slide1
Download
Skip this Video
Download Presentation
Jeanette G. Eggert Concordia University – Portland, Oregon

Loading in 2 Seconds...

play fullscreen
1 / 32

Jeanette G. EggertConcordia University - PowerPoint PPT Presentation


  • 246 Views
  • Uploaded on

Jeanette G. Eggert Concordia University – Portland, Oregon. A Comparison of Online and Classroom-based Developmental Math Courses. Developmental Math. Definition: Educational opportunities for students that lack the math skills needed for success in college-level math courses. Citation.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Jeanette G. EggertConcordia University ' - richard_edik


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
Jeanette G. Eggert

Concordia University – Portland, Oregon

A Comparison of Online and Classroom-based Developmental Math Courses

developmental math
Developmental Math

Definition:

Educational opportunities for students that lack the math skills needed for success in college-level math courses.

Citation

students in developmental math
Students in Developmental Math
  • Traditional and Non-traditional
  • Previous bad experiences with math
  • Gaps in their background
  • Low self-efficacy
  • High levels of math and test anxiety

Citation

math labs at concordia
Math Labs at Concordia
  • Placement test
  • Four half-semester courses
  • Cover basic skills through some intermediate algebra topics
  • Small class size
before 2005
Before 2005
  • Quizzes over each section
  • Large portion of class time spent in assessment supervision
  • Mastery-based, but time-sequencing problematic
  • Quiz re-takes placed additional demands on instructors
implementation of computer based quizzes
Implementation of Computer-based quizzes
  • Immediate feedback for students
  • Increased instructional time
  • More time for individual help
online math labs
Online Math Labs
  • Classroom notes
  • Textbook resources
  • Quizzes
  • Access to the instructor
    • Email
    • Phone
    • In-person
this study problem statement
This Study: Problem Statement

Use existing data to compare the effectiveness of online and classroom-based developmental math courses at a four-year liberal arts university.

theoretical framework i
Theoretical Framework I

Media Debate

  • Clark – 1983
    • Delivery truck analogy
  • Kozma – 1991
    • Instructional attributes

Citation

theoretical framework ii
Theoretical Framework II

Instructional alternatives are needed for developmental students.

Citation

research question 1
Research Question #1

Is there a significant difference in successful course completion for online and classroom-based sections of the developmental math courses during the stated interval?

research question 2
Research Question #2

Is there a significant difference in student satisfaction at the conclusion of each course with regard to their participation in online and classroom-based sections of the developmental math courses during the stated interval?

research question 3
Research Question #3

Is there a significant difference in academic achievement in a subsequent college-level mathcourse for those students who participated in online and classroom-based sections of the developmental math courses during the stated interval?

