INVESTIGATION OF INSTRUCTIONAL STRATEGIES ONLINE MATHEMATICS CLASSES. Ana M. Porro. The College of Education. Doctor of Philosophy

Transcription

1 INVESTIGATION OF INSTRUCTIONAL STRATEGIES DESIGNED TO PROMOTE ACHIEVEMENT AND RETENTION IN ONLINE MATHEMATICS CLASSES by Ana M. Porro A Dissertation Submitted to the Faculty of The College of Education In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Florida Atlantic University Boca Raton, Florida December 2011

2 Copyright by Ana M. Porro 2011 ii

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4 ACKNOWLEDGEMENTS Boundless gratitude goes to Dr. Valerie Bryan, a true mentor and leader in the field of adult education; her guidance and encouragement made this project come to fruition. I wish to express my thanks to Dr. Lucy Guglielmino, Dr. John Morris, and Dr. Ginger Pedersen for their guidance, sharing of knowledge and intellectual inspiration. I thank Dr. Robert Shockley for his willingness to serve at my dissertation defense at the last hour. I am also grateful to Dr. Gary Conti for permitting me to use the ATLAS instrument in my study. I thank Palm Beach State College for allowing my students to participate in the study. Special thanks to my students who chose to participate in this research endeavor. I have also been inspired by my colleague and mentor Dr. Seymour Samuels, who has provided endless hours of support in this long journey. I especially want to thank my children. Their love and encouragement have kept me going when the tough got tougher. I thank Marcel for many things, but especially for our many Sunday editing sessions by the phone. I am very grateful to Alex for holding up the fort, but above all for always knowing when I needed a big hug. I also thank Nico for understanding and making me laugh, but most of all for his back massage every time I was pressed for time. Lastly, I want to thank my parents, who taught me the value of education, both of whom believe in the pursuit of dreams. I will keep you always in my heart. iv

5 ABSTRACT Author: Title: Institution: Dissertation Advisor: Degree: Ana M. Porro Investigation of Instructional Strategies Designed to Promote Achievement and Retention in Online Mathematics Classes Florida Atlantic University Dr. Valerie Bryan Doctor of Philosophy Year: 2011 The purpose of this study was to investigate the effect of using learner selfassessment and multifaceted instructional strategies on student achievement and retention in online mathematics classes. The study used a quasi-experimental design. The study sample consisted of 35 students who were enrolled during the fall term 2010 in online Precalculus or Trigonometry classes at Palm Beach State College. Both treatment and comparison groups were taught by the researcher. Since the subjects in the study were not randomly assigned, the design was one of nonequivalent groups where the treatment group was compared to a similar group from the previous year. To limit researcher bias, the course exams were the same for both treatment and comparison groups. Five hypotheses were developed to examine the relationships between preferred learning strategies, the use of online tools, and achievement and retention. The five hypotheses were investigated with the following procedures respectively: ANOVA, v

6 linear regression, Pearson correlations, t-test and chi-square, and linear regression analysis with dichotomously coded variables. The findings indicated that the ATLAS groups did not show a preference for online tools, except for ebook. In addition, the use of most tools predicts achievement. The ebook is the only tool that is not significantly related to all the other tools. Achievement was not significantly different among treatment and comparison groups, but retention was. Retention for the treatment group surpassed retention for the comparison group by 15%. Results also pointed out that the ATLAS groups moderate the relationship between some of the online tools predicting achievement. vi

7 DEDICATION To my children, keep dreaming. Sometimes dreams really do come true. SHMILY.

8 INVESTIGATION OF INSTRUCTIONAL STRATEGIES DESIGNED TO PROMOTE ACHIEVEMENT AND RETENTION IN ONLINE MATHEMATICS CLASSES List of Tables... x List of Figures...xii CHAPTER 1: Introduction... 1 Conceptual Framework for the Study... 5 Problem... 6 Purpose of the Study... 8 Research Questions and Hypotheses... 8 Significance of the Problem Definitions Role of the Researcher Delimitations Limitations Summary CHAPTER 2: Literature Review Motivational Theory Historical Perspectives Current Views and Academic Success vii

