Walden University ScholarWorks Walden Dissertations and Doctoral Studies Walden Dissertations and Doctoral Studies Collection 2017 Examining the Relationship Between Math Achievement and Self-Efficacy in Developmental Math Students Ellen Prescott Walden University Follow this and additional works at: https://scholarworks.edu/dissertations Part of the Instructional Media Design Commons This Dissertation is brought to you for free and open access by the Walden Dissertations and Doctoral Studies Collection at ScholarWorks. It has been accepted for inclusion in Walden Dissertations and Doctoral Studies by an authorized administrator of ScholarWorks. For more information, please contact ScholarWorks@waldenu. Walden University College of Education This is to certify that the doctoral dissertation by Ellen Prescott has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.
Review Committee Dr. Dennis Beck, Committee Chairperson, Education Faculty Dr. Bettina Casad, Committee Member, Education Faculty Dr. Wade Smith, University Reviewer, Education Faculty Chief Academic Officer Eric Riedel, Ph.
Walden University 2017 Abstract Examining the Relationship Between Math Achievement and Self-Efficacy in Developmental Math Students by Ellen Louise Prescott MA, Northern Arizona University, 2011 BS, Arizona State University, 2008 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Education Walden University November 2017 Abstract Many students today lack preparedness for college-level math. Colleges and universities offer developmental math courses; however, students are failing these developmental courses and they often have low math self-efficacy. Educational technology and alternative classroom models are used to try to alleviate low success rates in developmental math courses. The purpose of this study was to examine the relationships between math self-efficacy and math achievement in students in developmental math courses that used the software platform Connect Math.
Research questions focused on self-efficacy and math achievement differences between students in computer mediated and traditional lecture-based developmental math courses, as well as differences in their opinion of Connect Math. Guided by self-efficacy theory, a quasi-experimental study was conducted and data from students in traditional lecture-based (n = 81) and computer- mediated (n = 76) developmental math courses was analyzed. ANCOVA analysis revealed a significant relationship between age and math self-efficacy, p = .042 and a significant relationship between class type and student’s perceived helpfulness of Connect Math, p =. Analysis also found a difference in GPA with computer- mediated students having a slightly higher GPA than traditional lecture-based students.
Furthermore, results indicated instructor significantly predicted student opinion of Connect Math, p =. Results suggest that greater access to technology did not significantly predict greater success in the developmental math course. With higher completion rates of developmental math courses, colleges and universities could see greater graduation rates for all students. Examining the Relationship Between Math Achievement and Self-Efficacy in Developmental Math Students by Ellen Louise Prescott MA, Northern Arizona University, 2011 BS, Arizona State University, 2008 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Education Walden University November 2017 Dedication This dissertation is dedicated to my children Nile, Kai, and Avery and my husband, Bennie for their never-ending love and support; to my parents, Ellick and Elke Ruffin, and my sister, Mary Ruffin, for believing in me.
I love you all very much. Acknowledgments Without the support, mentorship, and guidance of many people, this dissertation would not be possible. My father, Dr. Ellick Ruffin, encouraged me to pursue a doctorate degree while also providing advice and reassurance throughout the process.
I am proud to carry on his legacy of collegiate achievement. My mother, Elke Ruffin, was always there to listen to my thoughts and ideas on this process, and was very supportive of my endeavor. My husband, Bennie Prescott, and children, Nile, Kai, and Avery, for enduring the many, MANY hours spent at the library, coffee shops, or locked away in our home office. My dissertation committee contributed significantly to the scope, direction, and execution of this paper over the past several years.
My committee chairperson Dr. Dennis Beck is an exemplar for his roles as editor, mentor, and teacher. I would also like to acknowledge and thank Dr. Bettina Casad, my methodologist committee member.
Casad’s willingness to listen and provide critical feedback was greatly appreciated. Wade Smith for providing guidance on how to improve the dissertation so that it was succinct and pithy. I would also like to acknowledge all of my friends (who are like family) for their support. Lasana Hotep was always there to push me to continue, even when I had many moments of doubt.
My best friend Chelsi Porter and sorority sisters Fiory Tedla, Faith Gaines, Talei Hornback, and Sabrina Kinsella who always gave me words of encouragement throughout this process. I could not have done this by myself. Thank you all. Table of Contents List of Tables.
v List of Figures. vi Chapter 1: Introduction to the Study. 5 Purpose of the Study. 6 Research Questions and Hypotheses.
9 Nature of the Study. 9 Definition of Terms. 11 Scope and Delimitations. 13 Chapter 2: Literature Review.
14 Developmental Education Defined. 18 Technology Integration in Classrooms. 20 i Technology Integration in Developmental Math Courses. 29 Theory of Self-Efficacy.
33 Chapter 3: Research Method. 35 Research Design and Rationale. 38 Description of Population. 38 CPP Developmental Math Program.
39 Sampling and Sampling Procedure. 39 Procedures for Recruitment, Participation, and Data Collection. 40 Instrumentation and Operationalization of Constructs. 42 Threats to Validity.
48 Time Frame, Actual Recruitment, and Response Rates. 48 ii Descriptive Statistics for Demographic Characteristics. 65 Chapter 5: Discussion, Conclusions, and Recommendations. 68 Interpretation of the Findings.
