Computer Use, Self-Confidence, and Attitudes: A ... - Science Direct

Computers in Human Behavior; Vol. 9, pp. 427-440.1993 Printed in the U.S.A. AU rights reserved.

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0747-5632l93 $6.00 + .co 0 1993 Pergamon Press Ltd.

Computer Use, Self-Confidence, and Attitudes: A Causal Analysis Donald G. Gardner, Richard L. Dukes, and Richard Discenza University of Colorado at Colorado Springs

Abstract

- Based on attitude-behavior theory, it was hypothesized that computer use would enhance beliefs about self-perceived computer confidence, which would in turn affect attitudes towards computers. Primary level students (N = 723) completed self-report surveys that measured these three constructs. Covariance structural analyses revealed that (a) computer use positively affected computer confidence, and (b) computer confidence positively affected computer attitudes. Unexpectedly, direct computer use had a negative effect on computer attitudes, when confidence was held constant. Results suggest how computer educational environments might be improved.

Clearly the use of computers in schools, especially personal computers, has grown dramatically in recent years. Concomitant with that increase has been an associated intensification in research that is designed to evaluate how to best utilize the power of computers in the classroom setting. Some of this research has evaluated effectiveness of computer use by students as a function of their personal characteristics (e.g., Hativa, 1988; Woodward, Camine, & Gersten, 1988; Tobias, 1989). Other research has examined effects of computer use on students’ interpersonal behaviors (e.g., Clements & Nastasi, 1988). Yet another line of research has focused on the causes and consequences of attitudes and beliefs that students develop about computers. Personal characteristics such as gender, age, educational level, socioeconomic standing, general anxiety, mathematics anxiety, rigidity, and locus of control have been shown to be at least weakly related to computer attitudes and beliefs (e.g., Arndt, Feltes, & Hanak, 1983; Erickson, 1987; Howard, 1986; Loyd & Gressard, 1984; Popovich, Hyde, Zakrajsek, & Blumer, 1987; Raub, 1981; Tobias, 1988; Weil, Rosen, & Wugalter, 1990). A problem with much of this research is that it tends to be theoretically unclear, a point made by Igbaria and Parasuraman (1989). The result is that researchers end up knowing that, for example, males and females differ in their Requests for reprints should be addressed to Donald G. Gardner, College of Business, Colorado, Colorado Springs, CO 80933-7150. 427

University

of

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attitudes towards computers (Loyd & Gressard, 1984), but do not know why. The present study attempts to overcome this problem by empirically examining three core variables that affect how well computers are utilized in classrooms: (a) the degree to which students actually use computers for a variety of purposes, (b) students’ self-confidence in using computers, and (c) students’ attitudes towards computers. We consider these variables to be “core” in the sense that if any of the three is negative or deficient, then successful, positive, repeated experiences with computers are less likely to occur. In addition, the present study examines students in the dynamic ages at which the three constructs are being formed and/or experienced. We base our hypotheses about the causal relationships among the core variables in the attitude-behavior theory of Fishbein and Ajzen (Fishbein, 1979; Fishbein & Ajzen, 1975). Their theory of reasoned action has been widely used in psychological research. For example, their theory was validated in an applied study of managerial attitudes towards computer information systems (Pavri, 1988). In the Fishbein and Ajzen model, beliefs about an object lead to an attitude toward it. These beliefs arise from experiences with the object, what others have said about the object, and other sources of information. When beliefs about an object are favorable, attitudes also will be favorable. Attitudes in turn lead to certain behavioral intentions regarding the object (e.g., pursue, use, avoid, sabotage). These intentions, in turn, affect the actual behaviors manifested towards the attitude object. Finally, behaviors feed back onto beliefs, and can modify the beliefs about the attitude object. If the results from manifesting the behavior reinforce beliefs about the attitude object, then attitudes consequently are reinforced. If behaviors manifested toward the attitude object are not reinforced, beliefs about the attitude object may change (positively or negatively), and subsequently the resulting attitudes towards the object also change. If experiences with the attitude object are favorable, then beliefs about the utility of the object may be reinforced, and favorable attitudes toward the object may increase. Because of the feedback loop, the sequence of events in Fishbein and Ajzen’s model is essentially:

