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Explaining and PredictingResistance to ComputerAnxiety Reduction amongTeacher Education StudentsAlfred P. Rovaia & Marcus D. Childressb

a Regent Universityb Emporia State UniversityPublished online: 25 Feb 2014.

To cite this article: Alfred P. Rovai & Marcus D. Childress (2002) Explaining andPredicting Resistance to Computer Anxiety Reduction among Teacher EducationStudents, Journal of Research on Technology in Education, 35:2, 226-235, DOI:10.1080/15391523.2002.10782382

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Explaining and Predicting Resistance to Computer Anxiety Reduction

among Teacher Education Students

Alfred P. Rovai Regent University

Marcus D. Childress Emporia State University

Abstract This study investigated how resistance to the reduction of computer anxiety among teacher education students can be explained and reliably predicted Instrumentation for the study consisted ofsix self-report questionnaires: (I) the Computer Anxiety Scale, (2) the Computer Attitude Scale, (3) the Computer Experience Scale, (4) the Computer Knowledge Scale, (5) Rotter's Internal-External Control Scale, and ( 6) the trait form of the State- Trait Anxiety Inventory. A stepwise multiple regression using backward deletion was used to find the di­mensions along which computer anxiety can be explained and best predicted The results indicated that the best predictors of retained computer anxiety were computer confidence, trait anxiety, computer knowledge, and computer liking, together accounting/or 69% of the variance of computer anxiety following completion of a computer literacy course. The findings suggest that any efforts to treat retained computer anxie~y in teacher education students should focus on building computer confidence and expanding students' knowledge about computers. (Keywords: computers, computer anxiety, teacher education.)

A substantial number of teachers report little or no use of computers for in­struction despite the presence of more than 5.8 million computers in American schools (U.S. Congress, 1995), and ample research suggests that school use of computers can result in increased achievement and opportunities to learn (Kulik & Kulik, 1991). Furthermore, many of these teachers actively resist us­ing computers despite the availability of hardware and software and the knowl­edge that positive outcomes can follow computer use.

Positive outcome expectations regarding the use of computers are important (Schunk, 1989), but apparently they do not guarantee specific behavior. Al­though teachers may believe that computers will lead to improved teaching and learning, they may choose not to use this technology if they have low confi­dence in their abilities to use computers (low computer self-efficacy), if they fear computers (computer anxiety), or if they simply do not like computers (Delcourt & Kinzie, 1993; Savenye, Davidson, & Orr, 1992). In other words, given technology availability and requisite skills and knowledge to use it, perfor­mance may not occur without positive attitudes about computers, particularly high computer self-efficacy and low computer anxiety. Research evidence sug­gests that an intervenrion that provides computer knowledge and experience can decrease computer anxiety, although evidence also suggests that duration and content of the intervention and general likeliness to feel anxious (trait anxi-

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ety) can be mediating variables that influence outcomes (Howard, ~ 986; Over­baugh, 1993; Savenye, Davidson, & Orr). However, there may be additional mediating variables that can render some individuals resistant tc reduction of computer anxiety in spite of an appropriate intervention, such as a computer literacy course.

PROBLEM STATEMENT

This study addresses the following problem: How can resistance to reduction of computer anxiety among teacher education students be explained and reli­ably predicted? Howard (1986) theorized that the sources or roots of computer anxiety are (1) lack of operational experience, (2) inadequate knowledge about computers, and (3) psychological makeup. He asserted that lack of operational experience with computers is the easiest to treat, inadequate knowledge about computers is more difficult to treat, and psychological makeup is the most diffi­cult to treat. If Howard is correct, the best predictors of computer anxiety at the end of a computer literacy course are related to an individual's psychological makeup because that is the most difficult source of anxiety to treat. He identi­fied trait anxiety (i.e., generally anxious individuals), locus of control, and atti­tudes about computers as potentially important variables in this regard. Loyd and Gressard (1984) identified computer confidence, computer liking, and computer usefulness as important computer attitudes. Therefore, this study treated computer confidence, computer liking, computer usefiLness, locus of control, and trait anxiety as potentially important variables that are related to an individual's psychological makeup and, thus, may be useful in predicting com­puter anxiety at the end of a computer literacy course.

METHOD Sample

The sample consisted of 92 volunteers (91.09% volunteer rare) drawn from the experimentally accessible population of 101 undergraduate teacher educa­tion students enrolled in six sections of a computer literacy course. Eighty-six participants (6.52% mortality rate), of which 84o/o were female, completed the study. The mean age of participants was 23.61 (SD = 5.33).

