The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2011 Will the use of My Math Lab influence the students' attitude toward mathematics, course completion, and content comprehension of basic college mathematics? Sami B. Mejri The University of Toledo Follow this and additional works at: http://utdr.utoledo.edu/theses-dissertations Recommended Citation Mejri, Sami B., "Will the use of My Math Lab influence the students' attitude toward mathematics, course completion, and content comprehension of basic college mathematics?" (2011). Theses and Dissertations. 640. http://utdr.utoledo.edu/theses-dissertations/640 This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.
A Thesis entitled Will the Use of My Math Lab Influence the Students Attitude toward Mathematics, Course Completion, and Content Comprehension of Basic College Mathematics? by Sami B. Mejri Submitted to the Graduate Faculty as partial fulfillment of the Requirements for the Master of Education Degree in Secondary Education Charles Rop, Ph.D., Advisor Mark Templin, Ph.D., Committee Member Tod Shockey, Ph.D., Committee Member Patricia Komuniecki, Ph.D., Dean College of Graduate Studies University of Toledo August 2011
Copyright 2011, Sami B. Mejri This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.
An Abstract of Will the Use of My Math Lab Influence the Students Attitude toward Mathematics, Course Completion, and Content Comprehension of Basic College Mathematics? by Sami B. Mejri Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Master of Education Degree in Secondary Education The University of Toledo August 2011 The purpose of this study was to determine whether or not computer-aided learning using a software program called My Math Lab (Pearson, 2010) has a significant difference in students attitudes toward mathematics, their comprehension of mathematics, and their prospects for course completion. The study was based on a partial replication of similar recent studies with similar research goals. Based on principles of educational constructivism, the use of software programs must improve learning and acquisition of knowledge. Appropriate implementation of this software into curricular activities enhances students active engagement in the classroom and increases their participation levels in learning. Participants in this study were all college students who varied in age and socio-economic status. They were randomly placed into either a control group that learned in normal settings using the board and textbooks or a treatment group that was given computer supplemented instruction. The results of this study indicated that there was no statistically significant difference in terms of attitude toward mathematics between students enrolled in classes iii
using the software and students not using the software. The study also showed no effect on course completion between both instructional settings. However, similar to the finding of Loving, (2007), and unlike the finding of Moosavi, (2009), this study showed that students enrolled in developmental math classes using My Math Lab performed much better in terms of overall final grade than students in a traditional setting not using the software. These results have implications especially on the effective use of software programs when planning and assessing learning developmental mathematics. The findings of this study have implications on conditions and circumstances associated with effective application of software programs in mathematics for educators and their students. iv
Acknowledgements This paper was made possible by the continuous advising from Dr. Charles Rop and Dr. Templin for willing to be on the committee and overseeing this study. I also would like to thank Dr. Susan Modarai for the continuous monitoring of the writing process. Special Thanks to Dr. Shockey for all of his constructive editing and advising. v
Table of Contents Abstract... iii Acknowledgements...v Table of Contents... vi List of Tables... ix List of Charts...x I. Introduction...1 A. Background... 1 B. Statement of the Problem... 2 C. Purpose of the Study... 2 D. Organization of the Study... 4 E. Research Question... 4 F. Significance of the Study... 5 G. Limitations and Assumptions... 6 H. Definition of Terms... 7 I. Chapter Summary... 8 II. Review of the Literature...9 A. Theoretical Basis for Computer-Aided Instruction (My Math Lab )... 9 a. Constructivism.... 9 b. My Math Lab as a constructivist example of CAI.... 10 c. CAI in special education.... 10 d. Student roles in a constructivist approach.... 11 e. Theoretical framework summary.... 16 vi
B. Available literature on Software Technology in the Mathematics Classroom Instruction... 17 a. Influencing factors contributing determining the effectiveness of CAIs.... 18 b. Two case studies on traditional and computer-aided instruction.... 20 C. CAI is a Move from Traditional to a More Current Application... 25 a. The traditional instruction and the traditional learning styles.... 25 b. CAI as contemporary instruction and learning method.... 27 D. Chapter Summary... 28 III. Methodology...30 A. Introduction... 30 B. Conceptual Framework... 30 C. Research Design and Rationale... 32 D. Research Site and Participant Selection... 32 E. Data Collection Methods... 33 F. Data Analysis... 34 G. Role of the Researcher... 35 H. Chapter Summary... 35 IV. Results...37 A. Introduction... 37 B. Hypothesis Testing... 37 a. Testing H1: Attitudes toward mathematics inventory and survey.... 37 b. Testing H2: Student comprehension in MML courses compared to no MML.... 48 c. Testing H3: Course completion in MML courses compared to no MML.... 51 V. Discussion...54 A. Assumptions and Limitations of the Study... 54 B. Conclusions... 55 vii
C. Comparison of Study Results with Existing Similar Studies... 56 D. Recommendations... 57 E. Implications... 57 References...59 Appendix A: Survey...63 Appendix B...68 Appendix C...69 Appendix D...70 viii
List of Tables Table 1: Pre-Course Survey...38 Table 2: Post Course Survey...39 Table 3: Two-Sample Assuming Unequal Variances...49 Table 4: Final Course Grades...50 ix
List of Charts Chart 1: Pre No MML Disagree...40 Chart 2: Pre No MML Agree...41 Chart 3: Post No MML Disagree...42 Chart 4: Post No MML Agree...43 Chart 5: Pre MML Disagree...44 Chart 6: Pre MML Agree...45 Chart 7: Post MML Disagree...46 Chart 8: Post MML Agree...47 Chart 9: Summary for No MML...52 Chart 10: Summary for MML...52 x
Chapter One Introduction Background As a college mathematics educator, I am always searching for ways to modify and improve my teaching methods in order to meet the learning needs of my students. So far I have managed to minimize lecture time to twenty percent or less, I tend to guide and monitor the learning and the classroom discussion rather than lecturing the entire time. I allow students to work cooperatively and assign mathematics projects that are practical and meaningful to the students as some assignments involve other subject matters such as pharmacology for medical assistant students. Learning as much as possible about my students learning needs and what may go on in their daily struggles is challenging and sometimes an impossible task, but it makes my job much easier. More and more Colleges and Universities are implementing computer supplemented instruction in various levels of mathematics and my institution of employment is no different. I was introduced to the use of computer application in mathematics first hand at a local two year college in Northwest Ohio. Facilitating knowledge and planning assignments for developmental mathematics seemed challenging and I was skeptical about the students reaction to the use of computers in mathematics classes. I saw mixed reactions to the application of computer supplemented instruction in developmental mathematics classes. Some students complained about doing homework online due to availability of the Internet at their homes. Some students expressed frustration with specificity and complexity of the software such as their answers to questions were rejected if entered in an incorrect form even if it is correct. Some other students praised 1
the use of the programs and they were excited about this application and about the self paced, individualized guidance and the help it offered them. I heard similar statements and testimonials from other instructors about the application of software in mathematics classes. I became more interested in the advantages and the limitations of these computer aided programs and specifically My Math Lab that I used very often in my developmental mathematics classes. I wanted to examine the possible learning benefits and elements of dissatisfaction associated with use of computers in math. I also wanted to know what educational rational made us progress toward using computers as a learning choice in mathematics. Statement of the Problem Students are not one size fits all, the more you know them and interact with them; the easier it is to facilitate acquisition of knowledge for them. Humor is often a powerful tool to make abstract concepts more concrete and ease students frustrations with mathematical concepts. As a mathematics instructor, I am also interested in finding the role of embedding technology in instruction toward reshaping students' mindsets about mathematics. Some government studies, e.g. What Works Clearinghouse (2008), have shown that the implementation of software technology in mathematics classrooms has had a positive and statistically significant effect on student performance and comprehension. Purpose of the Study The purpose of this study is to determine if there is any statistically significant difference in terms of attitude, course completion, and comprehension between students enrolled in classrooms receiving Computer Assisted Instruction (CAI) and their peers in 2
classrooms not using CAI. The program used in this study and referred to as CAI is the widely used software My Math Lab and was developed by Pearson (2010). The choice of this specific program was simply familiarity with its application through multiple uses and that it is used generally for the mathematics level I teach. This present study is supported by existing literature on the relationship between technological support such as My Math Lab, I CAN learn, and Think Well software technology programs, in the classroom and increased student learning. A study by Siemens (2008) and Loving (2007) focused on how students learning abilities and styles differ greatly from one to another within any one classroom. Loving (2007) noted that the contemporary generation of students, who have been raised on the Internet and interactive technology, are accustomed to the individualized, customized, and active form of learning. Various study, such as the one by Spardlin (2009) researched education reform and found that improvement opportunities in mathematics classes, through the use of software technologies, rely on the human roles, i.e. the educator and learner, and the importance of educator training and willingness to incorporate properly and effectively CAI to mathematics specifically and education in general. Another related study by Moosavi (2009) compared both classroom instructional methods traditional and computerized assisted for a statistical difference in content comprehension and course completion. Like Spardlin (2009), Moosavi (2009) study found the critical role of the human element, such as instructor training. This study s literature review incorporates the results of existing studies such as these and examines the relevance of their findings to contribute this present study s survey findings. The study seeks to address the current issue that while some studies 3