These relationships form the basic hypotheses in the present study. Specifically, we reasoned that as students initially gain experience with computers, they develop certain beliefs about them (e.g., useful, challenging, fun). This, in time, leads to attitudes about computers, which may be favorable or unfavorable. Based upon these computer attitudes, behavioral intentions regarding computers are formed: Students may approach computers and use them, or attempt to avoid them. If these behaviors are reinforced through the various consequences (e.g., teacher praise, peer pressure) in the typical classroom computing environment, a number of events may occur. Beliefs about computers become strengthened. In the case of the student with favorable beliefs and attitudes towards computers, beliefs about them (e.g., their ability to make them work) strengthen and attitudes become more positive. In the case of the student having negative beliefs and attitudes about computers, beliefs about them (e.g., impossible to use) may also strengthen, and attitudes can become increasingly negative. As attitudes become more strongly embraced, behavioral intentions towards the computers become stronger: The student more actively uses or avoids using computers.

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In the present context, we hypothesize that the core concepts begin with behaviors that create the direct experience that students have with computers, such as the length of time one has used them, the different contexts in which computers are used, the breadth of experience in programming, and the frequency of computer usage (as have others: cf. Chen, 1986; Clarke, 1986; Guimaraes & Ramanujam, 1986; Howard, 1984; Maurer, 1983; Raub 1981). According to Fishbein and Ajzen (1975), these direct experiences with computers will result in beliefs about one’s attributes with regard to the computer. We further hypothesize that beliefs concerning oneself as they relate to the computer are a result of these direct experiences, and they are expressed as confidence in one’s ability to do well in activities that involve the computer, such as learning new material, using the computer (to do word processing, mathematics, to draw pictures, etc.), or teaching these skills to someone else (Chen 1986; Erickson 1987; Gilroy & Desai, 1986). Beliefs borne of direct experience also involve self-assurances that one can cope with a computer successfully. Nonanxious individuals believe that they can do well on challenging computer tasks, do advanced work with computers, perform well on a test about computers, and can avoid becoming intimidated by them (Howard, 1984; Loyd & Gressard 1984; Maurer, 1983; Raub, 1981). In contrast, extremely computer-anxious individuals may suffer “keyboard paralysis,” hyperventilation, vomiting, and other strong emotional reactions (Shore, 1985, chapter 1; Emurian, 1989). “Attitudes toward computers” refer to an individual’s location on evaluative or affective dimensions with regard to computers (Fishbein & Ajzen, 1975). As such they are influenced strongly by beliefs that one holds about computers. These attitudes include enjoying computers and computer problems, getting immersed in them, and being glad that computers are becoming more widely used (Bernet-, 1984; Chen, 1986; Clarke, 1986; Loyd & Gressard, 1984). Also, attitudes toward computers include one’s desire to be recognized for proficiency with them, such as not being concerned about possible stigmatization as a computer “nerd” or as a “brain” (Erickson, 1987). Computer attitudes also can include one’s evaluation of the utility of computers in society, such as their importance beyond school in performing a job and earning a living (Howard, 1984; Raub, 1981). Consistent with Fishbein and Ajzen’s model, we hypothesize that as students develop beliefs about their ability to work with computers, this will in turn affect the favorableness of their attitudes toward computers. Implicit in this model is the notion that attitudes will affect behavior in succeeding time segments (Fishbein & Ajzen, 1975; Jagodzinski & Clarke, 1984; Robey, 1979). Alternatively, it has been hypothesized that computer experience directly affects computer attitudes, as well as computer confidence (Loyd & Gressard, 1984; Sievert, Albritton, Roper, & Clayton, 1988). That is, because computer attitudes have been correlated with both computer experience and computer confidence, it has also been hypothesized that experiences with computers have direct, causal effects on computer attitudes. The research design and analyses in the present study allow testing of this competing view on causes of computer attitudes. That is, the hypothesized causal path from experience to attitudes can be evaluated, holding constant any effects attributable to computer confidence. Figure 1 illustrates the basic, causal model that we test. Because the design is not a longitudinal one, we omit the feedback loops which otherwise occur over time. Put simply, we predict that experience with computers will lead to (cause) self-confidence, which will in turn affect attitudes towards computers. Manifest indicators (boxes) of the core conceptual variables (encircled) are also included in Figure 1.

Figure 1. Hypothesized relationships between manifest and latent variables.

Conrguter

utility

Computer Liking

Sel8+Rated Confidence

Lack ot Anxiety

I 1 I

I

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We do not propose that our selection of the manifest indicators is complete, but we believe that they adequately reflect the three core variables, as required by statistical structural equation modeling techniques.