9 Cognitive Load Implications Online Learning Andragogy and Learning Theories Learner Characteristics Interpersonal and Cognitive Tools Theory into Practice Instructional Design Learning Strategies Designing and Implementing Strategies Summary CHAPTER 3: Methodology Research Design Setting/Subjects Procedures Instrumentation The survey ATLAS BlackBoard and MyMathLab Activity Reports Assignments and Chapter Exams Reflective Exercises Statistical Techniques Data Collection viii

10 Data Analysis Summary CHAPTER 4: Findings Introduction Participants Profile Hypothesis Testing Summary CHAPTER 5: Conclusions and Recommendations Overview of Findings Conclusions Recommendations for Future Research Summary Appendices Appendix A: Student Survey Letter on BlackBoard Appendix B: Student Survey Appendix C: ATLAS Flowchart Appendix D: Dissertations Using ATLAS Appendix E: Permission to Use ATLAS Appendix F: Reflective Exercise Appendix G: List of Reflective Exercise Comments by ATLAS Groups References ix

11 LIST OF TABLES Table 1. Instructional Strategies as Prescribed by ATLAS Table 2. Gender, Age, and Ethnicity for Treatment and Comparison Groups Table 3. Working Status, Prior Computer Knowledge, and Prior Elearning Experience for the Treatment Group Table 4. Mean and Standard Deviation for Academic Variables for Treatment and Comparison Groups Table 5. Distribution of Treatment Group and National ATLAS Responses Table 6. Frequency of Responses by ATLAS Group for Each Reflective Exercise Table 7. Distribution of Observed Hits of Online Tools and Average Hit per Student per ATLAS Group Table 8. ANOVA for ATLAS Groups and Announcements Table 9. ANOVA for ATLAS Groups and Calendar Table 10. ANOVA for ATLAS Groups and Chat Table 11. ANOVA for ATLAS Groups and Discussion Table 12. ANOVA for ATLAS Groups and Table 13. ANOVA for ATLAS Groups and MyMathLab Table 14. ANOVA for ATLAS Groups and Ebook x

12 Table 15. ANOVA for ATLAS Groups and PPP Lectures Table 16. ANOVA for ATLAS Groups and Videos Table 17. ANOVA for ATLAS Groups and Tutorial Table 18. ANOVA for ATLAS Groups and Examples Table 19. Summary of Correlations for Usage of Online Tools and Achievement Table 20. Means and Standard Deviations for Usage of Online Tools Table 21. Summary of Pearson Correlations for Usage of Online Tools Table 22. Frequency and Percent of Students Grades per Group Table 23. t-test for Equality of Means for Treatment and Comparison Groups Table 24. ATLAS Group Moderation Analysis Between Tool Use and Grade xi

13 LIST OF FIGURES Figure 1. A Multifaceted Approach to Online Mathematics Learning... 6 Figure 2. Frequency of Tool Usage by Preferred Learning Strategy xii

14 CHAPTER 1 Introduction There is a rapid growth in the use of technology in all aspects of society. Colleges have embraced technology in all operations including administration, communications, human resources, accountability, and teaching and learning. As colleges incorporated technology into teaching and learning, it was often the technology that provided the direction. This was frequently done without any evidence-based decision-making. Many organizations developed distance learning programs without regard to their effectiveness or suitability to the learning needs of their audience. Research on the effectiveness of distance learning classes often lagged far behind their implementation. In recent years, however, the focus has shifted from efforts to rapidly develop distance learning classes to refinement. The efficiency and effectiveness of distance learning programs is now being closely examined as instructional design strategies are incorporated into online instruction (Salas, Kosarzycki, Burke, & Stone, 2002). As more institutions develop and deliver distance learning classes, researchers have begun to ask more sophisticated and complex questions about this instructional modality. A synthesis of recent studies on online teaching and learning noted that some of the issues researchers are beginning to investigate are: (a) what motivates students to choose distance learning classes; (b) how learning styles be matched with 1

