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Title: Using Assessment to Improve and Evaluate Student Learning in Introductory Statistics


1
Using Assessment to Improve and Evaluate Student
Learning in Introductory Statistics
  • Bob delMas (Univ. of MN)

2
GAISE Guidelines
  • Assessment needs to be aligned with learning
    goals
  • Focus on learning key ideas (not only skills,
    procedures, and computed answers)
  • Include formative assessments as well as
    summative
  • Use timely feedback to promote learning
  • It is possible to implement good assessments even
    in large classroom settings

3
Suggestions for Teachers
  • Integrate assessment as an essential component of
    the course (timing of assessment and activities).
  • Use a variety of assessment methods to provide a
    more complete evaluation of student learning.
  • Assess statistical literacy using assessments
    such as interpreting or critiquing articles in
    the news and graphs in media.
  • Assess statistical thinking using assessments
    such as student projects and open-ended
    investigative tasks.

4
Suggestions for Large Classes
  • Use small group projects instead of individual
    projects.
  • Use peer review of projects to provide feedback
    and improve projects before grading.
  • Use items that focus on choosing good
    interpretations of graphs or selecting
    appropriate statistical procedures.
  • Use discussion sections for student presentations.

5
AAHE 9 Principles of Assessment
  • Assessment begins with educational values.
  • Treats understanding of learning as
    multidimensional, integrated, and revealed over
    time.
  • Requires clear, explicitly stated purposes.
  • Requires attention to experiences that lead to
    outcomes.
  • Works best when ongoing, not episodic.
  • Fosters wider improvement when representatives
    from across the educational community are
    involved (all stakeholders).
  • Represents issues and questions that people
    really care about.
  • Most likely to lead to improvement if part of a
    larger set of conditions that promote change.
  • Meets responsibilities to students and to the
    public.

6
Assessment Triangle National Research Council
(2001), Knowing what Students Know
  • Cognition the aspects of achievement or
    competencies that are to be assessed
  • Observation the tasks used to collect evidence
    about students achievement (i.e., the
    assessments)
  • Interpretation the methods used to analyze the
    evidence resulting from the tasks

The three elements are interdependent A
successful assessment synchronizes all three
elements
7
COGNITION
  • A theory or set of beliefs about
  • How students represent knowledge
  • How students develop competence
  • Used to identify important knowledge and skills
  • Based on a learning model that provides a level
    of detail sufficient to accomplish the assessment

8
OBSERVATION
  • What we typically consider to be the assessment
  • Careful design of tasks that will provide
    evidence that can be linked to the learning model
  • Context and Purpose are Important
  • A national assessment can indicate relative
    standing, but not sensitive to nuances of
    instruction
  • Instructor constructed assessment can be tied to
    classroom instruction, but may not generalize to
    a larger population

9
INTERPRETATION
  • Set of assumptions and models that are used to
    interpret evidence from observation
  • Links observations to competencies (cognition)
  • National assessments may use formal, statistical
    models that identify patterns indicative of
    competency levels
  • Classroom assessment is typically more
    qualitative and identifies categories of
    competency based on observations
  • Important to identify what has not developed as
    well as what has developed
  • Important to identify misunderstandings and
    misconceptions as well as correct understanding

10
Example Understanding p-Values
  • A p-value is the probability of obtaining results
    as or more extreme than the observed results,
    given that the null hypothesis is true.
  • A p-value IS NOT the probability that the null
    hypothesis is true.
  • A p-value IS NOT the probability that the
    alternative hypothesis is true.