Treatment The treatment consisted of six sections of a semester-long undergraduate com­

puter literacy course for education majors at a large southeastern university. In­struction took place in a computer lab. Four different experienced instructors taught the six sections of the course. Three sections met for 50 minutes three times per week, and the remaining three sections met for 75 minutes twice weekly for 16 weeks. All planned learning outcomes were from the cognitive dcmain and empha­sized providing computer knowledge and experience using comp-..1ters.

Instrumentation Instrumentation consisted of six self-report questionnaires. The first question­

naire used in this study was the Computer Anxiety Scale (C02vfPAS) (Oetting,

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1983), which consists of24 Semantic Differential scale items to measure com­puter anxiety. For each item the questionnaire used a statement followed by a Semantic Differential scale consisting of adjective pairs, with each adjective as an end anchor in a single 5-point continuum. For example, the first COMPAS statement is: just being around a computer [makes me feel}, with a 5-point con­tinuum anchored by the terms calm and tense. The instrument yields a score that ranges from 20 to 100, with higher scores reflecting greater levels of com­puter anxiety. Oetting reports a Cronbach alpha reliability of .93.

The Computer Attitude Scale (CAS) (Gressard & Loyd, 1985) measures the degree to which participants (1) have confidence in their abilities to use com­puters (computer confidence or self-efficacy), (2) like using computers (com­puter liking), and (3) view computers as a useful part of their future (computer usefulness). The instrument contains 10 Likert scale items to measure each atti­tude. Participants indicate the degree of agreement or disagreement with each statement. For example, an item that measures computer confidence starts with the statement Tm no good with computers, followed by the choices strongly agree, slightly agree, slightly disagree, and strongly disagree. Each CAS item is given a weighted score of 1 to 4 based on the test key. Item scores are then added to ob­tain the score for each scale. Scores can range from 10 to 40. Higher scores re­flect higher degrees of computer confidence, computer usefulness, and com­puter liking. Loyd and Loyd (1985) reported Cronbach alpha reliabilities of .89, .82, and .89, respectively, for the subscales of computer confidence, com­puter liking, and computer usefulness.

The Computer Experience Scale, developed by the primary author, consists of two items that operationalize experience using computers. The first item ad­dresses typical computer usage in hours per week; the second item addresses the length of time of regular computer usage in number of years. The total possible score ranges from 0 (no computer experience) to 25 (substantial computer experi­ence). The split-half correlation method using the equal-length Spearman­Brown formula yielded a coefficient of internal consistency of .68. The coeffi­cient of stability was .83 over a four-week period.

The Computer Knowledge Scale, also developed by the primary author, operationalizes computer knowledge. The subject enters a score that ranges from 0 (no knowledge) to 3 (substantial knowledge) for each of the following computer knowledge areas: computer networks, computer programming, data­base management programs, desktop presentation programs, desktop publish­ing programs, entertainment/ games, multimedia/hypermedia programs, spread­sheet programs, telecommunications, and word processing programs. The total score is obtained by adding all the responses entered by the subject. Scores for this scale range from 0 (no computer knowledge) to 33 (substantial computer knowledge). The coefficient of internal consistency using Cronbach's alpha was .90 and the coefficient of stability was . 77 over a four-week period.

Rotter's Internal-External (I-E) Control Scale (Rotter, 1966) operationalizes locus of control. The instrument is a 29-item, forced-choice test. It includes 6 filler items intended to make the purpose of the test more ambiguous. It pro­vides a single measure of the extent to which participants hold generalized con-

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trol beliefs. Each item consists of two choices: an internal choice and an exter­nal choice. For example, one test item is: "a. People's misfortunes result from the mistakes they make. b. Many of the unhappy things in people's lives are pardy due to bad luck" (Rotter, p. 11). The external response is choice b. The score is the total number of external choices. Scores range from 0 to 23. Lower sco:::-es rdlec:: stronger internality, and higher scores reflect stronger externality. Rotter reported an internal consistency coefficient (Kuder-Richardson 20) of . 70 obtained from a sample of 400 college students. Test-retest reliability for a one-month period using 60 college students was .72.

