1 Online Value and Self-Efficacy Scale Running head: ONLINE VALUE AND SELF-EFFICACY SCALE Development and Initial Validation of the Online Learning Value and Self-Efficacy Scale Anthony R. Artino Jr. and D. Betsy McCoach University of Connecticut Artino, A. R., & McCoach, D. B. (2007, April). Development and initial validation of the online learning value and self-efficacy scale. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.
2 Online Value and Self-Efficacy Scale Abstract The objective of this study was to develop a quantitative self-report measure of perceived task value and self-efficacy, and to establish reliability and validity evidence for the instrument. In Study 1 (n = 204), 28 survey items were created for the Online Learning Value and Self-Efficacy Scale (OLVSES), and an exploratory factor analysis (EFA) was completed on the items. Results suggested two interpretable factors: Task Value and Self-Efficacy. In Study 2 (n = 618), confirmatory factor analysis (CFA) suggested several survey modifications that resulted in a refined, more parsimonious version of the OLVSES. The resulting 11-item, two-factor scale appears to be psychometrically sound, with reasonable factor structure and good internal reliability. Instrument applications and suggestions for future research are discussed.
3 Online Learning Task Value and Self-Efficacy Scale 1 Development and Initial Validation of the Online Learning Value and Self-Efficacy Scale 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). 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, postsecondary institutions have recognized the utility of online learning. A recent survey of 2,200 U.S. colleges and universities by the Sloan Consortium (2006) found that 96% of large institutions (greater than 15,000 total enrollments) have some online offerings; 62% of Chief Academic Officers rated learning outcomes in online education as the same or superior to traditional, face-to-face instruction; 58% of schools identified online education as a critical long-term strategy; and overall online enrollment increased from 2.4 million in 2004 to 3.2 million in 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). 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 of their own learning (Hartley & Bendixen, 2001; Moore & Kearsley, 2005). Furthermore, a number of researchers have argued that online students, to an even greater extent than traditional learners, require well-
4 Online Learning Task Value and Self-Efficacy Scale 2 developed self-regulated learning skills to guide their cognition and behavior (Bandura, 1997; Hill & Hannafin, 1997). Self-regulated learners are generally characterized as active, motivated participants who efficiently control their own learning experiences in many different ways, including organizing and rehearsing information to be learned, and holding positive beliefs about their capabilities and the value of learning (Schunk & Zimmerman, 1994, 1998). Purpose of the Study The purpose of the present study was to develop a quantitative self-report measure of perceived task value and self-efficacy for learning within the context of self-paced, online training, and to establish reliability and validity evidence for the instrument. Investigations of this kind are particularly important because task value and self-efficacy have been shown to be significant predictors of students use of self-regulated learning strategies and academic achievement in traditional school settings (Pintrich, 1999). Furthermore, numerous experts have suggested that these motivational constructs may be even more critical in predicting student success in online, distance learning situations (Bandura, 1997; Schunk & Zimmerman, 1998). Study 1: Item Development and Exploratory Factor Analysis Item Development Based on the results of a literature review, initial items were developed for each of four subscales (see Table 1; Bandura, 1997; Eccles & Wigfield, 1995, 2002). Following initial item development, six content experts were recruited to participate in a content validation (DeVellis, 2003). Each content expert was provided with the 41 draft items and comprehensive instructions for completing the content validation. Results from the content validation yielded a 28-item instrument designed to measure the four hypothesized latent variables. All items employed a 7- point Likert-type agreement scale.
5 Online Learning Task Value and Self-Efficacy Scale 3 Insert Table 1 Here Participants Participants consisted of a convenience sample of 204 military and civilian personnel from the U.S. Navy. The sample included 150 men (74%) and 53 women (26%). The mean age of the participants was 39.0 years (SD = 9.3; range 22-69). Exploratory Factor Analysis Results A principal axis factor (PAF) analysis with oblique rotation (Oblimin; delta = 0) was carried out on the 28 items using SPSS 13.0 (see recommendations in Preacher & MacCallum, 2003). Oblique rotation methods allow for factors to be correlated, and the assumption was made that the four factors thought to be present in the OLVSES were related. The number of factors to extract was determined on the basis of several criteria, including parallel analysis, examination of the resulting scree plot, and eigenvalues greater than 1.0 (i.e., the K1 criterion; Hayton, Allen, & Scarpello, 2004). The parallel analysis suggested that two factors should be retained. Inspection of the scree plot, although subjective, seemed to suggest two or three factors, while the K1 criterion suggested four initial factors. Based on these results, it was determined that three factors would be retained a reasonable compromise considering the dangers of under-extracting and the tendency for the K1 criterion to over-extract (Hayton et al., 2004). The three initial factors extracted accounted for 57.9% of the total variance in the items. Inspection of the table of communalities revealed that the majority of the items had high extracted communalities (i.e., > 0.40; see Table 2), which indicates that much of the common
