Predicting Attitudes Toward Online Learning 1 Running head: PREDICTING ATTITUDES TOWARD ONLINE LEARNING Task Value, Self-Efficacy, and Experience: Predicting Military Students’ Attitudes Toward Self-Paced, Online Learning

Many would agree that learning on the Web – a highly autonomous learning environment – may be difficult for less motivated individuals (Hartley & Bendixen, 2001). Using a social cognitive view of self-regulated learning (Bandura, 1997; Schunk & Zimmerman, 1998), the objective of the present study was to investigate the relations between two motivational constructs, prior experience, and several adaptive outcome measures. Participants (n = 204) completed a survey that assessed their perceived task value, self-efficacy, prior experience, and a collection of outcomes that included their satisfaction, perceived learning, and intentions to enroll in future online courses. Results indicate that task value, self-efficacy, and prior experience with online learning were significantly related to positive outcomes. Additionally, results from several independent samples t-tests indicate that students’ reporting on a course they chose to take exhibited significantly higher mean scores on task value, satisfaction, perceived learning, and intentions to enroll in future courses than students reporting on a course they were required to complete. Educational implications and suggestions for future research are discussed. DRAFT Predicting Attitudes Toward Online Learning 3 Task Value, Self-Efficacy, and Experience: Predicting Military Students’ Attitudes Toward Self-Paced, Online Learning With the rapid expansion of Internet-based technologies, online learning has emerged as a viable alternative to traditional classroom instruction (Moore, 2003; Tallent-Runnels et al., 2006). As a subset of a much larger form of instruction – distance education – online learning has become the format-of-choice for countless institutions eager to provide students with the opportunity and convenience of learning from a distance (Moore & Kearsley, 2005; Simonson, Smaldino, Albright, & Zvacek, 2003). For example, the Department of Defense, an organization that spends more than $17 billion annually on military schools for almost three million personnel, recently committed to transforming the majority of its classroom training to computer-supported distance learning (United States General Accounting Office, 2003). Similarly, institutions of higher education have recognized the utility of online learning. A recent survey of 1,000 U.S. colleges and universities by the Sloan Consortium (2005) found that 63% of schools offering undergraduate face-to-face courses also offer undergraduate courses online; 56% of schools identified online education as a critical long-term strategy (up from 49% in 2003); and overall online enrollment increased from 1.98 million in 2003 to 2.35 million in 2004. The recent growth in online learning has resulted in a major shift in education and training from an instructor-centered to a learner-centered focus (Dillon & Greene, 2003; Garrison, 2003; Gunawardena & McIsaac, 1996). With this shift has come the suggestion that, in the absence of an ever-present instructor, students learning at a distance must take greater responsibility for the management and control of their own learning (Kearsley, 2000; King, Young, Drivere-Richmond, & Schrader, 2001; Schunk & Zimmerman, 1998). As Moore and Kearsley (2005) so aptly stated in their extensive book on distance education, “Students DRAFT Predicting Attitudes Toward Online Learning 4 frequently do not understand that they must take a large degree of responsibility for their learning in a distance education course” (p. 178). In light of these concerns, a number of researchers have argued that online students, to an even greater extent than traditional learners, require well-developed self-regulated learning (SRL) skills to guide their cognition and behavior (Bandura, 1997; Dillon & Greene, 2003; Hartley & Bendixen, 2001; Hill & Hannafin, 1997). Self-regulated learners are generally characterized as motivated participants who efficiently control their own learning experiences in many different ways, including establishing a productive work environment and using resources effectively; organizing and rehearsing information to be learned; and holding positive beliefs about their capabilities, the value of learning, and the factors that influence learning (Schunk & Zimmerman, 1994, 1998). The purpose of the present study was to investigate the relations between two motivational constructs, task value and self-efficacy; prior experience with online learning; and several adaptive outcomes. Task value, self-efficacy, and prior experience were chosen as predictors because past research in both traditional and online environments has found them to be positively related to students’ use of SRL strategies and academic achievement, as well as other adaptive outcomes, including satisfaction and intent to participate in future courses (see, for example, Joo, Bong, & Choi, 2000; Lim, 2002; Miltiadou & Savenye, 2003; Pintrich, 1999). The present study was designed to determine if these linkages extend