The Advances Of Telecommunication Technologies Education Essay

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During the 1990s the advances of telecommunication technologies and user-friendly Internet browsers began to influence the educational realm. In the early 1990s the appearance of self-contained distance education courses led to an explosion of web-based instructional formats. During the last decade the enrollment in postsecondary online distance learning courses has grown to a participation rate in the 1999- 2000 school year of nine percent of total enrollment in two-year public institutions (National Center for Educational Statistics, 2002). During the Fall 2002 semester, over 1.6 million postsecondary students enrolled in distance education courses with almost 600,000 of these taking only distance learning courses (Allen & Seaman, 2003).

Many factors have been identified as being key in the success of students in

online courses. Success in online learning environments (OLE's) is a combination of the interaction of human, technologic, course, pedagogic, and leadership factors (Menchaca

The purpose of this study is to examine 4 of the predictors of students' success in online classes. These predictors are self-efficacy for online technologies, self-regulation, cognitive style, and motivational beliefs. The researcher will use descriptive statistics and multiple linear regression models to determine if these predictors significantly influence students' final grade in the online technology course. In addition, this study will examine the relationships among students' final grade in the online course and students' age and gender. Descriptive statistics and simple t tests will be utilized to examine these relationships.

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Justification of Study

There have been numerous studies that focus on the predictors of student success in online courses. These studies have been very beneficial to students, instructors, and online course developers. It is important for institutions that are offering online courses to recognize the importance of advising students of the requirements needed for a non-traditional form of education. Gunawardena and Duphorne (2001) maintain that "paying close attention to the attitudes and skills they bring with them, and orienting them to the skills they need to function effectively in an online environment, will help ensure success in class and a more satisfying learning experience" (p. 16). Many of these studies used small sample sizes and the time-frames were short. This study utilizes a very large sample (2078 students) and a longer time-frame (5 years). These parameters can help to indentify significant differences among the variables of interest and can help to clarify the relationships among the groups of interest.

Need for Additional Research

As alluded to in the previous section, many studies in this area lack the magnitude and the time-frames that may be needed to develop reliable and valid results. This study has the potential to undercover findings that may have not been reported as of yet. Technology advances can have an influence on students' success in the online educational environment. The sample size and time-frames of this current study recognizes these advances. This study can fill in the gaps that have been unexplored in this area of distance education. Therefore, current additional research in this field is necessary.

Research Questions

1. Is self efficacy for online technologies a significant predictor of success in online courses?

2. Is self-regulation a significant predictor of success in online courses?

3. Is cognitive style a significant predictor of success in online courses?

4. Are motivational beliefs significant predictors of success in online courses?

5. Is there a significant difference in the final grade in an online course due to age of student?

6. Is there a significant difference in the final grade in an online course due to gender of student?

Theoretical Frameworks of Online Education

The researcher will explore several theoretical frameworks that describe the

relationships between various factors and the success of students in online courses. These

theoretical frameworks include: (a) Bandura's theory of self-efficacy (b) self-regulation, (c)

cognitive style, (d) motivational beliefs.

Bandura's Theory of Self-efficacy. The researcher will employ Bandura's theory of self-efficacy to explain self-efficacy for online technologies as a predictor of success of students in online courses. This theory of self-efficacy will be selected as a theoretical framework for this study because it is based on the concept that an individual's belief or perceived confidence for coordinating and carrying out a specific action influences whether a specific action is taken (Bandura, 1986). Self-efficacy postulates that two types of expectancies influence behavior: (a) efficacy belief or perceived self-efficacy, and (b) outcome expectation Perceived self-efficacy is the self-decision about the ability level in doing something. Outcome expectation is the self-decision about the positive or negative outcomes resulting from the behavior (Bandura, 1986). Traditional and non-traditional students should have the self-confidence that they can be successful in an online learning environment. This confidence is manifested in the concept that the student has the technical skills, the motivational beliefs, and the personal discipline to complete the online course.