15 instructional design; and, (c) how to best deliver this type of instruction (Tallent- Runnels et al., 2006). In addition, the final question remains: what motivates students to succeed in distance learning classes? Distance education has existed for over a century. As early as 1892, schools and universities initiated offering classes by correspondence using mail or the postal system as a vehicle to reach out those who lived far from higher education institutions, but wanted an education (Monolescu, Schifter, & Greenwood, 2004). As new technologies emerged, distance education opportunities were broadened by radio (1920s), instructional television (1950s), cable television (1970s), satellite downlinks (1970s and 1980s), and videoconferencing through interactive compressed video (1990s). The term distance learning is used to describe the interactions in any course that is delivered to students who are not physically present in the same room (Tallent- Runnels et al., 2006). Although distance learning classes may be delivered in many different formats, this investigation will examine Web-based or online learning classes, courses that are delivered completely over the Internet via a computermediated environment. This delivery method, a common distance learning approach, may interchangeably be referred to as Internet, electronic or elearning, or virtual learning (Bork, 2005, p. 31). In select cases the students may have the opportunity to see and chat with the instructor and other students at a distance. For the purpose of this research, online learning may or may not involve this component. In the last decade of the twentieth century, Web-based learning grew from 2

16 being almost absent to suddenly appearing everywhere (Hantula & Pawlowicz, 2004). As the trend in virtual enrollment continues in colleges and universities, the need to understand adult learners surges in an effort to compete effectively in a global community. To remain competitive, faculty and administrators must identify and understand the needs and preferences of adult learners as they relate to online learning and online instructional design (Ausburn, 2004). According to Knowles (1980), the father of andragogy, or the art of teaching adults, adult learners tend to be selfdirected. That is, they want some control over both the content of learning and the way the knowledge or skill is presented. One of the principal premises of andragogy is that the successful facilitator acknowledges that need and creates a highly interactive learning environment for adults. Therefore, andragogy tends to be process-driven more than content-driven. That is not to say that content is irrelevant to the adult learner, but that the adult learner acquires new material (content) or a taught skill more efficiently when the facilitator individualizes the learning experience. Because adults' readiness to learn is frequently affected by their need to know or do something, they tend to have a life-, task-, or problem-centered orientation to learning as opposed to a subject-matter orientation (Knowles, 1980). Other assumptions underlying Knowles andragogical model differentiate adults as having a growing reservoir of experience that can serve as a resource for learning. Adults are generally motivated to learn due to internal or intrinsic factors as opposed to external or extrinsic factors. A logical outcome of these assumptions is the use of a collaborative teaching model that involves the learner and facilitator as mutual 3

17 partners in the learning venture (Merriam & Caffarella, 1999). Offering a variety of resources and fostering a supportive environment are vital factors in successful adult learning (Hantula & Pawlowicz, 2004; Knowles, 1980; Merriam & Caffarella, 1999). An important issue to consider at the forefront of instruction is the learners processing of information. This can be known by using instrumented learning. Blake and Mouton (1972) defined instrumented learning as the means to gain understanding of self and others, improve performance, and enhance the processes of metacognition and learning how to learn. One tool currently used to assess the learner's processing of information is a self-assessment instrument termed ATLAS (Ausburn, 2004; Conti, 2009). Assessing The Learning Strategies of Adults (ATLAS) groups learners into one of the three preferred strategies or approaches to learning. The three strategies are named: Navigators, Problem Solvers, and Engagers. Navigators are viewed as result-oriented learners who favor efficiency and effectiveness via a plan. Problem solvers are critical thinkers who explore options (multiple paths) and avoid rapid closure. Engagers are learners who love to learn and approach the affective domain seeking connectedness and high involvement (Conti, 2009). The information garnered through this assessment can be valuable for the instructor to design the course in a way that it offers options to learners addressing individual differences, and also for learners to become aware of how they initiate a learning task. Munday (2002) concluded that adult learners can realize a positive impact in their academic achievement if they know their learning strategy preferences. By students having self-knowledge of preferred 4

