Printer Friendly


Measuring Self-Efficacy and Self-Regulation in Online Courses

In response to the current advancement in technology, there is no question that methods of online instruction are becoming an academic staple. Flexibility in class scheduling coupled with individualized learning opportunities places online classes at the peak of student demand (Botsch & Botsch, 2012). Therefore, as online instruction begins to play a lead role in students' educational experiences, it is important to examine how these drastic changes in educational practices are impacting the ways in which students learn. Given the sweeping dissimilarities between traditional classroom education and online education, attention should now be directed toward the development of instructional techniques that will promote student success through the advancing technological era.

Previous research on academic achievement in the classroom suggests that one of the best predictors of academic success is self-regulation and the use of self-regulatory strategies in educational environments (Pintrich & De Groot, 1990; Zimmerman, 2002; Zimmerman & Martinez-Pons, 1986). Students who are characterized as being self-regulated are active participants in the learning process. These students exercise control over their own learning experiences in a variety of ways, including self-monitoring and self-assessment, setting goals, organizing and rehearsing information, seeking help, and utilizing self-motivational strategies (Artino & Stephens, 2007; Caprara et al., 2008). In terms of online instruction, research has indicated that students who have a high self-efficacy for accomplishing academic tasks have proficient self-regulatory skills. Command of such abilities leads to more successful academic outcomes. In comparison, those with low self-efficacy tend to have weak self-regulatory skills which leads to poor academic outcomes due to the inability to control study habits (Bandura, 2002; Joo, Bong. & Choi. 2000; Pintrich & De Groot, 1990; Whipp & Chiarelli, 2004). Furthermore, students who proficiently and regularly interact with computers and the Internet tend to have higher self-efficacy and be better adapted to online coursework than those who interact with online technology less frequently (McCoy, 2010). Given the implications of the current research, further investigation concerning the impact of student self-regulation and self-efficacy on success in online courses is warranted.


One's self-efficacy emerges as a result of a multitude of factors, including personal life events and experiences, physiological and emotional states, events in one's external environment, and the behavior of oneself and others. An individual's self-efficacy determines their personal goals and aspirations and shapes the outcomes that people expect their efforts to produce. Those with high self-efficacy expect favorable outcomes as a result of their efforts and view weaknesses as being conquerable through effort, whereas those with low self-efficacy expect their efforts to fail. As a result, belief in one's self-efficacy can significantly influence one's ability to function optimally (Bandura, 2002). According to Bandura, self-efficacy can be impacted by a variety of factors such as mastery experiences, vicarious experiences, verbal persuasion and social influence, and physiological and affective states (Bandura, 1997). These four sources provide a valuable appraisal of the learner's skill set which will ultimately influence whether the learner pursues of avoids the task.

The first source, mastery experiences, affects the development of self-efficacy in that successful performance of a task strengthens a person's sense of self-efficacy, however, failure to adequately perform a task can undermine self-efficacy. Mastery experiences are the most influential factor in informing a person's self-efficacy beliefs because they give the learner concrete proof of whether he or she has the capability to master the task. The second source, vicarious experiences, are learned by observing the successes and failures of similar others, and alter efficacy beliefs through the "transmission of competencies" (Bandura, 1997). That is, witnessing someone similar to oneself succeed raises the observer's belief that they are also capable of succeeding at the same task. The third source, verbal persuasion and social influences, can convince an individual that he or she is. in fact, capable of successfully completing a task, thus alleviating feelings of self-doubt and increasing confidence. When verbal persuasion shapes a realistic goal, it can be significant enough to encourage self-change (Bandura, 1997). Finally, people interpret their own physiological and affective states in specific contexts to judge their capabilities. People often interpret physiological responses in stressful situations as indications of impending failure.