6 Online Learning Task Value and Self-Efficacy Scale 4 variance in the items can be explained by the three extracted factors (Pett, Lackey, & Sullivan, 2003). Insert Table 2 Here Several rules were used to determine the number of factors and individual items to be retained in the final solution: (1) factors needed to contain at least three items; (2) all factor pattern coefficients needed to be > 0.50 on at least one factor; and (3) items with factor pattern coefficients 0.30 on more than one factor were dropped (see recommendations in Pett et al., 2003). The factor pattern and structure coefficients from the PAF analysis are displayed in Table 2. The rotated pattern and structure coefficients were judged to have identical factor interpretations. The first factor (extraction eigenvalue = 11.86) included 16 items: TV-1, TV-3, TV-4, TV-6 to TV-17, and TV-19. Although item TV-2 loaded highly on Factor 1, it also loaded on Factor 3 and was therefore dropped from the final solution. The second factor (extraction eigenvalue = 3.52) included eight items: SE-1 to SE-7 and SE-9. Although item SE-8 loaded highly on Factor 2, it also loaded on Factor 3 and was therefore dropped from the final solution. The third factor (extraction eigenvalue = 0.83) had no items with pattern coefficients > 0.50, and, therefore, Factor 3 was dropped from the final solution. The correlation between the two remaining factors was Reliability Analysis Based on the results of the PAF, a reliability analysis was run on the 16 items retained in the Task Value subscale. The Cronbach s alpha for these 16 items was However, further inspection of the inter-item correlation matrix revealed some considerable redundancy in items
7 Online Learning Task Value and Self-Efficacy Scale 5 TV-12 and TV-17. Each of these items was highly correlated (r > 0.70) with four other items in the subscale. Therefore, these two items were deleted. The Cronbach s alpha for the resulting 14- item Task Value subscale was 0.95 (see Table 3). Insert Table 3 Here Next, a reliability analysis was run on the eight items retained in the Self-Efficacy subscale. The Cronbach s alpha for these eight items was However, further inspection of the inter-item correlation matrix revealed that item SE-2 had low correlations (r < 0.40) with four other items in the subscale. Inspection of the item-total correlation for the item confirmed this result. Therefore, item SE-2 was deleted. The Cronbach s alpha for the resulting seven-item Self- Efficacy subscale was 0.89 (see Table 3). Conclusions Results from the EFA did not reproduce the conceived survey structure. Instead of four factors, as hypothesized, results suggested only two interpretable factors: Task Value and Self- Efficacy for Learning with Self-Paced, Online Training. The 14 items that make up the Task Value subscale (see Table 4) assess the respondent s belief that a self-paced, online course is valuable. High scores on this subscale indicate the person finds the online course interesting, important, and useful. The seven items that make up the Self-Efficacy subscale (see Table 4) assess the respondent s confidence in his/her ability to learn the material presented in a selfpaced, online course. High scores on this subscale indicate the person is completely confident he/she can learn the material presented in a self-paced, online format. Insert Table 4 Here
8 Online Learning Task Value and Self-Efficacy Scale 6 Participants Study 2: Confirmatory Factor Analysis In Study 2, CFA was used to test the two-factor solution identified in Study 1. Participants for this study consisted of a convenience sample of 646 military personnel from the U.S. Navy. The sample included 514 men (80%) and 113 women (18%). The mean age of the participants was 20.4 years (SD = 1.0; range 18-24). Confirmatory Factor Analysis Results Using AMOS 4.0, correlations among the 21 OLVSES items were calculated. Listwise deletion of cases with missing data was used. There were 618 cases with no missing values on the 21 items. The two latent variables were the two factors identified by the previous EFA. The 21 observed variables were the actual OLVSES items. Parameters led to each item from the factor hypothesized to represent that item. Additionally, a parameter connected the two factors. The second column of Table 5 provides a summary of the resulting goodness-of-fit indices for the two-factor model. Chi-square was statistically significant; Chi-square/degrees of freedom ratio was > 2.0; Tucker Lewis index (TLI) and comparative fit index (CFI) were < 0.9; and root mean square error of approximation (RMSEA) was > 0.08; all indicating that the model did not fit the data well (see recommendations in Hu & Bentler, 1999). Table 6 provides parameter estimates for the original two-factor model. Overall, the pattern coefficients were consistent with the hypothesized model. Except for SE-4, which had a pattern coefficient of , all factor pattern coefficients were moderate (> 0.55 ) to high (> 0.75 ) on their corresponding factor. Insert Table 5 Here
9 Online Learning Task Value and Self-Efficacy Scale 7 In an attempt to improve model fit, standardized residuals and modification indices (MIs) were examined. Standardized residuals represent differences between the implied covariance matrix and the observed covariance matrix and reflect possible sources of model misfit (Netemeyer et al., 2003). Standardized residuals greater than are considered statistically significant (Netemeyer et al., 2003), and a number of items had standardized residuals greater than Inspection of the MIs also revealed that these same items had large correlated errors (MI > 40.0) with other items. Based on these results, items TV-1, TV-4, TV-9, TV-14, and SE-4 were deleted and the CFA was run again. Insert Table 6 Here The third column of Table 5 provides a summary of the resulting goodness-of-fit indices for this revised model. All fit indices improved as a result of this change, approaching recommended standards. Examination of the MIs again revealed a number of items with large correlated errors (MI > 20.0). Based on these results, items TV-2, TV-5, TV-6, TV-11, and SE-1 were deleted and the CFA was run a final time. The fourth column of Table 5 provides a summary of the resulting goodness-of-fit indices for this revised model. Once more, all fit indices improved as a result of this change. Using the recommended standards of Hu and Bentler (1999), overall model fit was deemed adequate. The Chi-square/df ratio (3.15) approached the recommended level of 2.0, TLI (0.96) and CFI (0.97) were > 0.95, and RMSEA (0.06) was < 0.08.
