The concept of motivation is one of the most important components of learning in any educational environment (Maehr, 1984). Questions of why students engage, pursue, and accomplish certain goals or tasks, or why they avoid others, have been the subjects of scholarly inquiry since the writings of Socrates, Plato, and Aristotle. There are many constructs of motivation that have emerged from different theoretical approaches during the last quarter of the twentieth century. Social-cognitive learning theory defines motivation in terms of the students' (a) self-efficacy beliefs about their abilities to engage, persist, and accomplish specific tasks (Bandura, 1986; Stipek, 1988), (b) goal-setting activities (Dweck & Leggett, 1988) and (c) learning strategies and cognitive and metacognitive processes (Schunk, 1995; Pajares & Kranzler, 1995).

Overview of the problem

The purpose of this study is to investigate whether three motivational constructs can be identified as predictors of success for students enrolled in an online classroom. The three motivational constructs in question are (a) students' self-efficacy beliefs in the context of online education, (b) their achievement goal orientation, and (c) their self-regulation.

Online education is the result of the exponential growth of digital technology and computer networks over the last fifteen years. This type of distance education utilizes the Internet and computer-mediated communications (CMC) systems as means of delivering instructional materials (Riel & Harasim, 1994). The present trend and market demand for education is toward distance education and specifically online courses. This trend is highlighted in PetersonÕs 1999 Distance Learning Guide, which provides details for two thousand degree and certificate programs available from nearly 900 institutions. This number can be compared to 762 institutions in 1997 and 93 institutions in 1993. Many educational institutions offer a wide variety of online courses and provide the opportunity for students to enroll in certain online courses as part of a degree. Other institutions offer complete Bachelors and Masters degrees through distance education. The use of communications technology is still a recent development in education and more information is needed to identify motivational attributes of students who enroll and succeed in online courses.

The growth of online courses has also introduced the problem of high attrition rates that educational institutions and online students face. Attrition rates in distance education courses are 40%-50% higher than the ones in traditional face-to-face classrooms (Dille & Mezack, 1991; Parker, 1994). Distance education requires students to monitor and regulate their own learning. Students control their own educational experience and pace. It is therefore important to identify the motivational characteristics of online students that would help educational institutions predict their success.  A review of the literature uncovered very few studies that examined predictors of success for online students despite the need for these studies.

Self-Regulated Learning

Self-regulation is a fairly new construct of motivation and it refers to "learning that occurs from students' behaviors that are systematically oriented toward attainment of learning goals" (Schunk, 1990, p. 3). Self-regulated learners not only need to posses cognition (knowledge to build upon), and metacognition (the knowledge and monitoring of learning strategies), but they must also be motivated to use their metacognitive strategies to build upon their understandings of instructional material (Pintrich & De Groot, 1990).

Self-regulation has been studied in traditional classrooms in order to provide an understanding of how students use their cognition, metacognition, and motivation in order to experience successful learning. Cognitive and metacognitive strategies provide the building blocks for constructing knowledge within a learning environment. Motivation, especially within the distance education context, provides the fuel for student engagement.  Without motivation students will not think about nor organize their knowledge because of the separation of students and the instructor by time and place. Research conducted by Blocher (1997) has shown that self-regulated students have a strong desire to learn and are goal directed.

Self-efficacy

Bandura (1986) defined self-efficacy as "people's judgements of their capabilities to organize and execute courses of action required to attain designated types of performance" (p. 391). Self-efficacy refers to people's beliefs about their capability to perform certain actions in a specific domain (Bandura, 1986; Bandura, 1993). Locke & Latham (1990) stated that self-efficacy is a significant determinant of achievement, operating independently of the individuals' underlying skills in a specific context (Schunk, 1984). Bandura (1993) stated that individuals with high self-efficacy "heighten and sustain their efforts in the face of failure" (p. 144).

Zimmerman, Bandura, & Martinez-Pons (1992) and Zimmerman & Bandura (1994) presented studies that showed that self-efficacy for self-regulated learning influenced self-efficacy for academic achievement. Using a statistical path model, Garcia & Pintrich (1991) found that intrinsic motivation (comparable to learning goal orientation) had a substantial positive effect on self-efficacy (?=.36), and that both intrinsic motivation and self-efficacy had moderate positive effects on self-regulated learning (?=.24 and ?=.26). Malpass et al. (1996) found that self-efficacy was positively related to self-regulated learning and mathematics achievement. The interaction of self-efficacy with other motivational constructs makes self-efficacy an important variable in this study.