11
Example Understanding p-Values
A research article reports the results of a new
drug test. The drug is to be used to decrease
vision loss in people with Macular Degeneration.
The article gives a p-value of .04 in the
analysis section. Indicate if the following
interpretation of this p-value is valid or
invalid.
Statement (N 1617) Valid Invalid
The probability of getting results as extreme or more extreme than the ones in this study if the drug is actually not effective.
12
Example Understanding p-Values
A research article reports the results of a new
drug test. The drug is to be used to decrease
vision loss in people with Macular Degeneration.
The article gives a p-value of .04 in the
analysis section. Indicate if the following
interpretation of this p-value is valid or
invalid.
Statement (N 1617) Valid Invalid
The probability of getting results as extreme or more extreme than the ones in this study if the drug is actually not effective. 57.7 42.3
13
Example Understanding p-Values
A research article reports the results of a new
drug test. The drug is to be used to decrease
vision loss in people with Macular Degeneration.
The article gives a p-value of .04 in the
analysis section. Items 25, 26, and 27 present
three different interpretations of this p-value.
Indicate if each interpretation is valid or
invalid.
Statement (N 1617) Valid Invalid
25. The probability of getting results as extreme or more extreme than the ones in this study if the drug is actually not effective. 57.7 42.3
26. The probability that the drug is not effective.
27. The probability that the drug is effective.
14
Example Understanding p-Values
A research article reports the results of a new
drug test. The drug is to be used to decrease
vision loss in people with Macular Degeneration.
The article gives a p-value of .04 in the
analysis section. Items 25, 26, and 27 present
three different interpretations of this p-value.
Indicate if each interpretation is valid or
invalid.
Statement (N 1617) Valid Invalid
25. The probability of getting results as extreme or more extreme than the ones in this study if the drug is actually not effective. 57.7 42.3
26. The probability that the drug is not effective. 39.9 60.1
27. The probability that the drug is effective. 45.2 54.8
  • 9 of all students made an incorrect choice for
    all 3 items
  • Of those who chose valid for Item 25 (N 933)
  • 55 chose valid for at least one of the other
    items
  • 7 chose valid for all 3 items.

15
Assessment Cycle (from Beth Chance see G.
Wiggins 1992, 1998)
  • Set goals
  • What should students know, be able to do?
  • At what point in the course?
  • Identify assessable learning outcomes that match
    goals
  • Select methods
  • Identify an assessment that matches the type of
    learning outcome
  • Consider minute papers, article reviews,
    newspaper assignments, projects, short answer
    items, multiple choice
  • Can the assessment be built into the activity?
  • Gather evidence (i.e., administer the assessment)
  • Draw inference
  • Dont use results just to assign a grade
  • Consider what responses indicate about student
    understanding
  • Take action
  • Provide feedback
  • What can be done to remedy a misunderstanding (an
    activity extra reading more experience with a
    procedure or a concept)
  • Re-examine goals and methods

16
Embedding Assessment into Classroom Activities
  • Sorting Distributions
  • Goal Learn to associate labels with shapes of
    distributions
  • Normal Distribution
  • Goal Learn to find areas for the standard normal
    distribution
  • Sampling Distributions
  • Learn the characteristics of sampling
    distributions
  • Understand effect of sample size

17
ARTIST Website https//app.gen.umn.edu/artist
  • Item Database (Assessment Builder) A collection
    of about 1100 items, in a variety of item
    formats, organized according to statistical topic
    and type of learning outcome assessed.
  • Resources Information, guidelines, and examples
    of alternative assessments. Copies of articles or
    direct links to articles on assessment in
    statistics. References and links for other
    related assessment resources.
  • Research Instruments Instruments that can be
    used for research and evaluation projects that
    involve assessments of outcomes related to
    teaching and learning statistics.
  • Implementation issues Questions and answers on
    practical issues related to designing,
    administering, and evaluating assessments.
  • Presentations Copies of conference papers and
    presentations on the ARTIST project, and handouts
    from ARTIST workshops.
  • Events Information on ARTIST events.
  • Participation Ways to participate as a class
    tester for ARTIST materials.

18
ARTIST Topic Tests
  • There are 11 scales, consisting of 8-12
    multiple-choice items, that can be administered
    online. Our goal is to develop high quality,
    valid and reliable scales that can be used for a
    variety of purposes (e.g., research, evaluation,
    review, or self-assessment).
  • TOPICS
  • Data Collection (data types, types of study,
    study design)
  • Data Representation (choose appropriate graphs,
    interpret graphs)
  • Measures of Center (estimate, when to use,
    interpret, properties)
  • Measures of Spread (estimate, when to use,
    interpret, properties)
  • Normal Distribution (characteristics, empirical
    rule, areas under the curve)
  • Probability (interpret, independence, relative
    frequency, simulation)
  • Bivariate Quantitative Data (scatterplots,
    correlation, descriptive and inferential methods,
    outliers, diagnostics, influential observations)
  • Bivariate Categorical Data (two-way tables and
    chi-square test, association)
  • Sampling Distributions (types of samples, sample
    variability, sampling distributions, Central
    Limit Theorem)
  • Confidence Intervals (interpret, confidence
    level, standard error, margin of error)
  • Tests of Significance (hypothesis statements,
    p-values, Type I and II error, statistical and
    practical significance)

19
Comprehensive Assessment of Outcomes in
Statistics (CAOS)
  • Forty item test that can be administered as an
    online test to evaluate the attainment of desired
    student outcomes.
  • CAOS items are designed to represent the big
    ideas and the types of reasoning, thinking and
    literacy skills deemed important for all students
    across first courses in statistics.
  • Unifying focus is on reasoning about variability
    in univariate and bivariate distributions, in
    comparing groups, in samples, and when making
    estimates and inferences.
  • Not intended to be used exclusively as a final
    exam or as the sole assessment to assign student
    grades.
  • CAOS can provide very informative feedback to
    instructors about what students have learned and
    not learned in an introductory statistics course
    (e.g., administered as pretest and posttest).