10 Online Learning Task Value and Self-Efficacy Scale 8 Reliability Analysis Based on the results of the CFA, reliability analyses were run on the six items retained in the Task Value subscale and the five items retained in the Self-Efficacy subscale; Cronbach s alphas for the two subscales were 0.85 and 0.87, respectively (see Table 7). Conclusions Insert Table 7 Here Results from the CFA suggested several survey modifications that resulted in a refined, more parsimonious version of the OLVSES. The resulting 11-item, two-factor scale appears to be psychometrically sound, with reasonable factor structure and good internal reliability. Table 8 provides a summary of the psychometrics for the two subscales that make up the OLVSES. Table 9 presents the final items retained in each subscale. Insert Table 8 Here Relating the Present Study to Past Research In their previous work on expectancy-value theory, Eccles and Wigfield (1995, 2002) found that attainment, interest, and utility value were separable in both EFA and CFA. Results from the present study did not support this conclusion. Instead, these findings suggest that participants did not differentiate between the three theoretically distinct components of task value. That being said, results of the present study are consistent with much earlier research by Parsons (1980), who conducted an EFA and also found no empirical distinctions between the three positive components of task value.
11 Online Learning Task Value and Self-Efficacy Scale 9 Insert Table 9 Here Educational Implications and Future Directions Although factor analysis failed to confirm the hypothesized four-factor model, the resulting two-factor scale appears to have reasonable factor structure and good internal reliability (see reliability guidelines in Gable & Wolfe, 1993). Therefore, the ultimate goal of the present study was achieved; namely, to produce a psychometrically sound survey for measuring respondents perceived task value and self-efficacy with respect to self-paced, online learning. Using the OLVSES, researchers have another tool for exploring the relations between online learners motivational beliefs, as measured by perceived task value and self-efficacy, and other dependent variables of interest. Future validation of the OLVSES should include replication of the current study on a more diverse population. Additionally, future research should examine other forms of validity evidence, such as convergent, discriminant, and predictive validity, which could ultimately improve the functioning of the instrument.
12 Online Learning Task Value and Self-Efficacy Scale 10 References Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company. DeVellis, R. F. (2003). Scale development: Theory and applications (2nd Ed.). Thousand Oaks, CA: Sage Publications. Dillon, C., & Greene, B. (2003). Learner differences in distance learning: Finding differences that matter. In M. G. Moore and W. G. Anderson (Eds.), Handbook of distance education (pp ). Mahwah, NJ: Lawrence Erlbaum Associates. Eccles, J., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents achievement task values and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21, Eccles, J. S., & Wigfield, A. (2002) Motivational beliefs, values, and goals. Annual Review of Psychology, 53, Gable, R. K., & Wolfe, M. B. (1993). Instrument development in the affective domain: Measuring attitudes and values in corporate and school settings. Boston: Kluwer Academic Publishers. Hartley, K., & Bendixen, L. D. (2001). Educational research in the Internet age: Examining the role of individual characteristics. Educational Researcher, 30(9), Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, Hill, J. R., & Hannafin, M. J. (1997). Cognitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4),
13 Online Learning Task Value and Self-Efficacy Scale 11 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, Moore, M. G. (2003). Preface. In M. G. Moore and W. G. Anderson (Eds.), Handbook of distance education (pp. ix-xii). Mahwah, NJ: Lawrence Erlbaum Associates. Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view (2nd ed.). Belmont, CA: Wadsworth. Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks, CA: Sage Publications. Parsons, J. E. (1980). Self-perceptions, task perceptions and academic choice: Origins and change. Unpublished final technical report to the National Institution of Education, Washington, DC. (ERIC Document Reproduction Service No. ED186477). Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research. Thousand Oaks, CA: Sage Publications. Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31, Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift s electric factor analysis machine. Understanding Statistics, 2, 1, Schunk, D. H, & Zimmerman, B. J. (Eds.). (1994). Self-regulation of learning and performance: Issues and educational applications. Hillsdale, NJ: Lawrence Erlbaum Associates. Schunk, D. H, & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: From teaching to self-reflective practice. New York: The Guilford Press.
14 Online Learning Task Value and Self-Efficacy Scale 12 The Sloan Consortium. (2006, November). Making the grade: Online education in the United States, Retrieved March 14, 2007, from survey/pdf/making_the_grade. Tallent-Runnel, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M, & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), United States General Accounting Office. (2003). Military transformation: Progress and challenges for DOD s advanced distributed learning programs (GAO Publication No ). Washington, DC: Author.
