Computer Technology Training for Prospective Teachers: Computer Attitudes and Perceived Self-Efficacy.

To be effective users of computer technologies and be models for students' computer use, teachers must have positive computer attitudes and feel self-efficacious in using them. The computer training that teachers receive through their teacher education program is likely to foster positive computer affect, yet the change may require time and development. This longitudinal study examined prospective teachers' changes in perceived anxiety/discomfort with and usefulness of computer technology, frequency of using word processing, e-mail, spreadsheets, database management, statistical packages, and CD-ROM databases, and perceived self-efficacy with the six selected computer technologies over three years of study. Statistical analyses of data involved three time periods and two student-teacher cohorts. Significant time effect, cohort effect, and time by cohort interaction effect are discussed.

Technology is having a significant influence on the future of education. In some cases, computer technology has manifested its potential in helping teachers facilitate students' problem-solving skills, scientific inquiry, understanding of abstract mathematical concepts, communication skills, and workplace competencies (Office of Technology Assessment, 1995). Current learning theories, such as constructivism, emphasize using computer technology as a tool to access and organize information, and to construct personal knowledge. On the other hand, the Office of Technology Assessment (1995) reported that "despite technologies available in schools, a substantial number of teachers report little or no use of computers for instruction" (p. 1). Marcinkiewicz (1994) found that more than half of the teaching population displayed a pattern of under use or non-use of computer-related resources in schools. Evans-Andris (1995) examined 72 elementary teachers' computing styles. What was found was that more than half (60%) o f the teachers avoided using computers and treated computer lab visits as an aimless activity. Many teachers have doubts about computer technology and their own ability because computer technology was not part of their learning experience. In addition, the rapid evolution of computer applications in education appears to further complicate the situation. Therefore, teachers must have positive computer attitudes and feel self-efficacious in using computer technologies in order to model effective computer use for their students.

THEORETICAL AND RESEARCH BACKGROUND

Computer Affect

Affect such as attitudes, values, and self-judgments can exert a profound effect on behaviors. Negative emotions are more likely to deter a person from trying new things, while positive affect tends to strengthen a person's belief and promote productive behavior (Campbell & Williams, 1990). The construct of computer attitudes has multiple dimensions. A substantial amount of literature suggests that computer anxiety can be a great obstacle for educators' computer adoption (Gabriel & MacDonald, 1996; Hakkinen, 1995; Handler, 1993; Hunt & Bohlin, 1993; Kernan & Howard, 1990; Koohang, 1989; Liu, Reed & Phillips, 1992; Loyd & Gressard, 1984; McInerney, McInerney & Sinclair, 1994; Reed & Overbaugh, 1992; Reed & Overbaugh, 1993; Robertson, Calder, Fung, Jones & O'Shea, 1995; Stimmel, Connor, McCaskill & Durrett, 1981). Stressful encounters with computers are likely to cause an aversive predisposition toward the object. Many studies also measured attitudes toward computer usefulness to help predict computer behavior . Campbell (1992) found that college students' attitudes toward the usefulness of computers were predictive of their enrollments in college computer courses. Marcinkiewicz (1994) reported that teachers' perceived relevance of computers for teaching was one of the factors that influenced their motivation to integrate computers into teaching.

Perceived self-efficacy is defined as "personal judgments of one's capability to organize and implement actions in specific situations that may contain novel, unpredictable, and possible stressful features" (Schunk, 1984, p. 48). Studies show that perceived self-efficacy with computers is a critical predictor for the use of computer technology (Delcourt & Kinzie, 1993; Ertmer, Evenbeck, Cennamo, & Lehman, 1994; Jorde-Bloom, 1988; Kinzie, Delcourt & Powers, 1994; Olivier & Shapior, 1993). In the past, a number of self-efficacy scales were developed to measure different types of self-efficacy. Olivier (1985) developed computer efficacy scales to assess subjects' separate efficacy beliefs on programming and instruction. Murphy, Coover, and Owen (1989) later devised the Computer Efficacy Scale for measuring general efficacy beliefs with computers. Unlike attitudes, the interpretation of self-efficacy is identified with a set of clearly defined skills or behaviors (Bandura, 1986; Murphy et al., 1989). Ashton and Webb (1986) pointed out that "the development of perceived efficacy fluctuates with the specific educational experiences of teacher-education students" (p. 154). Delcourt and Kinzie (1993) recommended that when measuring computer self-efficacy, results with regard to each specific technology should be interpreted individually.