to military students learning in the context of self-paced, online training. Review of the Literature Self-regulated learning refers to “learning that occurs largely from the influence of student’s self-generated thoughts, feelings, strategies, and behaviors, which are oriented toward 1 Self-paced, online training is a specific type of online learning in which students use a Web browser to access a course management system and complete Web-based courses at their own pace. While completing these courses, students do not interact with an instructor or other students. DRAFT Predicting Attitudes Toward Online Learning 5 the attainment of goals” (Schunk & Zimmerman, 1998, p. viii). Academic self-regulation has been studied in traditional classrooms as a means of understanding how successful students adapt their cognition, motivation, and behavior to improve learning. In general, investigators have found moderate to strong positive relations between motivational components of self-regulation, use of learning strategies, and academic achievement (Pintrich, 1999; Pintrich & De Groot, 1990; Pintrich & Garcia, 1991). Motivational Influences on Self-Regulation and Performance While most SRL theorists acknowledge the influence of motivation on self-regulation, Pintrich’s (2000, 2003) model of SRL stresses the importance of motivation in all phases of selfregulation. Pintrich and his colleagues have demonstrated that effective and less effective selfregulated learners differ in several motivational processes. For example, their research suggests that learners’ task value (i.e., the extent to which they find a task interesting, important, and/or valuable) relates positively to several adaptive outcomes, including students’ use of SRL strategies, future enrollment choices, and, ultimately, academic performance. Similarly, Wigfield (1994) reported that achievement values appear to relate to students’ choices about whether or not to become cognitively engaged in a learning task, as well as their intentions to enroll in similar courses in the future (choice behaviors). In short, research findings suggest that students who view a learning task as valuable are more likely to experience superior academic outcomes (Pintrich, 1999). Self-efficacy is another important motivational construct that has been shown to predict adaptive learning outcomes. According to Schunk (2005), “self-regulated learners are more selfefficacious for learning than are students with poorer self-regulatory skills; the former believe that they can use their self-regulatory skills to help them learn” (p. 87). For example, in a study DRAFT Predicting Attitudes Toward Online Learning 6 of middle school students, Pintrich and De Groot (1990) found that students’ self-efficacy beliefs were positively related to their cognitive engagement and academic performance. In part, their results indicated that students who believed they were capable of learning were more likely to report use of SRL strategies and to persist longer at difficult academic tasks. In a more recent study of college students in an online course, Lynch (2003) found that students’ efficacy beliefs were among the best predictors of academic achievement, as measured by final course grades. Finally, results from a recent meta-analysis of more than 100 empirical studies conducted over the last 20 years found that of nine commonly researched psychosocial constructs, academic selfefficacy was the strongest single predictor of college students’ academic performance (Robbins et al., 2004). Prior Experience with Online Learning The influence of prior experience on students’ success with online learning is well documented (Hannafin, Hill, Oliver, Glazer, & Sharma, 2003). In general, research has revealed that successful online learners possess more technology knowledge than their less successful counterparts (Kearsley, 2000; Simonson et al., 2003). For example, in a study of adult learners’ use of cognitive strategies in an open-ended, online learning environment, Hill and Hannafin (1997) found that system knowledge impacted students’ ability to successfully find and use resources. Furthermore, the linkages between prior experience and learner success have been well documented within the motivation literature (Pintrich & Schunk, 2002). Specifically, Bandura (1986, 1997) and his associates (Pajares, 1996; Schunk, 1991) have shown that previous personal experience with a given task is often the strongest predictor of one’s confidence and attitude toward that task. With these considerations in mind, previous experience with self-paced, DRAFT Predicting Attitudes Toward Online Learning 7 online learning was explored in the present study as a potentially important predictor of students’ attitudes toward online instruction. Study Objectives and Research Questions Taken together, much of the research on academic self-regulation supports the hypothesized linkages between motivation, self-regulation, and adaptive academic outcomes. The objective of the present study was to explore the relations between students’ motivation, prior experience, and a collection of outcomes, seeking to determine if the pattern of relationships are