Self-regulation refers to the cognitions and behaviors that coincide with achieving personal goals (Gazzaniga, Heatherton, & Halpern, 2010; Zimmerman 2000). Self-regulation plays an important role in motivation; it requires that people plan, monitor, and modify their behaviors and cognitions in terms of personal goals (Pintrich & De Groot, 1990). People who are able to self-regulate successfully reflect on the effectiveness of their cognitive and behavioral strategies. Upon seeing that these strategies are bringing one closer to his or her goals, motivation for continued improvement increases. When specific strategies prove ineffective for promoting goal attainment, self-regulators will modify these strategies and continue to adapt them to changing demands until the strategies result in goal attainment. Due to their superior motivation (as compared to those who fail to self-regulate) and adaptive strategies, self-regulated students are more likely to succeed academically (Zimmerman 2002).

Research has shown that students who wish to self-regulate their learning need to direct self-awareness and self-motivation toward accurately applying knowledge and skill sets (Zimmerman, 2002). Additionally, self-regulated learning requires that these skill sets be adaptive to accommodate different learning tasks. Zimmerman (2002) suggests that the following skills are necessary for self-regulated learning:
(a) setting specific proximal goals for oneself, (b) adopting powerful
strategies for attaining the goals, (c) monitoring one's performance
selectively for signs of progress, (d) restructuring one's physical and
social context to make it compatible with one's goals, (e) managing
one's time use efficiently, (f) self-evaluating one's methods, (g)
attributing causation to results, and (h) adapting future methods.
(p. 66)

Finally, the self-motivation that is a necessary component of self-regulation depends on perceived self-efficacy as well as intrinsic interest in the task at hand. This motivational factor can be enhanced if self-regulatory processes are employed, like self-monitoring (Zimmerman, 2002). Through self-monitoring of their learning, self-regulators will take note of their progress as a result of successful application of effective learning strategies, and feelings of efficacy for learning will increase. In this sense, Zimmerman argues that self-regulation is necessary for self-efficacy.

Conversely, Bandura asserts that self-efficacy is necessary for self-regulation. Generally speaking, the four sources of self-efficacy inform one's belief in their capabilities to organize and execute the course of action required to accomplish a task (1997). Once a person has a particular efficacy for a task, Bandura suggests that self-regulation is closely linked to these beliefs and has the power to directly influence the way in which a person engages in a task. Bandura (1997) typically suggests that those individuals who have high self-efficacy are likely to demonstrate high self-regulatory behaviors which lead to favorable academic outcomes. However, those with low self-efficacy typically demonstrate poor self-regulatory behaviors which yield negative academic results. The temporal relationship between self-efficacy and self-regulation remains unclear, however the theory that these two constructs are indeed strongly related stands firm.

Many studies have demonstrated the relationship between self-efficacy and self-regulatory skills in academic settings. Pintrich and De Groot (1990) provided evidence for the beneficial effects of high self-efficacy and self-regulation on student success in traditional face-to-face courses. The researchers defined self-regulated learners as those students employing metacognitive strategies which include planning, monitoring, and modifying their cognition. Results indicated that higher levels of student self-efficacy and an intrinsic value of the presented task (a belief that the task is interesting and important) were correlated with higher levels of cognitive strategy use by students, higher levels of metacognitive activity, higher levels of student self-regulation, and higher levels of student achievement in a variety of tasks including seatwork assignments, quizzes, teacher-made tests, lab problems, and essays.

In a study investigating the pertinence of the self-efficacy theory to Web-based instruction, Joo et al. (2000) discovered that several components of self-efficacy and self-regulation operate in tandem to influence academic achievement in a Web-based course. Self-efficacy for self-regulated learning, defined by the researchers as one's perceived capability to use a variety of self-regulated learning strategies, was positively correlated with academic self-efficacy (the belief that one is capable of performing successfully at appropriate levels), self-regulation strategy use. and Internet self-efficacy. The researchers also found that students" standardized test scores were correlated with self-efficacy for self-regulated learning, academic self-efficacy, and self-regulation strategy use. Furthermore, in accordance with previous research, student self-efficacy beliefs and self-regulated learning strategies were found to be context-dependent. Because self-efficacy beliefs are multi-faceted, they should be measured according to their various circumstances and the demands of tasks specific to each circumstance (Bandura, 1997). Additionally, the use of self-regulated learning strategies is dictated by the features unique to each specific learning environment (Whipp & Chiarelli, 2004).