Finally, the trait form of the State-Trait Anxiety Inventory (STAI) (Spielberg­er, 1983), which contains 20 Liken-scale items, was used to measure trait anxi­ety. Participants indicate the degree of agreement or disagreement with each star:ement. For example, one trait form item states: I feel pleasant, followed by the chc·ices almost never, sometimes, often, and almost always. Each trait form item is given a weighted score of 1 to 4. Scores can vary from a minimum of 20 to 3. maximum of 80, with higher scores reflecting higher levels of trait anxiety. Sp1elberger reported trait form Cronbach alpha reliabilities of .90 and .91, re­spectively, for male (N = 324) and female (N = 531) college students. Addition­ally, he reported test-retest reliabilities of .73 and .77, respectively, for male and female college students over a six-month period.

Design and Analysis This study used a correlation design in order to explain and predict computer

anxiety immediately following a computer literacy treatment based on a set of predictor variables. Basically, a regression solution was sought so that, on the basis c.f the subject's status on a set of predictors, his or her level on the criterion variable could be predicted. The predictor variables were locus of control and trait a:J.x:iety measured at the pretest observation and computer confidence, computer experience, computer knowledge, computer liking, and computer usefulness measured at the posttest observation. All predictor variables were se­le::ted 0::1 the basis of theoretical considerations. Since locus of control and trait anxiety were not expected to vary significantly during one's lifetime (Gaudry & Spielberger, 1971; Rotter, 1966), they were measured at the pretest observation in orcer to help balance testing time between observations. The criterion vari­able, measured at the posttest observation, was computer anxiety. In addition to measuring computer anxiety, computer confidence, computer experience, com­puter knowledge, computer liking, and computer usefulness as posttest mea­surements for the regression analysis, these variables were also measured as pre­tests in order to determine the effect of the computer literacy course on these nriables. The procedures used for each analysis are described in the results sec­t~on below.

RESULTS Table 1 displays the descriptive statistics and the results of tests of significance

between paired means for pretest and posttest measurements of the 86 study partidpants. The differences between the pretest and posttest means of com-

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Table 1. Means, Standard Deviations, and Results of Tests of Significance between Paired Means for Pretest and Posttest Measurements

Pretest Posttest

Variable M SD M SD t

Computer anxiety 54.05 15.22 46.84 11.05 6.32* Computer confidence 31.09 5.80 32.52 5.35 3.03** Computer experience 9.40 7.40 9.92 4.69 1.60 Computer knowledge 9.02 5.94 13.76 4.83 34.09* Computer liking 30.87 4.81 31.31 5.00 .99 Computer usefulness 36.74 2.75 36.48 2.99 .85 Locus of control 10.86 4.09 Trait anxiety 38.53 9.58

N = 86 Possible ranges for each scale are computer anxiety, 20 to 1 00; computer confidence, com­puter liking, and computer uj-efolness, 10 to 40; computer experience, 0 to 25; computer knowl­edge, 0 to 33; locus of control, 0 to 23; and trait anxiety, 20 to 80. Higher scores reflect increased presence of the measured trait. For locus of control, higher scores reflect higher externality. Locus of control and trait anxiety were measured only at the pretest observation. * p < . 001. ** p < . 01.

purer anxiety, computer confidence, and computer knowledge were statistically significant. These results provide evidence to suggest that the computer literacy course was effective in red:King computer anxiety and increasing both com­puter confidence and computer knowledge in students.

A multiple regression was performed between computer anxiety, the criterion variable measured at the posttest observation, and the following predictor vari­ables: computer confidence, computer experience, computer knowledge, com­puter liking, computer usefulness, locus of control, and trait anxiety. All the predictor variables were m·=asured at the posttest observation except for locus of control and trait anxiety, which were measured at the pretest observation. Tabachnick and Fidell (200 1) write that the ratio of cases to predictor variables in a multiple regression analysis must be substantial, and a bare minimum re­quirement is to have at least five times more subjects than predictor variables. Using this criterion, the bare minimum number of subjects needed for this analysis was 35. Because 86 cases were used, the bare minimum criterion was exceeded by a comfortable margin. Data screening revealed that ( 1) means and standard deviations of all variables were plausible, (2) no out-of-range values were identified, and (3) no data were missing. Boxplots for each variable con­firmed that there were no extreme univariate outliers. Additionally, no multi­variate outliers were identitied among any of the cases using the criterion of p < .001 for Mahalanobis distance.

Table 2 is a correlation matrix that shows the Pearson r correlations for the criterion variable and all predictor variables. Of particular interest were any large intercorrelations bervveen pairs of predictor variables, since such correla­tions can adversely affect the results of multiple regression analysis. No such correlations were found.