15 Online Learning Task Value and Self-Efficacy Scale 13 Tables Table 1 Construct Categories and Conceptual Definitions for Each of the Four Subscales Construct Category Conceptual Definition I. Attainment Value/Importance II. Intrinsic Interest Value III. Extrinsic Utility Value IV. Self-Efficacy for Learning with Self-Paced, Online Training Attainment value (or, more simply, importance) is defined as the importance of doing well on a task in terms of one s self-schema and core personal values. Intrinsic interest value is defined as the inherent enjoyment or pleasure one gets from engaging in an activity, or simply a person s subjective interest in the content of a task. Extrinsic utility value is defined as the usefulness of a task in terms of one s short- and long-term goals, including academic and career goals. Self-Efficacy for Learning with Self-Paced, Online Training is defined as an individual s confidence in his or her ability to successfully learn the material presented in a self-paced, online learning format.
16 Online Learning Task Value and Self-Efficacy Scale 14 Table 2 Results from the Exploratory Factor Analysis with Oblique Rotation (Oblimin; delta = 0) Item Communality Factor TV-10 I was very interested in the content of this course (.87) -.13 (.21).00 (.00) TV-17 I enjoyed learning the material presented in this online course (.88).00 (.39) -.12 (-.13) TV-12 The material presented in this course is useful for me to know (.85).00 (.29).26 (.25) TV-15 It was important for me to learn the material in this course (.83).00 (.27).23 (.23) TV-11 I felt that doing well in this self-paced, online course was imperative for me (.79).00 (.29) -.14 (-.15) TV-16 The knowledge I gained by taking this course can be applied in many different situations (.75).00 (.21).00 (.00) TV-3 I will be able to use what I learned in this course in my job (.79).00 (.31).22 (.21) TV-6 In the long run, I will be able to use what I learned in this course (.82).13 (.40).19 (.817 TV-7 I really enjoyed completing this self-paced, online course (.80).13 (.42) -.22 (-.24) TV-4 It was personally important for me to perform well in this course (.74).00 (.30).00 (.00) TV-2 Understanding the material in this course was important to me (.75).00 (.29).33 (.32) TV-13 Completing this course moved me closer to attaining my career goals (.70).00 (.22).00 (.00) TV-9 This course provided a great deal of practical information (.72).00 (.30).00 (.00) TV-19 Finishing this online course gave me a sense of accomplishment (.74).13 (.39) -.17 (-.19) TV-8 Performing well in this course made me feel good about myself (.74).16 (.42) -.25 (-.26) TV-14 This self-paced, online course included many interesting activities (.71).00 (.35) -.24 (-.25) TV-1 I liked the subject matter of this course (.66).00 (.26).00 (.00) TV-18 The information I learned in this course has very little use in my daily life. (REV) (.46) -.12 (.00).00 (.00) TV-5 This online course was very boring. (REV) (.45).20 (.35) -.28 (-.30) SE-6 I am confident I can do an outstanding job on the activities in a self-paced, online course (.36).80 (.82).00 (.00) SE-4 I am confident I can learn without the presence of an instructor to assist me (.24).79 (.77).00 (.00) SE-7 I am certain I can understand the most difficult material presented in a self-paced, online course (.30).78 (.79).00 (-.12) SE-3 Even in the face of technical difficulties, I am certain I can learn the material presented in an online (.24).77 (.75).00 (.00) course. SE-1 I can perform well in a self-paced, online course (.21).77 (.75).00 (-.13) SE-9 Even with distractions, I am confident I can learn material presented online (.35).68 (.73) -.15 (-.20) SE-8 I am confident I can successfully navigate through a self-paced, online course (.30).64 (.64).35 (.30) SE-5 I find it difficult to comprehend information presented in a self-paced, online learning format (.16).57 (.56) -.11 (-.16) (REV) SE-2 I am confident I can successfully log in to an online course management system (.31).54 (.56).19 (.15) Note. N = 204. Pattern coefficients are presented first, followed by structure coefficients in parentheses. Entries in bold indicate pattern coefficients >.50 on at least one factor and pattern coefficients.30 on only one factor.
17 Online Learning Task Value and Self-Efficacy Scale 15 Table 3 Reliability Statistics for Each Subscale in the Final Solution for Study 1 Subscale # Items Cronbach s Alpha 95% Confidence Interval Lower Upper Mean Inter-Item Correlations SD of Inter-Item Correlations Task Value Self-Efficacy Table 4 Items Retained in Each Subscale Based on Results of Study 1 Task Value (TV) TV-1 TV-3 TV-4 TV-6 TV-7 TV-8 TV-9 TV-10 TV-11 TV-13 TV-14 TV-15 TV-16 TV-19 I liked the subject matter of this course. I will be able to use what I learned in this course in my job. It was personally important for me to perform well in this course. In the long run, I will be able to use what I learned in this course. I really enjoyed completing this self-paced, online course. Performing well in this course made me feel good about myself. This course provided a great deal of practical information. I was very interested in the content of this course. I felt that doing well in this self-paced, online course was imperative for me. Completing this course moved me closer to attaining my career goals. This self-paced, online course included many interesting activities. It was important for me to learn the material in this course. The knowledge I gained by taking this course can be applied in many different situations. Finishing this online course gave me a sense of accomplishment Self-Efficacy for Learning with Self-Paced, Online Training (SE) SE-1 SE-3 SE-4 SE-5 SE-6 SE-7 SE-9 I can perform well in a self-paced, online course. Even in the face of technical difficulties, I am certain I can learn the material presented in an online course. I am confident I can learn without the presence of an instructor to assist me. I find it difficult to comprehend information presented in a self-paced, online learning format. (Reverse Coded) I am confident I can do an outstanding job on the activities in a self-paced, online course. I am certain I can understand the most difficult material presented in a self-paced, online course. Even with distractions, I am confident I can learn material presented online.