In a more comprehensive study, Siegle et al. (2006) investigated factors that have historically shown to impact student success. In comparing gifted students across 5 treatment conditions, the researchers found that students in the self-efficacy and self-regulation treatment group--whose treatment consisted of focusing on study skills, test-taking strategies, and time-management skills--showed little improvement in grades, even among students reporting problems in those areas. On the other hand, treatments related to increasing student connections to school, thereby making school more meaningful to the students, produced the greatest improvements in academic grades. This suggests that tailoring instruction to students' interests and creating an intellectually challenging environment is vital to increasing passion in schools, leading to further student investment in their education and, as a result, improving grades even more so than promoting positive student self-efficacy or teaching effective self-regulation strategies.

The discrepancies in current research concerning the roles that self-regulation and self-efficacy play in contributing to student success have driven the current study. Additionally, it is unclear as to what extent these characteristics influence student achievement online. The Online Academic Success Indicators Scale (OASIS) is an instrument designed to measure student levels of self-regulation and self-efficacy specific to online courses. We hypothesized positive correlations between the OASIS, preexisting measures of student self-regulation and self-efficacy, Internet self-efficacy, and, subsequently, higher final grades in online courses (Caprara et al., 2008; Debowski, Wood, & Bandura, 2001; Joo, et al., 2000; Pintrich & De Groot, 1990; Yukselturk & Bulut, 2007; Zimmerman, Bandura, & Martinez-Pons, 1992).



The participants were 266 undergraduate students at a small university in south Georgia with ages ranging between 18 and 43 years (M = 21.67). The sample consisted of 213 females and 52 males. One participant did not indicate their sex. Of the 266 participants, 143 were Caucasian, 92 were African-American, 10 were multiracial, six specified their race as 'Other', five were Asian/Pacific Islander, four were Hispanic, three were Arabic, and one was Latino/Latina. Two participants did not provide their ethnicity. Additionally, 115 people were Psychology majors, 88 were Education majors, 39 were Speech and Language Pathology majors, three were Nursing majors, two were Biology majors, three were Business majors, two were Exercise Science majors, four were Criminal Justice majors, one was an English major, and eight participants listed their major as 'Other'. One participant did not list a major. The participants indicated the number of online and web-hybrid courses they have completed with responses ranging between zero and 30 courses (M = 2.35). Participants were sampled from undergraduate courses in psychology and were offered extra credit for participating in the study. Students who elected not to participate in the study were given other opportunities to earn extra credit in the course. The instructor of each course sampled determined the form of extra credit, and subsequently awarded the extra credit to participants upon completion of the survey.


Internet Self-Efficacy Scale. Adapted from Ertmer, Evenbeck, Cennamo. & Lehman (1994) and Murphy, Coover, & Owen (1989), the Internet Self-Efficacy Scale comprises 13 items designed to measure self-efficacy for using the Internet (Joo et al., 2000). Each item in the scale is a statement related to the participant's perceived ability to interact with the Internet. Sample items were as follows: "I feel confident starting an Internet program," and "I feel confident linking to desired screens by clicking." Participants are instructed to respond to each of the 13 statements using a Likert scale, with responses ranging from 1 (not at all true) to 5 (very true). Scores on this scale have revealed high internal consistency with a Cronbach's alpha of .95.