show definitive classroom and learning enhancement through the implementation of software technology, students are individuals with individual learning needs that can be facilitated through the educators personal knowledge and interaction. Organization of the Study This research project is presented as a thesis in five chapters. The first chapter introduces the topic of this study, its significance, its purpose, and the main research question that was posed. Some key words utilized throughout this paper were also explained and defined in this chapter. In the second chapter, a review of the relevant literature and the summary of findings in two similar studies are discussed. Some recommendations for future research in literature were taking into consideration in basing and organizing this study. In chapter three, the research methods are outlined. The results of the research are offered in chapter four. Finally, the research problem is summarized; interpretation of results is listed along with future research recommendations in chapter five. Research Question Computer aided instruction software like My Math Lab may be a more positive and effective method in achieving a classroom characterized by individual, self paced learning styles than instruction with no software programs. This study seeks to answer the following research question: 1) Is there a statistically significant difference between two methods of instruction in terms of course completion, comprehension, and attitudes toward mathematics? 4
Significance of the Study Government research committees such as What Works Clearinghouse (2008), found that software technology has a positive and statistically significant effect on student learning and improvement. Programs such as I CAN learn Pre-Algebra and Algebra implementation and My Math Lab were found to enhance student comprehension and course completion as compared to students who only received partial or no computer aided instruction. Considering existing literature findings, such as those discussed above, that computer based programs cannot substitute for the student-teacher interaction, this present study may contribute additional findings to determine the extent that My Math Lab can offer students the opportunity to be actively engaged and enhance their interest and enthusiasm for the subject. According to Wretch (1985), software technology in college level developmental mathematics education can enhance learning while increasing student engagement with the subject matter. Through incorporating the same learning techniques and principles within the primary educational system, it could be possible to engage student interest and enhance student comprehension and retention earlier in their educational experience. Existing literature has found that social constructivism views each learner as a unique individual with unique needs and backgrounds. Furthermore, learning theoretically occurs in the context of student background knowledge, is self paced. As such, this study examines whether well designed, individualized software instruction may be the answer to how to reach the diverse needs of a diverse group of learners. Through the use of recent findings on the effects of CAI and specifically software programs such as My Math Lab, I will use the results of this study and other research 5
findings (Loving, 2007; Spardlin, 2009; Moosavi, 2009) on Computer assisted Instruction (CAI), to enhance the quality of lesson planning, classroom management, and overall interactions with students in the classroom as compared to traditional classroom instruction. The findings of this study may help instructors make better decisions on how and when to appropriately implement the program in their planning. Furthermore, within contemporary literature in this field, researchers such as Spardlin (2009) have suggested that, with the proper training, CAI, such as My Math Lab, is ideal for students with learning disabilities as it my offer self paced guidance and continuous feedback to them on their progress. This study seeks to examine this possibility and determine any possible contributions it may have to improve contemporary special education. Limitations and Assumptions This study assumes that students going into this course with the aid of computer instruction have a certain level of computer literacy. This is not a computer literacy course, so it is assumed that these students have the necessary competence with which to mount an active pursuit of computer-aided instruction in mathematics. Limitations of this study include students whose literacy is not at the same level as other students, as well as those students who are resistant to any form of mathematics learning in general. As a mathematics teacher, I deal with such limitations on a daily basis, so I hope that it will be no different in the course of this research, and that, perhaps, this software will aid in enhancing these students' motivation. There are other limitations in this study; the sample size of participants here is too small for the study to be generalized to other pools of students. Participants in this study 6
were diverse but remained predominantly from African American decent. Another limitation is that only My Math Lab was tested in this study and thus the findings of this study may not reflect similar results if other CAI were tested. Definition of Terms Computer Aided Instruction (CAI): Usually computers are used as supplement to the instruction vs. Traditional form of instruction where learners interact with their educator without the use of software programs. CAI takes a variety of forms, such as distance learning and telecourses. For this study, CAI only refers to the use of My Math Lab. Student-centered learning: A teaching style in which the student is the center of focus and instruction is planned around the learning needs of the student. Traditional methods: A teaching style in which the use of software technology is very limited, lecture and textbooks are the primary source of information and delivery. Think Well, My Math Lab a software program containing comprehensive courses for mathematics and statistics with online tutorials, homework and assessment, interactive e-textbook and rich media learning aids. Net Generation: an age group that has grown up with information technology at hand. The aptitudes, attitudes, expectations, and learning styles of the net generation students reflect the environment in which they were raised. Social constructivism: is a sociological theory of knowledge that applies the general philosophical constructionist into social settings, wherein groups construct knowledge for one another, collaboratively creating a small culture of shared artifacts with shared meanings. 7
Millennial learners: also known as the net generation or generation y, Characteristics of this generation vary by region, depending on social and economic conditions. However, it is generally marked by an increased use and familiarity with communications, media, and digital technologies. Digital learners: someone who relies on information technology to communicate, comprehend or deliver information. Linear regressions: is a statistical approach to modeling the relationship between a scalar variable y and one or more variables denoted X. In linear regression, data are modeled using linear functions, and unknown model parameters are estimated from the data. Coefficient of determination R 2 : is the square of the sample correlation coefficient between the outcomes and their predicted values. Chapter Summary Using software technologies such as Think Well, My Math Lab and I Can Learn in mathematics classrooms may be an attractive alternative to traditional methods of instruction. The benefits of computers include giving learners the opportunity to individualized self paced instructions which may be valuable for students with learning disabilities. This study was based on research recommendations from two similar dissertations and determines whether a statistically significant difference in terms of attitude toward mathematics, course completion and comprehension of mathematics exist between classes using traditional instruction or supplemented instruction using My Math Lab. 8
Chapter Two Review of the Literature Theoretical Basis for Computer Aided Instruction (My Math Lab ) Constructivism The main theoretical framework for this present study is Constructivism, an educational philosophy that may contribute towards understanding how CAI can help student learn mathematics in introductory courses. Constructivist philosophy posits that individuals learn within personal contexts of experiences and interpersonal interactions. The contemporary generation has been raised on technology and experiences a lesser degree of personal interaction than previous generations, due to electronic modes of communications. According to constructivism, it is appropriate to examine the contributions of computerized programs as well as the educator s role within the context of the current student body as the so-called net generation. As such, this philosophy provides the appropriate framework to examine the learning affects of computerized programs within the classroom in conjunction with teacher interaction. Constructivism is a theory of knowledge founded on the premise that, by reflecting on our experiences, we construct our own understanding of the world we live in. As constructivist Ausubel stated, If I had to reduce all of educational psychology to just one principle I would say this: The most important single factor influencing learning is what the learner already knows (as cited in Bodner, 1986, p. 873-878). This is particularly relevant to mathematical learning as real world situations can form the basis for classroom lessons and help teach how the lessons are relevant to the learner. 9
My Math Lab as a constructivist example of CAI Within this philosophical model is Social Constructivism, which has been strongly influenced by the work of Vygotsky as cited by (Wretch, 1985) and suggests that knowledge is first constructed in a social context and then appropriated by individuals. Social constructivism encourages the learner to search for the truth using their own experiences. Social constructivism also views each learner as a unique individual with exceptional needs and backgrounds. In a constructivist mathematics classroom, learners are encouraged to participate actively to construct their own knowledge and reflect on their own learning. This approach describes the ineffectiveness of standardized curriculum as one size fits all and encourages the implementation of unique, individualized and customized curriculum even when teaching highly standardized material such mathematics. Assessment in constructivism is not based on standardized tests; instead the self paced learning process becomes a tool of assessment in itself. The emphasis is not changing standardized mathematics but changing the way in which standardized material is taught into customized methods to accommodate individualized needs. My Math Lab is one CAI that has been developed to address this need. CAI in special education Various educational studies have been conducted on CAIs. There are positive and negative views concerning effectiveness. A recent study published by the International Dyslexia Association (2009), revealed that computer technology may also be part of the long-term solution for dyslexic and other at risk students. The use of software technology may have the capacity to provide unique learning options and specific methods of instructions at individual pace. However, due to the special needs of this student 10