18 Online Learning Task Value and Self-Efficacy Scale 16 Table 5 Fit Indices for Confirmatory Factor Analysis Models Tested in Study 2 CFA Model Index Original 2-Factor Model 2-Factor Model, TV-1, TV-4, TV-9, TV-14, & SE-4 Deleted 2-Factor Model, TV-2, TV-5, TV-6, TV-11, & SE-1 Deleted Chi-square df Probability Chi-sq/df ratio TLI CFI RMSEA Chi-sq
19 Online Learning Task Value and Self-Efficacy Scale 17 Table 6 Standardized Factor Pattern Coefficients for the Original 21-Item, Two-Factor Model Observed Variables (Items) Task Value Factor Self- Efficacy TV-1: I liked the subject matter of this course TV-2: I will be able to use what I learned in this course in my job TV-3: It was personally important for me to perform well in this course TV-4: In the long run, I will be able to use what I learned in this course TV-5: I really enjoyed completing this self-paced, online course TV-6: Performing well in this course made me feel good about myself TV-7: This course provided a great deal of practical information TV-8: I was very interested in the content of this course TV-9: I felt that doing well in this self-paced, online course was imperative for me TV-10: Completing this course moved me closer to attaining my career goals TV-11: This self-paced, online course included many interesting activities TV-12: It was important for me to learn the material in this course TV-13: The knowledge I gained by taking this course can be applied in many different situations TV-14: Finishing this online course gave me a sense of accomplishment SE-1: I can perform well in a self-paced, online course SE-2: Even in the face of technical difficulties, I am certain I can learn the material presented in an online course SE-3: I am confident I can learn without the presence of an instructor to assist me SE-4: I find it difficult to comprehend information presented in a self-paced, online learning format. (Reverse Coded) SE-5: I am confident I can do an outstanding job on the activities in a self-paced, online course SE-6: I am certain I can understand the most difficult material presented in a selfpaced, online course SE-7: Even with distractions, I am confident I can learn material presented online Note. Parameter estimates fixed to be zero are reported as dashes (-). Table 7 Reliability Statistics for Each Subscale in the Final Solution for Study 2 Subscale # Items Cronbach s Alpha 95% Confidence Interval Lower Upper Mean Inter-Item Correlations SD of Inter-Item Correlations Task Value Self-Efficacy
20 Online Learning Task Value and Self-Efficacy Scale 18 Table 8 Subscale Summary Statistics Based on Participant Scores from Study 2 Subscale Name # Items Cronbach s Alpha M SD Subscale Correlation Task Value Self-Efficacy Note. N = 638. *p < * Table 9 Items Retained in Each Subscale Based on Results of Studies 1 and 2 Task Value TV-3 TV-7 TV-8 TV-10 TV-12 TV-13 It was personally important for me to perform well in this course. This course provided a great deal of practical information. I was very interested in the content of this course. Completing this course moved me closer to attaining my career goals. It was important for me to learn the material in this course. The knowledge I gained by taking this course can be applied in many different situations. Self-Efficacy for Learning with Self-Paced, Online Training SE-2 SE-3 Even in the face of technical difficulties, I am certain I can learn the material presented in an online course. I am confident I can learn without the presence of an instructor to assist me. SE-5 SE-6 SE-7 I am confident I can do an outstanding job on the activities in a self-paced, online course. I am certain I can understand the most difficult material presented in a self-paced, online course. Even with distractions, I am confident I can learn material presented online.