Motivated Strategies for Learning Questionnaire (MSLQ). The version of the Motivated Strategies for Learning Questionnaire used by Pintrich and De Groot (1990) was an adaptation of the final version developed by Pintrich, Smith, Garcia, and McKeachie (1993), which was still under evaluation at the time of the study. Items on the MSLQ are designed to measure students' motivational beliefs and self-regulated learning. Three sub-scales of this modified version of the MSLQ were utilized in the present study, including the Self-Efficacy subscale, the Cognitive Strategy Use subscale, and the Self-Regulation subscale. There were 9 items for the Self-Efficacy, 12 items for the Cognitive Strategy Use, and 9 items for the Self-Regulation scales. Sample items for Self-Efficacy were: "Compared with other students in an online class, I expect to do well," and "I'm certain I can understand ideas taught in an online course." Sample items for Cognitive Strategy use were: "When I study I put ideas into my own words," and "I copy my notes over to help me remember material." Sample items for Self-Regulation were: "I ask myself questions to make sure I know the material I have been studying," and "Before I begin studying 1 think about the things I will need to do to learn." Students are instructed to rate themselves on a 7-point Likert scale, from 1 (not at all true of me) to 7 (very true of me). Cronbach's alphas for each of the individual scales in the MSLQ were all greater than .70 indicating good internal reliability. Predictive validity of the MSLQ was determined by correlating sub-scale scores with student final course grades. The scale correlations with final grades were significant.

Self-Efficacy for Self-Regulated Learning. One subscale from the Children's Multidimensional Self-Efficacy Scales (Bandura, 1989) was selected for use in this study: self-efficacy for self-regulated learning. This scale includes 11 items that measure students' perceived capability to use self-regulated learning strategies. Sample items were as follows: "How well can you finish homework assignments by deadlines?" and "How well can you arrange a place to study without distractions?" On each of the 11 items, students rate their perceived self-efficacy according to a 7-point Likert scale, with possible responses ranging from 1 (not well at all) to 7 (very well). This scale has been shown to be highly reliable, obtaining a Cronbach's alpha of .87.

Positive Affect Negative Affect Schedule (PANAS). The PANAS (Watson, Clark, & Tellegen, 1988) consists of two 10-item mood scales designed to measure positive affect and negative affect. Participants are instructed to rate the extent to which they have experienced each emotion within the specified time period on a 5-point Likert scale, from 1 (very slightly or not at all) to 5 (very much). In the current study, the time-frame adopted was 'during the past week'. The PANAS has been shown to have high internal reliability, with the Positive Affect subscale obtaining a Cronbach's alpha of .89 and the Negative Affect subscale achieving a Cronbach's alpha of .85. Regression and correlational analysis were used to determine the relationship between the PANAS and measures of depression and anxiety.

Online Academic Success Indicators Scale (OASIS). Self-efficacy beliefs for online courses and students' perceived ability to utilize self-regulated learning strategies in online courses were assessed by 23 items (see Appendix A). Items were selected from 3 published studies on the basis of their relevance to an online learning environment: 2 items were adapted from the Internet Self-Efficacy scale developed by Joo, Bong, and Choi (2000); 4 were adapted from the MSLQ items published in Pintrich and De Groot (1990); 7 were adapted from the Self-Efficacy for Self-Regulated Learning Scale by Zimmerman et al. (1992); and 10 items were developed by the researcher based on the interactive elements of online courses. On each of the 23 items, students are instructed to rate their level of confidence in their ability to complete each of the statements relating to tasks required in an online course on a 7-point Likert scale, from 1 (not confident) to 7 (very confident). A total of 10 items are related to perceived student self-regulation (Items: 1, 2, 3, 5, 10, 15, 16, 17, 19, and 20) and 13 items are related to student self-efficacy beliefs toward online classes (Items: 4, 6, 7, 8, 9, 11, 12, 13, 14, 18, 21, 22, 23). The OASIS has been shown to be highly reliable, with the Self-Efficacy subscale obtaining a Cronbach's alpha of .91 and the Self-Regulation subscale obtaining a Cronbach's alpha of .88. Predictive and discriminant validity of the OASIS was determined by correlating sub-scale scores with scores on previous measures of self-efficacy, self-regulation, and positive and negative affect (see measures listed above). The scale correlations with previous scores were significant and in the predicted direction.


