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The Influence of Enrollment in Agriscience Education on High School Student Achievement in Mathematics

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... opportunities in a contextual fashion (Conroy, Trumbull, & Johnson, (1999) ... in classroom/lab settings, community-based SAEs, and FFA activities ... – PowerPoint PPT presentation

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Title: The Influence of Enrollment in Agriscience Education on High School Student Achievement in Mathematics


1
The Influence of Enrollment in Agriscience
Education on High School Student Achievement in
Mathematics
  • Dr. Paul J. Theriot, Louisiana Department of
    Education
  • and
  • Dr. Joe Kotrlik, Louisiana State University

2
Introduction
  • The level of mathematic literacy needed to
    understand and make informed decisions concerning
    the use of technology is continually increasing
    (McLure McLure, 2000 National Research
    Council, 1996).
  • Assessments of student mathematics achievement
    often shows performance below the levels demanded
    in the workplace (Frome, 2001 National
    Assessment of Education Progress, 2000)

3
Achievement
  • Measuring achievement is part of the education
    process and is the gauge used by educators to
    guide students through the education process
    (National Research Council, 1999)
  • Standardized tests have become a primary method
    of evaluating both student and school performance
    (National Research Council, 1999)

4
Variables Related to Achievement
  • Certain ethnic groups have lower standardized
    test scores (Lareau, 2002 Stanton-Salazar, 2001)
  • There is no longer a gender gap in mathematics
    achievement in any ethnic group (Coley, 2001)
  • Socioeconomic status may be the strongest
    predictor of student achievement (Coleman,
    Campbell, Hobson, et al., 1996)

5
Provide Meaning Relevance
  • Mathematics is often taught in abstract terms,
    lacking real world connections and relevance
    (Mullis, Martin, Gonzalez, et al, 1999))
  • Providing sufficient context is the key to
    improving student achievement (Balschweid, 2001)
  • Ag Ed provides formal and informal learning
    opportunities in a contextual fashion (Conroy,
    Trumbull, Johnson, (1999)

6
Informal Learning Activities
  • Higher mathematics achievement is linked to
    participation in out of school math related
    activities (McLure McLure, 2000)
  • Students provided with everyday context for math
    problems develop higher order math skills (Lesh,
    1985)
  • Ag Ed provides formal and informal opportunities
    in classroom/lab settings, community-based SAEs,
    and FFA activities (Edwards, Leising, Parr,
    2002)

7
Purpose
  • Purpose of this study was to compare the
    mathematics achievement of high school students
    in Louisiana by whether they were identified as
    an agriscience education student.

8
Objectives
  • Obj 1 - Describe the students who completed the
    GEE
  • Obj 2 - Describe the academic achievement of 10th
    grade students as measured by scores on the math
    GEE
  • Obj 3 - Compare the academic achievement of 10th
    grade agriscience students to non-agriscience
    students as measured by scores on the math GEE

9
Objectives
  • Obj 4 Determine if selected variables explain
    significant portions of variance in math
    achievement as measured by scores on the math GEE

10
Methodology
  • Target population and frame was all public high
    school students in LA, except special education
    students
  • SPED omitted since the database did not include
    information on the specific exceptionalities
    (gifted, mild mentally handicapped,
    severe/profound, etc.)
  • Research shows a disproportional amount of SPED
    students in CTE programs (Elliot, Foster,
    Franklin, 2005)

11
Methodology
  • Accessible population was defined as all 10th
    grade students (except SPED) who took the math
    GEE in 2005 and had valid scores in the LDE
    database.

12
Data Analyses
  • Descriptive statistics were utilized for
    objectives 1 2
  • Inferential t-tests were used for objective 3
    alpha level set a priori at .05
  • Forward multiple regression analyses were used
    for objective 4 alpha level set a priori at .05
  • Effect sizes for the t-tests and multiple
    regression analyses were interpreted according to
    Cohens guidelines

13
Findings - Obj 1- Demographics
  • 54.9 (n 19,871) were female
  • Five ethnic groups were represented with
    Caucasians (n 19,931, 55.0) and
    African-Americans (n 14,691, 40.6) being the
    largest ethnic groups
  • Students classified as 504 made up a small
    portion of the subjects (n 952, 2.6)

14
Findings - Obj 1- Demographics
  • Participation in the free/reduced lunch program
    was used as a measure of SES
  • Most (n 21,371, 59.0) paid full price
  • Over a third (n 12,374, 34.2) received free
    lunch
  • A small number (n 2,461, 6.8) paid reduced
    price
  • 2,485 (6.9) of the students were identified as
    agriscience students

15
Findings - Obj 2 - Science Achievement
  • Scaled scores are classified as Unsatisfactory,
    Approaching Basic, Basic, Mastery, and Advanced
  • The minimum scaled score to achieve Approaching
    basic is 286 scores ranged from 100 to 500 with
    a mean scaled score of 314.91
  • 9,513 students (23.11) did not attain at least
    the Approaching Basic level

16
Findings - Obj 3 - Ag vs. Non-Ag
  • Ag students had higher scores on the total math
    exam (t -3.49, p lt.001) and the following
    domains Numbers Number Relations (t -3.95, p
    lt.001) Measurement (t -5.99, p lt.001)
    Geometry (t -4.67, p lt.001) Data Analysis (t
    -2.68, p lt.001)
  • There were no differences in scores on the
    Algebra or Patterns, Relations, Functions
    domains
  • Cohens d revealed a negligible effect size in
    each of these areas

17
Findings - Obj 4 - Explaining Variance
  • Regression analyses were performed on the total
    math score and for each of the math sub-tests
  • Pearson product-moment correlation coefficients
    were used to measure the relation between
    potential explanatory variables and the dependent
    variable.
  • Variables with an r value at or above 0.10 were
    entered into model 1

18
Findings - Obj 4 - Explaining Variance
  • For the second phase, whether or not the student
    was classified as an ag student was then entered
    into model 2 to determine the amount of variance
    explained by participation in Ag Ed
  • In all cases, participation in Ag Ed was a
    positive significant explanatory variable.
  • In all cases, the effect size was negligible
    according to Cohens standards

19
Conclusions
  • Most students pass the math GEE
  • Before controlling for other variables, ag
    students score better than or as well as non-ag
    students on the total math GEE and on all
    sub-tests
  • Being enrolled in Ag Ed courses has a significant
    positive effect on math achievement this should
    be read with caution since the effect size is
    small

20
Conclusions
  • The math achievement of ag students is at least
    equal to that of non-ag students
  • Couple these conclusions with the other benefits
    of participating in Ag Ed courses, it becomes
    clear that Ag Ed contributes to student success

21
Whats Next?
  • These results achieved with little to no
    professional development for teachers to
    enhance the math concepts in their classes.
  • What would be the effect if integration models
    such as the Math In CTE Model (NRCCTE) are
    implemented?
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