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Self- regulation theory. The researcher will employ the social cognitive perspective of self-regulation to explain self- regulation as a predictor of success of students in online courses. The social cognitive perspective of self-regulation provides a framework for online education research that can offer insights into the functioning of autonomous learners (Lynch & Dembo, 2004). Working within this perspective, Zimmerman (1989) defined academic self-regulation as the extent to which learners are meta-cognitively, motivationally, and behaviorally active in achieving their learning goals. Self-regulated learners are active, adaptive constructors of meaning who control important aspects of their cognition, behavior, and environment in attaining their learning goals (Pintrich, 2000). Schunk and Zimmerman (1998) observed that significant differences exist between skillful and naïve self-regulators: Perhaps the most important performance control process that distinguishes skillful from naïve self-regulators is self-monitoring. This process involves keeping track of key indicators of personal effectiveness as one performs.

Skillful self-monitors recognize their performance level. This allows them to modify their efforts to retain higher performance levels, without relying on social assistance or external factors. However, a misconceived notion of performance or an inflated ego can have negative consequences in skillful self-monitors. The tendency for people with high self-esteem to make inflated assessments and predictions about themselves carries the risk of making commitments that exceed capabilities, thus leading to failure (Baumeister, Heatherton, &Tice, 1993). Those naive self-regulators do not monitor their performance levels. Instead, they depend on incomplete information to assess continuing efforts. Many of these naive self-regulators misjudge their levels of success. This misjudgment may manifest itself in misplaced optimism and poor study habits. Many factors contribute to poor self-regulation. According to Behncke (2002), "self-regulation failure may occur if any of the above mentioned factors, such as goal setting, self-monitoring, activation and use of goals, discrepancy detection and implementation, self-evaluation, self-consequation, self-efficacy, meta-skills, and boundary conditions have been inappropriately implemented or insufficiently utilized." pp 243-248.

Cognitive styles. The researcher will examine several theories of learning to explain cognitive styles as a predictor of students' success in online courses. Cognitive learning theories are best understood when one understands learning styles. Sternberg (1997) describes some basic concepts of learning styles in his book, Thinking Styles:

Styles are preferences, not abilities

Styles are not "good" or "bad," but rather matters of fit between learner and teacher or learner and material

Styles can vary across tasks and situations

People differ in strengths of stylistic preferences

People differ in stylistic flexibility

Styles are socialized

Styles can vary across the life span-they are not fixed

Styles are measurable

Styles are modifiable

What is valued in one time and place may not be valued in another

Messick (1984) defines cognitive style as an individual's consistent approach to organizing and processing information during thinking. Riding & Sadler-Smith (2002) surmise that style does not appear to be related to intelligence and reflects qualitative rather than quantitative differences between individuals in their thinking processes. The theories of cognitive learning that the researcher will examine includes: Gardner's Multiple Intelligence Theory, Mayer's Cognitive Theory of Multimedia Learning, The Constructivist Theory of Learning.

Gardner's Multiple Intelligence Theory. The principal concept behind Howard Gardner's Multiple Intelligence Theory is that people learn, not through one or two modalities, but through eight distinct modalities. Gardner defined the first six of the multiple intelligences in his book, Frames of Mind, in 1983.  He added the last two multiple intelligences his book, Intelligence Reframed, in 1999.

The eight modalities of learning that Gardner proposed include

(1) visual/spatial (2) verbal/linguistic (3) musical/rhythmic

(4) logical/mathematical (5) bodily/kinesthetic (6) interpersonal

(7) intrapersonal (8) naturalistic. Although individuals possess all

eight intelligences, each has their own particular mix of intelligences,

with some dominating over others, but they are not fixed and can

change over time (Barrington, 2004). p 422

A condensed definition of the eight distinct modalities of the Multiple Intelligence Theory is explained in an article by Gardner, (1997),

Verbal/linguistic intelligence: the production of language, abstract reasoning, symbolic thinking, conceptual patterning, reading, and writing

Logical/mathematical intelligence: the capacity to recognize patterns, work with abstract symbols (e.g., numbers, geometric shapes), and discern relationships or see

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connections between separate and distinct pieces of information