Undergraduate students were asked to participate in an undergraduate study about the roles of self-regulatory techniques and perceived self-efficacy in online courses. Those who chose to participate provided their informed written consent while those that declined were thanked politely. Students who elected not to participate in the study were given other opportunities to earn extra credit in the course; at any time during the term, students could register online to participate in a variety of departmental studies, through which they could earn extra credit toward their course. Participants were supplied with a packet containing the Internet Self-Efficacy Scale (Joo et al., 2000), the Motivated Strategies for Learning Questionnaire (Pintrich & De Groot, 1990), the Self-Efficacy for Self-Regulated Learning Scale (Zimmerman et al., 1992), the Positive Affect Negative Affect Scale (Watson, Clark, & Tellegen, 1988), and the new Online Academic Success Indicators Scale. The scales were presented in a packet to the students in this order. Students were not informed of the nature of these scales in order to avoid potential responses based on social desirability. Participants were instructed to complete the packet honestly and at their leisure. After students completed the packet, they were informed of the nature of the study, the researchers' hypotheses, and the potential implications that the study would have on future research.


Normality of variance assumptions were assessed by examining histograms and the skewness and kurtosis for each of the measures. The Cognitive Strategy subscale of the MSLQ, the Self-Regulation subscale of the MSLQ, and the Positive Affect subscale of the PANAS were all normally distributed. The Internet Self-Efficacy scale, the Self-Efficacy subscale of the MSLQ, the Self-Efficacy for Self-Regulated Learning scale, the OASIS, and the Negative Affect subscale of the PANAS, however, were slightly skewed. Nevertheless, all of these measures were utilized in the study because they have been previously validated and served as measures of concurrent and discriminant validity for the OASIS.

Bivariate correlations were conducted between each of the measures (see Table 1). There were strong, positive correlations between the OASIS and all other measures utilized in the study except for the PANAS. The PANAS was used as a measure of discriminant validity, and the correlations between both the Positive and Negative subscales of the PANAS and the OASIS were not significant enough to indicate a relationship between the measures. Additionally, the Self-Efficacy subscale and the Self-Regulation subscale of the OASIS were highly correlated with each other. These results suggest that the scales are highly related on concepts of self-efficacy, self-regulation, and cognitive strategy use.

An independent samples t-test was performed to see if the number of online courses completed by participants influences participants' self-efficacy as it pertains to online courses. The online course variable was split dichotomously with participants having taken either zero or one online course grouped as "light" in their online coursework and participants having taken two or more online courses grouped as "heavy". Using a two-tailed .05 criterion, the null hypothesis was rejected. There was a significant difference in Self-Efficacy scores on the OASIS between participants having taken light online course loads (M = 76.43, SD = 11.85) and participants having taken heavy online course loads (M = 81.56, SD = 9.94), t(260) = -3.44, p =.001, d= 0.47 (see Figure 1). There was also a significant difference in Self-Regulation scores on the OASIS between participants having taken light online course loads (M = 53.23, SD = 10.42) and heavy online course loads (M = 56.44, SD = 10.37), t(261) = -2.34, p =.020, d = 0.31 (see Figure 2). Participants who had taken two or more online courses had higher self-efficacy beliefs in their ability to perform successfully in an online course than did students who had not taken any online courses or students who had taken only one online course. Likewise, participants in the heavy online course group perceived themselves to be more self-regulated than the students in the light online course group.


Because the OASIS correlates with measures of self-regulation and self-efficacy in traditional classrooms and various online environments, it can be used to provide additional feedback on levels of student self-regulation and self-efficacy in online courses. This instrument is validated by the strong correlations between previous measures of self-efficacy and self-regulation and the OASIS as well as by discriminant validity with the PANAS. Utilizing the OASIS in an online course benefits the instructor of the course by revealing critical characteristics about the students enrolled, and should ultimately benefit the students via specified instruction based on the results of the OASIS. By using the OASIS as a pretest to the course, instructors will be more aware of their students' self-efficacy and self-regulatory capabilities upon entering the class, and be better able to alter their methods of instruction accordingly. For example, if the majority of students have poor self-regulatory skills, instructors will be aware that they need to design the course in a way that will encourage self-regulatory behavior across the entire class.