Achievement Goal Orientation

Students set goals in order to accomplish a task. Dweck (1990) found that students would set either learning goals or performance goals. According to Dweck (1990), students who set learning goals seek to increase mastery of something new, whereas students who set performance goals do so in an attempt to obtain favorable, or to avoid negative, judgements of their competence. In the former case intrinsic motivation for success and understanding is critical, while in the latter case extrinsic motivation is responsible for exhibited behavior (Paris & Newman, 1990). Dweck & Leggett (1988) found that students who adopted a learning orientation increased perceptions of self-efficacy and success in their courses. Furthermore, studies have clearly shown that students oriented toward learning goals have demonstrated high-levels of self-regulated learning (Meece, 1994; Schunk & Zimmerman, 1994). Dweck (1986) and Schunk & Zimmerman (1994) contended that learning goal orientation is positively related to self-regulated learning and self-efficacy.

Computer-mediated communications (CMC) and the online classroom

CMC refers to the use of networked computers for communication, interaction, and exchanging of information (Kerr & Hiltz, 1982). Examples of CMC include electronic mail, bulletin boards, newsgroups, and computer conferencing. The rapid growth of computer networks and the evolution of the Internet have increased the use of CMC, which plays a significant role in web-based delivery of instruction. Studies have shown that online learners interact with their peers, instructors, and content experts in ways that allow them to develop their critical and problem solving skills (Riel, 1993). The online classroom, supported by CMC, constitutes a rich learning environment in which students construct their own understanding of the world (Harasim, 1996).

Self-regulation, self-efficacy, and achievement goal orientation are three motivational constructs that need to be further investigated in order to identify predictors of success for students enrolled in CMC-supported online courses.

Because of the need to identify predictors of success for online students and the high attrition rates in web-based courses, questions arise as to the importance of self-regulation, self-efficacy, and achievement goal orientation in the distance education context. The purpose of this study is to answer the following research questions:

1. Is there a relationship among self-efficacy, goal orientation, and self-regulation as predictors of success in the online classroom?

2. What is the relationship between the three motivational constructs (self-efficacy, goal orientation, and self-regulation) and (a) course completion, (b) student satisfaction, and (c) student achievement?

In order to identify books, book chapters, dissertations, and articles from research journals about self-regulation, self-efficacy, achievement goal orientation, and online courses, the researcher conducted computerized literature searches in four electronic databases: (a) Educational Resources Information Center (ERIC) (1966-1998), (b) Education Abstracts (1983-1999), (c) PsycINFO (1966-1999), and (d) EBSCOHost 3.0 (1990-1999). Different sets of keywords were used. The first set of keywords, "student motivation and research and (self-regulation or self-efficacy or goal orientation)," produced about three hundred references of which about one hundred and fifty were selected based on criteria about the credibility of the researchers, the type of research design, etc. The second set of keywords, "(distance education or online class* or web-based instruction) and (self-regulation or self-efficacy or goal orientation)," surprisingly produced only a handful of references. The third set of keywords, "(self-regulation or self-efficacy or goal orientation) and (measurement or scale or instrument or questionnaire or survey)," produced about three hundred and fifty references, of which about fifty were selected based on criteria about the type of instrument used in each study and its applicability to this study.

The following sections provide a review of the research studies that investigated the motivational constructs of self-regulation, self-efficacy, and achievement goal orientation mostly in traditional face-to-face classrooms.

Self-regulation refers to the process whereby students are cognitively, metacognitively, and motivationally active participants in their own learning environment (Zimmerman, 1989). Self-regulated learning is comprised of two components, (a) learning strategies, which include cognition and metacognition, and (b) motivation. Self-regulated students who employ cognition and metacognition plan, organize, self-instruct, and self-evaluate at various stages during the process of information acquisition. Students who are motivated perceive themselves as self-efficacious, intrinsically motivated, and goal directed (Zimmerman, 1989). As a result, self-regulated students are academically superior to students who are not self-regulating their learning experiences (Pintrich & De Groot, 1990; Zimmerman, 1986; Zimmerman & Martinez-Pons, 1986; Zimmerman & Martinez-Pons, 1988).