study parameters
Study Parameters
  • Ten semesters: Summer 2005 – Summer 2008, inclusive
  • Census of all students who completed developmental math courses
  • Parallel instructional methodologies
human subjects safeguarding
Human Subjects Safeguarding
  • Existing data
    • Coded to remove student and faculty identifiers
  • IRB approval
    • George Fox University
    • Concordia University - Portland
data analysis rq 1 s uccessful course completion
Data & Analysis: RQ #1Successful Course Completion
  • N = 718
    • Classroom n = 357
    • Online n = 361
  • Independent samples t - test
  • Levene’s Test for Equality of Variances
results rq 1 s uccessful course completion
Results: RQ #1Successful Course Completion
  • Classroom-based
    • Mean = 0.80; Standard deviation = 0.398
  • Online
    • Mean = 0.83; Standard deviation = 0.373
  • No statistically significant difference at an alpha level of 0.05 (t = – 1.039, n.s.)
  • Null hypothesis supported
data analysis rq 2 student satisfaction
Data & Analysis: RQ #2Student Satisfaction
  • N = 222
    • Classroom n = 100
    • Online n = 122
  • Two scales; reliability via Cronbach’s Alpha
    • Satisfaction with course; 6 Likert-scale items
    • Satisfaction with the instructor; 8 items
  • Independent samples t - test
  • Levene’s Test for Equality of Variances
results rq 2 first scale satisfaction with course
Results: RQ #2 - First ScaleSatisfaction with Course
  • Cronbach’s Alpha = 0.942 for the 6 items.
  • Classroom-based
    • Mean = 25.34; Standard deviation = 6.189
  • Online
    • Mean = 26.55; Standard deviation = 4.398
  • No statistically significant difference at an alpha level of 0.05 (t = – 1.698, n.s.)
  • Null hypothesis supported
results rq 2 second scale satisfaction with the instructor
Results: RQ #2 - Second ScaleSatisfaction with the Instructor
  • Cronbach’s Alpha = 0.971 for the 8 items.
  • Classroom-based
    • Mean = 37.29; Standard deviation = 6.091
  • Online
    • Mean = 37.89; Standard deviation = 4.613
  • No statistically significant difference at an alpha level of 0.05 (t = – 0.828, n.s.)
  • Null hypothesis supported
data analysis rq 3 college level math gpa
Data & Analysis: RQ #3College-Level Math GPA
  • N = 118
    • Classroom n = 58
    • Online n = 60
  • Independent samples t - test
  • Levene’s Test for Equality of Variances
results rq 3 college level math gpa
Results: RQ #3College-Level Math GPA
  • Classroom-based
    • Mean = 2.448; Standard deviation = 1.1275
  • Online
    • Mean = 2.978; Standard deviation = 0.9076
  • Statistically significant difference in the means (t = – 2.818, p < 0.05)
  • Both the null hypothesis and the alternative hypothesis were rejected
summary of results
Summary of Results
  • No significant difference based on:
    • Successful course completion
    • Student satisfaction
  • Online instructional delivery was more effective for higher levels of academic achievement in a subsequent college-level math course.
implications
Implications
  • Supports continuation of both instructional delivery systems
  • Revise online courses
    • Mastery-based
    • Hyperlinked
  • Revise classroom-based courses
    • Utilize web-based options
    • Unique face-to-face opportunities
acknowledgments
Acknowledgments
  • My students and colleagues at Concordia University – Portland
  • My parents, Richard & Myra Gibeson
  • My husband, John Eggert
  • My dissertation committee at George Fox University:
      • Dr. Scot Headley
      • Dr. Terry Huffman
      • Dr. Linda Samek
graphics
Graphics
  • Clip-Art from the Microsoft Collection
  • WebCT view from Concordia University’s Online Math Lab course
references
References
  • Berenson, S. B., Carter, G., & Norwood, K. S. (1992). The at-risk student in college developmental algebra. School Science and Mathematics, 92(2), 55-58.
  • Brown, D. G. (Ed.). (2000) Teaching with technology: Seventy-five professors from eight universities tell their stories. Bolton, MA: Anker Publishing Company.
  • Brown, D. G. (Ed.). (2003) Developing faculty to use technology: Programs and strategies to enhance teaching. Bolton, MA: Anker Publishing Company.
references page 2
References page 2
  • Clark, R.E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445-459.
  • Dotzler, J. J. (2003). A note on the nature and history of post-secondary developmental education.Mathematics and Computer Education,37(1), 121-125.
  • Duranczyk, I. M., & Higbee, J. L. (2006). Developmental mathematics in 4-year institutions: Denying access. Journal of Developmental Education, 30(1), 22-29.
references page 3
References page 3
  • Hodges, D. Z., & Kennedy, N. H. (2004). Editor\'s choice: Post-testing in developmental education: A success story. Community College Review, 32(3), 35-42.
  • Kinney, D. P., & Robertson, D. F. (2003). Technology makes possible new models for delivering developmental mathematics instruction. Mathematics and Computer Education, 37(3), 315-328.
  • Kozma, R. B. (1991). Learning with Media. Review of Educational Research, 61(2), 179-211.
references page 4
References page 4
  • Mallenby, M. L., & Mallenby, D. W. (2004). Teaching basic algebra courses at the college level. Primus: Problems, Resources, and Issues in Mathematics Undergraduate Studies, 14(2), 163-168.
  • Manto, J. C. (2006). A correlations study of ACCUPLACER math and algebra scores and math remediation on the retention and success of students in the clinical laboratory technology program at Milwaukee Area Technical College. Unpublished master’s thesis, University of Wisconsin – Stout, Menomonie, WI.
references page 5
References page 5
  • Reese, M. S. (2007). What’s so hard about algebra? A grounded theory study of adult algebra learners. Unpublished doctoral dissertation, San Diego State University – University of San Diego, San Diego, CA.
  • Tanner, J., & Hale, K. (2007). The “new” language of algebra. Research & Teaching in Developmental Education, 23(2), 78-83.
  • Weinstein, G. L. (2004). Their side of the story: Remedial college algebra students. Mathematics and Computer Education, 38(2), 230-240.
ad