The correlation between the number of online courses completed and the degree of student self-efficacy is likely explained by mastery experience. Students who were successful in their online courses have evidence that they have what it takes to succeed. On the other hand, students who do not have mastery experiences in their online courses, that is, students who either were not successful in their online courses or have no prior experience in taking online courses, are less likely to feel that they possess the necessary capabilities to do well in this context.

Previous research suggests that self-efficacy is an important motivational influence on self-regulated learning strategy use (Artino & Stephens. 2006; Whipp & Chiarelli, 2004). Furthermore, Zimmerman (1998, 2000) states that self-regulated learning begins with goal setting and planning, which are carried out on the basis of self-efficacy beliefs. For example, students who have low self-efficacy are less likely to ask for help or seek assistance when they are struggling, where help-seeking is a self-regulatory strategy (Artino & Stephens, 2007; Bandura, 1997; Caprara et al. 2008). Students' value of a presented task, coupled with their self-efficacy beliefs are positive predictors of their metacognitive strategy use, which in turn, influence student self-regulatory behavior (Artino & Stephens, 2006). Because of the direct relationship between task value, self-efficacy, and self-regulation, it is safe to say that students who feel highly connected to school and who have high self-efficacy beliefs for online courses are likely to more effectively use self-regulated learning strategies to complete their coursework than students with minimal connections to school and low self-efficacy. Students who value their education and who have high self-efficacy will consequently earn higher final course grades. However, promoting positive associations to education may be more difficult in an online environment than in a traditional face-to-face environment because of the lack of interpersonal interaction with peers and instructors (Pintrich 2000). Instructors should assign coursework that allows students to investigate how the content of the class relates to their chosen field of study in order to demonstrate the relevance of the course, and thereby increasing student connections.

This study, in line with the results from previous research, has concluded that students who are successful in online classes are active participants in learning (Pintrich & De Groot, 1990; Whipp & Chiarelli, 2004; Yukselturk & Bulut. 2007; Zimmerman, 2002; Zimmerman & Martinez-Pons, 1986). Improving students' self-efficacy beliefs may be a good start to increasing student performance on online academic tasks. By improving their self-efficacy beliefs, students will begin to use more of the self-regulatory strategies that are vital to success in an online course, thereby improving their overall performance. Implications for online instructors include providing individual and timely feedback to students concerning their performance in the course and offering constructive criticism along with guidance on how a student can improve will provide a foundation for a resilient sense of efficacy (Bandura, 1997; Yukselturk & Bulut, 2007). Teachers can also guide self-regulatory behavior in the way that they assign course-work. For example, instead of setting a single due date for a project, teachers should set several due dates throughout the course for sections of the project. In this way, students learn to self-regulate enough to meet each of the required deadlines over time.

A potential limitation of this study is the researchers' inability to draw from a sample of students currently enrolled in an online course. Monitoring these students and observing their direct interaction with the course would provide details concerning their self-efficacy and the self-regulatory strategies they employ throughout the course. Future research should utilize an online course throughout the entire duration of the course, terminating with students' final course grades. Researchers should correlate students' final course grades with scores on the OASIS so as to establish a predictive relationship between the OASIS and academic achievement.

In summary, the results provide empirical evidence for the importance of considering both self-efficacy and self-regulated learning in online educational environments. Student self-efficacy beliefs are closely tied to students' ability to self-regulate their behavior, which directly influences academic outcomes. Educators need to be aware of the relationship between self-efficacy, self-regulatory behaviors, and student academic outcomes so that they may benefit the largest number of students possible. Recognizing the signs of low self-efficacy and working to increase self-efficacy is vital to promoting success in any classroom setting. While educators may continue to teach self-regulatory behavior, these learned behaviors will not be cemented in the foundation that self-efficacy provides and may not persist once a class, and the self-regulatory scaffolding, has ended. Furthermore, online instructors need to be aware of the environmental differences between traditional instruction in a face-to-face classroom setting and an online learning environment, and alter their instructional methods accordingly. Online instructors need to address self-efficacy and self-regulation even more than traditional instructors and also need to incorporate strategies that allow for sufficient interpersonal interaction.