Online Task Value and Self-Efficacy Scale Running head: ONLINE TASK VALUE AND SELF-EFFICACY SCALE Final Report: Development and Initial Validation of the Online Learning Task Value and Self-Efficacy Scale
Motivation in Online Military Training Running head: MOTIVATION IN ONLINE MILITARY TRAINING Online Military Training: Using a Social Cognitive View of Motivation and Self-Regulation to Understand Students
Original article doi: 10.1111/j.1365-2729.2007.00258.x Motivational beliefs and perceptions of instructional quality: predicting satisfaction with online training* A.R. Artino Department of Educational
The Relationship Between Epistemological Beliefs and Self-regulated Learning Skills in the Online Course Environment Lucy Barnard Baylor University School of Education Waco, Texas 76798 USA HLucy_Barnard@baylor.edu
Validation of the MUSIC Model of Academic Motivation Inventory: A Measure of Students Motivation in College Courses Brett D. Jones & Gary Skaggs Virginia Tech Research presented at the International Conference
ABSTRACT Assessing a theoretical model on EFL college students Yu-Ping Chang Yu Da University, Taiwan This study aimed to (1) integrate relevant language learning models and theories, (2) construct a theoretical
Overview of Factor Analysis Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone: (205) 348-4431 Fax: (205) 348-8648 August 1,
University of Connecticut DigitalCommons@UConn NERA Conference Proceedings 2009 Northeastern Educational Research Association (NERA) Annual Conference 10-22-2009 The Effect of Mathematics Self-Efficacy
Review of the MSLQ 1 Running head: REVIEW OF THE MSLQ A Review of the Motivated Strategies for Learning Questionnaire Anthony R. Artino Jr. University of Connecticut Review of the MSLQ 2 A Review of the
Perceptions of Online Course Communications and Collaboration Lucy Barnard Texas Tech University firstname.lastname@example.org Valerie Osland Paton Texas Tech University email@example.com Kristyn Rose Mesa State
1. Introduction A Brief Introduction to Factor Analysis Factor analysis attempts to represent a set of observed variables X 1, X 2. X n in terms of a number of 'common' factors plus a factor which is unique
Allied Academies International Conference page 7 THE IMPORTANCE OF TEACHING PRESENCE IN ONLINE AND HYBRID CLASSROOMS Richard Bush, Lawrence Technological University Patricia Castelli, Lawrence Technological
The Role of Community in Online Learning Success William A. Sadera Towson University Towson, MD 21252 USA firstname.lastname@example.org James Robertson University of Maryland University College Adelphia, MD USA Liyan
The Use of Survey Research to Measure Student Satisfaction in Online Courses Elaine Strachota Abstract Although student satisfaction in online courses is of concern to institutions of higher learning attempting
Motivation and Self-Regulation in Online Courses: A Comparative Analysis of Undergraduate and Graduate Students Anthony R. Artino, Jr. and Jason M. Stephens University of Connecticut Abstract This study
Internet and Higher Education 12 (2009) 1 6 Contents lists available at ScienceDirect Internet and Higher Education Measuring self-regulation in online and blended learning environments Lucy Barnard a,,
FACTOR ANALYSIS EXPLORATORY APPROACHES Kristofer Årestedt 2013-04-28 UNIDIMENSIONALITY Unidimensionality imply that a set of items forming an instrument measure one thing in common Unidimensionality is
Learner Self-efficacy Beliefs in a Computer-intensive Asynchronous College Algebra Course Charles B. Hodges Georgia Southern University Department of Leadership, Technology, & Human Development P.O. Box
UNIVERSITAT ROVIRA I VIRGILI How to report the percentage of explained common variance in exploratory factor analysis Tarragona 2013 Please reference this document as: Lorenzo-Seva, U. (2013). How to report
Factorial Invariance in Student Ratings of Instruction Isaac I. Bejar Educational Testing Service Kenneth O. Doyle University of Minnesota The factorial invariance of student ratings of instruction across
International Management Journals www.managementjournals.com International Journal of Applied Management Education and Development Volume 1 Issue 3 Factors that Influence Students Learning Attitudes toward
Community College Journal of Research and Practice, 37: 333 338, 2013 Copyright# Taylor & Francis Group, LLC ISSN: 1066-8926 print=1521-0413 online DOI: 10.1080/10668926.2012.754733 EXCHANGE The Community
Farhana Khurshid PhD scholar, King s College London Aim of the study The main aim of the study is: To examine the online collaboration and selfregulation of learning among the students of Virtual University,
Journal of Educational Psychology 1990, Vol. 82, No. 1, 60-70 Copyright 1990 by the American Psychological Association, Inc. 0O22-O663/90/SO0.75 Predictors of Math Anxiety and Its Influence on Young Adolescents'
Walter Wymer, Helena Maria Baptista Alves 143 Walter Wymer, Helena Maria Baptista Alves, A Review of Scale Development Practices in Nonprofit Management and Marketing, Economics & Sociology, Vol. 5, No
Appendix 1 Exploratory factor analysis We fitted exploratory factor analysis models with 1 to 6 factors using maximum likelihood estimation and an oblimin (oblique) rotation. Some fit statistics for these