Visual/spatial intelligence: visual arts, navigation, mapmaking, architecture, and games requiring the ability to visualize objects from different perspectives and angles

Bodily/kinesthetic intelligence: the ability to use the body to express emotion, to play a game, and to create a new product

Musical/rhythmic intelligence: capacities such as the recognition and use of rhythmic and tonal patterns and sensitivity to sounds from the environment, the human voice, and musical instruments

Interpersonal intelligence: the ability to work cooperatively with others in a small group, as well as the other people

Intrapersonal intelligence: the internal aspects of the self, such as knowledge of feelings, range of emotional responses, thinking processes, self-reflection, and a sense of intuition about spiritual realities

Naturalistic intelligence: the ability to recognize patterns in nature and classify objects, the mastery of taxonomy, sensitivity to other features of the natural world, and an understanding of different species

Existential intelligence: the human response to being alive in all ways (Gardner is still not satisfied that he has enough physiological brain evidence to conclusively establish this as an intelligence)

Mayer's Cognitive Theory of Multimedia Learning. Mayer's Cognitive Theory of Multimedia Learning was developed from research in cognitive science that emphasizes three assumptions that describe how the human mind works. These assumptions include the dual channel assumption, the limited capacity assumption, and the active processing assumption.

The human information-processing system consists of two separate channels--an auditory/verbal channel for processing auditory input and verbal representations and a visual/ pictorial channel for processing visual input and pictorial representations (Mayer & Moreno, 2003). This concept is the core of Paivio's Dual-Coding Theory and Baddeley's Theory of Working Memory.

Each channel in the human information-processing system has limited capacity-- only a limited amount of cognitive processing can take place in the verbal channel at any one time, and only a limited amount of cognitive processing can take place in the visual channel at any one time (Mayer & Moreno, 2003). These concepts are the core of Chandler and Sweller's Cognitive Load Theory and Baddeley's Theory of Working Memory.

Mayer (2005) describes the five cognitive processes in multimedia learning:

selecting relevant words from the presented text or narration

selecting relevant images from the presented illustrations

organizing the selected words into a coherent verbal representation

organizing selected images into a coherent pictorial representation

integrating the pictorial and verbal representations and prior knowledge

Constructivist Theory of Learning. Jean Piaget, a Swiss psychologist and philosopher, is generally credited with the development of the Constructivist Theory of Learning. According to constructivism, particularly radical constructivism, the child functions in relation to its environment, constructing, modifying and interpreting the information s/he encounters in her/his relationship with the world (Glaserfeld, (von) E. (1995). Other researchers have developed various interpretations of the main constructs of the Constructivist Theory of Learning. Al-Weher (2004) suggests that

knowledge, from a constructivist' point-of-view, is not separated from the knower; rather, it is a representation of the real world in our minds as a result of experiencing it. Cobern (1995) says that the process of learning is a process of interpretation, and when we learn we build our own understanding of the material studied depending on our interpretation of it. Cho, Yager, Park & Seo (1997) add:

Students have to participate actively in their learning, either through

discussion, problem solving and exploration of knowledge, or

through the design or implementation of projects. This enables them

to construct their knowledge by themselves, establishes them as

independent learners who know that others have their own views

that may differ from theirs, so they may be expected to respect

those views and to be brave enough to accept being wrong during

the learning process. p. 400

Motivational beliefs. The researcher will examine students' motivational beliefs as predictors of success in online courses. It is generally agreed upon that there are two main motivational theories of learning. These are intrinsic and extrinsic. Intrinsic motivation refers to motivation that comes from inside an individual rather than from any external or outside rewards. The learner's motivation comes from a deep need to satisfy the innate psychological needs for competence and autonomy. According to Cox and Williams (2008), "Students whose motivation is relatively self-determined are more likely to engage in activities because they are fun and personally important and less apt to engage in activities to gain approval." (p. 223). Extrinsic motivation is unrelated to the task being completed. Learners who lean more toward extrinsic motivation are seeking something external and tangible. These learners may crave good grades or praise from the instructor. In addition, a student's motivation to learn or not to learn may be determined by the contents of the material, itself.