segment, researchers Bennett and Matont (2008) argued that it is unsatisfying and insufficient to simply state that students must utilize software technology to learn and to communicate in today s academic settings. That is, just learning to utilize technology based activities may not prepare students well for academic practices. Students may need additional drill and practice with the use of software programs and how it may reinforce content retention and more importantly mastering practical skills useful in everyday life. Student roles in a constructivist approach. Learners often have multiple paths to content and they want learning that is fast paced, multimedia and interactive (Richter, Anderson-Inman 2008). As the traditional classroom setting is transforming, student are encouraged to communicate and interact intimately with their instructors, peers and more importantly with the subject matter. In an effort to increase the students success and engagement in the learning process, many instructors are re-designing and re-developing their roles in the classroom. As access to information becomes more available, through software technology, the role of instructors must shift from primary sources of information and content knowledge to facilitators and coaches. As an example of coaching the learning process, mathematics educators may briefly present new concepts and then allow students to practice individually or in small groups using My Math Lab. Students will then have an opportunity to inquire about newly learned skills and practice until mastery is achieved. This method of instruction will also be great for educators since it frees him or her from preaching and allows more time to monitor students progress. Likewise, the technologically changed method of teaching changes not only the way that students learn but also the role of the learner in this new educational context which is more interactive and responsible. 11
Technologically driven role changes for learners In order to promote learning and faster access to knowledge, the students function in the classroom need to change. Based on work of Edgier (2007), students are observably moving progressively from passive recipients of information to more interactive learners and questioners of the validity and usefulness of the information given to them. As emerging technologies allow students to connect with each other and the knowledge sources, traditional training and educational models are called into question concerning whether or not they meet the needs of these millennial learners (Siemens, 2008). As students become more dependent on technology to acquire knowledge, e.g. through the Internet, and practice skills, e.g. through computer programs, the role of the educator will need to shift and training will need to accommodate such a change. As constructivism encourages learners to relate to their own experiential contexts to construct their knowledge, then digital contemporaries may be well suited to adjust effectively to relying on software technology in their learning. Perhaps the educator s training and new role would include preparing and guiding millennial learners to adjust effectively to their role as more independent learners responsible for the knowledge gained through technology. Observable trends in the field of education emphasize the central focus of mathematics and science in contemporary classroom instruction and learning. However, through the observed experience of this researcher as an educator, a significant portion of contemporary students build anxiety toward mathematical concepts and fear of repeated failure partially due to the nature of content delivery and to the traditional formats of instruction. Yet, Loving (2009) noted that students recently began to express a 12
heightened interest in mathematics following the increased integration of computer technology. Role changes as millennial learners. The common theme here between Siemens (2008) and Loving (2007) findings is that today s learners learn and retrieve information differently. The so called millennial learners or net generation learners fit the characteristics of individualized, customized and active form of learning. Furthermore, Shank and Cleary (2008) found that educators may look at concepts that relate to today s world, one where there is so much to know that it is likely that a student will have to direct his or her own education out of practical necessity. If computers are utilized in almost every aspect of life business, communication, medical field, social interactions, and media then Shank and Cleary (2008) argued that computers should be a dominant form of delivery in the classroom and specifically in college mathematics. The study by Loving (2007) supports this position in that millennial learners are programmed by a digital era to receive and process information compatible with computer based learning. Constructivist approach to a critical assessment into CAI study results Some of the criticism toward the ineffectiveness of CAI is found in research conducted by the U.S Department of education (2008) which focuses on cost versus achievements rather than the factors and conditions favoring such achievements. These studies may have ignored the complex conditions from which innovations, such as software technologies in education, become valuable and effective and are focused on the cost of innovations. When the full context of the studies is accounted for, there could remain a positive role for CAI. For example, students could take advantage of computer 13
supplemented instruction to submit homework and group projects in mathematics electronically and avoid printing documents. Online instruction and computer aided instruction has the capacity to change the learning and teaching processes and help established a new medium of interaction among students and instructors (Berge, 2007). Berge (2007) stated that such technological changes in the learning environment and student expectations usually required changes in how teachers needed to promote student motivation. Contemporary students are more diverse than ever before and as emerging technologies allow students to connect and communicate with each other; traditional teaching strategies are now something of the past. Students may no longer favor learning environments where they are perceived as note takers and knowledge recipients in a unidirectional learning style. In the search for richer and more engaging strategies in the classroom, educators are often faced by the need to implement technological resources and other forms of communications. Research conducted on the effectiveness of computer assisted instruction as compared to instruction without the use of computers, in terms of student comprehension and persistence to course completion is limited. Studies by Siemens (2008) highlighted the positive effects of CAI and view it as a promising shift to the student centered educational approach. (Berge, 2007) argues that the financial cost of implementing software technology in classrooms outweighs the state, and national level of improvements on test results and course completion. Christensen (2008) acknowledged the popularity and effectiveness of CAI especially My Math Lab and think well in education but criticized research that focused on the technology itself and ignored the human elements such as students, instructors, 14
corporations, policy makers, and effective training. Yet, CAIs may be strategically developed to incorporate both human and technologically focused objectives. Without a clear implementation of academic objectives, without educator training on how to implement the use of computers in the classroom, the outcomes of CAI may not achieve its purpose. Christensen (2008) viewed computer based-learning as an emerging disruptive force and a promising opportunity to change the way we learn. Furthermore, Christensen noted the proper use of technology as a platform for learning offers a chance to modularize the system and thereby customize learning. Additionally, Christensen describes effective shifting from traditional learning into computer-based learning as a disruptive innovation rather than sustaining innovation. The way to implement an innovation, (computer based-learning), that will transform an organization, (education generally and mathematics specifically), is to implement it disruptively, not by using it to compete against, and serve existing customers (Christensen, 2008). According to the U.S Department of Education, Institute of Education Sciences, (2009), the computer software that educators may be trained in to implement in math education, called I CAN learn, was used by more than a million students across the nation in high school and college pre-algebra classes. Kirby (2006) indicated that 27 studies reviewed by What Works Clearinghouse, meet the evidence standards and concluded that I CAN learn Pre-Algebra and Algebra implementation had a positive and statistically significant effect on student learning and improvement. The study showed an improvement index of +5 percentile points in a range of -7 to +16 percentile points (IES, 2009). In a doctoral dissertation at the University of Southern Mississippi (Loving, 2007), on which this project will try to partially duplicate. The results indicated that the use of I 15
CAN learn as well as My Math Lab both enhanced student comprehension and course completion as compared to students who only received partial or no computer aided instruction In 2007, The US department of education appointed a group of education professionals, researchers and law makers to examine and suggest on ways to improve mathematics learning across the nation. The research group National Mathematics Advisory Panel (2008), presented various suggestions regarding mathematics achievement based on computer aided instruction. First, research on instructional software has generally shown positive effects on students achievement in mathematics as compared with instruction that does not incorporate such technologies. This study showed that technology-based drill and practice and tutorials can improve student performance in specific areas of mathematics. Secondly, teaching computer programming to students can support the development of particular mathematical concepts, applications, and problem solving (National Mathematics Advisory Panel, U. S Department of Education (2008). Theoretical framework summary Given the theoretical background established in this review, this current study will seek to show that Computer aided instruction (CAI) and specifically My Math Lab have many applications in the modern mathematics classroom. Private and public colleges and universities are working with educational software industries to meet learning needs and a variety of instructions using computers to serve a diverse population of students. The reason for this widespread adoption is manifold: First, colleges and universities are exploring more ways to maximize profit and reduce the cost of content 16