Artino, A. R., Jr. & Stephens, J. M. (2006). Learning online: Motivated to self-regulate? Academic Exchange Quarterly, 10(4) 176-182.

Artino. A. R., Jr. & Stephens, J. M. (2007, October). Motivation and self-regulation in online courses: A comparative analysis of undergraduate and graduate students. Paper presented at the annual meeting of the Association for Educational Communications and Technology, Anaheim, CA.

Bandura, A. (1989). Multidimensional scales of perceived self-efficacy. Unpublished test. Stanford University, Stanford, CA.

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W. H. freeman and Company.

Bandura, A. (2002). Growing primacy of human agency in adaptation and change in the electronic era. European Psychologist, 7(1), 2-16.

Borsch, R. E. & Botsch C. S. (2012). Audiences and outcomes in online and traditional American government classes revisited. PS: Political Science and Politics, 45(3), 493-500.

Caprara, G. V., Fida. R., Vecchione, M., Del Bove, G., Vecchio, G. M, Barbaranelli, C., & Bandura. A. (2008). Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement. Journal of Educational Psychology, 100(3). 525-534.

Debowski, S., Wood. R. E., & Bandura. A. (2001). Impact of guided exploration and enactive exploration on self-regulatory mechanisms and information acquisition through electronic search. Journal of Applied Psychology, 86(6), 1129-1141.

Ertmer, P. A., Evenbeck, E., Cennamo, K. S., & Lehman, J. D. (1994). Enhancing self-efficacy for computer technologies through the use of positive classroom experience. Educational Technology Research and Development, 42(3), 45-62.

Gazzaniga, M.S. Heatherton. T.F. & Halpern, D. (2010). Psychological sciences: Mind, brain. and behavior (3rd edition). W. W. Norton & Company.

Joo. Y. J., Bong. M., & Choi. H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and Internet self-efficacy in web-based instruction. Educational Technology Research and Development, 48(2), 5-17.

McCoy, C. (2010). Perceived self-efficacy and technology proficiency in undergraduate college students. Computers & Education. 55, 1614-1617.

Murphy. C. A., Coover. D., & Owen. S. V. (1989). Development and validation of the Computer Self-Efficacy scale. Educational and Psychological Measurement, 49. 893-899.

Pintrich P. R. (2000) An achievement goal perspective on issues in motivation terminology, theory, and research. Contemporary Educational Psychology, 25, 92-104.

Pintrich, P. R. & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40.

Pintrich, P. R., Smith. D. A. P., Garcia. T., & McKeachie. W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801-813.

Siegle, D., Reis. S. M., & McCoach, D. B. (2006. June). A study to increase academic achievement among gifted underachievers. Poster presented at the 2006 Institute of Education Sciences Research Conference, Washington, DC.

Watson. D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 47, 1063-1070.

Whipp, J. L. & Chiarelli, S. (2004). Self-regulation in a web-based course: A case study. Educational Technology Research and Development, 52(4), 5-22.

Yukselturk, E. & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology & Society, 10(2), 71-83.

Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Sehunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1-19). New York: Guilford.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich & Moshe Zeidner (Eds), Handbook of self-regulation (pp. 13-39). New York: Academic Press.

Zimmerman, B. .1. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2). 64-70.

Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29(3), 663-676.

Zimmerman, B. J. & Martinez-Pons, M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategics. American Educational Research Journal, 23, 614-628.

Zimmerman, B. J. & Martinez-Pons. M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82(1), 51-59.