This is a relatively new theory concerning learners' motivational operatives. According to Kuhn (2007)

In contrast to an earlier time in which motivation was regarded

strictly as an attribute of the learner, motivation theorists now

focus directly on what the subject matter is that students may

(or may not) have the motivation to learn and more specifically

what the relation may be between a particular student's

disposition and the particular subject matter we would like that

student to master. (p.109)

Looking at learners in this context, the motivation rests within the interaction between the learner and the subject matter, not with the learner's intrinsic or extrinsic state. Therefore, if the learner has a concrete interactive experience with the learning materials, motivation is developed from this relationship.

CHAPTER II

Review of Related Literature

The review of literature concerning the predictors of success in online courses will be conducted in the context of the theoretical frameworks of online education, discussed in the previous section. Researchers have identified several predictors of success in the online learning environment. This study focuses on four of these success predictors; self-efficacy for online technologies, self-regulation, cognitive style, and motivational beliefs.

Self-efficacy for Online Technologies

Self-efficacy is based on the concept that an individual's belief or perceived confidence for coordinating and carrying out a specific action influences whether a specific action is taken (Bandura, 1986). Self efficacy beliefs are rooted in personal variables such as motivation, perceived values, and perceived outcomes. Self-efficacy beliefs, in turn, act as a motivational influence and affect individual action, performance, and behavior (Puzziferro, 2008). Joo, Bong, and Choi (2000) examined academic self-efficacy, self-efficacy for self-regulated learning, and Internet self-efficacy on learners' performance in Web-based instruction. The study did find that computer self-efficacy (a skills-level measure) is a key variable that may determine student success in distance education. Another study by Wang and Newlin (2002) found that self-efficacy for understanding course content and self-efficacy for meeting the technological demands of an online course can predict performance. Specifically, they found that self-efficacy was related to the reasons students reported enrolling in Web-based courses.

Other studies related to self-efficacy in online technologies have shown that this factor is a poor predictor of success in online education. Deture (2004) utilized a series of multiple regression and correlational analyses that focused on self-efficacy for online technologies as a determinant of final course grade. In this particular study, Deture found that self-efficacy for online technologies was found not to be a statistically significant indicator of final course grades. In addition, a study by Hodges, Stackpole-Hodges and Cox (2008) found that self-efficacy for online technologies were not a significant predictor of student success in online courses that were delivered via podcasting. They stated that the history of the influence that self-efficacy has on academic achievement makes the lack of significant findings in this study surprising. (p. 150).

Research of the literature on the study of self-efficacy in technology as a predictor of student achievement in online courses yields opposing views. Many findings concur that this factor is an excellent predictor of student success, while other studies have found that this factor has very little influence on students' success in online courses. This study will add to this literature and fill in the research gaps concerning the magnitude of sample size and the time-frames of previous studies.

Self-regulation in Online Courses

Lynch and Dembo (2004) identified three factors that are included in the concept of self-regulation in the academic environment. These factors include time management, study environment management, and learning assistance management.

The importance of time management is stressed in a book by Palloff and Pratt (1999), in which they pointed out that interacting in a Web-based course can require two to three times the amount of time investment than in a face-to-face course. Those students who are good time managers are aware of the need to evaluate how their study time is spent and to reprioritize as necessary (Zimmerman and Risemberg, 1997).

Self-regulated learners are proactive in managing not only their study time, but also their study environment. They are sensitive to their environment and resourceful in altering or changing it as necessary (Zimmerman and Martinez-Pons, 1986). In other words, online learners must be able to develop their personal learning environment to suit their needs and goals. Those students who work on their online course from their homes can decide where the computer is located, such as in a bedroom or a more common room. Those students, who for whatever reason, cannot work from home on the online course can access the course from a computer at a public library or from the university's computer lab.

Self-regulated online learners realize the importance that others play in their success. They realize that seeking assistance from the instructor and other students is essential. Online learners must be able to determine where and how to seek help, and make decisions concerning the most appropriate sources for such help (Lynch and Dembo, 2004).