delivery. Profitability is also a primary motive for software companies that conduct research that may favor its acceptance and popularity in academic institutions. One may consider independent, nonprofit studies to determine the effectiveness of such software in mathematics and conditions associated to its success. In the following sections of this paper, I will move away from the theoretical discussion on CIAs and present literature on recent studies into the effectiveness of computer aided programs in the classroom and examine contemporary knowledge on how technology affects learning. Available literature on Software Technology in the Mathematics Classroom Instruction Scholarly literature highlights the positive effects of computer aided instruction and the widespread acceptance of this software by academic institutions and by educators and learners to help promote success. These alternative routes to education offer methods adaptable to all forms of learning and mathematics educators have been swift to embrace methods that will allow them to become more effective and increase their student proficiency (Manochehri & Young, 2006). Manochehri& Young (2006) view the use of new technological tools in the classroom, as an emerging opportunity to meet the needs of a diverse culture of learners. Their study show a specific interest in the power of computer supplemented instruction and its effectiveness as regard to content retention and proficiency in a subject matter. Other study conducted separately by Moosavi (2009) and Loving (2007) focused more on the results of a relatively short-term study aimed at a specific group of students. Unlike Manochehri & Young (2006) who highlighted the long-term educational opportunities and possibilities to math educators and to learners using computer supplemented instructions, other Moosavi (2009), has shown that 17
students enrolled in traditional instruction performed better than those enrolled in classes using software technology (My Math Lab and Think Well) programs regardless of whether performance was measured by final exam scores, semester test scores, or final course average. Influencing factors contributing determining the effectiveness of CAIs. Manochehri and Young (2006) stressed the role of cultural setting and attitude toward technology implementation as it may affect the outcome of its purpose of software application in mathematics classrooms. The study was conducted in Qatar where the use of distance learning and CAI in mathematics classrooms has never been higher in a nation where The Gross Domestic Product per capital was over $90,000 (Willoughby, 2010). Inconsistencies and differences in the results and findings of the studies conducted by Manochehri and Young (2006), and Moosavi (2009) may be affected by the conditions of each and the variables of their settings. So what factors, if any, other than resources and financing may influence the educational outcomes of the use of these programs? Educator related factors. Educators may consider the importance of educators workshops, training, and financial incentives as they may motivate classroom instructors to put old routines to rest and use computers to enhance learning especially in developmental mathematics classes. According to Willoughby, effective technology integration is achieved when the use of technology is routine and transparent and when technology supports course objectives. This may explain why Spardlin, (2009) found that education reform and chances for improvement in mathematics classes through the use of software technologies, rely on the 18
human roles (educator and learner), in terms of training and of their wiliness to incorporate properly and effectively CAI to mathematics specifically and education in general. Student related factors. One study examined factors influencing African American students attitude, comprehension and course completion in computerized mathematic courses. This first study was performed by Marquise Loving in 2007 at the University of Southern Mississippi. Results from this study are discussed later in the literature review, and the focus here is on how this study differs from the next one, how it is similar, and more importantly what gaps and recommendation will be gained from both studies to enhance the settings and conditions of this study. Another study was a dissertation by Seyed Moosavi at the University of Alabama in 2009. The purpose of this study is to compare two methods of instructions at a private college and determine statistically the effects (if any) played by computerized instruction in content comprehension and course completion in a twelve week period. The difference between both studies is that Loving (2007) limited the study to African American students, and the effects of age, gender and comfort ability with using computers on the mathematics achievements. Moosavi (2009) investigated the enrollment process and procedure through which students are placed into mathematics classes. Both authors used a comparative approach to the instructional methods (CAI and traditional instruction), and they both used My Math Lab and Think well programs as software programs to conduct these comparative studies. The overall purpose of both studies was to compare the mathematics achievement of college students who were taught in classrooms using 19
My Math Lab and their peers who were taught by traditional instruction. One may rely on the findings of both of these studies to make instructional decisions or conduct further research student learning. The validity of the findings of both studies could be questioned not in terms of data collection or in terms of accuracy but in terms of the time span through which these studies were done. Loving (2007) study aimed at measuring students attitude toward mathematics as they may differ across the two forms of instruction, one may consider such limitations that the length of the course (a 6-12 week course) may impose on the nature of the results obtained. If Moosavi (2007) finding s show that students enrolled in CAI classes have a significantly higher drop-out rate than those enrolled in classrooms with no computer program, one may consider the effect of factors and variables such as placement exams into the course, familiarity with software technology, student access to resources such as tutoring and individual assistance with the software? Both studies failed to mention any preliminary or additional training received by the students on how to use the software in mathematics classes. Moosavi (2009) acknowledged the importance of instructor training to incorporate these programs in math classes; he also elaborated on how to design curricula that carefully match students needs and abilities while using innovative technology. Two case studies on traditional and computer aided instruction. Two separate studies were conducted recently and searched, under different settings for differences in students achievements and comprehension in mathematics classrooms through the variation of instruction methods. 20
Study 1: Marquise Loving at the University of Southern Mississippi in 2007. One study examined factors influencing African American students attitude, comprehension and course completion in computerized mathematic courses. This study was done by Marquise Loving in August 2007 at the University of Southern Mississippi. Results from this study is discussed later in the literature review, and the focus here is on how this study differ from the next one, how it is similar, and more importantly what gaps and recommendation will be gained from both studies to enhance the settings and conditions of this study. Study 2: Seyed Moosavi at the University of Alabama in 2009. The second study was a dissertation by Seyed Moosavi at the University of Alabama in 2009. The purpose of this study is to compare two methods of instructions in a private college and determine statistically the effects (if any) played by computerized instruction in content comprehension and course completion in a twelve week period. The difference between both studies is that Loving limited the study to African American students, and the effects of age, gender and comfort ability with using computers on the mathematics achievements. Moosavi on the other hand investigated the enrollment process and procedure through which students are placed into math classes. Both authors used a comparative approach to the instructional methods (CAI and traditional instruction), and they both used My Math Lab and Think well programs as software programs to conduct these comparative studies. The overall purpose of both studies was to compare the mathematics achievement of college students who were taught in classrooms using My Math Lab and their peers who were taught by traditional instruction. One may rely on the findings of both of these studies to either make 21
instructional decisions or conduct further research how students learn. One may also argue and questions the validity of the findings of both studies not in terms of data collection or in terms of accuracy but in terms of time span through which these studies were done. A critical case study comparison as related to the present study The purpose of this study is to determine if there is any statistically significant difference in terms of attitude course completion and comprehension between students enrolled in classrooms receiving CAI and their peers in classrooms with no CAI. This study will be similar to the studies discussed earlier in terms of software technology used (My Math Lab ) and the same inventory measurement of students attitude toward mathematics. What may be different than the previously cited studies is that in this research students will be receiving three day training on how to use the program in the CAI class. The training will involve demonstrations and individual presentations to make sure that every student is capable of navigating the software. As students develop depth and frequent interaction with software technology, they may develop a positive attitude toward the use of these programs and consequently toward mathematics. This may eliminate frustration and computer phobia especially for student with little or no knowledge of computers and millennial tools. In one of the doctoral dissertations that this paper is using as a benchmark, Loving (2007), focused at identifying factors influencing students attitude comprehension, and course completion in developmental mathematics receiving different methods of instructions. The attitude toward mathematics and course completion for this study was measured using a 60-item Likert-scale instrument and each student s final grade respectively. The hypotheses presented in Loving (2007) research 22
were similar to those proposed in this study and the variables included self-confidence, value placed on mathematics in career, enjoyment in mathematics pursuit and enjoyment do mathematics related problems. Supporting variables were confidence to use computers, gender, age, and socio-economic status. The purpose of the second doctoral dissertation (Moosavi, 2009) was to examine the use of My Math Lab and remedial college algebra. Specifically, this study compared student achievement in classes where predominantly computer-based instruction was used as opposed to the achievement of students through traditional lecture classes. This study also compared achievement in terms of course grades and how they differ between two levels of computer aided instructions. The independent variable in this study was the method of instruction and within this variable, there were traditional and two levels of CAI. To measure the effectiveness of such instructional method, Moosavi used the final course grade as an indicator. Two of the null hypotheses in this study were similar to those of the first one and to those stated at the beginning of this paper. One null hypotheses was used to compares the effectiveness of instructional methods across time. Based on the overall final examination scores, the results in the first study showed that students enrolled in the courses using My Math Lab demonstrated a mean of 84.94 (SD = 8.44, n=31), those enrolled in the traditional course demonstrated a mean of 80.25 (SD = 10.23, n = 70). Data analysis showed no significant difference in terms of attitude toward mathematics between both methods of instructions. Data showed statistically significant difference between CAI and traditional instruction in terms of comprehension, but not difference in terms of course completion. 23