A Task Technology Fit Model on e-learning Linwu Gu, Indiana University of Pennsylvania, email@example.com Jianfeng Wang, Indiana University of Pennsylvania, firstname.lastname@example.org ABSTRACT In this research, we propose
1 Calculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship) I. Authors should report effect sizes in the manuscript and tables when reporting
Factors affecting professor facilitator and course evaluations in an online graduate program Amy Wong and Jason Fitzsimmons U21Global, Singapore Along with the rapid growth in Internet-based instruction
Peer Assessment for Developing Students' 2D Animation Skills SONGSHEN YEH 1, JUNG-CHUAN YEN 1, JIN-LUNG LIN 2 & SHUN-DER CHEN 3 Department of Multimedia Design, Takming University of Science and Technology
Students Track Their Own Learning with SAS Data Notebook Lucy Kosturko SAS Institute, Inc. Lucy.Kosturko@sas.com Jennifer Sabourin SAS Institute, Inc. Jennifer.Sabourin@sas.com Scott McQuiggan SAS Institute,
A Brief Introduction to SPSS Factor Analysis SPSS has a procedure that conducts exploratory factor analysis. Before launching into a step by step example of how to use this procedure, it is recommended
The Multifaceted Nature of Online MBA Student Satisfaction and Impacts on Behavioral Intentions MEGAN L. ENDRES SANJIB CHOWDHURY CRISSIE FRYE EASTERN MICHIGAN UNIVERSITY, YPSILANTI CHERYL A. HURTUBIS ALTA
Examples: Exploratory Factor Analysis CHAPTER 4 EXAMPLES: EXPLORATORY FACTOR ANALYSIS Exploratory factor analysis (EFA) is used to determine the number of continuous latent variables that are needed to
2003 Midwest Research to Practice Conference in Adult, Continuing, and Community Education Calculating, Interpreting, and Reporting Cronbach s Alpha Reliability Coefficient for Likert-Type Scales Joseph
Journal of Adolescence Journal of Adolescence 30 (2007) 523 528 www.elsevier.com/locate/jado Brief report: Intimacy, passion, and commitment in romantic relationships Validation of a triangular love scale
INTERNAL MARKETING ESTABLISHES A CULTURE OF LEARNING ORGANIZATION Yafang Tsai, Department of Health Policy and Management, Chung-Shan Medical University, Taiwan, (886)-4-24730022 ext.12127, email@example.com
For more resources click here -> Building Online Learning Communities: Factors Supporting Collaborative Knowledge-Building Joe Wheaton, Associate Professor David Stein, Associate Professor Jennifer Calvin,
Assessing Online Asynchronous Discussion in Online Courses: An Empirical Study Shijuan Liu Department of Instructional Systems Technology Indiana University Bloomington, Indiana, USA firstname.lastname@example.org
Factor Analysis and Structural equation modelling Herman Adèr Previously: Department Clinical Epidemiology and Biostatistics, VU University medical center, Amsterdam Stavanger July 4 13, 2006 Herman Adèr
, pp.61-66 http://dx.doi.org/10.14257/astl.2014.48.12 An Empirical Study on the Effects of Software Characteristics on Corporate Moon-Jong Choi 1, Won-Seok Kang 1 and Geun-A Kim 2 1 DGIST, 333 Techno Jungang
Attitude, Behavioral Intention and Usage: An Empirical Study of Taiwan Railway s Internet Ticketing System Wen-Hung Wang Department of Shipping and Transportation Management National Taiwan Ocean University,
A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to
The Validity and Reliability Study of the Turkish Version of the Motivated Strategies for Learning Questionnaire Þener BÜYÜKÖZTÜRK *, Özcan Erkan AKGÜN **, Özden ÖZKAHVECÝ ***, Funda DEMÝREL **** Abstract
Conceptualising and Modelling Virtual Experience for the Online Retailer: The 3D Technology INTRODUCTION Previous studies (e.g., Jiang & Benbasat, 2005; Algharabat & Dennis, 2010 a, b, c) regarding online
Factor Analysis in SPSS To conduct a Factor Analysis, start from the Analyze menu. This procedure is intended to reduce the complexity in a set of data, so we choose Data Reduction from the menu. And the
Constructing a Technology Readiness Scale for Sports Center RFID Door Security System Users Mu-Cheng Wu, National Chin-Yi University of Technology, Taiwan Chao-Chien Chen, Asia University, Taiwan Chih-Hung
1 Neuendorf Factor Analysis Assumptions: 1. Metric (interval/ratio) data 2. Linearity (in the relationships among the variables--factors are linear constructions of the set of variables; the critical source
Internet and Higher Education 16 (2013) 70 77 Contents lists available at SciVerse ScienceDirect Internet and Higher Education University students' online academic help seeking: The role of self-regulation
Factors influencing Adoption of Biometrics by Employees in Egyptian Five Star hotels Ahmed Abdelbary Iowa State University Ames, IA, USA email@example.com and Robert Bosselman Iowa State University
An exploratory study of student motivations for taking online courses and learning outcomes ABSTRACT Sarath A. Nonis Arkansas State University Grant H. Fenner Arkansas State University An investigation
2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore Examining acceptance of information technology: A longitudinal Study of Iranian