Over the last several decades, there have been numerous studies that were focused on self-regulation as a predictor of success in online learning environments. Chang (1994) found a self-monitoring form for recording study time and environment was used in addition to predicting test scores, a test-taking strategy. Chang's findings indicated that students who employed the self-monitoring strategy obtained higher scores on their course material comprehension

tests and the measure of motivational beliefs than those who did not employ the self-monitoring strategy. Williams and Hellman (2004) conducted a study that compared the self-regulation strategies of first generation college online students to second generation college online students. The researchers stated that they were not surprised that their findings were supported by previous research on first-generation college students, namely that they do not have the same skill level as second-generation students and therefore are less successful. One other study by Yukselturk and Bulut (2009) found that there were not statistically significant mean differences among motivational beliefs, self-regulated learning variables and achievement in programming with respect to gender. In a similar study, Hargittai and Shafer's (2006), found that female self-assessed their skills significantly lower than men evaluated their skills. The review of relevant literature in self-regulation was overwhelming focused on the concept that students' self-regulation strategies are a good predictor of success in the online environment. However, the findings of several studies did not agree with these studies.

Mason, (2002) found that most traditional forms of assessment are not appropriate for testing learning outcomes that online courses are promoting. In other words, Mason promoted the idea that the most useful applications of assessment in online environments are formative and self-assessment strategies rather than summative and graded assessment. In addition, Benson (2003) suggests that many other forms of assessment of self-regulation strategies are available rather than the common survey instrument. These include virtual discussions, concept mapping, e-portfolio assessment, writing, field experiences, individual and group projects, informal student feedback and peer assessment.

Many findings concur that the self-regulation factor is an excellent predictor of student success, while other studies have found that this factor has very little influence on students' success in online courses. Realizing that each student has their own priorities and circumstances, this study will add to the existing literature and will illicit further research in this field.

Cognitive Styles in Online Classes

The theories of cognitive learning displayed in the online learning environment that the researcher will examine includes: Gardner's Multiple Intelligence Theory, Mayer's Cognitive Theory of Multimedia Learning, The Constructivist Theory of Learning. This list is not exhaustive; however, it includes the key theories of learning that may influence the success of students' in online classes.

Cunningham-Atkins, Powell, Moore, Hobbs and Sharpe (2004) investigated the impact of students' cognitive style on their effective uses of text-based, computer-mediated conferences. In this study, the researchers found that students who responded to images sent more messages in computer-conferencing and students who learn verbally were less likely to complete the courses. This study explored the relationship of student participation in the context of Gardner's Multiple Intelligence Theory and to Mayer's Cognitive Theory of Multimedia Learning. The importance of student participation in online classes has a direct link to the student's success in the class. In an article by Mestre (2006), the concepts of global learners and millennial learners were discussed. She proposed that "global learners are likely to benefit from exploratory links that provide practical examples of course material to help them make connections. They are also likely to benefit from creative activities that allow them to identify how the pieces of information fit together". (p. 30) In addition, Mestre (2006) proposed that "the millennial learners are used to multitasking, tend to be visual learners, and benefit from lots of tactile experiences. They prefer a lot of interactivity, the use of mobile tools, and social networking". (p. 30) These learning strategies are best developed through the Constructivist Theory of Learning, in which learners take control of their learning experience by actively building on their understanding of the material through personal interpretation of the world around them.

In a study by Mupinga, Nora, and Yaw (2006), the researchers did not identify a particular learning style to be predominant with respect to success in online classes. However, they did suggest that the design of online learning activities should strive to accommodate multiple learning styles in an effort to ensure that the majority of students can be successful in the class.

The majority of the literature on cognitive styles as predictors of success in online courses has come to the conclusion that cognitive learning styles are positively correlated with student success. However, there is a gap in identifying the specific learning styles that have this positive correlation. This study hopes to fill in this gap with findings that lead to the identification of learning styles that are positively and negatively correlated with student success in online courses.