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Table 2. Correlation Matrix

Variable 1 2 3 4 5 6 7 8

1. Computer anxiety -.77 -.27 -.44 -.65 -.33 ns .49 2. Computer confidence .24 .40 .77 .53 -.25 -.36 3. Computer experience .59 ns ns ns ns 4. Computer knowledge .23 ns ns ns 5. Computer liking .56 -.22 -.25 6. Computer usefulness ns ns 7. Locus of control .39 8. Trait anxiety

N = 86. ns = not significant at the . 05 level

A stepwise multiple regression using backward deletion was performed. Step­wise multiple regression was used to develop a subset of predictor variables that would be useful in predicting posttest computer anxiety and to eliminate those predictor variables that did not provide significant additional prediction. At Step 1, all variables were entered into the regression equation. Thereafter, the variables were removed one at a time based on the probability ofF of .1 0 or greater. At Step 2, computer experience, the variable with the smallest partial correlation co­efficient at Step 1, was removed because the probability of its t, p = .70, was greater than the removal criterion of .10. At Step 3, locus of control, the variable with the smallest partial correlation coefficient at Step 2, was removed because the prob­ability of its t, p = .51, was greater than the removal criterion. At Step 4, com­puter usefulness, the variable with the smallest partial correlation coefficient at Step 3, was removed because the probability of its t, p = .29, was greater than the removal criterion. At Step 4, none of the remaining predictors met there­moval criterion of .1 0, so the backward deletion stopped at this point.

Table 3 displays the unstandardized regression coefficients (B), the standard errors of the predicted values (SE B), the standardized regression coefficients (~), and the t ratios for Step 1 and Step 4 (the first and last steps of the regres­sion analysis). The multiple regression analysis yielded the following equation:

y' = 84.04- .87x1 - .51x2 - .45x1 + .33x4

where y' is the predicted posttest computer anxiety score, X 1 is the computer confidence score, x2 is the computer knowledge score, x

3 is the computer liking

score, and x4 is the trait anxiety score. The null hypothesis that the multiple regression in the population was zero

was tested using an ANOVA. Table 4 provides a summary of this analysis. It displays the source of variability (i.e., the observed variability attributable to the regression [labeled Regression] and the observed variability that was not at­tributable to the regression [labeled Error]), degrees of freedom, and the F ra­tio. Because the regression solution was highly significant, the null hypothesis was rejected.

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Table 3. Summary of Stepwise Regression Analysis Using All Predictor Variables to Predict Posttest Computer Anxiety

Variable B SEB 13 t

Step 1 Computer contldence -.93 .23 -.45 -3.96* Computer experience -.07 .17 -.03 -.39 Computer knowledge -.44 .19 -.19 -2.31 ** Computer liking -.53 .23 -.24 -2.34** Computer usefulness .29 .29 .08 1.01 Locus of control -.14 .19 -.05 -.73 Trait anxiety .33 .08 .28 3.93*

Step 4 Computer contldence -.87 .23 -.42 -3.84* Computer knowledge -.51 .16 -.22 -3.19*** Computer liking -.45 .22 -.21 -2.10** Trait anxiety .33 .08 .28 4.22*

R = . 84 and R2 = . 70 for Step I through Step 3; R = . 83 and R2 = . 69 for Step 4. N = 86 * p < .OOI. ** p < .05. *** p < .OI.

Table 4. Analysis ofVariance for the Multiple Linear Regression Model Using Backward Deletion

Source

Regression Error

df

4 81

F

45.71 * (39.30)

The value enclosed in the parentheses is the mean square error. N = 86 * p < . 000 I.

The analysis conducted for this research question used ordinary least squares regression to provide e\'idence that computer confidence, computer knowledge, computer liking, and trait anxiety can reliably explain and predict posttest com­puter anxiety at the end of a computer literacy course for teacher education stu­dents. The relationship between the predictor variables and the criterion vari­able was highly significant, F(4, 81) = 45.71,p < .0001, indicating that the observed relationship was not simply an unlikely chance occurrence.

Regression assumptions were tested and found to be tenable. Consequently, the least squares predictors of the regression solution have several desirable properties, including unbiasedness and efficiency, and can be used for statistical inference such as conducting tests of statistical significance or calculating confi­dence intervals (Berry, 1993; Berry & Feldman, 1985).