http://evaluation.wmich.edu/jmde/ Articles Using Principal Components Analysis in Program Evaluation: Some Practical Considerations J. Thomas Kellow Assistant Professor of Research and Statistics Mercer
Learner-Valued Interactions: Research into Practice Kathryn Ley University of Houston Clear Lake Box 217, UHCL, 2700 Bay Area Blvd. Houston TX 77058, USA firstname.lastname@example.org Ruth Gannon-Cook De Paul University,
Examining the Savings Habits of Individuals with Present-Fatalistic Time Perspectives using the Theory of Planned Behavior Robert H. Rodermund April 3, 2012 Lindenwood University 209 S. Kingshighway, Harmon
Using The Theory Of Planned Behavior To Understand In-Service Kindergarten Teachers Behavior To Enroll In A Graduate Level Academic Program I Ju Chen, (E-mail: email@example.com), Chal Yang University
Principals Use of Computer Technology 85 Lantry L. Brockmeier James L. Pate Don Leech Abstract: The Principal s Computer Technology Survey, a 40-item instrument, was employed to collect data on Georgia
Validation of the Core Self-Evaluations Scale research instrument in the conditions of Slovak Republic Lenka Selecká, Jana Holienková Faculty of Arts, Department of psychology University of SS. Cyril and
Perceptions of Compressed Video Distance Learning (DL) Across Location and Levels of Instruction in Business Courses CONSTANCE R. CAMPBELL CATHY OWENS SWIFT GEORGIA SOUTHERN UNIVERSITY STATESBORO, GEORGIA
Document name: Theoretical perspectives: Eccles expectancy-value theory Document date: 2013 Copyright information: Proprietary and used under licence OpenLearn Study Unit: OpenLearn url: Physical activity:
2011 2 nd International Conference on Economics, Business and Management IPEDR vol.22 (2011) (2011) IACSIT Press, Singapore The Effectiveness of Ethics Program among Malaysian Companies Rabiatul Alawiyah
Constructing Indices and Scales Hsueh-Sheng Wu CFDR Workshop Series Summer 2012 1 Outline What are scales and indices? Graphical presentation of relations between items and constructs for scales and indices
PROC FACTOR: How to Interpret the Output of a Real-World Example Rachel J. Goldberg, Guideline Research/Atlanta, Inc., Duluth, GA ABSTRACT THE METHOD This paper summarizes a real-world example of a factor
1 E-learning: Students perceptions of online learning in hospitality programs Sungmi Song Hospitality Management Iowa State University Robert Bosselman Hospitality Management Iowa State University ABSTRACT
Judgment and Decision Making, Vol. 7, No. 2, March 2012, pp. 181 188 Validation of the Domain-Specific Risk-Taking Scale in Chinese college students Xiaoxiao Hu Xiaofei Xie Abstract Using college student
Running head: SAMPLE FOR STUDENTS 1 Sample APA Paper for Students Interested in Learning APA Style 6th Edition Jeffrey H. Kahn Illinois State University Author Note Jeffrey H. Kahn, Department of Psychology,
USING AMOS GRAPHICS TO ENHANCE THE UNDERSTANDING AND COMMUNICATION OF MULTIPLE REGRESSION CUNNINGHAM, Everarda G and WANG, Wei C Swinburne University of Technology Australia Support for the saying a picture
Principal Components Analysis (PCA) Janette Walde firstname.lastname@example.org Department of Statistics University of Innsbruck Outline I Introduction Idea of PCA Principle of the Method Decomposing an Association
A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to
Statistics Corner Questions and answers about language testing statistics: Principal components analysis and exploratory factor analysis Definitions, differences, and choices James Dean Brown University
A STRUCTURAL EQUATION MODEL ASSESSMENT OF LEAN MANUFACTURING PERFORMANCE Tipparat Laohavichien Department of Operations Management, Faculty of Business Administration Kasetsart University, Thailand email@example.com
A Casual Structure Analysis of Smart phone Addiction: Use Motives of Smart phone Users and Psychological Characteristics Dept. of Social Welfare, BaekSeok Culture University, firstname.lastname@example.org Abstract
2015, TextRoad Publication ISSN: 2090-4274 Journal of Applied Environmental and Biological Sciences www.textroad.com SEM Analysis of the Impact of Knowledge Management, Total Quality Management and Innovation
Instructor and Learner Discourse in MBA and MA Online Programs: Whom Posts more Frequently? Peter Kiriakidis Founder and CEO 1387909 ONTARIO INC Ontario, Canada email@example.com Introduction Abstract: This
Introduction to Data Analysis in Hierarchical Linear Models April 20, 2007 Noah Shamosh & Frank Farach Social Sciences StatLab Yale University Scope & Prerequisites Strong applied emphasis Focus on HLM
ISSN 1308 8971 Special Issue: Selected papers presented at WCNTSE EVALUATION OF PROSPECTIVE SCIENCE TEACHERS COMPUTER SELF-EFFICACY a Nurhan ÖZTÜRK, a Esra BOZKURT, b Tezcan KARTAL, c Ramazan DEMİR & d
16 FACTOR STRUCTURE OF THE ATTITUDES TOWARD RESEARCH SCALE 6 ELENA C. PAPANASTASIOU Intercollege, Cyprus firstname.lastname@example.org SUMMARY Students at the undergraduate level usually tend to view research methods
The Relationship Between Engineering Students Self-Directed Learning Abilities And Online Learning Performances: A Pilot Study Pao-Nan Chou, National University of Tainan, Taiwan ABSTRACT This study aimed
Your consent to our cookies if you continue to use this website.