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Teaching Basic Skills Mathematics: Outcome Assessment

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Teaching Basic Skills Mathematics: Outcome Assessment Xi Zhang, Campus Based Researcher, City College Jenny Kimm, Associate Professor, City College – PowerPoint PPT presentation

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Title: Teaching Basic Skills Mathematics: Outcome Assessment


1
Teaching Basic Skills Mathematics Outcome
Assessment
Xi Zhang, Campus Based Researcher, City College
Jenny Kimm, Associate Professor, City College
San Diego Community College District
Presented at the 2009 Strengthening Student
Success Conference San Francisco, CA October 7,
2008
2
Introduction of the District
  • San Diego Community College District
  • 2nd largest district in the state
  • Three 2-year colleges and eleven Continuing
    Education campuses
  • Serves approx. 100,000 students each semester

3
Introduction of the San Diego City College
  • San Diego City College is the first established
    community college in San Diego.
  • San Diego City College is a public, two-year
    community college administered by the San Diego
    Community College District.
  • Serving as the educational cornerstone of
    downtown San Diego, the college offers more than
    100 majors, 100 certificate programs and 1,500
    classes each semester to 16,000 students.

4
Purpose of the Presentation
  • The primary purpose of this presentation is to
    share the research methodology and innovative
    techniques for data analysis and instrument
    refinement for SLO assessment rather than to
    disseminate results of the study.
  • A second purpose is to support the teaching of
    Basic Skills Math in community colleges.

5
Purpose of the Study
  • Demonstrate SLO assessment cycle in the Math
    Department at San Diego City College
  • Draw attention to measurement issues in assessing
    SLOs.
  • Demonstrate methods of item analysis that has
    multiple advantages.

6
Research Design
  • SLO assessment cycle
  • A repeated measure design pre and post design

7
SLO Assessment in the Math Department
City College Student Learning Outcomes Assessment
Cycle 6 Column Form For
Developmental Math Program- Math 35, 95, 96
Year 2008/2009
8
Research Methodology
  • Rasch Model
  • Estimate both item difficulty and person ability
  • Map both parameters on the same scale
  • Inform instrument refinement
  • Paired Sample t-test
  • Statistically significant improvement from
    pretest to posttest

9
Target Population and Sample Size
  • Developmental math courses Pre-Algebra,
    Beginning Algebra, and Intermediate Algebra.
  • This totals to over 1000 students taking the
    developmental math courses
  • In Fall 2008, we collected pre/post paired data
    from about 250 students

10
Data Collection Instrument
  • Developed one pre test and a similar post test
  • TestGen testbank software
  • 8 multiple choice questions
  • Topics of the questions
  • A Sample Instrument

11
Data Collection Test Administration
  • Pre-test administration
  • Pre-test grading
  • Post-test administration
  • Post-test grading

12
Data Management
  • For each student, itemized response per question
    and a total score were entered and paired in
    Excel
  • Data then were exported to Winsteps for model
    fitting
  • Estimates of item difficulty and student ability
    were analyzed in SPSS to compare pre test results
    to the post.

13
Data Analysis
  • Fit itemized responses rather than the total
    scores with the Rasch Model to obtain estimates
    of item difficulty and student ability.

14
Data Analysis
  • Map both item difficulties and person abilities
    on the same scale to produce the Item-Person Map.
  • Compare Pre test and Post test
  • Paired sample t-test
  • Anchoring item difficulties

15
Measures
  • Item difficulty
  • Student ability

16
Results (Pre-algebra FALL 2008 DATA)
  • Pre-test
  • Item difficulty
  • Student ability

17
Results (Pre-algebra FALL 2008 DATA)
  • Pre-test
  • Person-item map

18
Results (Pre-algebra FALL 2008 DATA)
  • Post-test
  • Item difficulty
  • Student ability

19
Results (Pre-algebra FALL 2008 DATA)
  • Post-test
  • Person-item map

20
Results (Pre-algebra FALL 2008 DATA)
  • Pre and post comparison
  • Anchoring item difficulties to produce a new set
    of student ability estimates

21
Results (Pre-algebra FALL 2008 DATA)
  • Pre and post comparison
  • Anchoring item difficulties to produce a new set
    of student ability estimates

22
Results (Pre-algebra FALL 2008 DATA)
  • Pre and post comparison
  • Paired sample t test

23
Findings
  • Results revealed that students scored
    statistically significantly higher in the post
    test compared to their performance in the
    pretest.
  • Content areas that the instructors need to
    emphasize for teaching.
  • Information for instrument refinement.

24
INSTRUMENT REFINEMENT
25
Use of Results for Programmatic Improvement
  • Imbed the post test into the final exam to
    increase sample size. Also provide online version
    of pre test to collect data from online
    developmental classes.
  • Rewrite or revise test questions based on results
    of item analysis.
  • Identify difficult topics and disseminate the
    information to developmental math instructors for
    future teaching.
  • Also disseminate the information to instructors
    of higher level math course for their preparation
    and planning.

26
Discussion
  • Advantages of item analysis
  • Solve measurement issues
  • Conduct meaningful comparisons
  • Bank good test items for constructing future
    tests
  • Diagnostic function provides insight of the
    strength and weakness of student content
    knowledge.

27
Limitations of the Research
  • Small sample size
  • Small number of test items
  • Item stability
  • Generalizability of the results

28
Questions?
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