20
Data Representation Item (page 19)
A baseball fan likes to keep track of statistics
for the local high school baseball team. One of
the statistics she recorded is the proportion of
hits obtained by each player based on the number
of times at bat as shown in the table below.
Which of the following graphs gives the best
display of the distribution of proportion of hits
in that it allows the baseball fan to describe
the shape, center and spread of the variable,
proportion of hits?
21
Data Representation Item (page 19)
A baseball fan likes to keep track of statistics
for the local high school baseball team. One of
the statistics she recorded is the proportion of
hits obtained by each player based on the number
of times at bat as shown in the table below.
Which of the following graphs gives the best
display of the distribution of proportion of hits
in that it allows the baseball fan to describe
the shape, center and spread of the variable,
proportion of hits?
What percents would you predict for your students?
RESPONSE PERCENT (N 1643)
Graph A
Graph B
Graph C
Graph D
22
Data Representation Item (page 19)
A baseball fan likes to keep track of statistics
for the local high school baseball team. One of
the statistics she recorded is the proportion of
hits obtained by each player based on the number
of times at bat as shown in the table below.
Which of the following graphs gives the best
display of the distribution of proportion of hits
in that it allows the baseball fan to describe
the shape, center and spread of the variable,
proportion of hits?
RESULTS US undergraduates 2005-2006
RESPONSE PERCENT (N 1643)
Graph A 11.1
Graph B 46.4
Graph C 29.1
Graph D 13.4
23
Data Representation Item (page 20)
A local running club has its own track and keeps
accurate records of each member's individual best
lap time around the track, so members can make
comparisons with their peers. Here are graphs of
these data. Which of the graphs allows you to
most easily see the shape of the distribution of
running times?
RESPONSE PERCENT (N 1345)
Graph A
Graph B
Graph C
All of the above
24
Data Representation Item (page 20)
A local running club has its own track and keeps
accurate records of each member's individual best
lap time around the track, so members can make
comparisons with their peers. Here are graphs of
these data. Which of the above graphs allows you
to most easily see the shape of the distribution
of running times?
RESPONSE PERCENT (N 1345)
Graph A 43.8
Graph B 48.9
Graph C 3.6
All of the above 3.7
25
First Small Group Exercise
Designate one person to be the recorder. Discuss
the following questions (pages 21-22 of handout)
with respect to the Data Representation
items Why do you think students are selecting
the incorrect responses for these items? (3-5
minutes) Outline an instructional activity to
help students develop the correct understanding.
(10 minutes)
26
Second Small Group Exercise
  • Choose a Topic
  • Sampling Variability
  • Confidence Intervals
  • Tests of Significance
  • Bivariate Quantitative Data
  • Discuss the following questions
  • Why do you think students are selecting the
    incorrect responses for each item? (5-10 minutes)
  • Outline an instructional activity to help
    students develop the correct understanding.
    (10-15 minutes)