70 Limitations of the Study. 93 Appendix B: Initial Email. 101 Appendix C: Second Email. 102 Appendix D: Introduction to Study Paragraph.
103 Appendix E: Survey Permission #1. 104 iii Appendix F: Survey Permission #2. 106 iv List of Tables Table 1. Demographics for Overall Sample (N = 157).
Math Self-Efficacy Responses. Overall Means of Student Opinion of Connect Math. 63 v List of Figures Figure 1. Highest completed math course in high school……………………………….64 vi 1 Chapter 1: Introduction to the Study Background For centuries, societies have depended on their ability to create advances in exploration/commerce, health/medicine, defense, finance, and technology (US Department of Education, 2008).
Quantitative reasoning is an important skill to possess in order to be successful in any of the aforementioned areas (Elrod, 2014). A lack of US citizens available for these types of jobs can result in job outsourcing and/or understaffing of positions. Additionally, those without a college degree (on average) make less than those with college degrees (U. Bureau of Labor Statistics, 2017).
With a college degree, people can become positive, successful, contributing members of the US economy, while also increasing their median earnings (U. Bureau of Labor Statistics, 2017). Higher education has become progressively more accessible since the establishment of community colleges. However, all colleges are facing a challenge.
With an increase in the number of students who have access to higher education, there are greater variances in the education level and preparedness of the students (Holt, Holt, & Lumadue, 2012). A growing number of students are entering college who are underprepared for college-level coursework (National Association for Developmental Education, 2013). Developmental math has one of the highest enrollments of the three types of developmental classes available, English, reading, and math (Bonham & Boylan, 2012). Developmental education programs (also known as remedial course programs) were created at community colleges and universities to help underprepared students in math, English, writing, and sometimes science, be able to achieve the dream of 2 attending college and earning a higher education degree (Bonham & Boylan, 2012).
Developmental courses are designed to help students who are not properly prepared for college level work by completing a course (or sequence of courses) that will provide the necessary background information to take college courses (National Association for Developmental Education, 2013). While providing college-prep courses seems like the perfect solution for underprepared students, colleges still face a problem with student success rates (Daiek, Rusinko, & Talbert, 2012). Peterson, Woessmann, Hanushek and Lastra-Anadon (2011) stated that the U. ranked 32nd in the world for proficiency in mathematics, in addition to only 32% of U.
eighth graders being proficient in mathematics. Colleges and universities have provided a way for underprepared students to be able to attend their institutions, yet students are still failing these developmental math courses. Mathematics education is important, not only for society, but also for the individual, as it offers a greater number of college and career options (US Department of Education, 2008). America’s math students have been struggling.
Community colleges across the nation experience low levels of student success in developmental math courses (Gerlaugh, Thompson, Boylan, & Davis, 2007). In higher education, students take a placement test or have previous coursework be considered before placement into developmental math courses (Blum et al. Once students are placed in developmental math courses, successful completion of these developmental mathematics courses are necessary to take ensuing college-level courses (Blum et al. The outcome of these courses can affect college success/graduation.
Of US high school graduates, fewer than half are prepared to take college-level math courses (Bissell, 2012). 3 Adelman (2004) estimated that 41% of college students enroll in remedial courses at some point during their college career. Some students are unable to attain their educational goals because they do not succeed in these courses (Bonham & Boylan, 2012). Remedial courses have been found to be beneficial to underprepared students, according to studies that examine academic outcomes of students in developmental math courses.
After tracking students long-term, Waycaster (2011) found that developmental math students and nondevelopmental math students achieved similar success in later math courses. Likewise, Bahr (2008) found no significant difference when he compared the long-term academic outcomes of successful remediated students to those who achieved college-level math skill without remediation. This may indicate that remedial math programs are highly effective for those students who actually complete the program. Studies that examine student academic outcomes in developmental math courses that use educational technology have found mixed results.
Kodippili and Senaratne (2008) sought to examine whether computer-generated homework (via MyMathLab) led to an increase in academic performance in comparison to traditional instructor-generated homework and found no significant difference in performance on homework assignments between the students in the MyMathLab group and controls; however, results showed that students in the MyMathLab group had higher success rates than the traditional students (70% success rate in the final grade for MyMathLab students vs. 49% success rate with traditional-based students). MyMathLab is an interactive computer-learning environment that includes course materials and tools (Pearson Education, 2012). Bissell (2012) suggested that in order to create a more personalized teaching 4 and learning experience for both regular and developmental math students, instructors should make use of open educational resources.
Trenholm (2006) examined how computer technology can be used to increase learning success and retention, but results did not indicate a clear relationship between learning efficacy and computer use. While various educational technology tools are being used to help students be more successful in their developmental math courses, there are mixed results as to their effectiveness. While student motivation and hard work are key components to student success, low self- efficacy toward coursework can hinder students’ ability to successfully complete the coursework and eventually graduate (Spence & Usher, 2007). Self-efficacy, the strength of one’s belief in their ability to complete a task or goal, is a significant predictor of math success and plays an important role in developmental math courses (Spence & Usher, 2007).
The experience of long- term negative emotions toward mathematics and math anxiety caused by frequent course failures should be addressed to improve students’ success in math courses (Taylor, 2008). In order to achieve success in math courses, students need to have positive self-efficacy.