consistent with those that have been found in traditional academic settings. The specific research questions addressed by this study are: (1) When considered individually, how are task value, self-efficacy, and experience with online learning related to students’ overall satisfaction, perceived learning, and intentions to enroll in future online courses? (2) How accurately can a linear combination of task value, self-efficacy, and experience with online learning predict students’ overall satisfaction, perceived learning, and intentions to enroll in future online courses? (3) Are there significant differences in the predictor and outcome variables when comparing students’ reporting on required courses versus learners reporting on courses they chose to complete? Methods Participants A convenience sample of 475 personnel from the U.S. Navy were invited to participate in the present study. A total of 204 individuals completed the survey (response rate = 43%). The sample included 150 men (74%) and 53 women (26%); 1 person did not report gender. The mean DRAFT Predicting Attitudes Toward Online Learning 8 age of the participants was 39.0 years (SD = 9.3; range 22-69). Participants reported a wide range of educational experience, including: High School/GED (n = 21, 10%), Some College (n = 51, 25%), 2-Year College (n = 24, 12%), 4-Year College (B.S./B.A.; n = 25, 12%), Master’s Degree (n = 48, 24%), Doctoral Degree (n = 15, 7%), and Professional Degree (n = 16, 8%). Information regarding ethnicity was not collected as part of this study. Procedures Naval personnel were contacted via email and invited to complete an anonymous, online survey concerning their experiences with self-paced, online learning. Participants were asked to respond to survey items while keeping in mind what they considered to be the most effective self-paced, online course they had completed within the last two years. This approach was necessary because the survey could not be given at the end of a specific course. One benefit of this approach was that some participants were reporting on a course they chose to complete (i.e., a personal elective), while others were reporting on a course they were required to complete (i.e., a Navy requirement). Participants were asked to indicate which type of course they were reporting on (personal elective or Navy requirement), and these two groups were then used as independent variables in the analysis. Measures The first section of the survey was composed of 25 items with a Likert-type response scale ranging from 1 (completely disagree) to 7 (completely agree; see Appendix). A principle axis factor analysis with oblique rotation (Oblimin; delta = 0) was carried out on the 25 items from the first section of the survey. Results from the exploratory factor analysis suggested three interpretable factors accounting for 61.6% of the total variance in the items. The resulting threefactor solution included: (1) a 14-item task value subscale that assessed learners’ judgments of DRAFT Predicting Attitudes Toward Online Learning 9 how interesting, useful, and important a recent self-paced, online course was to them (α = .95); (2) a 7-item self-efficacy subscale that assessed learners’ confidence in their ability to learn the material presented in a self-paced, online format (α = .89); and (3) a 4-item satisfaction subscale that assessed learners’ overall satisfaction with a recent self-paced, online course (α = .91). Sample items from these three subscales include “I liked the subject matter of this course” (task value); “Even in the face of technical difficulties, I am certain I can learn the material presented in an online course” (self-efficacy); and “Overall, I was satisfied with my online learning experience” (satisfaction). The second section of the survey contained background and demographics items. This section also included three individual items used as variables in the present study: (1) Experience. Experience was assessed with a single self-report item: “In your estimation, how experienced are you with self-paced, online learning?” The response scale ranged from 1 (extremely inexperienced) to 7 (extremely experienced). (2) Perceived Learning. Perceived learning was assessed with a single self-report item: “In your estimation, how well did you learn the material presented in this course?” The response scale ranged from 1 (not well at all) to 7 (extremely well). Although controversial, some research evidence has suggested that self-reports can be a valid measure of student learning (Mabe & West, 1982; Pace, 1990), particularly when used to assess military training and when coupled with anonymity (Barker & Brooks, 2005; Wisher & Curnow, 1996). Therefore, because a more direct measure of student learning was not accessible, perceived learning was used as a measure of student learning in the present study. DRAFT Predicting Attitudes Toward Online Learning 10 (3) Choice. Choice was assessed using a single self-report item: “What is the likelihood that you will enroll in another self-paced, online Navy course if you are not required to do so?” The response scale ranged from 1 (definitely will not enroll) to 7 (definitely will enroll).