Training, Computer Attitudes, Perceived Self-Efficacy and Computer Use

Computer affect tends to be influenced by training experience and is likely to exert an influence on future computer behavior. In a review of the computer attitudes literature, Dupagne and Krendl (1992) stated that "teachers who have had computer training are more likely to show positive attitudes toward computer use in the classroom" (p. 423), while those who had no prior experience exhibited high anxiety towards using computers (Liu et al., 1992). Reed and Overbaugh (1992) found that both an introductory computer course and a content-specific computer course influenced teacher education students' attitudes to change. Others (Hunt & Bohlin, 1993; McInerney et al., 1994) reported that exposure to a variety of computer applications, word processing in particular, seemed to also have an effect on attitude change. Among studies on computer self-efficacy, Miura (1987) found that "completion of a high school computer programming course, and current and past enrollment in college computer science classes, were pos itively related to the self-efficacy scale" (p. 307). Ertmer et al., (1994) reported that students who were more familiar with computers and utilized word processing and e-mail technology more frequently had judged themselves as more effective users of those technologies.

Training and educational practices can provide an important influence on a person's sense of efficacy. However, "experience, per se, is not likely to exert a direct influence on people's decisions to learn about or use computers, unless computer efficacy beliefs have been affected" (Hill, Smith, & Mann, 1987, p. 310). Adoption of computer technology requires time and development (Dwyer, Ringstaff & Sandholz, 1990; Faison, 1996; Hall, George, & Rutherford, 1977; Sheingold & Hadley, 1990). Therefore, it is essential to provide teachers with adequate computer training early in the educational process. Salomon (1984) cautioned educators not to overlook the long-range cumulative effects of computers. Studies on the relationship between computer training, development of computer affect, and computer use over time are needed.

PURPOSE OF THE STUDY

The purpose of this longitudinal panel study was to examine the effects of computer training that two cohorts of prospective teachers received through their teacher preparation process at the University of Virginia. Most participants were required to complete three two-hour, hands-on, computing tool class sessions, and a two-credit, full-semester, Introduction to Instructional Technology course. Responses toward 14 selected computing variables (frequency of using six types of computer technologies, two computer attitudes and perceived self-efficacy with the six computer technologies) were analyzed to help understand prospective teachers' changes in computer affect and computer use over time.

METHODS

Subjects

Participants in this study were prospective teachers who entered the five-year teacher education program. These students first enrolled as regular liberal arts students in the College of Arts and Sciences, and then applied for permission to enter the teacher education program in their second semester or during their second year of undergraduate study. After completing all requirements, they received both a Bachelor of Arts/Science degree and a Master's of Teaching at the end of their fifth year. Besides technology courses, prospective teachers in this study observed and participated in various instructional technology activities. For example, the third-year students were encouraged to participate in the computer-based Curry Teaching Simulations, which fostered basic classroom behavior-management skills (Strang, Landrum, & Ulmer, 1991). Faculty in different concentration areas demonstrated computer technology usage in the method courses. Teaching internships provided opportunities for prospective teachers to apply computer technology to classroom teaching.

Procedure

The Computer Technology Survey (Kinzie et al., 1994) was used to collect data. The two prospective teacher cohorts received similar computer training. Panel One was admitted to the teacher education program a year before Panel Two. Both panels were surveyed three times. The first survey (pretest) took place in the first semester (or Summer term) after these prospective teachers entered the teacher education program (usually in their second year of undergraduate studies). Most prospective teachers completed the computing tool class sessions (on word processing, electronic mail, and bulletin board systems) in their first semester of study in the program. The majority of prospective teachers enrolled in the mandatory Introduction to Instructional Technology course in the Fall semester of their third year. The second survey (posttest) was administered at the end of this course. The course content included word processing, electronic mail, spreadsheets, computer graphics, instructional software, library databases , and audiovisual material production and utilization. The last survey (follow-up test) was given at the end of student teaching semester (Fall semester of their fifth year). Only a proportion of senior students took a three-credit elective advanced computer technology course.

Instrumentation 

The Computer Technology Survey includes three parts: demographic information and two instruments--Attitudes Toward Computer Technologies (ACT) and Self-Efficacy with Computer Technologies (SCT) (see Appendix). The demographic section contained questions to elicit the student's identification number, age, sex, racial/ethnic status, degree/program enrolled, major, employment, frequency of use and course experience in using six common technologies (word processing, e-mail, spreadsheets, database management software, statistical packages, and CD-ROM databases). Frequency of using each computer technology was assigned to five levels (never = 1; at least once/year = 2; at least once/month = 3; at least once/week = 4; daily = 5). Responses for course experience with the six types of computer technologies were coded as No = 0 and Yes 1.

