Learner readiness for online learning: Scale ... - Anitacrawley.net

M.-L. Hung et al. / Computers & Education 55 (2010) 1080–1090 1085Table 2Reliability, AVE, CR of confirmatory factor analysis.Measures Items Composite reliability Average variance extracted (AVE)Computer/Internet self-efficacy 3 0.736 0.486Self-directed learning 5 0.871 0.577Learner control 3 0.727 0.477Motivation for learning 4 0.843 0.573Online communication self-efficacy 3 0.867 0.686statistically significant. Thus, results constitute evidence pointing toward the construct validity of the instrument for learner readiness in anonline learning format.4.2. Validity and reliabilityWe evaluated the OLRS measurement model by examining the composite reliability and the convergent and discriminant validities.Studies have suggested that 0.7 is an acceptable value for a reliable construct (Fornel & Larcker, 1981). The values of composite reliability forthe five subscales given in Table 2 were acceptable.The factor loadings from the CFA provide evidence for convergent validity as all items load sufficiently high on the correspondingconstructs. We also evaluated convergent validity by using average variance extracted (AVE), which should exceed 0.50 (Fornel & Larcker,1981). As indicated in Table 2, all indicator factor loadings exceed the threshold value of 0.50 suggested by Peterson (2000). AVE ranged from0.486 to 0.686. Two constructsdcomputer/Internet self-efficacy and learner controldwere slightly below 0.50. For discriminant validity,the square root of the AVE of each construct should be greater than the correlation shared between the construct and other constructs in themodel and should be at least 0.50 (Fornel & Larcker, 1981). Table 3 displayed the correlations among constructs, with the square root of theAVE on the diagonal. All constructs satisfactorily pass the test, as the square root of the AVE (on the diagonal) is larger than the crosscorrelationswith other constructs. The convergent and discriminant validities of the constructs of the OLRS model are thus acceptable.4.3. Difference among students’ scores of five readiness dimensions on the OLRSTable 4 presents students’ mean scores and standard deviations on the five subscales. To calculate each student’s mean score for everyfactor (dimension), we identified the sum of the answers to each item in that factor, and then divided the sum by the number of that factor’sitems. As Table 4 indicates, all students’ average scores relative to the different dimensions range from 3.75 to 4.37 on a 5-point Likert-typerating scale, indicating that on average these online learners exhibited above-medium levels of readiness toward online learning. In order toinvestigate the differences among the five factors (dimensions) of the OLRS, we conducted a multivariate, repeated one-way ANOVA. Bycomparing the mean of those five dimensions, the higher the mean score, the more online learning readiness the self-evaluating studentsassigned to themselves. The comparisons of these five mean scores can indicate the rank of students’ readiness of the five dimensions. Theresults show that Hottelling’s Trace was significant (F ¼ 237.323, p < 0.001). A post hoc test further revealed that the mean score of factorMFL (motivation for learning) was greater than the mean scores of factors SDL, OCS, and LC; that the mean score of factor CIS (computer/Internet self-efficacy) was greater than the other four factors’ mean scores, the mean score of factor OCS was greater than the mean scores offactors SDL and LC; and that the mean score of factor SDL was greater than the mean score of factor LC. Table 4 shows the results ofa multivariate, repeated, one-way ANOVA and of a post hoc test of the OLRS factors.4.4. Gender difference in online learning readinessTo test for gender differences in the OLRS constructs, we ran a Multivariate Analysis of Variance (MANOVA) that revealed no significantdifference between male and female students, as shown in Table 5.4.5. Grade difference in online learning readinessThis study furthermore analyzed the relationships between students’ grade (i.e., level of accumulated academic credits) and the OLRSdimensions. This study divided the sample students into three groups according to student grade: (1) freshmen and sophomores, (2) juniors,and (3) seniors. The freshman and sophomore students were in the same group because there were not many students in even thecombination of the two groupings (about 7.8% were freshman and sophomore students, 30.5% junior students, and 61.7% senior students).Table 3Correlations among constructs (square root of AVE in diagonal).Computer/Internetself-efficacySelfdirectedlearningLearnercontrolMotivation forlearningComputer/Internet self-efficacy 0.697Self-directed learning 0.087 0.760Learner control 0.052 0.661 0.691Motivation for learning 0.266 0.572 0.511 0.757Online communication self-efficacy 0.151 0.459 0.412 0.621 0.828Online communicationself-efficacyNote: diagonal elements (in bold) represent the square root of the average variance extracted (AVE). Off-diagonal elements represent the correlations among constructs. Fordiscriminant validity, diagonal elements should be larger than off-diagonal elements.