METHOD Subjects

and Procedure

Subjects were 723 fifth- through ninth-grade students from 36 classes in 12 schools in the San Francisco Bay area. All of the students had some exposure to personal computers in their classrooms, but this exposure was not controlled (in the present study) with regard to either quality or frequency. Teachers administered the questionnaires to students in their own classrooms and in those of their colleagues as part of an in-service program that focused on computers and equity in the classroom. The questionnaires were not used for the evaluation of either the teachers or of the in-service program. Measures In addition to demographic information unrelated to the purposes of this study (e.g., age, grade, sex), the following self-report measures were obtained: Computer experience. Subjects indicated when they first had used a computer by marking one of the following alternatives: (a) “this school year,” (b) “last school year,” (c) “last summer,” (d) “before that,” and (e) “It’s so long I can’t remember.” This variable was scored on a 5-point scale. A score of 5 represented the greatest amount of experience with computers, and a score of 1 represented the least amount of experience with them.

Programming experience. Subjects indicated whether or not they used the computer at school to (a) write programs in BASIC, (b) write programs in Pascal, and/or (c) write programs in other computer programming languages. Answers to these items did not form an internally consistent scale (coefficient alpha was .27), so the items were analyzed separately. (This alpha and subsequent ones refer to the present study.) Frequency of use. Subjects indicated when they used computers at school by checking the following response categories: (a) before school, (b) during recess or free periods, (c) after school, and/or (d) during lunch. The scale of frequency of computer use was computed by summing the items that were answered in the affrrmative. Coefficient alpha was only .34 for this scale; nevertheless, it formed meaningful relationships in the analyses discussed below. For reasons of parsimony, the summed scale was used in the analyses reported below. School uses. Subjects indicated whether or not they had used computers in school to (a) play video games; (b) play educational games; (c) write stories, letters, or reports; and/or (d) draw pictures or graphs. Responses to these items were summed to form a “variety of school uses” scale. Higher scores on the scale represented a greater variety in use of computers. Coefficient alpha was .76. Confience. Subjects rated their confidence on eight different computer-related tasks (e.g., make a picture using a computer, use a computer to type a letter).

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Subjects indicated their degree of confidence on a scale that ranged from 10 (Yrn not confident at all - no way can I do that”) to 100 (“No problem. I can do that.“). Data for these items were converted into deciles (1 to 10) and summed before analysis. Coefficient alpha was .79 for this scale. The remaining measures were taken from the BELCAT-36 (Erickson, Lowery, & Blomberg, 1988), an empirically developed instrument that was designed to measure attitudes toward the computer and reactions to computer usage (see Dukes, Discenza, & Couger, 1989, for additional validity evidence). All BELCAT-36 items use 5-point Likert-type scales anchored by the descriptors agree strongly and disagree strongly. tack o~#~ie~. The Anxiety subscale from the BELCAT-36 was reverse-scored to measure freedom from anxiety (i.e., higher scores indicate lower anxiety). The nine items that make up this scale reflect the degree to which subjects were unafraid of computers (e.g., “Computers don’t scare me at all”) or were confident in using computers (e.g., “I have a lot of self-~on~dence when it comes to using a computer”). Coefftcient alpha was .74 for this scale. Computer liking. This subscale from the BELCAT-36 consists of six items that reflect the degree to which subjects exhibit positive affect about using computers (e.g., “I like computer problems”). Coefficient alpha was .78 for this scale. Computer undo. This subscale from the BELCAT-36 consists of eight items that reflect the degree to which subjects perceive computers to be useful (e.g.,“Knowing about computers will help me earn a living”). Coefficient alpha was .79 for this scale. Computer success. This subscale from the BELCAT-36 consists of seven items that measure the degree to which subjects would like to be recognized for their proficiency in using computers (e.g., “I’d like people to think I was smart with computers”). Coefficient alpha was .74 for this scale. Data Analyses

Means, standard deviations, and intercorrelations were computed for all variables. In addition, a several equation analysis using the computer program LISREL VI (Joreskog & Sorbom, 1984) was used to test the causal relations among the core variables described above. Structural equation analysis allows researchers to examine causal relationships among hypo~eti~al ~ons~cts (termed latent var~a~~e~),as opposed to only the product-moment correlations among the measured (or manifest) variables. Foremost among the well-documented advantages of structural equation analysis (see James, Mulaik, & Brett, 1982; Long, 1983a, 1983b) is that random measurement error is removed from the estimated relationships among the latent variables. Thus, the magnitudes of the statistical associations among them are not attenuated by a lack of measurement reliability. Therefore, structural equation analyses provide estimates of relationships that are less biased than they would be if one used only the measured variables of a conventional co~elational technique. In a structural equation model the latent variables are hypothesized to cause variation in the measured variables. The degree to which the causal relationships specified by the theory actually account for observed variances and covariances are assessed using several measures of fit {termed goodness of$t irdices) because of problems that can occur when only a single measure is used (e.g. parsimony, type