18 learning strategies as Navigators, Problem Solvers, or Engagers as prescribed in ATLAS, they will be able to choose interpersonal and cognitive tools that best match their learning journey. On October 19, 2010, CNN reported that despite the souring economy, more and more Americans are buying high-speed Internet service. As more people get on the information highway, it is likely that distance learning programs will continue to grow. In an effort to compete effectively in a global community, colleges and universities need to value adult learners and understand why they choose distance learning classes. More importantly, schools need to understand what motivates students to succeed in online classes. Conceptual Framework for the Study The researcher takes a motivational perspective to learning and embeds it within the context of instructional strategies. Figure 1 helps define the conceptual framework for the study. 5

19 Instructional Strategies Learning & Motivation Student Success Self-Knowledge Choices Interpersonal Tools Cognitive Tools Reflection Figure 1. A multifaceted approach to online mathematics learning. Problem While enrollment in online classes continues an uphill path, administrators and faculty are being faced with issues and challenges related to retention and success of online students. Issues concerning student retention and success involve motivation and connectedness to the information delivered in the course (Sanacore, 2008). In a South Florida state college, distance learning, also called elearning, classes have 6

20 significantly higher withdrawal rates in all courses across terms. This low retention rate affects the success rate in the courses as measured by the grade (or number of A/B/C/P issued) in the classes (PBCC Academic Affairs, 2006). The student success rate in traditional college mathematics courses lags behind other courses. Particularly in online mathematics classes, student retention and success are a serious concern. Online Precalculus classes have a 32% withdrawal rate and a 47% success rate, while traditional face-to-face Precalculus classes have an 18% withdrawal rate and a 70% success rate (PBCC Institutional Research & Effectiveness, 2008). These results do not differ significantly from other online courses nationwide regardless of subject content (Wadsworth, Husman, Duggan, & Pennington, 2007; PBCC, 2006). Courses with more than 30% withdrawal rate are considered high-risk and deserve special attention (Bambara, Harbour, Davies, & Athey, 2009). Other researchers (Wadsworth et al., 2007) also noted that although online learning enrollment is growing, very little research has been conducted that examines the use of cognitive strategies and their effects on student learning and achievement in online courses. Present research on motivating online mathematics students appears to have a narrow focus and is still in the developmental stage. Little research appears to address the issue that learners in online mathematics courses must deal with two major tasks: (a) learning the course mathematics content and (b) learning how to maneuver in an elearning environment. In an effort to improve retention and success rates in online mathematics classes, there is a need to conduct research and develop evidence-based 7

21 recommendations for instructional design (Li & Edmonds, 2005; van Merriënboer & Ayres, 2005). It is essential to recognize that one size does not fit all and to understand learning styles and learning theory in the context of motivation. Employing a multifaceted approach may lead to greater opportunities for success. There is a need to investigate the application of motivational strategies, such as preferred learning strategies, to the design of online instruction and learning, specifically by integrating aspects that promote self-knowledge, choices, and the interaction of instructional conditions (Wadsworth et al., 2007). As research is growing to improve retention in online classes, the exploration of a multifaceted approach to enhance student achievement is viable and needed research that should provide successful strategies for learners. The hypothesis that motivational strategies promote achievement in online mathematics classes is supported by recent investigators (Bellon & Oates, 2002; Kelsey & D Souza, 2004; Patall, Cooper, & Robinson, 2008; Sanacore, 2008; Skinner, 2005). These researchers suggested the need to investigate the effectiveness of incorporating motivational strategies in the implementation of online instructional designs. Purpose of the Study The purpose of this study was to investigate the effect of using learner selfassessment and multifaceted instructional strategies on student achievement and retention in online mathematics classes. 8