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DISCUSSION

Howard (1986) identified three sources of computer anxiety: (a) lack of op­erational experience with computers, (b) inadequate knowledge about comput­ers, and (c) psychological makeup. He theorized that computer anxiety based on the lack of operational experience with computers is the easiest to treat, computer anxiety arising from knowledge-based origins is of intermediate diffi­culty to treat, and computer anxiety based on an individual's psychological makeup is the most difficult to treat. The predictors &elected for this analysis address all three sources of computer anxiety. If Howard's theory of computer anxiety is accurate, computer experience should be the least significant predic­tor of posttest computer anxiety because it is related to the easiest source of computer anxiety to treat and the treatment placed emphasis on providing computer experience. Computer knowledge should be a more significant pre­dictor because it is more difficult to treat and the treament provided limited computer knowledge (e.g., computer programming was not taught). Finally, predictors related to psychological makeup (i.e., locus of control, trait anxiety, and the attitudes of computer confidence, computer liking, and computer use­fulness) as a group should be the strongest predictors because they address the most difficult source of computer anxiety to treat. Furthermore, there were no plans for the treatments to address the psychological makeup source of com­puter anxiety, although such uncontrolled factors as classroom discussions may have had an indirect effect.

The significant predictors of posttest computer anxiety were related to the psychological makeup of subjects and their computer knowledge. Computer confidence, (3 = -.42, was the strongest predictor, followed by trait anxiety, (3 = .28, computer knowledge, (3 = -.22, and computer liking, (3 = -.21. Taken together, these variables accounted for 69% of the variance of posttest computer anxiety, indicating a very substantial relationship. Computer experience, com­puter usefulness, and locus of control did not account for any significant addi­tional variance.

These findings provide support for Howard's (1986) theory of computer anxi­ety and suggest that attitudes about computers, particularly computer confi­dence or self-efficacy, are useful in explaining retained computer anxiety follow­ing a computer literacy course designed to provide computer knowledge and experience. The inability of locus of control to reliab~y predict posttest com­puter anxiety can possibly be explained by a combination of the following fac­tors: (1) locus of control and trait anxiety were related, r = .38; (2) trait anxiety was a significant predictor of posttest computer anxiety; and (3) the unique contribution of locus of control in predicting posttest computer anxiety was sta­tistically insignificant.

Although computer usefulness was not a significant predictor of posttest com­puter anxiety, it was highly related to computer confidence, r = .53, and to computer liking, r = .56, which were significant predictors. As was the case with

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locus of control, the unique contribution of computer usefulness to predicting posttest computer anxiety was statistically insignificant.

The findings in our study suggest that any efforts to treat retained computer anxiety in teacher education students at the completion of a computer literacy course should focus on building computer confidence and expanding students' knowledge of computers. Any efforts to treat trait anxiety would appear to be difficult, because it is a trait characteristic that Gaudry and Spielberger (1971) assert is unlikely to vary significantly during the course of an individual's life­time. With this in mind, teacher education computer literacy courses might strive to address computer knowledge and confidence by providing meaningful and relevant, yet easily attainable, assignments for students. Tasks might include creating lesson plans that integrate computers into the students' certification area. This allows students to use new computer knowledge within their already familiar area of expertise. Assignments increasing productivity in the school set­ting might also be useful in increasing confidence. Such tasks may increase computer confidence and knowledge and ultimately decrease computer anxiety. Short-term counseling sessions for individuals with more resistant forms of computer anxiety might assist them in coping with a technological society.

We assumed that participants in this study would be typical teacher education students and that the instructors, course designs, and pedagogy sampled by the study would be representative of computer literacy courses. However, these as­sumptions may not have been completely valid. Consequently, generalizability of the findings beyond the present study is limited because only one university was sampled and the learner characteristics, course content, course design, and pedagogy used by the instructors in the study may not have been fully represen­tative of other instructors and other settings. Furthermore, all the limitations as­sociated with correlation designs also apply to this study. In particular, the re­sults of the present study should not be construed to suggest that there is a causal relationship between posttest computer anxiety and computer confi­dence, trait anxiety, computer knowledge, and computer liking. Correlation is a necessary, but not a sufficient, condition for determining causality. Ill

Contributors

Dr. Alfred P. Rovai is an associate professor of education at Regent University, Virginia Beach, Virginia, where he presently teaches courses in research, statis­tics, and program evaluation. His research interest is to understand how and why the sense of community varies in different learning environments and courses and how it is related to achievement and persistence in distance educa­tion programs. Dr. Marcus D. Childress is an associate professor of education at Emporia State University, Emporia, Kansas, where he teaches courses in in­structional design and instructional technology. His research interests include distance education, online instruction, and using technology as a catalyst for school reform. (Address: Alfred P. Rovai, School of Education, Regent Uni­versity, 1000 Regent University Dr., Virginia Beach, VA 23464-9800; alfrrov@regen t.edu.)

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