The second study (Moosavi, 2009) revealed that students using traditional instruction methods performed better than those enrolled in classes using software programs despite calculations and measurements were based on final exam scores, test scores, or final course averages. Data analysis shows however, that there were statistically significant differences between the two levels of computer aided instructions. Students using Think well did slightly better than those using My Math Lab Final course grades showing the traditional method with a Mean final course grade 77.62 (SD=12.01, n= 185), Think well 66.04 (SD= 27.63, n=214) and My Math Lab 61.34 (SD=24.55, n= 237). These two studies aim at measuring and comparing comprehension, and academic achievement in mathematics between traditional instructional methods and those using My Math Lab in mathematics classrooms. Although Loving study focused on one racial group and the socio-economic factor, no correlation was found on attitude toward mathematics to support these variables. Students in CAI did better than those in traditional classrooms. The Loving study revealed that students in traditional classroom (No supplemental instruction with computers) performed better than those in classes using My Math Lab. It was interesting to notice that results in the Moosavi study varied in regards to the type of software program used. These results were supportive of learning theories on computer programs and that the latter one may enhance retention if embedded properly. The second study showed that CAI has a positive effect on classroom sizes and individualized attention, but the educator presence to assess learning is crucial. Finally, both studies acknowledged the positive outcomes of implementing these computer 24
programs in mathematics classrooms and its power to enhance and structure a more customized learning process. Both authors also recognized, despite the differences in results, that more research is needed to reach successful academic strategies in coupling CAI with traditional instruction. This current study will aim at comparing classrooms with and without CAI methods. CAI is a Move from Traditional to a More Current Application The traditional instruction and the traditional learning styles Traditional learning is defined in the American Heritage Dictionary (2000) as a process where learners and knowledge providers are physically present in the same place. This educational setting and traditional form of interaction, involves an educator lecturing or monitoring the learning process in a unidirectional way. Students often have little or no input in classroom discussions and topics presented. A face to face interaction between students and educators may or may not involve the use of computer supplemented forms of instructions. Students receiving face to face instruction without being formally and actively involved in the learning process, may experience disinterest in the material especially in mathematics and the sciences. This is partially due to the great deal of routines and practices that students witness every day, year after year. The basis for the above statement is the criticism toward mathematics education in the late fifties and again in the past decade by Edgier (2007). The early criticisms led to the wave of modern school mathematics shortly after the 1958, when the National Education Act was passed. Similar criticism re-emerged in recent years as U.S fourth and eighth grade average scores in mathematics lacked those of many Asian and European nations in the 2007 Trends in 25
Mathematics Study and Sciences (TIMSS), (U.S. Department of Education, 2007). Presently, there is criticism of mathematics being boring with its emphasis being placed upon memorizing for annual mandated tests (Edgier, 2007). High school education may represent the foundation of higher education. If students are not doing well in middle and high school mathematics classes; they are likely to struggle in college. The struggle, frustration that students encounter in remedial developmental mathematics classes may be limited or eliminated if they grasp and comprehend better in high school. With an increasing number of students taking college credit courses, research conducted at the high school level or the first year in college may be similar and effective to both levels of settings. Direct instruction usually includes the presentation of material, thinking aloud by the teacher, guided practice, correction and feedback and modeling by the teacher (Kinney & Robertson, 2003). The instructor plays the role of the main supplier of knowledge and the expert in the classroom. The teacher decides what, when, and how students should learn (Brown, 2003; Kinney & Robertson, 2003). The one-way interaction between the learner and the educator has shown to be a non-productive instructional method. Students may appreciate some form of lecturing and guidance in the classroom when they are introduced to new content and as they meet lower levels of Blooms Taxonomy that constitute knowledge and comprehension. The difficulty is that college instructors and professors tend to lecture in math classes most of the time. What Brown is indicating here is that the instructor is in control of both the teaching and learning process. Students should be actively involved and in control of their own learning as they communicate and interact with one another and with their instructors. In 26
this digital age, student may take advantage of the internet and a variety of software technology programs to share ideas, solve problems, and monitor their own progress electronically. Well designed My Math Lab may help students gain understanding of mathematical concepts using their monitors and without listening to long boring lectures. CAI as contemporary instruction and learning method The fact that software technology programs such as My Math Lab and Think Well offer step by step explanations of mathematics concepts, student are now given the virtual one-on-one attention and the opportunity to listen to explanatory videos as needed. Literacy in mathematics is a byproduct of students having access to powerful mathematical ideas and instruction that can help them develop as mathematical thinkers and learners (Hill, 2008). Students need to contribute in constructing knowledge because not only practice make perfect but also it may enhance memorization and mastery of mathematical concepts. If students are passive, they may lose the sense of intrinsic motivation especially when research shows that the attention span in an average hourly class is about fifteen minutes (Weimer, 2009). Some instructional strategies and techniques such as cooperative learning, group studies, and projects may be credited for enhancing the quality of the traditional classroom experience but more needs to be done in terms of individualized self paced instruction. Despite the current issues and criticism with traditional instruction, we cannot undermine the importance, effectiveness, and the role of research conducted in the past decade that aimed at enhancing the quality of traditional education and specifically improving the mathematics classroom experience. Some research compared and analyzed the benefits of utilizing high school methodology teachings in developmental 27
mathematics classes (Phyllis, Wilson, 2008). Traditional instruction may be more effective at the high school level because teachers tend to have a personal interaction with their students and they tend to lecture less than college professors. Other research concentrated on adopting new, and improved state standards (Skinner, 1938). Although traditional lecture alone adopted in many classroom settings has not been effective with developmental college students, there is evidence in literature that many instructional techniques other than lecture tend to enhance students comprehension, attitude, and course completion of courses and specifically mathematics. Cooperative learning, meaningful classroom discussions, real world examples and appropriate use of humor have positive results (Skinner, 1938). The question remains, as the basis for this current study, if there any theoretical basis for the use of software technology such as My Math Lab as it is the program used in classrooms of this study? Chapter Summary This literature review examined various studies into the effectiveness of computer aided instruction and highlighted CAI positive effects as well as the widespread acceptance of this software by academic institutions, educators, and learners to help promote its successful integration. However, research conducted on the effectiveness of CAI as compared to instruction without the use of computers, in terms of student comprehension and persistence to course completion, remains limited to independent and unrelated studies. Still, some common themes emerged. Siemens (2008) and Loving (2007) both found that contemporary students learn and retrieve information consistent to the digital era. These so-called millennial learners, or net generation learners, fit the characteristics of individualized, customized, and active form of learning that CAI 28
promotes. My Math Lab, I CAN learn and Think Well are commonly used CAIs and incorporate a social constructivist approach to mathematics education. As access to information becomes more available, through software technology, the role of instructors must shift from primary sources of information and content knowledge to facilitators and coaches. Two case studies were utilized in the critical analysis into the actual integration and affects of computer aided instruction in contemporary mathematics classroom. The study by Marquise Loving (2007) provided a data analysis that showed no significant difference in terms of attitude toward mathematics between traditional and CAI methods of instructions. Yet, data did show a statistically significant difference between CAI and traditional instruction in terms of comprehension but not difference in terms of course completion. They study by Seyed Moosavi (2009) revealed that students using traditional instruction methods performed better than those enrolled in classes using software programs despite calculations and measurements were based on final exam scores, test scores, or final course averages. However, data analysis showed that there were statistically significant differences between the two levels of computer-aided instructions. Students using Think well did slightly better than those using My Math Lab 29
Chapter Three Methodology Introduction Chapter three provides the research design and methods used in this study. The methods and procedure are arranged in the following manner: (a) conceptual framework; (b) research design and rational; (c) research site and participant selection; (d) data collection methods and procedures; (e) data analysis; and (f) the role of the researcher. The methods and procedures aimed at answering these three hypotheses: H1: There is a significant difference between student attitudes toward developmental mathematics courses with computer- aided instruction using My Math Lab as compared to that of students enrolled in a traditional course setting H2: There is a significant difference between the levels of comprehension between student enrolled in developmental math classes using My Math Lab and that of students in traditional course setting H3: There is significant difference between persistence to course completion of student enrolled in math classes with CAI and traditional course settings. Conceptual Framework In order to reach student success in developmental mathematics classes, many educators are assessing the effectiveness and significance (if any) of the use of computer programs as a tool to enhance the classroom experience (Spardlin, 2009). Recent studies show that proper implementation of technology in college mathematics classrooms may enhance learning (What Works Clearinghouse, 2008) 30