22 Research Questions and Hypotheses The study attempted to discern what benefits and advantages emerge, as well as what limitations and challenges are identified, when using a multifaceted motivational design in online mathematics classes by finding answers to the following questions: Question 1: Are Navigators, Problem Solvers, and Engagers, as prescribed by ATLAS, different in their preferences for interpersonal and cognitive tools? Question 2: Is frequency of usage of interpersonal and cognitive tools a significant predictor of academic performance? Question 3: Is frequency of usage of interpersonal tools related to frequency of usage of cognitive tools? Question 4: Are there differences in the achievement and retention of the treatment group when compared to a similar comparison group from the previous year? Question 5: Does the preferred learning style moderate the relationship of usage of tools and academic performance? The following hypotheses were addressed: H 0 1: There is no significant relationship between students preferred learning strategy (N, P, E) and types of interactive tools chosen. H 0 2: There is no significant relationship between frequency of interpersonal and cognitive interactions in an online mathematics course and achievement. H 0 3: There is no significant relationship between use of interpersonal tools and 9

23 use of cognitive tools. H 0 4: There is no significant difference between student achievement and retention in the treatment group versus student achievement and retention in the comparison group. H 0 5: The preferred learning strategy is not a significant moderator of the relationship of usage of tools and academic performance. Significance of the Problem The virtual world is here to stay. The Internet opened doors to a global community without physical barriers. This is clearly visible in the enrollment growth of online education (Bambara et al., 2009; Wadsworth et al., 2007). For example, in the last ten years at a South Florida state college, elearning enrollment had an increase from barely existent to almost five thousand students per year (PBCC Academic Affairs, 2006). This rapid growth has given rise to issues and concerns regarding student achievement. There is a vital need to increase retention and success in online education, particularly in online mathematics classes (Wadsworth et al., 2007). Motivation has been shown to be an important attribute for success and, therefore, warrants further investigation as an integral component of instructional design. This research used a design of learning strategies to promote interpersonal and cognitive interactions in two online mathematics classes. The results of this study added some new evidence-based answers and direction to the task of increasing student success in online mathematics classes. Moreover, if the implementation of 10

24 motivational strategies in an online instructional design promotes achievement, the study serves as a foundation for further research. Definitions Achievement Achievement rates refer to the percentage of completed assignments. Student achievement, as well as success rate, will be measured by the final average grade for the course of 70% or higher on all assignments. These two courses are general education and a lower grade is not acceptable. ATLAS Assessing The Learning Strategies of Adults is an instrument that can be used to quickly identify learning strategy preferences. Learners are clustered into Navigators, Problem Solvers, or Engagers. Cognitive interactions These are the student connections within the mathematical content website. MyMathLab is a student-focused interactive online learning environment that delivers the subject content and is designed to be correlated with a title-specific textbook (Speckler, 2009). Cognitive tools Available in MyMathLab, these tools consist of e-book, videos, PowerPoint lectures, tutorials, and examples. Interpersonal interactions These refer to the social connections in the virtual classroom. BlackBoard is a course management system where the virtual classroom is held and where all social connections take place. Interpersonal tools Available in BlackBoard, they include announcements, calendar, chat room, discussion board, and functions. 11

25 Learning strategies These are techniques and skills that a student elects to use in order to accomplish a specific learning task, and they vary by individual and by learning objective (Ausburn, 2004; Conti, 2009; Munday, 2002). Online tools These are the interpersonal and cognitive tools used for learning. Online, Web-based, Internet, and elearning These classes refer to a distance education modality where all interactions among students, instructors, and content take place in the virtual world. Face-to-face contact with the instructor or other students occurs only when initiated by the student. The only exception is when students take their online exams in a proctored academic environment located anywhere in the world, and approved by the instructor. Retention This rate is the percentage of students who registered for the course and attempted to complete all assignments. Retention rate only excludes those students who officially withdrew from the course (Speckler, 2009). Withdrawal The withdrawal policy on the syllabus states The last day to withdraw from a College course with a "W" grade is November 4, It is the responsibility of the student to complete and submit the necessary forms to the Registrar's Office. An official withdrawal would entitle the student to a grade of "W" in the course. Role of the Researcher For more than twenty years, the researcher has been teaching developmental mathematics at a South Florida state college. Having been an international student 12

