Education and Human Sciences, College of (CEHS)

 

Date of this Version

11-2009

Comments

A DISSERTATION Presented to the Faculty of The Graduate College in the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Educational Studies (Teaching, Curriculum, and Learning); Under the Supervision of Professor David Fowler
Lincoln, Nebraska: November, 2009
Copyright (c) 2009 Carrie A. Campbell

Abstract

This mixed-methods investigation studied the learning effects of example problems based on college algebra student interests. The study spanned two semesters and included three groups of students. The first group was presented with algebraic procedural examples and assessments without context. The second group was presented with algebraic class examples in contexts related to student majors and hobbies, but assessments without context. The third group was presented with class examples in contexts related to student majors and hobbies and also assessments with context.

Learning growth as measured by performance scores on examinations was analyzed quantitatively. Student comments regarding learning progress were analyzed qualitatively, using grounded theory. Performance improvement was higher for Group 3 than for Group 2 than for Group 1 as context increased, but these most differences were not statistically significant and could have occurred by chance. A large effect size (>0.80) between Group 3 students presented with class examples and homework problems based on student interests and Group 1 (control) students for 50% of quizzes given.

Student engagement was also studied. Results from scaled student survey including questions from the National Survey of Student Engagement were analyzed quantitatively. Participation in completing learning logs provided a measure of student engagement. Students in higher context groups had higher participation rates, Group 3 having 65% participation, Group 2 at 58% average participation, and Group 1 only averaging 36% of students returning learning logs.

Advisor: David Fowler

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