I am a mathematics and science instructor in Northwest Ohio. I am interested in comparing my student s opinions on the advantages, and disadvantages of face-to-face instruction without the use of technology as compared to education which is facilitated by computer-assisted instruction. To do this, I taught two sections of basic mathematics without technology and two sections with technology and used a survey instrument to measure attitudes before and after instruction in all classes. The outcome of this study may be beneficial to mathematics instructors and their students in terms of planning, assessment, and mastery of new skills. In order to compare students attitude toward mathematics between students receiving computer supplemented instruction and students in similar math classes not using computers, the two groups of students were separated by classes. The survey used to determine any differences was requested from the designer Tapia, (1996) and approved for use in this study. The instrument is composed of forty questions on students likes and dislikes, confidence, and preferences in math classes. Participants responses were recorded at the beginning of the course and again at the end to see if their attitude toward mathematics has changed over the course of the session. Phase I: Traditional face-to-face instruction (non technology enhanced) For this test group, instruction looked quite traditional and familiar to students because lessons include face-to-face lecture; demonstrations, group work and problem solving. Homework was assigned from the textbook and grades were based on homework assignments (35%), Final comprehensive exam (15%), in-class and group work (35%) and attendance (15%). There were two classes of approximately 25 students each for a total of 50 students enrolled in this part of the study. 31
Phase II: Face-to-face using My Math Lab (Technology enhanced) In these two basic mathematics classes I used My Math Lab software developed by Pearson Education (2010), through which students accessed the text book and homework assignments online. The students reported to class regularly just like in phase I but they were guided and assisted in their home work by the step-by-step (help me solve, show me an example, etc) provided by the software. Grading was exactly like in phase I except that homework was then graded by the software and students were given the opportunity to view their grades and progress on each problem and exercise. The mathematics content is basically the same in both phases. Research Design and Rationale This was a pre-post survey study in which students in both forms of instructions (Phases I & II discussed above), were given to students. The attitude toward mathematics survey was given to approximately 98 students enrolled in four basic mathematics classes. I taught several of these 6-week Basic Math classes every year. There were an equal number of students surveyed in both types of instructions (phase I and phase II). A student volunteer was assigned and she distributed the survey to students where she matched names to numbers written on the survey. All numbers matching names were stored in a safe place and locked until the end of the six-week academic quarter. Research Site and Participant Selection The selection of participants was based on the students enrolled in a six week basic mathematics class during the spring of 2011. When student went through the admission process, they had no prior knowledge of what type of technology could be embedded into their classes, although admission staff may have told the students about 32
the process vaguely. The courses were offered on a six week basis as a requirement for general education diploma and for students pursuing an associate degree in medical administration or medical coding and billing. The courses were offered at a small private college in Northwest Ohio serving a diverse population of students of varying age and low socio-economic status. In conducting this study, several variables may have had reasonable effects on this study and the outcome of the Data collected and that student may have developed prior sense of phobia and intimidation to the use of computers and that may have played a role in their performance and level of comprehension. Other supporting variables were age in terms of interaction with other student, gender, and socio-economic status and its effect on attendance and the level of intrinsic motivation. The student comprehension of the content was based on an ongoing instructor evaluation and assessment throughout the session. Based on recommendations by Moosavi (2009), participants received three days of training on how to use the program in the CAI class. The training involved demonstrations and individual presentations to make sure that every student was capable of navigating the software. As students developed depth and had frequent interactions with software technology, they also developed a sense of ease toward the use of these programs and consequently toward mathematics. This early exposure may have eliminated frustration and computer phobia especially for students with little or no knowledge on computers and millennial tools. Data Collection Methods Permission to collect data was requested from the administration at the local College. The Attitude toward Mathematics Index (Tapia, 1996), a reliable and effective metric (reliability measures ranging from 0.88 to 0.95), was used at the beginning of the 33
quarter and again at the end. The survey was the primary instrument to evaluate students attitudes toward mathematics. The respondents were given the following instructions: Directions: This inventory consists of statements about your attitude toward mathematics. There are no correct or incorrect responses. Read each item carefully. Please think about how you feel about each item. Darken the circle that most closely corresponds to how the statements best describes your feelings. Use the following response scale to respond to each item: strongly agree, and strongly disagree. Students comprehension was measured by the grades submitted at the end of the session and it represented the average of in class work and online homework. Completion of the course was measured by the final grade calculations and by the number of students attending until the last day of the course. All documents and student personal and educational information remained anonymous and instruments relating to this study were destroyed after the analysis procedure was concluded. Final scores obtained from final exam and chapter quizzes in the form of ongoing assessments given by the instructor (in this case the researcher) was used to determine content comprehension. A student earning 60% and above is considered passing the course and therefore comprehended the content. Data Analysis The survey data was analyzed using techniques of linear regressions and analysis of variance. The total number of responses and the nature of the responses (positive or negative) toward mathematics were summarized in Tables 1 and 2). The hypotheses in this study were tested and evaluated using a Microsoft software technology and Minitab. Results from this study were compared to the findings of Moosavi (2009) and also to 34
Loving (2007). Student comprehension and overall class average was compared between both phases of instruction to determine if the second hypothesis is supported or rejected. The data collected from the survey was also compared based on whether MML was used in the classroom. Role of the Researcher The researcher was the main instructor of these classes and because I was the sole assessor of grades and do not want any perception of bias; I never saw names associated with the data. The student volunteer redistributed the surveys and matched names with corresponding numbers. When the quarter ended, and the surveys were completed, the names associated to each survey were removed. I only saw numbers on the completed survey instruments and that ensured confidentiality and anonymity. There was absolutely no repercussions, penalty or advantage for students who do or do not participate in the study. Chapter Summary This chapter aimed at answering the main question of the thesis and test the hypotheses presented. The chapter also provided an overview of the research design in terms of participant selection, site of the study and theoretical reasons to conduct this study. This chapter also provided techniques involved in data collection and the protection of subject confidentiality in this process. Finally the chapter provided the role and believes of the researcher and the appropriate ways to conduct the survey specifically, the study in general without any biases and minimum risk to the participants. The survey used in this study was originally designed by Tapia, (1996) and used to 35
determine students attitude toward mathematics similar to the study done by Loving, (2007). 36
Chapter 4 Results Introduction This chapter presents the survey results and tests the hypotheses. Data are presented in tables and discussed in order to accept or reject H: 1, H: 2 and H: 3. First, the attitudinal responses are discussed as gathered at the beginning of the course and then at the end and then as gathered from no My Math Lab (MML) courses and MML courses. Second, course comprehension is discussed from the findings of course grades as gathered from no MML courses and MML courses. Finally, completion findings are discussed as responses are gathered from no MML courses and MML courses. Hypothesis Testing Testing H1: Attitudes toward mathematics inventory and survey A 40-question survey (see Appendix A) designed by Tapia (1996) was used to evaluate students attitudes toward mathematics and how the responses varies depending on the method of instruction. The survey was given at the beginning and at the end of the six week session to students enrolled in two basic mathematics classrooms using My Math Lab. The same survey was also given to students enrolled in two basic math classrooms not using the software. Both ends of responses (strongly agree, and strongly disagree), from both phases of instructions, will be compared to the overall weighted average. The data collected is analyzed using simple regression to determine the effect of methods of instruction on students attitude toward mathematics. 37
The tables below summarized all variables associated with the attitude toward mathematics survey given to students enrolled in classrooms using My Math Lab and students enrolled in classrooms not using My Math Lab. The first table (Table 1: Pre-Course Survey) summarized the students responses at the beginning of the class regardless of the method of instruction. Analysis of Total number of responses to questions in the survey as well as average responses per student indicated that at the beginning of the course students responded positively to the questions. At the beginning of the course, the number of negative response to the survey was slightly higher in classrooms not using MML (297) than those of students using MML. This was a slight contradiction to studies Moosavi, (2009) and Loving, (2007) suggesting that students attitude toward mathematics may be negatively affected by the use of computers and the phobia associated with software literacy. Table 1: Pre-Course Survey Summary Classrooms using MML Classrooms not using MML Pre-Course responses Summary of Negative responses (Strongly disagree) Summary of Positive responses (Strongly agree) Summary of Negative responses (Strongly disagree) Summary of Positive responses (Strongly agree) Total # of responses 248 448 297 472 Average/student 5.167 9.3 6.19 9.83 Std dev, σ 4.78 7.46 3.81 6.92 The second table (Table 2: Post Course Survey) summarized the students responses at the end of the course. The results in the following table indicated that the number of 38