Zimmerman (1994) identified four attributes of self-regulated learning: (a) self-motivation, (b) self-monitoring, and (c) manipulation of social and physical environment, and (d) self-confidence. Self-motivation refers to motivation that is derived from the students' self-efficacious perceptions and their use of self-regulatory learning processes, such as setting goals. Self-monitoring refers to the students' awareness and self-checking during a learning process. Manipulation of the social and physical environment refers to the students' ability to both seek help from people who they know are capable, and also organize and restructure their skills in order to optimize learning. Self-confidence refers to the planned or automated methods of learning. O' Neil (1978) called these methods of learning as learning strategies, and Weinstein & Mayer (1986) classified them into two major categories: (a) learning strategies associated with outcome goals, and (b) learning strategies associated with process goals (such as monitoring, controlling, planning, organizing, transforming, rehearsing, and memorizing). Zimmerman & Martinez-Pons (1986) have defined the latter category of learning strategies as self-regulation. Pintrich & De Groot (1990) included several of these strategies in their Self-Regulated Learning Strategies scale. This is one of the two scales that compose the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, & McKeachie, 1991).

A review of the literature on self-regulation uncovered numerous theoretical and empirical studies (Garcia, 1995; Pintrich & Garcia, 1991; Schunk & Zimmerman, 1994). Garcia (1995) proposed that students use their self-efficacy to fuel their motivational strategies. Pintrich & De Groot (1990) found that increased levels of self-efficacy stimulate self-regulated learning. Meece (1994) suggested that self-regulated learners possess motivational attributes in their goal orientation that affect their learning experiences. For example, some students are intrinsically motivated to engage in academic activities, while others are extrinsically motivated to maintain their engagement.

However, few studies have explicitly linked the components of self-regulated learning to academic achievement (Schunk, 1984; Pajares & Kranzler, 1995; Pajares & Miller, 1994; Pajares & Miller, 1995). Schunk (1984) conducted an experiment on 4th grade children and posited that students who adopt a learning goal experience higher self-efficacy for skill improvement and engage in activities they believe enhance learning. Pajares & Kranzler (1995) studied high school students and found that self-efficacy had a significant direct impact on mathematics performance (r = .349, p < .05). In a similar study, Pajares & Miller (1994) found a significant direct correlation from self-efficacy to academic achievement (r = .349, p < .05). In a later study, Pajares & Miller (1995) found a significant correlation between mathematics self-efficacy and problem-solving performance (r = .69, p < .05). Brackney & Karabenick (1995) and Malpass & al. (1996) obtained very similar results to the previous studies.

The evidence presented in the above studies point towards the importance of self-regulation and its components as a predictor of academic achievement in traditional face-to-face classrooms. One area of study concerning self-regulation that has not yet been completely examined is that of its effects on students' achievement and satisfaction of online courses, as well as course completion.

Self-efficacy is a major component of Bandura's social cognitive learning theory (1986), which asserts that behavior is strongly stimulated by self-influence. Bandura (1986) describes self-efficacy as individuals' confidence in their ability to control their thoughts, feelings, and actions, and therefore influence an outcome. These perceptions of self-efficacy influence individuals' (a) thoughts, (b) emotions, (c) choices of behavior, and (d) amount of effort and perseverance expended on an activity. According to Bandura (1986), individuals make personal ability judgements and evaluations through a cognitive appraisal system that is unique to the individual, the task, and the particular situation at any given moment. Bandura (1986) cautioned that, because efficacy judgements are task-specific, a self-efficacy measure must asses the specific skills needed for performing an activity, and it must be administered during the time that the performance is being assessed.

According to the social-cognitive learning theory, individuals acquire information to assess self-efficacy from four principal sources: (a) actual experiences, (b) vicarious experiences, (c) verbal persuasion, and (d) physiological indexes.

Individuals' own performances, especially past successes and failures, offer the most reliable source for assessing efficacy. Typically, successes raise efficacy appraisals and failures lower them. However, once individuals develop a strong sense of efficacy, a failure may not have much impact (Bandura, 1986).

Vicarious experiences affect self-perceptions of efficacy. According to Schunk (1989b), peers offer the best basis for comparison. Observation of similar peers performing a task conveys to observers that they too are capable of accomplishing that task. In a study by Schunk & Hanson (1985), children who were having difficulty with subtraction problems observed either a same-sex peer or a teacher demonstrate mastery. The results of the study showed that the children who observed a peer had a higher self-efficacy for learning the procedure as opposed to those who observed the teacher. Information acquired vicariously typically has a weaker effect on self-efficacy than performance-based information (Bandura, 1986). Bandura (1986) also indicated that a vicarious increase in self-efficacy could be negated by subsequent failures.