26 herself, the researcher is mindful of the struggles that students have when learning English as an adult. Mathematics, being the language of numbers and logic, presents similar difficulties as learning another language. As an educator, the researcher sees students struggling in mathematics every day. As chair of the college's reaffirmation for accreditation process in 2001, the researcher gained insight in the importance of assessment and using the results of these for improvement. In addition to being a doctoral student and having family and social responsibilities, the researcher teaches mathematics full-time. This personal experience contributes to understanding the role of the adult learner with multiple obligations and potential hurdles/obstacles. The researcher is also the instructor of record for all groups under study and is aware of potential bias for the study. To minimize the bias, the researcher gave both groups the same assignments and exams among other factors. On the other hand, the same instructor or the researcher was more cognizant of pros and cons, and reflective of the real classroom situation as opposed to a laboratory research effort. The researcher had a significant understanding of the students' needs and total college requirements. As a result, the researcher was continuously available for the students of the experimental and comparison groups in order to meet their needs. Her awareness and concerns of the potential bias, in many ways, kept her actions from contaminating the study. Whatever biases result within the study was carefully assessed and noted for the readers. 13

27 Delimitations Both experimental and comparison groups are limited to self-enrolled students in a South Florida state college. In addition, the instructor is the same for both groups. Moreover, the courseware chosen, Blackboard and MyMathLab, have both had positive results linked to their use but are not the only venues available. Limitations The study involved a very small convenient sample. The research project used a quasi-experimental design with no formal control group, and no accounting for prior online course experience. The students who reported on their computer knowledge may not have accurately reported their level. It was not possible to control for prior experience in online learning. Summary This chapter explained the need to investigate the application of instructional strategies in a multifaceted motivational design in online mathematics classes. Presenting the significance and purpose of the study and outlining the problem and the research questions, Chapter One also provided a conceptual framework that formed the basis of the study. The next four chapters provide the literature review, methodology, findings, and conclusions and recommendations 14

28 CHAPTER 2 Literature Review The review of the literature on the role of instructional strategies in online mathematics learning addresses the following major areas and their related subsets: motivation and academic success; online learning and andragogy; and instructional design and learning strategies. It is noted that the hypothesis that motivational strategies promote achievement and success in online mathematics courses is supported by recent investigators who suggested the need to research the effectiveness of incorporating motivational strategies in the implementation of online instructional designs (Bellon & Oates, 2002; Kelsey & D Souza, 2004; Patall et al., 2008; Sanacore, 2008; Skinner, 2005). Additionally, the limited role documented on motivation in online mathematics courses by both mathematicians and educators is included. Next, discussions of online learning, andragogy and learning theories, learners' characteristics and heuristics in interpersonal and cognitive tools are addressed. Lastly, laying the foundation of theory into practice, considerations for instructional designs are included along with suggestions for design and implementation of learning strategies. Motivational Theory There is a vast field of investigation providing definitions of motivation 15

29 (e.g., Encyclopedia Britannica, McGraw-Hill Encyclopedia of Science and Technology, & The Columbia Electronic Encyclopedia). A common thread among the definitions of motivation is that motivation is the driving force of human behavior. For the purpose of this study motivation is defined as the ability to change behavior (Motivation, n.d.) in students who need encouragement to strive for achievement. Motivation in an elearning environment is the major factor that determines success (Bellon & Oates, 2002; Chang, 2005; Paas, Tuovinen, van Merriënboer, & Darabi, 2005; Patall et al., 2008; Sanacore, 2008). Student motivation is not new to teachers and college professors. All professors are aware of their role in motivating students to learn. Student motivation is perceived as the basis for student learning and success. The role of motivation needs to be recognized as the facilitator of meaningful learning. When instructors focus on learners strengths and provide them with wellmatched and structured resources across the curriculum, they increase opportunities for motivation and for successful learning (Bellon & Oates, 2002; Ozcelik & Yildirim, 2005; Sanacore, 2008; Wadsworth et al., 2007). Easy access to quality resources is not enough to promote competency and lifetime learning, but the balance of instructional materials with effective teaching sets the stage for success (Sanacore, 2008). Historical perspectives. Behavior is an extremely complex subject matter (Skinner, 2005). From Plato ( BC) and Mencius ( BC) to Maslow ( ) and Skinner (