positive responses were significantly higher in classes using MML than those of students not using MML. The students positive response at the end of the course may reflected a level of enjoyment to this method of learning and translated a sense of satisfaction overall to their learning experience. The survey results at the end of the course may however have been biased and only highlighted the attitude of those who finished the course and generally they are more committed. Table 2: Post Course Survey Summary Classrooms using MML Classrooms not using MML Post-course Summary of Summary of Summary of Summary of responses Negative responses (Strongly disagree) positive responses (Strongly agree) Negative responses (Strongly disagree) positive responses (Strongly agree) Total # of responses 167 388 348 271 Average/student 3.54 8.08 8.09 6.03 Std dev, σ 5.53 8.10 6.23 3.75 The following Tables 3 through 6 summarize student responses to the survey on attitude toward mathematics in two classrooms where My Math Lab was not used. Results are presented as pre and post weighted averages. Chart 1: Pre no MML disagree describes strongly disagree responses at the beginning of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a statistical regression line describing strongly disagree 39
responses as compared to the sample weighted average. Answers are clustered around the weighted average for the class showing a general negative attitude towards mathematics in classes without MML before courses begin. Chart 1: Pre No MML Disagree No MML course pre-weig average Vs. Strongly disagree responses pre E/5 = - 34.25 + 5.039 Pre Weig ave 25 20 S 4.15539 R-Sq 65.5% R-Sq(adj) 64.7% 15 pre E/5 10 5 0 6 7 8 9 Pre Weig ave 10 11 Chart 2: Pre no MML agree describes strongly agree responses at the beginning of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly disagree responses as compared to the sample weighted average. Answers are scattered around the weighted average for classes without MML before courses begin are not closely aligned with the weighted average of a negative slope. 40
Chart 2: Pre No MML Agree 18 16 14 12 No MML course pre-weig average Vs. Strongly agree responses A1 No MML = 20.96-1.688 Pre Weig ave S 3.39522 R-Sq 24.2% R-Sq(adj) 22.5% A1 No MML 10 8 6 4 2 0 6 7 8 9 Pre Weig ave 10 11 Chart 3: Post no MML disagree describes strongly disagree responses at the end of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly disagree responses as compared to the sample weighted average. Answers are scattered strongly at the high end of negative attitudes towards mathematics in classes without MML after courses as compared with the weighted average with a positive slope. 41
Chart 3: Post No MML Disagree No MML course post-weig average Vs. Strongly disagree responses post E/5 = - 0.987 + 0.9331 Post Weig Ave 20 15 S 3.25739 R-Sq 37.8% R-Sq(adj) 36.5% post E/5 10 5 0 0 2 4 6 Post Weig Ave 8 10 Chart 4: Post no MML agree describes strongly agrees responses at the end of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly agree responses as compared to the sample weighted average. Answers are scattered strongly at the high end of positive attitudes towards mathematics in classes without MML after courses yet not around the weighted average, characterized by almost no slope. 42
Chart 4: Post No MML Agree No MML course post-weig average Vs. Strongly agree responses post A/1 = 6.547 + 0.0988 Post Weig Ave 25 20 S 6.53364 R-Sq 0.2% R-Sq(adj) 0.0% post A/1 15 10 5 0 0 2 4 6 Post Weig Ave 8 10 The following Charts 5 through 8 summarize student responses to the survey on attitude toward mathematics in two classrooms where My Math Lab was used. Results are presented as pre and post weighted averages. Chart 5: Pre MML disagree describes strongly disagree responses at the beginning of the courses compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly disagree responses as compared to the sample weighted average. The weighted average has a negative slope and answers are moderately clustered. 43
Chart 5: Pre MML Disagree Pre-MML course wei ave Vs. strongly disagree pre A/1 = 15.51-1.209 Wei Av 20 S 4.56410 R-Sq 12.9% R-Sq(adj) 11.0% 15 pre A/1 10 5 0 3 4 5 6 7 8 Wei Av 9 10 11 12 Chart 6: Pre MML agree describes strongly agree responses at the beginning of the course as compared to the sample weighted average in classrooms with My Math Lab. This is a simple statistical regression line describing strongly disagree responses as compared to the sample weighted average. The answers are mostly clustered showing a strong positive attitude around all aspects of a mathematics course integrating MML before the course begins. 44
Chart 6: Pre MML Agree 30 20 pre-mml course weig av vs. strongly agree pre E/5 = - 21.62 + 3.618 Wei Av S 5.51020 R-Sq 47.7% R-Sq(adj) 46.6% pre E/5 10 0-10 3 4 5 6 7 8 Wei Av 9 10 11 12 Chart 7: Post MML describes strongly disagree responses at the end of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly disagree responses as compared to the sample weighted average. Answers are clustered across all aspects showing a general agreement in attitude towards mathematics using MML while the weighted average has almost no slope. 45
Chart 7: Post MML Disagree 25 20 post-mml course weig av vs. strongly disagree post A/1 = 3.307 + 0.0304 Wei Av S 5.65198 R-Sq 0.0% R-Sq(adj) 0.0% post A/1 15 10 5 0 0 2 4 6 Wei Av 8 10 12 Chart 8: Post MML agrees chart describes strongly agree responses at the end of the course as compared to the sample weighted average in classrooms without My Math Lab. This is a simple statistical regression line describing strongly agree responses as compared to the sample weighted average. The weighted average has a positive slope and the positive attitudes are strongly clustered. 46
Chart 8: Post MML Agree 35 30 Post-MML course weig ave Vs. strongly agree post E/5 = - 3.149 + 1.453 Wei Av S 6.79454 R-Sq 32.7% R-Sq(adj) 31.2% 25 post E/5 20 15 10 5 0-5 0 2 4 6 Wei Av 8 10 12 Interpretation Hypothesis One predicted a significant difference between student attitudes toward developmental mathematics courses with computer aided instruction using My Math Lab as compared to that of students enrolled in a traditional course setting. These results presented above in Charts 1-8, show a significant difference between student attitudes toward developmental mathematics courses with computer aided instruction using My Math Lab as compared to that of students enrolled in a traditional course setting. Regression lines for classes not using MML at the beginning of the session showed strong correlation between weighted average responses and strongly agree responses and strongly disagree responses. This is indicated by the high R 2 Value: 47
65.5%. (Where R is the correlation coefficient and R 2 is the strength of a linear association), is the square of R): 65.5%. This correlation reflected students attitude (slightly resentful and negative responses toward the course not using MML. The rest of the regression lines whether prior to or after taking the course and regardless of the form of instruction were heavily affected by outliers and residuals. There is no statistically clear indication in this study that attitude toward mathematics was affected by the method of delivery or by the time students were surveyed. These results are in agreement with Loving (2007) findings in terms of students attitude toward mathematics and H1 in this case is rejected. Testing H2: Student comprehension in MML courses compared to no MML. Table 3: Two sample assuming unequal variances summarizes the results of a t-test for two samples assuming unequal variances where variable one describes the outcome of final grades of 49 students observed in classrooms using My Math Lab. Variable two summarizes is a statistical analysis of final grades in classrooms not using the software. (StDev = 17.0, No MML, N = 48, Mean = 66.9, StDev =21.7). The median final grade for students using MML was also higher than those enrolled in classes not using MML (Median for MML= 80.5, Median No MML = 74.0). 48
Table 3 Two Sample Assuming Unequal Variances 49
Table 4: Final course grades presents the range of grade percentiles next to absolute number of students within each grade percentile in the no MML course and then with the MML course. Each class comprised of 48 students and the grade percentiles are defined identically for both courses. Most students ranked in the higher-grade percentile in both courses. This data represents the level of comprehension at the end of each course. Table 4: Final Course Grades Hypothesis 2 predicted a significant difference between the levels of comprehension between student enrolled in developmental mathematics classes using My Math Lab and that of students in traditional course setting. The results of this survey show a significant difference between the levels of comprehension between student enrolled in developmental math classes using My Math Lab and that of students in traditional course setting statistical analysis of final grades in classrooms not using the 50
software. These results are different than those of Loving and Moosavi where students in classroom with no computer aided instruction did better than those using software and that means H2 is accepted under the conditions of this study. Testing H3: Course completion in MML courses compared to no MML. Chart 9: Summary for no MML and Chart 10: Summary for MML both present findings on course completion for courses using the Anderson Darling Normality Test for classrooms using My Math Lab. Five students did not complete the course in classes not using MML. (5/48 or 10.42%). Six students did not complete the course in classes using MML (6/48 or 12.5%). Although more students discontinued coming to class In the MML group, the reason(s) why they stopped attending may or may not be related to the method by which they were learning. Factors and reasons why students discontinued attending class may be a limitation of this study and could represent an opportunity for further research. 51
Chart 9: Summary for No MML Chart 10: Summary for MML 52
Hypothesis 3 predicted a significant difference between persistence to course completion of student enrolled in math classes with CAI and traditional course settings. In terms of course completion, this hypothesis was tested by simply relying on the number of student who dropped from either class. Based on these results there is no statistically significant difference in terms of course completion between students enrolled in MML classes and students receiving instruction with no MML and therefore H3 is rejected. 53
Chapter Five Discussion The general purpose of this study was to compare attitudes toward mathematics, comprehension, and course completion in developmental math classes. The comparison was based on whether student enrolled in these course were using My Math Lab or not. The study was conducted at a two year post secondary institution during the spring semester of 2011. Data on 100 students who enrolled in basic mathematics classes was collected and analyzed. Attitude toward mathematics was measured by a forty question survey given to all students at the beginning and again at the end of the course. Correlation between method of instruction and content comprehension was measured by comparing the overall final grades of students enrolled in MML classes and those in classes not using MML. Measurement of course completion between the two class settings was based on the number of students attending until the last day of the session. Assumptions and Limitations of the Study Developing this study was based on the assumption that all students entering a two year program have had similar experiences in mathematics classes. Further more students enrolled in classes using computers were assumed to have similar and prior exposure to software technologies. Comparing students comprehension between students enrolled in classes using MML and students enrolled in classes not using MML was based on the assumption that students will attend classes on a regular basis. Three day training, on how to use My Math Lab as described in chapter three for students enrolled in MML sections were assumed to cover the needs of navigating the website by all students. 54