Verbal persuasion is the third factor that influences self-efficacy judgements. Persuasion is when individuals are often encouraged to believe that they posses the capabilities to perform a task (e.g. being told "you can do this"). Verbal persuasion is not likely to be effective unless it is realistic and reinforced by real experience. Furthermore, verbal persuasion enhances self-efficacy, but this increase will be temporary if subsequent efforts result in failure (Bandura, 1986).

The last factor influencing self-efficacy judgements are the physiological indexes. Individuals acquire efficacy information from physiological indexes such as heart rate and sweating. Bodily symptoms signaling anxiety and fear might be interpreted by the students themselves to indicate their own lack of skills.

Information acquired from the above sources does not necessarily influence self-efficacy. According to Bandura (1986), the information gained through these sources is cognitively appraised before an efficacy judgement is made. Efficacy evaluation is an inferential process, in which persons weigh and combine the contributions of personal and situational factors. These factors are: (a) their perceived ability, (b) the difficulty of the task, (c) the amount of effort expended, (d) the amount of external assistance received, (e) the number and pattern of successes and failures, (f) their perceived similarity to models, and (g) the persuader credibility (Schunk, 1989b).

Researchers in academic environments have studied the relationship among self-efficacy and constructs such as goal orientation (Urdan, Pajares, & Lapin, 1997; Anderman & Midgley, 1992; Zimmerman et al., 1992) and self-regulation (Schunk, 1989d; Pintrich & De Groot, 1990). Results in this area indicate that students who believe they are capable of performing certain tasks use more cognitive and metacognitive strategies and persist longer than those who do not feel they are capable of performing certain tasks (Pintrich & Garcia, 1991). For example, Pintrich & De Groot (1990) reported that academic self-efficacy correlated with academic outcomes such as exam scores and final year grades. In a similar manner, Schunk (1991) stated that individuals who have a high sense of self-efficacy for accomplishing a task work harder and persist longer when they encounter difficulties, whereas those who do not feel efficacious may quit or avoid a task. In the same context, Bandura (1993) stated that when individuals with high self-efficacy are challenged by a difficult situation they are more likely to attempt different strategies, or develop new ones, and are less likely to give up than people with a low sense of self-efficacy.

Individuals who feel they lack the ability to affect an outcome act in ways that prevent learning and performance. For example, individuals with low self-efficacy beliefs tend to avoid activities they believe are beyond their capabilities and therefore, choose easier tasks where the chances for success are greater. Furthermore, individuals with low self-efficacy do not try to develop new skills and strategies, as they feel uncertain about their previous skills (Bandura, 1986). These individuals invest less effort in an outcome and give up more quickly than those with high self-efficacy. This poor performance reinforces low self-efficacy, which leads to poorer performance, and so on. Some researchers call this "learned helplessness" (Bandura, 1982; Bandura, 1986; Bandura, 1993; Peterson, Maier, & Seligman, 1993). Goal orientation theorists refer to learned helplessness as a "helpless response" and state that these responses are the result of high performance goal orientation in addition to low efficacy (Dweck, 1986; Dweck & Leggett, 1988; Nicholls, 1984; Nicholls, 1989).

Researchers have established that self-efficacy is a strong predictor of academic performance. Multon, Brown, & Lent (1991) (cited in Pajares, 1995) reviewed a comprehensive list of studies that examined self-efficacy in achievement situations. Findings in this area suggest that self-efficacy beliefs were positively related to academic performance (r = .38). In the same context, Ames (1984) and Nicholls & Miller (1994) have found that students' self-perceptions of ability are positively related to achievement and student motivation.

Summing up the literature on self-efficacy beliefs, it is evident that the construct plays a significant role in predicting academic achievement. Pintrich & De Groot (1990) suggested that the improvement of students' self-efficacy beliefs leads to increased use of cognitive and metacognitive strategies and, thereby, higher academic performance. Self-efficacy is closely related to self-regulation, and both are especially useful in the context of online education where increased levels of self-efficacy beliefs toward the technology utilized are needed by the students in order to be able to communicate and interact with their peers and the instructor. No studies have been found when reviewing of the literature on self-efficacy which address this area of research.

An achievement goal is what an individual is striving to accomplish (Locke & Latham, 1990). According to Locke & Latham (1990), there are four main reasons why goal setting improves performance: (a) they direct the students' attention to the particular task, (b) they engage effort, (c) they increase persistence, and (d) they promote the development of new strategies when old strategies fail.