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of estimation method, sample size, non-normality, and Type I errors; cf. Bentler & Bonnett, 1980; James et al., 1982; Joreskog & Sorbom, 1984; La Du & Tanaka, 1989; Mulaik et al., 1989). The present study uses a relatively large sample to examine a single structural equation model using maximum likelihood estimators. Based upon recommendations of the authors cited above, the following three goodness of fit indices were used: (a) Joreskog and Sorbom’s GFI (a measure of the relative amount of observed variance and covariance that is accounted for by the theoretical model); (b) the root mean square residual (RMSR) (the average residual covariance remaining after covariances predicted by the model have been subtracted); and (c) the obtained chi-square divided by its associated degrees of freedom. The degrees of freedom serve as a standard by which to judge the cl&square, and it has been suggested that ratios less than 5.00 can be considered a good fit of a model to the data (Wheaton, Muthen, Alvin, & Summers, 1977). The procedure followed in using the LISREL VI program was based on the recommendations of James et al. (1982). After variables were standardized, using listwise deletion of data, the covariance matrix S was computed for the observed variables (which in effect is a correlation matrix). Then the measurement model was tested to see if the hypothesized latent variables explained the observed covariantes as predicted - which it did in our analyses (see below). Once the measurement model is supported, then the metric for the latent variables must be set. This procedure is required because covariance structural analysis cannot simultaneously estimate loadings on - and relationships between - latent variables without a known metric, however arbitrary it might be (Long, 1983a). This problem was overcome (as is standard practice) by arbitrarily setting to 1.00 the loading of one measured variable for each latent variable (Joreskog & Sorbom, 1984). Following this, LISREL VI was used to estimate the parameters illustrated in Figure 1. Goodness-of-fit indices were then computed to evaluate the proposed model.

RESULTS

Means, standard deviations, and intercorrelations for all of the measured variables are presented in Table 1. Note that the average correlation among the measured variables is a modest .146. Perhaps uncontrolled measurement error (discussed above) attenuated these correlations, so the results are especially conducive to further analysis with programs like LISREL VI. The correlations presented in Table 1 served as input to the LISREL VI program, and the remaining results are based on these data. The first step in the structural equation analyses is to evaluate the measurement model. It was hypothesized above that three latent variables would account for the observed variances and covariances of the manifest variables, as illustrated in Figure 1. This measurement model was tested, and the results are presented in Table 2. As can be seen from Table 2, the measurement model was an excellent one for the latent variables of computer confidence and computer attitudes, and it was acceptable for the degree of computer use. The measurement model was accepted as specified. The next step was to evaluate the causal relationships illustrated in Figure 1. Loadings for each of the three latent variables were arbitrarily set to one (as suggested by Joreskog & Sorbom, 1984, and by Long, 1983a), and the model was analyzed. The results are presented in Figure 2. The chi-square for this model is 94.90,

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Table 1. Means, Standard Variable 1. 2. 3. 4. 5. 6. 7. 8. 9.

x

Lack of anxiety Self-rated confidence Computer liking Computer utiti Computer success School uses Basic experience Pascal experience Other programming experience 10. Frequency of computer use 11. Computer experience

Deviations,

SD

25.73 6.74 55.78 16.18 21.70 5.30 30.22 6.06 27.18 5.16 2.30 1.18 0.47 0.50 0.05 0.21 0.06 0.24

1

2

and Intercorrelations 3

4

5

(.74)= .53 (.79) 44 34 (.78) 34 .23 .49 f.79) 33 .25 44 .43 (.74) .15 .2f .08 .ll .03 .14 .16 -66 .Ol -.03 .lO .ll -.Ol .Ol .Ol .lO .12 -.02 -.02 -.15

6

of Study Variables 7

(.76) .29 1.00 .13 .13 .17 .05

8

9

1.00 .15

1.00

10

0.88

0.66

.lQ

.15

.20

.16

.07

.24

.Ol

.13

.OQ (34)