Appendix A

Online Academic Success Indicators Scale (OASIS)

Write a number from 1-7 using the box below to answer the following questions about your ability to navigate an online course. If you have never taken an online course, please predict your behavior if you were enrolled in an online course and answer the questions according to your prediction.
1          2   3           4    5           6   7

Not            Not too          Pretty          Very
Confident      Confident        Confident       Confident

In regard to taking an online collegiate class, how confident are you that you could successfully...

1. Effectively use a calendar/planner to organize your online classwork. ____

2. Manage your time on the computer. ____

3. Maintain focus on an assigned task (ex: not surfing other webpages while working on an assignment). ____

4. Learn material presented in an online class. ____

5. Eliminate distractions that interfere with a suitable learning environment. ____

6. Upload an assignment. ____

7. Post a comment on a discussion board. ____

8. Post a reply on a discussion board. ____

9. Compose an email. ____

10. Meet online deadlines for course requirements. ____

11. Download and save files posted for course. ____

12. Communicate/network with classmates via email. ____

13. Communicate/network with classmates via discussion boards. ____

14. Problem solve when experiencing technical difficulties. ____

Write a number from 1-7 using the box below to answer the following questions about your ability to navigate an online course. If you have never taken an online course, please predict your behavior if you were enrolled in an online course and answer the questions according to your prediction.
1          2  3          4  5          6  7

Not           Not too       Pretty        Very
Confident     Confident     Confident     Confident

In regard to taking an online collegiate class, how confident are you that you could successfully...

15. Ask for help from your online teacher. ____

16. Ask for help from your online peers. ____

17. Take notes on presented material during an online class. ____

18. Take a test or quiz online. ____

19. Motivate yourself to persevere throughout the length of the online course. ____

20. Use external resources to gather information for class (ex: library). ____

21. Recall information presented in the online course at a later date. ____

22. Receive a good grade. ____

23. Understand material presented in the online course. ____




Valdosta State University
Table 1. Correlation Matrix for Self-Efficacy and Self-Regulation
Measures, PANAS, and OASIS Scores

Measure         1    2          3          4          5

l. OASIS_SE     -   .77 (**)   .45 (**)   .64 (**)   .40 (**)
2. OASIS_SR     -   .30 (**)   .62 (**)   .40 (**)   .41 (**)
3. Internet_SE  -   .37 (**)   .36 (**)   .23 (**)   .15 (*)
4. MSLCQ_SE     -   .36 (**)   .32 (**)   .31 (**)   .33 (**)
5. MSLQ_Cog     -   .54 (**)   .55 (**)   .26 (**)  -.20 (**)
6. MSLQ_SR      -   .59 (**)   .28 (**)  -.21 (**)
7. SEforSR      -   .30 (**)  -.19 (**)
8. PANAS_Pos    -  -.18 (**)
9. PANAS_Neg    -

Measure           6          7          8          9

l. OASIS_SE      .28 (**)   .35 (**)   .23 (**)   -.18 (**)
2. OASIS_SR      .56 (**)   .28 (**)  -.18 (**)
3. Internet_SE  -.01       -.17 (**)
4. MSLCQ_SE     - 32 (**)
5. MSLQ_Cog
7. SEforSR
8. PANAS_Pos
9. PANAS_Neg

(*) Correlation is significant at the p < .05.
(**) Correlation is significant at the p < .001
Note. The Internet Self-Efficacy Scale is from Joo et al. (2000): the
Motivated Strategies for Learning Questionnaire (MSLQ) is from Pintrich
and De Groot (1990): the Self-Efficacy for Self-Regulated Learning
Scale is from Zimmerman et al. (1992): the Positive Affect Negative
Affect Schedule is from Watson et al. (1988).
COPYRIGHT 2017 Project Innovation (Alabama)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Bradley, Rachel L.; Browne, Blaine L.; Kelley, Heather M.
Publication:College Student Journal
Article Type:Report
Date:Dec 22, 2017

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters