, Lessons Plans, and Materials will be available at the end of summer 2008 at:

http://www.tc.umn.edu/~aims/