This study has few limitations associated with it. First, the duration of the session, (six weeks), was short and may not reflect students long term progress and adaptation to any of the two methods of instructions studied here. Second, the participants in this study were predominantly students seeking a two-year certificate or degree in a small two-year college. This latter limitation may limit the findings of this research to similar colleges and exclude large year institutions. Third, the fact that all students enrolled in these basic mathematics classes and involved in this study were assigned to one instructor who happens to be the researcher is limitation in the sense of randomized selection of studentinstruction. The fourth possible limitation to this study was to differentiate between students who did not complete assignments due to comprehension or lack of computer skills or outside factors. Conclusions Students enrolled in classrooms using MML performed better than those enrolled in classrooms not using MML based on overall final grades. Linear regressions representing weighted averages of students responses versus strongly agree or strongly disagree was adapted in this study but not in the studies in Loving (2007) or Moosavi (2009). The measurement of attitude toward mathematics prior to and after the course being taught showed no definite pattern as presented in eight regression tables (See Appendix). The coefficients of correlation R and its square R 2 were very low which indicated no correlation here between these methods of instruction and attitude toward mathematics. R 2 ranging from 0.0% to 47% was low enough to reject H1. Comparison in terms of course completion between both phases of instruction showed statistically better outcomes for students enrolled in courses not using MML. 87.5% of students enrolled in 55
classes using MML completed the course, while 89.6% of students enrolled in classes not using MML completed the course. There was a statistically significantly difference in terms of course completion between both phases of instruction but there is no clear indication that students course completion was directly linked to whether or not software programs were used in mathematics classes and therefore H3 was rejected. Comparison of Study Results with Existing Similar Studies The findings of this study were different than those of Moosavi, (2009) but similar to the findings of Loving, (2007) in terms of performance and comprehension. This study was in agreement with loving findings and that students enrolled in math classes using software technologies performed better than students enrolled in classes not using the program. There were also similar results between this study and that of Loving in terms of attitude toward mathematics. Both studies found no relation between attitude toward mathematics and whether or not software technology programs were used in instruction or not. This study was slightly different than that of loving and that student enrolled in classes using MML had a higher drop rate than students enrolled in classrooms not using MML. The difference was remarkable but not enough to accept H3. This study was a partial replication of Loving (2007) and Moosavi (2009) work so the latter one focused on finding the difference between two brands of software and that was not the focus of this study. Recommendations to expose and train students on how to use the software at the beginning of course session may have contributed to a better outcome in this study and that was not the case in Moosavi findings. Unlike Moosavi (2009) findings, students participating in this study and enrolled in MML classes did better in terms of overall final grades than students enrolled in classes not using MML. 56
Recommendations The use of software technology programs in the classroom and specifically in mathematics are designed to amplify the students visual and mental capacities. These tools are meant to increase the students involvement in learning and expand their cognitive abilities. Computer supplemented instruction like any educational tool, requires proper planning and implementation into the course curriculum. Further research is needed on the use of MML and other programs to properly identify more advantages and limitation of this technology and to better meet the need of the learner. Educators and learners seeking to use software programs in their classrooms may consider the following: Computer supplemented instruction must be carefully crafted to the college curriculum Students and instructors must have minimal experience with computers and the programs Assignments must be designed in a way that it serves individualized, self paced learning need of every student Instructors should not rely on software programs as the main form of student assessments Implications This study compared attitudes toward mathematics, performances based on overall final grades, and course completion of students enrolled in basic mathematics classrooms using MML and students not using MML. The study found no statistically significant differences in attitude toward the subject. One reasonable explanation is that students enter the course and leave the course with varied experiences and therefore their 57
responses to the survey was random and unpredicted which lead to no correlation or definite pattern. Students enrolled in classes using MML did much better than those in classes not using MML in overall final grades. Students using the program may have benefited from the tools associated with it such as help me solve, view an example and what section is this in the textbook. The tools may have helped students using the program explore their own understanding and were actively engaged as suggested by the theory of social constructivism. This study found no association between method of instruction and course completion even though one more student dropped out of MML classes than in no MML classes. Unless students are tracked and asked why they stopped attending classes, we cannot assume that the drop or absence is directly linked to the method of instruction. On a personal level, I will continue to use the program MML in mathematics classes and encourage students to explore the benefits of using technology in learning. My Math Lab may not be a primary factor on whether students will continue to attend class, but I am convinced it is a good motivational tool in the classroom. I am also convinced that the use of my Math Lab in mathematics courses supports the mastery of other computer, typing, reading, and communication skills. Finally, educators have a responsibility to provide multiple paths to learning and I am convinced that MML is an effective and clear path for the learning needs of many students. 58
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Appendix A: Survey A Strongly disagree B Disagree C Neutral D Agree E Strongly Agree 1. Mathematics is a very worthwhile and necessary subject. 2. I want to develop my mathematical skills. 3. I get a great deal of satisfaction out of solving a mathematics problem. 4. Mathematics helps develop the mind and teaches a person to think. 5. Mathematics is important in everyday life. 6. Mathematics is one of the most important subjects for people to study. 7. High school math courses would be very helpful no matter what I decide to study. 8. I can think of many ways that I use math outside of school. 63
Appendix A Continued 9. Mathematics is one of my A Strongly disagree B Disagree C Neutral D Agree E Strongly Agree most dreaded subjects. 10. My mind goes blank and I am unable to think clearly when working with mathematics. 11. Studying mathematics makes me feel nervous. 12. Mathematics makes me feel uncomfortable. 13. I am always under a terrible strain in a math class. 14. When I hear the word mathematics, I have a feeling of dislike. 15. It makes me nervous to even think about having to do a mathematics problem. 16. Mathematics does not scare me at all. 17. I have a lot of selfconfidence when it comes to mathematics 64
Appendix A Continued 18. I am able to solve A Strongly disagree B Disagree C Neutral D Agree E Strongly Agree mathematics problems without too much difficulty. 19. I expect to do fairly well in any math class I take. 20. I am always confused in my mathematics class. 21. I feel a sense of insecurity when attempting mathematics. 22. I learn mathematics easily. 23. I am confident that I could learn advanced mathematics. 24. I have usually enjoyed studying mathematics in school. 25. Mathematics is dull and boring. 26. I like to solve new problems in mathematics. 27. I would prefer to do an assignment in math than to write an essay. 65
Appendix A Continued 28. I would like to avoid A Strongly disagree B Disagree C Neutral D Agree E Strongly Agree using mathematics in college. 29. I really like mathematics. 30. I am happier in a math class than in any other class. 31. Mathematics is a very interesting subject. 32. I am willing to take more than the required amount of mathematics. 33. I plan to take as much mathematics as I can during my education. 34. The challenge of math appeals to me. 35. I think studying advanced mathematics is useful. 36. I believe studying math helps me with problem solving in other areas. 37. I am comfortable expressing my own ideas on how to look for solutions to a difficult problem in math 66
Appendix A Continued 38. I am comfortable A Strongly disagree B Disagree C Neutral D Agree E Strongly Agree answering questions in math class. 39. A strong math background could help me in my professional life. 40. I believe I am good at solving math problems. 1996 Martha Tapia 67
Appendix B Pre Weig ave A1 No MML pre E/5 Post Weig Ave post A/1 post E/5 10.80 2.00 20 8.60 7.00 10.00 6.73 10.00 3 0.00 0.00 0.00 10.53 1.00 19 8.27 5.00 6.00 8.80 5.00 6 8.13 2.00 3.00 7.13 11.00 5 9.33 1.00 4.00 8.67 6.00 3 8.47 10.00 13.00 8.93 5.00 11 8.20 4.00 8.00 7.87 9.00 2 0.00 0.00 0.00 7.73 10.00 8 9.27 2.00 7.00 9.67 5.00 10 9.40 0.00 8.00 10.47 7.00 19 9.33 3.00 6.00 9.80 6.00 20 7.73 5.00 4.00 8.27 1.00 5 7.87 7.00 4.00 7.73 11.00 8 9.47 2.00 10.00 10.20 4.00 16 0.00 0.00 0.00 9.33 7.00 18 9.40 3.00 12.00 10.47 0.00 13 9.93 0.00 8.00 8.87 1.00 10 9.13 8.00 15.00 9.53 0.00 5 8.67 3.00 2.00 8.87 10.00 13 7.53 12.00 9.00 6.47 16.00 0 6.13 14.00 3.00 8.00 3.00 5 7.13 5.00 5.00 8.73 4.00 5 0.00 0.00 0.00 9.00 6.00 10 6.47 15.00 6.00 9.93 3.00 17 8.60 13.00 18.00 9.27 1.00 10 8.27 9.00 7.00 9.53 8.00 20 4.40 23.00 0.00 8.53 9.00 10 6.20 17.00 2.00 9.07 10.00 19 0.00 0.00 0.00 7.00 12.00 0 7.33 16.00 3.00 8.20 5.00 2 6.40 10.00 0.00 8.07 10.00 10 8.13 11.00 9.00 7.33 8.00 3 8.87 0.00 3.00 9.07 8.00 18 7.47 11.00 8.00 9.73 10.00 20 7.53 14.00 7.00 8.40 15.00 19 7.27 20.00 10.00 7.67 7.00 4 6.53 18.00 3.00 9.27 0.00 7 9.93 0.00 6.00 7.13 5.00 1 7.00 11.00 3.00 7.53 2.00 1 7.07 10.00 3.00 9.00 7.00 5 6.07 20.00 3.00 10.80 2.00 23 7.00 10.00 9.00 9.60 8.00 16 8.40 0.00 4.00 7.53 5.00 2 8.73 4.00 8.00 9.00 7.00 8 7.87 7.00 8.00 7.80 3.00 4 8.20 0.00 4.00 7.73 7.00 0 7.67 4.00 5.00 10.13 5.00 19 7.80 12.00 5.00 68
Appendix C 69