3.77

1.20

.12

.16

.Ol

.04

.03

.16

.12

.04

.08

11

.12 1.00

Note. IV= 596 listwise. Correlations greater than .07 are significant at p < .05. aReliabilii

estimates (coefficient alphas) appear on diagonal where appropriate.

with 41 degrees of freedom (p < .OS). The GFI was .972, suggesting a very good fit of the model to the observed variances and covariances (1.00 is perfect). The RMSR was .046, indicating that the average correlation remaining after controlling for predicted relationships was quite small (0 is perfect). This finding is further evidence of a model that fits the data well. The chi-~u~e/degrees of freedom ratio of 2.32 also suggests a very good fit of the model to the data (Marsh & Hocevar, 1985). Lastly, inspection of the t ratios for individual parameters indicated that all of the free parameters to be estimated were significant at p < .OS. Overall, the model was accepted as being a very good fit to the observed data. The predicted effect of computer use on reducing anxiety and increasing confidence was supported, The standardized parameter estimate between the latent use and confidence variables was .64, suggesting that experience in using computers bolsters confidence in one’s abilities to use them. Also supporting our hypotheses was a standardized parameter estimate of .75 between the latent variables of confidence and computer attitudes. It appears that as students become more confident in their abilities to use computers (i.e., they are less anxious about the prospects of using them), they develop favorable attitudes toward computers. The Fishbein and Ajzen (1975) model is supported with respect to these two causal connections. Table 2. Results

From Test of Measurement

Model

Latent Variable

Manifest Variable School uses Basic experience Pascal experience Other experience Frequency of use Computer experience Confidence Lack of anxiety Computer liking Computer utility Computer success

Computer Use

Computer Confidence

Computer Attitudes

.80 .54 .38 .39 .47 .40 .74 .89 .89 .76 .72

2 k

I

V

Computer Utility

Figure 2. LISREL estimates of parameters for cornplate modal; latent variable metrics fixed.

Progreme

school USES

ottmr

We also specified a causal path from computer use to computer attitudes. Fishbein and Ajzen (1975) posit that effects of use on attitudes will be mediated by changes in beliefs about computers. However, it has also been hypothesized that computer use could lead directly to favorable attitudes (Loyd & Gressard, 1984). The obtained parameter estimate of -.29 suggests just the opposite: The more experience students had with computers, the less they liked them, even as this experience increased confidence. In this study, familiarity tended to breed contempt. This unexpected finding is consistent with the results of Collis (1985) and Dambrot, Watkins-Malek, Silling, Marshall, and Carver (1985), but does not support Fishbein and Ajzens’ model, the foundation for our study. However, while the direct effect of experience on attitudes is negative, the overall effect is positive, direct + indirect = -.29 + (X34)f.75) = .19, consistent with the Fishbein and Ajzen model. That is, higher use results in higher confidence, which in turn results in higher positive attitudes. On the other hand, when con~dence is held constant, greater use results in more negative attitudes.

DISCUSSION

Our study provides support for the hypothesis that experiences with computers affect beliefs about them, which in turn affect attitudes about computers. These findings also support the focus on the types of experiences students have when they first begin to use computers. For beliefs to be positive in response to experience, the experience must be pleasant, rewarding, important, and without coercion. In this study, the quality of the experience with computers was uncontrolled, so many of these factors may have been missing from the encounters that students in our sample had with machines prior to their responses to our survey. Students apparently felt that they were becoming more skilled in using computers as their experience increased, but apparently many of them did not enjoy the experience (or the increased competence). In terms of the Fishbein and Ajzen (1975) attitude model, the causal relationship between behaviors (in the form of experiences with computers) and beliefs (about ability to use computers) was supported. Contrary to the model was a direct relationship between experiences and attitudes. For many subjects, the experience of using computers led to negative attitudes about them. While one study is hardly convincing evidence that the model is in need of revision, it is nonetheless worth exploring this unexpected finding. Recent research in the area of self-concepts and self-evaluations could shed some light on this finding. Pelham (1991) discusses how experiences with attitude objects affect beliefs about one’s self-competence, which in turn affect how people behave in relationship to the attitude object. If experiences with an object, like a computer, are consistent over a period of time, then stable beliefs about self-competence vis-&vis the object form. Individuals who consistency succeed in using computers are predicted to develop strong and stable self-perceptions of competence in using them. This in turn should motivate them to seek out situations in which they can reaffh-m this self-belief, and should also cause positive attitudes towards computers. People self-verify stable beliefs about themselves. But what if early experiences with the attitude object are negative? Pelham (1991) theorizes that people will (a) be motivated to avoid the object, and (b) consider competence with the object to be relatively unimpo~ant to their self-evalua-