Dweck (1986) and Dweck & Leggett (1988) have identified two motivational patterns that are associated with differences in individuals' goal orientation. The first is the "mastery response," associated with learning goal orientation (Elliot & Dweck, 1988), in which challenging tasks are sought and effort is increased in the face of difficulty. Learning goals have also been called mastery goals (Ames & Archer, 1988), task incentives (Maehr & Braskamp, 1986), and task involvement (Nicholls, Patashnick, & Nolen, 1985). The second pattern is the "helpless response," associated with performance goal orientation (Ames & Archer, 1988; Elliot & Dweck, 1988), in which challenging tasks are avoided and performance decreases when difficulty is encountered. Performance goals have also been called ego incentive (Maehr & Braskamp, 1986), or ego involvement (Nicholls et al., 1985). Research on the relationship between learning and performance goals has indicated that these two types of goals are independent of one another (Hagen & Weistein, 1995), rather than opposite to one another as suggested in early motivational research (Meece & Holt, 1993). Pintrich & Garcia (1991) suggested that this independence means that it is possible for students to have both learning and performance goals at the same time.

A review of the literature on achievement goal orientation has shown that learning goals are positively related to self-efficacy (Hagen & Weistein, 1995; Urdan et al., 1997), and self-regulation (Ames & Archer, 1988; Hagen & Weistein, 1995; Meece, Blumenfeld, & Hoyle, 1988; Nolen, 1988). In a study by Schunk (1995), results indicated that learning goal orientation led to higher self-efficacy and self-regulated performance than performance goal orientation. Individuals with a learning goal orientation strive to master a particular task and to improve themselves no matter how many mistakes they make. Their primary goal is to obtain knowledge and improve their skills. Consequently, they process information at a deep level (Miller, Behrens, Greene, & Newman, 1993; Nolen, 1988). These individuals extend their learning processes beyond the minimum required and pursue the learning process as long as they perceive progress. The combination of these factors enable task involved individuals to learn more and perform better than individuals motivated only to perform better than others. Learning goal oriented students are more likely to engage in self-regulatory activities such as the use of monitoring, planning, and deep-level cognitive strategies (Anderman, 1992; Ames & Archer, 1988; Meece, Blumenfeld, & Hoyle, 1988; Nolen, 1988; Graham & Golan, 1991). Students who adopt learning goals also tend to find the topic under study more intrinsically rewarding (Meece et al., 1988; Miller et al., 1993; Nicholls & Miller, 1994). Furthermore, students with a learning goal orientation tend to achieve higher on tasks and persist longer after failure as compared to performance goal oriented students (Diener & Dweck, 1978; Dweck & Leggett, 1988).

Individuals oriented toward performance goals are concerned with positive evaluations of their abilities in comparison to others. They are focused on how they are judged by others (peers, teacher, or parents). They want to look smart, and they try not to seem incompetent. For these reasons, they may avoid challenging tasks and exhibit low persistence when they encounter difficult work (Ames & Archer, 1988; Elliot & Dweck, 1988; Dweck & Leggett, 1988; Nicholls, 1989; Maehr & Midgley, 1991). By doing so, they adopt failure-avoiding strategies such as pretending not to care, making a show of "not really trying", or simply giving up (Jagacinski & Nicholls, 1987; Pintrich & Schunk, 1996). The evaluation of their performance is what matters to them, instead of the course material or their efforts. Individuals with performance goal orientation tend to process information at a superficial level and generally fail to pursue learning beyond the level necessary to achieve positive recognition. Consequently, they frequently fail to retain the information they learn (Nolen, 1988; Miller et al., 1993; Greene & Miller, 1996; Pintrich & Garcia, 1991).

Summarizing the literature on achievement goal orientation, it is obvious that goal orientation is an important way of conceptualizing the motivation of students. Web-based instruction in higher education is one area concerning goal orientation that has not been completely examined.

One of the most important topics of current research in distance education is the use of CMC in both online classrooms and as a supplement to face-to-face instruction. Berge & Collins (1995) define CMC as "the ways in which telecommunication technologies have merged with computers and computer networks to give us new tools to support teaching and learning" (p. 1). CMC offers many advantages as a delivery medium in online environments. Among those advantages is the fact that CMC has the potential for high levels of student-student and student-instructor interaction, and the fact that it is both synchronous and asynchronous. The synchronous aspect allows for  "live" discussions between students and their peers and students and their teacher, while the asynchronous aspect allows for ample time for the students to think about the topic in discussion. Examples of CMC include electronic mail, computer conferencing, bulletin boards, and newsgroups.