27
Sampling Variability Item 1
A certain manufacturer claims that they produce
50 brown candies. Sam plans to buy a large
family size bag of these candies and Kerry plans
to buy a small fun size bag. Which bag is more
likely to have more than 70 brown candies?
RESPONSE PERCENT (N 1608)
Sam, because there are more candies, so his bag can have more brown candies. 5.3
Sam, because there is more variability in the proportion of browns among larger samples. 11.6
Kerry, because there is more variability in the proportion of browns among smaller samples. 32.4
Kerry, because most small bags will have more than 50 brown candies. 1.7
Both have the same chance because they are both random samples. 48.9
28
Sampling Variability Item 2
Consider the distribution of average number of
hours that college students spend sleeping each
weeknight. This distribution is very skewed to
the right, with a mean of 5 and a standard
deviation of 1. A researcher plans to take a
simple random sample of 18 college students. If
we were to imagine that we could take all
possible random samples of size 18 from the
population of college students, the sampling
distribution of average number of hours spent
sleeping will have a shape that is
RESPONSE PERCENT (N 872)
Exactly normal. 18.8
Less skewed than the population. 34.4
Just like the population (i.e., very skewed to the right). 34.7
It's impossible to predict the shape of the sampling distribution. 12.0
29
Confidence Interval Item 1
Suppose two researchers want to estimate the
proportion of American college students who favor
abolishing the penny. They both want to have
about the same margin of error to estimate this
proportion. However, Researcher 1 wants to
estimate with 99 confidence and Researcher 2
wants to estimate with 95 confidence. Which
researcher would need more students for her study
in order to obtain the desired margin of error?
RESPONSE PERCENT (N 1296)
Researcher 1. 51.9
Researcher 2. 25.9
Both researchers would need the same number of subjects. 9.1
It is impossible to obtain the same margin of error with the two different confidence levels. 13.1
30
Confidence Interval Item 2
A high school statistics class wants to estimate
the average number of chocolate chips per cookie
in a generic brand of chocolate chip cookies.
They collect a random sample of cookies, count
the chips in each cookie, and calculate a
confidence interval for the average number of
chips per cookie (18.6 to 21.3). Indicate if the
following interpretations are valid or invalid.
Statement (N 1609) Valid Invalid
We are 95 certain that each cookie for this brand has approximately 18.6 to 21.3 chocolate chips. 51.2 48.8
We expect 95 of the cookies to have between 18.6 and 21.3 chocolate chips. 34.1 65.9
We would expect about 95 of all possible sample means from this population to be between 18.6 and 21.3 chocolate chips. 53.1 46.9
We are 95 certain that the confidence interval of 18.6 to 21.3 includes the true average number of chocolate chips per cookie. 75.7 24.3
31
Test of Significance Item 1
A newspaper article claims that the average age
for people who receive food stamps is 40 years.
You believe that the average age is less than
that. You take a random sample of 100 people who
receive food stamps, and find their average age
to be 39.2 years. You find that this is
significantly lower than the age of 40 stated in
the article (p lt .05). What would be an
appropriate interpretation of this result?
RESPONSE PERCENT (N 1101)
The statistically significant result indicates that the majority of people who receive food stamps is younger than 40. 33.8
Although the result is statistically significant, the difference in age is not of practical importance. 50.5
An error must have been made. This difference is too small to be statistically significant. 15.7
32
Test of Significance Item 2
A researcher compares men and women on 100
different variables using a two-sample t-test. He
sets the level of significance to .05 and then
carries out 100 independent t-tests (one for each
variable) on data from the same sample. If, in
each case, the null hypothesis actually is true
for every test, about how many "statistically
significant" findings will this researcher report?
RESPONSE PERCENT (N 1160)
0 30.2
5 45.7
10 7.1
None of the above 17.1
33
Bivariate Quantitative Data Item 1
The number of people living on American farms has
declined steadily during the last century. Data
gathered on the U.S. farm population (millions of
people) from 1910 to 2000 were used to generate
the following regression equation Predicted Farm
Population 1167 - .59 (YEAR). What method
would you use to predict the number of people
living on farms in 2050.
RESPONSE PERCENT (N 1591)
Substitute the value of 2050 for YEAR in the regression equation, and compute the predicted farm population. 19.8
Plot the regression line on a scatterplot, locate 2050 on the horizontal axis, and read off the corresponding value of population on the vertical axis. 15.6
Neither method is appropriate for making a prediction for the year 2050 based on these data. 28.4
Both methods are appropriate for making a prediction for the year 2050 based on these data. 36.2
34
Bivariate Quantitative Data Item 2
A statistics instructor wants to use the number
of hours studied to predict exam scores in his
class. He wants to use a linear regression model.
Data from previous years shows that the average
number of hours studying for a final exam in
statistics is 8.5, with a standard deviation of
1.5, and the average exam score is 75, with a
standard deviation of 15. The correlation is .76.
Should the instructor use linear regression to
predict exam scores from hours studied?
RESPONSE PERCENT (N 850)
Yes, there is a high correlation, so it is alright to use linear regression. 21.2
Yes, because linear regression is the statistical method used to make predictions when you have bivariate quantitative data. 27.1
Linear regression could be appropriate if the scatterplot shows a clear linear relationship. 46.2
No, because there is no way to prove that more hours of study causes higher exam scores. 5.5
35
Assessment Builder Search
36
Assessment Builder Results
37
Assessment Builder Results
38
Assessment Builder Item Set
39
Assessment Builder Download
40
Assessment Builder Download
41
ARTIST Website https//app.gen.umn.edu/artist
We invite you to contact the ARTIST team with any
comments and suggestions you have regarding this
presentation, or any of the materials at the
ARTIST website. Thank you for your participation
in todays session.
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