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437

tion. If the negative experiences cannot be avoided, then negative self-perceptions of competence will develop. On the other hand, if individuals can avoid the object, weak beliefs about the object develop. Applying this logic to the present study, if some students’ early experiences with computers were negative, they may have simply avoided them. Therefore, they never would have developed strong and stable self-beliefs about their abilities to use computers. It is possible that the brief experiences may have been sufficient to cause these students to dislike computers, as reflected in their negative attitudes towards them. Students in this state of attitude equilibrium would have weakened the empirical relationship between experiences and confidence (and the resulting link to attitudes), because they lacked sufficient experience to have stable, accurate views of their computer abilities. They would know enough, though, to know that they do not like computers, accounting for the weak but negative link between experiences and attitudes. In the context of the Fishbein and Ajzen (1975) model, this line of reasoning suggests that time and experience dimensions should be added to the model. Certainly beliefs about attitude objects develop in part from experiences with the objects. However, these beliefs become stable and confidently held only after multiple, consistent, usually positive experiences. If an individual can successfully avoid continued, negative experiences, stable beliefs may not develop. Individuals may have doubts as to whether their competence vis-a-vis the object is as bad as they thought, but they still can harbor negative attitudes toward the object. Given the limits of our data, this argument is largely conjecture on our part, but it may be worth exploring in future studies of computer training. Would forcing students to continue using computers after initial negative experiences strengthen effects on subsequent confidence, and then attitudes? Many attempts to mitigate negative reactions to computers have focused on the issue of early experiences. Some of these attempts have involved the environments in which computers were used. A seminal article by Nickerson (1981) presented a taxonomy of issues relating to computer environments. Closely related are improvements in hardware, software, and user support (e.g., Dambrot et al., 1985; Guimaraes & Ramanujam, 1986). Combining these areas is the emerging area of human-computer interaction (e.g., Paxton & Turner, 1984). Certainly, improvements in this area have the potential to greatly increase the quality of early experiences that users have with the computer (Shore, 1985). Other researchers have focused on the process by which computer training is delivered. The way that new information is provided is critical to learning: It must be presented in ways that are nonthreatening and understandable. This notion is basic to all theories of education. For example, class size, pace and progressive difficulty, and allowing trainees input into selection of hardware, software, and applications have been studied by a number of authors (e.g., Arndt et al., 1983; Barrow & Karris, 1985; Castellano & Awad, 1987; Howard, Murphy, & Thomas, 1986; Rowe & Kleiner 1987). These aspects of the learning process by no means exhaust the list, but they do give insight into dimensions that seem worthy of future research. Our results also support the finding by Weil and colleagues (1990) that “computet-phobia” often can be traced to childhood. In our study there was considerable variation in measures of anxiety, negative attitudes towards computers, and (echoing Weil et al., 1990) “self-critical dialogues . . . when contemplating future computer interaction” (p. 352). In some of our students the seeds of computerphobia were already sown. It will take much additional effort to transform these uncomfortable computer users into confident, comfortable users.

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In addition, our finding of the negative relationship between experience and attitudes also support Weil and colleagues’ (1990) recommendation that introduction of computers in public schools be done with extreme care. They suggest that computers be introduced by teachers who themselves are comfortable with using them. They further suggest that these “introducers” be skilled in computer use, nonthreatening, and nonevaluative. These actions are predicted to result in favorable beliefs about computers and, in turn, favorable attitudes and use. In sum, our results suggest that increased computer usage causes increases in computer self-confidence, which in turn causes favorable attitudes towards computers. Teachers and trainers should build upon early successful experiences that students have with computers. We would suggest that computer trainers strive to make the computing experiences as successful for students as possible, while simultaneously emphasizing the importance of computers to society and to students’ lives. However, our results also suggest that if early experiences with computers are negative, and computers are subsequently avoided, students develop negative attitudes toward them. Early failures should be a major focus of trainers’ attention so that they do not develop into stable negative attitudes towards computers. Future research should focus on ways in which trainers can effectively prevent such processes from occurring. Acknowledgments - Data for this research were gathered under a grant from the National Science Foundation, Division of Materials Development and Research, #MDR 84-70529. The authors express their gratitude to Tim Erickson for making the data available to them.

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