Context. This descriptive study was designed to assess the motivational beliefs, specifically, self-efficacy for technology—herein referred to as Computer Self-Efficacy(Compeau & Higgins, 1995; Hasan, 2003), self-regulation, and engagement of students enrolled in at least one online English, math, science, or social science course at the high school level in the United States. Research Questions. RQ#1: What is the relationship between students' computer self-efficacy, self-regulation, and engagement in online learning? RQ #2 (used as four separate sub-questions in the study—one for each of the following subjects: English, social science, math, and science): What is the relationship between online learners' computer self-efficacy, self-regulation, and their behavioral, emotional, and cognitive engagement and expected achievement in English, social science, math, and or science? Methods. Participants completed one online survey as the method for data collection. The sample consisted of 194 students at one of two virtual schools located in the Southern and Western United States. Of this total, 183 attended Southern Virtual School (name changed for study) and 11 attended Western Virtual School (name changed for study). As the focus of the study was on high school students, the grade level breakdown of participants was as follows: 9th grade students comprised 14.6% (n=28), 10th grade students comprised 26.8% (n=52), 11th grade students comprised 32.5% (n=63), and 12th grade 25.3% (n=49). Two (2) students did not indicate a grade level. Three separate surveys were used to gather data about students' computer self-efficacy, self-regulation, and engagement. These assessments were scored using a five point Likert-type scale with respondents selecting a 1 to indicate that they "Strongly Disagree" with the statement, and selecting a 5 to indicate that they "Strongly Agree" with the statement. Participants were given the self-regulation subscale of the Motivated Strategies for Learning Questionnaire (MSLQ) survey instrument that included nine items (Pintrich et al., 1993). An initial internal consistency coefficient of the modified subscale revealed an acceptable Cronbach's alpha of .77. Two subscales from the Web-User's Self-Efficacy scale (Eachus and Cassidy, 2006), "information retrieval" and "communication," comprised of 10 items each, were used to measure participants' computer self-efficacy; reliability was calculated with an acceptable Cronbach's alpha of .774. A 19-item five-point Likert-scale instrument was used to measure three types of engagement: behavioral, emotional, and cognitive (Fredricks et al, 2004). Exploratory factor analysis was conducted to determine what underlying structure exists for the modified items in relation to the original scale. An initial internal consistency coefficient of the modified scale revealed an acceptable Cronbach's alpha of .77. Students were asked to indicate what they expected their Fall 2008 achievement to be for English, social science, math or science by selecting the traditional letter grade they expected to receive for that semester (A, B, C, D, F, or incomplete). Though not self-reported grades, it should be noted that Kuncel and colleagues (Kuncel, Crede, & Thomas, 2005) asserted that self-reported grades may not be accurate measures of actual earned grades and that self-reported grades may also reflect learning, persistence, achievement, and etc. Results. A Pearson's correlation was conducted to establish the observed correlations between the variable being examined. Single path analyses were used to examine the relationships between the variables. In order to conduct the single path analyses, entry regression was used to create path correlations for the purposes of writing effect decompositions in order to calculate the indirect effects of computer self-efficacy and self-regulation on English, Social Science, Math, and Science achievement. Multiple regression analysis indicated that there was a significant relationship between computer self-efficacy and self-regulation in the math and science achievement models, but not in the English and social science models. Self-regulation was significantly related to the three types of engagement in all path models. Emotional engagement was the only engagement type that related to English achievement; none of the engagement types significantly related to social science achievement; behavioral and emotional engagement significantly related to math achievement; and all three types of engagement significantly predicted science achievement. Path analysis indicated that computer self-efficacy showed significant indirect effect on science achievement, and self-regulation had significant indirect effects on English, math, and science achievement when it was the primary exogenous variable. Conclusions. This study supports previous research that suggests that computer self-efficacy is positively connected to self-regulation and engagement, suggesting that prior experience, ease of use of software, and training are vital in preparing students to use distance learning software and tools. However, this study also suggests that as the ease of use of software increases, the demands of computer self-efficacy may decrease, thus making it more likely that a learner would be successful in completing distance learning tasks. Discussion/Interpretation. Online education is growing lightning fast. As the race to offer online learning outpaces the understanding of how it works, it will be imperative for future researchers to find a way to keep up and solve problems and embrace opportunities as they arise, identify best practices, and understand that the malleability of technology has changed how future learning will occur. It is important to realize that the transformation technology is making in education is significant (Christensen, Horn, and Johnson, 2008). Whereas past perceptions of technology in education have been met with apprehension, disdain, or seen as only a cursory element in the grand scheme, the current influence on how curriculum is developed, delivered and accessed, and how teachers are trained is paramount to the success of future learning. Finally, discovering the tacit components of highly self-regulated students is essential as this has a direct influence on course design and instruction. Goals: Participants will obtain a better conceptual understanding of how these variables relate to each other and how they influence expected achievement. As well, participants will learn several techniques in order to foster behavioral, emotional, and cognitive engagement, and how to bolster students' self-regulation. This presentation is geared for intermediate and advanced online educators.