Motivational Beliefs in Online Classes

Miltiadou and Savenye (2003) conducted several studies of the motivational aspects of engaging in online education. In all their findings, one concept was common; there are two types of motivation, internal and external. Intrinsic motivation (internal motivation) stems

from factors such as interest and curiosity, describing students' natural tendency to seek

out and conquer challenges (Deci and Ryan, 1985). Extrinsic motivation (external motivation) would be identified as being the opposite of intrinsic; response to an outside stimulus with a desirable outcome.

Closely linked with intrinsic and extrinsic motivation is the Expectancy Value Theory. A fundamental premise of expectancy value theory is that people engage in specific activities due to the perceived value of likely consequences (Atkinson, 1982). When given the choice between multiple options, expectancy value theory posits that people will select the behavior that they believe will result in the greatest combination of success and value (Wighting, Liu, and Rovai, 2008).

These concepts of motivation have been the focus of many studies that examine motivation as a predictor of student success in online courses. The importance of student motivation in online courses was addressed by ChanLin (2009), to support students' self-directed learning in a Web-based learning context, the motivational issues related to students' devotion to the lessons and courses should be analyzed. Motivation has been previously noted as a key component to student success in the traditional classroom (Robbins, Lauver, Le, Davis, Langley, & Carlstrom, 2004), and thus would likewise be expected to be influential on success in an online course.

Motivational attributes in students can be intrinsic or the extrinsic. Students may have differing reasons for completing a certain task. The psychological and metacognitive aspects motives may vary from student to student. This study examines these motivational factors and focuses on their relationships to success in distance education.

In conclusion, it is evident that many factors may determine a student's success in an online course. The review of related literature identifies several of these factors. Self efficacy is identified in several studies in the online learning environment. Self-efficacy is the self-decision about the ability level in doing something. Outcome expectation is the self-decision about the positive or negative outcomes resulting from a behavior. Self-regulation is identified in several studies in the online learning environment. Academic self-regulation is identified as the extent to which learners are meta-cognitively, motivationally, and behaviorally active in achieving their learning goals. Various theories of learning styles have been identified in numerous studies in the online learning environment. Research concludes that a student's learning style influences comprehension, retention, and transference of skills and knowledge. Motivational beliefs influence students' interests and may have a direct effect on academic achievement. In addition, age and gender may have an influence on academic achievement.

As mentioned in the introduction section of the study, the skills needed to be successful an online course are different than the skills needed in the traditional classroom. Previous research into this area has been limited to small samples with small time-frames. The sample size of this study, along with the extended time-frame, will initiate a more comprehensive and focused study of the 4 predictors of students' success in online courses: self efficacy, self-regulation, cognitive style, and motivational beliefs. In addition, the relationships between final grade in an online course and the age and gender of students will be examined.

The following chapter describes the methodology and procedures that will be used to conduct this study. This chapter includes the following sections: participants, research design, variables of the study, instrumentation, data collection, and data analysis, limitations, and threats to validity.

CHAPTER III

Methodology

The purpose of this study is to examine 4 of the predictors of students' success in online classes. These predictors are self-efficacy for online technologies, self-regulation, cognitive style, and motivational beliefs. The researcher will use descriptive statistics and multiple linear regression models to determine if these predictors significantly influence students' final grade in the online technology course. In addition, this study will examine the relationships among students' final grade in the online course and students' age and gender. Descriptive statistics and simple t tests will be utilized to examine these relationships.

Participants

The participants of the study will be students enrolled in a split-level online technology course from the fall semester 2005 through the fall semester 2009 in 3 southeastern universities. The researcher has determined that the sample size will be 2078 students. The sample will consist of 1184 males (57%) and 894 females (43%). There will be 5 age group levels under consideration in this study. These include: 18-19 years of age, 19-20 years of age, 21-25 years of age, 26-35 years of age, and greater than 35 years of age. The percent of students in the 18-19 years of age group is 17% (353 students). The percent of students in the 19-20 years of age group is 34% (706 students). The percent of students in the 21-25 years of age group is 27% (561 students). The percent of students in the 26-35 years of age group is 17% (353 students). The percent of students in the greater than 35 years of age group is 5% (105 students). The percent of Caucasian students will be 46% (956 students). The percent of African-American students will be 36% (748 students). The percent of Asian students will be 12% (249 students). The percent of Hispanic students will be 3% (62 students).The percent of those who identified themselves as "other' will be 3% (63 students). The institutional data of the students will be obtained from the Registrar's Office at the 3 universities. The demographic information on the students will include age, gender, number of previous online classes, and cumulative grade point average. The researcher will obtain access of students' grades through written permission from the Registrars of the 3 universities.