The issue of the selection and utilization of the appropriate technology in a distance learning environment and specifically the topic CMC is widely reported in the literature on online instruction. Some of the reasons for the extensive research in the field are the rapid growth of technology and computer networking, the cost effectiveness of online courses, and the easier access to educational materials by students who otherwise would not be able to participate in classes. Currently, traditional face-to-face courses are being enhanced by distance learning methods of web-based delivery of instructional materials, and interaction is being accommodated via CMC. Educators believe that computer networks will someday provide an alternative to traditional education as virtual classrooms (Moore & Kearsley, 1996). Harasim (1996) argues that computer networks and online educational developments may change the future of educational institutions.

Distance education has a unique nature and CMC is prominent within that environment. Therefore, it is imperative that more research is conducted in order to identify which motivational attributes of distance learners are vital in order to succeed in such environment. Very few studies have been identified in the literature on various motivational constructs and the use of CMC in online courses. Parker (1994) examined students' locus of control and personal characteristics as predictors of attrition in online college courses in Maricopa county. Results indicated that internal locus of control was positively correlated with low attrition rates. In another study, Blocher (1997) examined studentsÕ CMC engagement in regard to gender, motivation, and self-regulation of strategies. Findings showed that CMC enhanced interaction and social presence in an Internet supported class.

Summing up the review of the literature on self-regulation, self-efficacy, achievement goal orientation, and CMC, it is imperative that more research is needed in order to shed light on which motivational constructs can be identified as predictors of success in an online environment. The lack of sufficient studies coupled with the rapid growth of online courses demand more investigation in the area so that attrition rates of online learners decrease.

Based on the abundance of online courses offered throughout the United States and the world, and the and high attrition rates that these courses have introduced, this proposed research study is designed to investigate three motivational constructs (self-regulation, self-efficacy, and goal orientation) and their relationship to online learners' success. After ascertaining the validity of the testing instruments, data will be collected. Background information on the participants will also be collected.

The research design will employ both quantitative and qualitative methods in order to establish triangulation. Three questionnaires measuring the students' self-regulation, self-efficacy, and goal orientation will be administered during the first week of the courses. Data from the questionnaires will be analyzed using the Statistical Package for the Social Sciences (SPSS).

Follow up interviews will be used with a selection of students that have and have not completed the course. Information will be categorized, and then analyzed using the Miles and Huberman approach.

The sample for the pilot study will consist of two intact classes during the fall 1999 semester. The first class is EMC 637 (n=60), and the second class is LNT 591 (n=20). Both classes meet online only. During the summer of 1999 the researcher will pilot test and validate the new instrument for measuring self-efficacy beliefs in the online context (SOT). The sample for the validation of the instrument will consist of about 50 students enrolled in EMC 637.

Independent Variables

The researcher will develop and validate a new instrument called Self-efficacy for Online Technologies (SOT). The SOT consists of about 45, 4-point Likert-type scale items, and will be designed to measure the learner's perceptions of their abilities in the online context.

Goals Inventory (GI) (Roedel, Schraw, & Plake, 1994). The GI consists of 25, 5-point Likert-type scale items, reflecting attitudes and behaviors that are associated with the learning and performance goal orientation.

Motivated Strategies Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, & McKeachie). The MSLQ consists of 81, 7-point Likert-type scale items: 31 items in the Motivation section and 50 in the Learning Strategies section. The MSLQ is designed to discover the learner's motivation and their cognitive and metacognitive strategy use for a specific course.

Dependent Variables

Course completion: the researcher will receive the class roster from the online course instructor at the end of the semester. The roster will include students who completed or dropped the course.

Student satisfaction: the researcher will develop a questionnaire with about ten attitude items.

Student achievement: the researcher will receive the class roster from the online course instructor at the end of the semester. The roster will detail the students percentage grades earned in the course.

The quantitative data will be analyzed using SPSS. First, Exploratory Data Analysis (EDA) will be used in order to investigate the raw data. The procedure includes creating graphs and correlation tables, and testing for the assumptions of multivariate regression and MANOVA in order to develop a model that best predicts success from self-regulation, self-efficacy, and goal orientation. Second, Confirmatory Data Analysis (CDA) will be used to test the study's hypotheses.

Coding and analysis of selected follow up interviews with students who successfully completed the online course and with students who dropped out will be based on the Miles and Huberman's approach.

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