Research Design

This study will use a combination of quantitative statistical analyses and survey designs. Survey instruments and statistical procedures will be utilized to examine the predictors of student success in online classes. In addition, survey instruments and statistical analysis will be used to examine the 2 other research questions: Is a significant difference in the final grade of an online course due to the age of student? Is there a significant difference in the final grade of an online course due to the gender of student? Therefore, the research designs of this study will be correlational and causal-comparative.

Variables of the Study

The independent variables for the first 4 research questions in this study are (a) self-efficacy for online technologies (b) self-regulation (c) cognitive style, and (d) motivational beliefs. The dependent variable is students' final grade in the course.

The independent variable for the fifth research question (Is a significant difference in the final grade of an online course due to the age of student?) is final grade in course and the dependent variable is age of student. This dependent variable will have 5 levels: 18-19 years of age, 19-20 years of age, 21-25 years of age, 26-35 years of age, and greater than 35 years of age.

The independent variable for the sixth research question (Is a significant difference in the final grade of an online course due to the gender of student?) is final grade in course and the dependent variable is gender of student. This variable will have 2 levels: male and female.

Instruments

The researcher will use several proven instruments to obtain the data used in this study. The first survey used in this study is the Online Technologies Self-Efficacy Scale (OTSES), developed by Miltiadou and Yu (2000). This survey will be used to access the participants' self-efficacy for online technologies, by measuring students' self-efficacy with communication tools commonly used in distance education. This 30-question survey is divided into four subscales that assess their confidence in utilizing the Internet, synchronous tools, and two levels of asynchronous tools. Construct validity and internal consistency of the OTSES was established by Miltiadou (2001). In developing the OTSES the instrument was administered to approximately 330 college students who were enrolled in several online courses at five educational institutions. This instrument has both content validity provided by a group of content experts and survey designers who reviewed and provided input during the development of the instrument and construct validity. Factor analysis on all items revealed that they load on a single construct. The reliability estimate of this instrument was found to be .95 (Cronbach's coefficient alpha) (Miltiadou, 2001).

To access the participants' self-regulation patterns, the researcher will utilize the Academic Delay of Gratification Scale developed by Bembenutty and Karabenick (1998). The ADOGS assesses students' delay preferences for an immediately available attractive option versus a delayed alternative. An example is "Delay studying for an exam in this class the next day even though it may mean getting a lower grade, in order to attend a concert, play, or sporting event" versus "Stay home to study to increase your chances of getting a high grade on the exam." Students responded on a four-point scale. Bembenutty reported a Cronbach's coefficient alpha of .78 for this survey instrument.

The Group Embedded Figures Test (GEFT) is used in this study to analyze the individual learning styles of the participants. The GEFT developed by Witkin, et al. (1971) was designed to measure individuals' levels of field independency by tracing simple forms in the larger complex figures. The test instrument consists of three sections with 25 items: the first section contains seven items for practice, and the second and the third sections contain nine items each for scoring.

The GEFT is a standardized instrument with validity and reliability established by the instrument's developers (Witkin, et al., 1971). The validity of the GEFT was established by determining its relationship with the "parent" test, the Embedded Figures Test (EFT), as well as the Rod and Frame Test (RFT), and the Body Adjustment Test (BAT) (Witkin et al, 1971). The GEFT is a timed test, therefore internal consistency was measured by treating each section as split-halves (Spearman-Brown reliability coefficient of .82) (Witkin et al, 1971).

The Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich, et al. (1991) was used to assess students' self-regulated learning skills and motivational orientation. The MSLQ is a self-report instrument that has been used extensively in previous research that investigated college student motivation and learning strategies. The questionnaire consists of 81 items, 31 that assess motivational beliefs, 31 items focused upon learning strategies and motivation, and 19 items concerning resource management. All of the items direct the respondent to the course in which they receive the survey, rather than their motivation and study strategies across several courses. Pintrich reported a Cronbach's coefficient alpha of .82 for this survey instrument.

Data Collection

Prior to distributing the 4 survey instruments, the research proposal will be submitted to the Institutional Review Board (IRB) at the 3 universities for approval. According to Ary, Jacobs, and Razavieh (1996), when using a questionnaire, the goal is to have a 100% return rate, "although a more reasonable expectation may be 75-90% returns" (p. 436). The 4 surveys will be distributed to participants as an online survey and availability of the survey, along with a request for participation in the study, will be solicited. At one week intervals, a reminder email will be sent to members of the population requesting that the survey be completed. Over the course of the survey period, three such reminder emails will be sent. The participants will need approximately 60 minutes to complete the survey instruments. Respondents will be asked to complete and return the surveys via email.

Data Analysis

This study will use SPSS 17 to analyze quantitative data collected from the survey instruments. An alpha level of .05 will be set for all significance tests in the study. Stepwise multiple regression analyses will be implemented to access the predictive nature of the independent variables to the dependent variable in the first four research questions. For the first research question, a stepwise multiple regression analysis will be conducted to determine if the self-efficacy for online technologies variable is predictive of the final grade in an online course. The second research question will utilize a stepwise multiple regression analysis to determine if the self-regulation variable, is predictive of the final grade in an online course. The third research question will utilize a stepwise multiple regression analysis to determine if the variable cognitive style is predictive of the final grade in an online course. The fourth research question will utilize a stepwise multiple regression analysis to determine if the predictor variable, motivational beliefs, is predictive of the final grade in an online course. The dependent variable, students' success scores, was derived from scores on three assignments given in the course and a traditional paper-based final examination at the end of the course.

In addition, simple t-tests will be used to determine whether there are significant differences in students' final grade based on age and gender. (Wikiversity.org. 2010) describes a

t test as a statistical analysis that compares the means of groups to determine if there is a statistically significant difference among the groups.

Limitations

The results of this study can only be generalized to the students who have taken the split-level online technology course at one of the 3 universities during the fall semester 2005 through the fall semester 2009. In addition, any inference to correlational findings or causal-comparative findings can only be generalized to the participants of the study and to the 3 universities.

Threats to Validity

Threats to internal validity for this study include: subject characteristics, validity of survey instruments, non-representativeness, data collection bias, history, non-response, and administration mode.

Because the participants of this study will be intact classes, no measure of randomization will be possible. To equalize any differences among the groups due to age or gender an analysis of covariance will be utilized. The threats to the validity and the reliability of the survey instruments were addressed in the instrument section of this proposal. Cronbach's-alpha coefficients are given. The threat of non-representativeness is validated by the sample size, 2078 students. The threat to data collection bias is addressed through the use of only one data collector, the researcher. Since the data will be collected a numerous times, an internal threat of history may be present. In other words the data may be affected because it will be collected at different points in time. To address this threat, the researcher will use analysis of covariance to identify any differences in data collection over time. To address the threat of non-response, the researcher will send out reminder e-mails to the population sample. Because the administration mode of the survey instruments (via e-mail) may affect the results of the study, the researcher can only generalize the results of the study to the students who took the split-level online technology course at one of the 3 universities.

CHAPTER IV

Implications

The factors examined in this study are common to all students, whether they be in an online environment or in a face-to-face educational environment. This study can be utilized by online course developers to examine the factors that contribute to students' online success. By knowing these factors, online course developers will have tools to assist them in constructing effective and efficient online course materials. In addition, this study can be used by curriculum developers to assist them in constructing applicable and relevant curriculums that contain appropriate online courses.