The ACT scale consists of 19 items to assess perceived comfort/anxiety with (8 items) and perceived usefulness of (11 items) computer technologies. To obtain the ACT score, responses to the negatively phrased item stems were re-coded (1=4, 2=3, 3=2, and 4=1) and summed for each subscale. The highest possible score for the comfort/anxiety scale was 32, and 44 for the perceived usefulness scale (the highest possible total score was 76). The SCT scale contains 46 items: 10 items on word processing, nine items on email, seven items on spreadsheet software, seven items on database management software, seven items on statistical packages, and six items on CD-ROM databases. The SCT scores were summed and reported separately for each type of computer technology. For the purpose of interpretation, the four-point scale discriminated four distinct levels of perceived self-efficacy with a computer technology: very low (strongly disagree), low (slightly disagree), high (slightly agree), and very high (strongly agree). Co nstruct validity was tested using Principal Component Analysis with varimax rotation. Sets of item loadings indicated a valid measure of each factor extracted. High internal consistency reliability (alpha) estimates for the factors on the ACT scale and the SCT scale were also reported (Kinzie et al., 1994).

Data Collection and Analysis

The data collection process started in the Fall semester of 1991. All surveys were conducted in a classroom setting, so the response rates were generally high. Only those who failed to take the class or were absent from the class when the survey took place did not respond. The same data collection strategy was used with the next prospective teacher panel in the Fall semester of 1992. The last set of data for this cohort was collected in the fall of 1995. Data collected over the three time periods were combined and matched by preservice teachers' student IDs. Only responses from those who completed all three surveys were selected for statistical analyses. After data screening and matching, Panel One (n=108) was designated Cohort 1 (n=44), and Panel Two (n=104) was Cohort 2 (n=42). Demographic variables such as age, sex, ethnic background, and computer course experience were analyzed using descriptive, t-test, and Chi-Square statistics. Fourteen individual univariate analyses (ANOVA) were conducted to analyze change over time. Each ANOVA was a repeated-measure two-factor (time by cohort) design. No control or manipulation was administered to the study sample.

RESULTS

The data screening and matching process yielded an unexpectedly high data attrition rate (59%), even though each response rate was high. Possible explanations included: (a) a proportion of students dropped out of the program after the first or second survey or did not complete the program at the same pace as their peers did; (b) some students joined the program late (in their third year instead of second year), but still graduated after the fifth year; (c) during the fifth-year students' teaching internships, each contentendorsement group met at a different schedule, and attendance (and completion of the research measures) was subject to students' teaching schedules; (d) a number of post-baccalaureate Master's degree students were present at the teaching internship meeting with the fifth-year students and completed the measures. To find out if the selected sample was significantly different from the rest of the panel, each panel was divided into seven groups (based on their participation in the three surveys ) for statistical comparisons. Significant group differences were found in Panel One between those who completed all three surveys (n=44) and the group (n=21) who participated only in the last survey (Lin, 1997). Nineteen out of the 21 respondents did not identify themselves as fifth-year preservice students. It was likely that members of this group were Master's program students whose responses were collected by accident. The decision of only analyzing responses from those who completed all three surveys was retained.

The selected study sample consisted of 86 subjects (Cohort 1: n=44; Cohort 2: n=42). The results of t-test and Chi-Square comparisons indicated no significant cohort differences in age, gender ratio, proportions of ethnic backgrounds, and course exposure to six selected computer technologies (word processing, e-mail, spreadsheets, database management, statistical packages, and CD-ROM databases) at each time period. However, it was documented that Cohort 1 learned to use e-mail with an old Prime computer mail system and Cohort 2 with the newer VA-PEN system (Lin, 1997). Results of 14 ANOV As indicated that changes in these variables were statistically significant. Tukey post hoc tests were performed to identify when the change occurred. In the meantime, statistically significant differences between the two cohorts were found in some variables.

Change in Frequency of Use Over Time

A significant time effect was found in frequency of using all six selected computer technologies. Between Time 1 and Time 3, frequency of using statistical packages was the only variable that did not change significantly. From Time 1 to Time 2, only frequency of using database management software did not show significant increase, while from Time 2 to Time 3, only frequency of word processing continued to increase significantly. Table 1 contains the mean scores and summary of the Tukey post hoc tests for the six frequency-of-use variables. When looking at the variables individually, all three post hoc comparisons of frequency of word processing were significant (Time 1 [less than] Time 2 [less than] Time 3). For frequency of using e-mail, spreadsheets, and CD-ROM databases, significant increases were found between Time 1 and Time 2 but not between Time 2 and Time 3 (Time 1 [less than] Time 2 = Time 3). Frequency of using database management software did not show significant increase from Time 1 to Time 2 and from Time 2 to Time 3. However, the overall increase between Time 1 and Time 3 was significant (Time 1 = Time 2; Time 2 = Time 3; Time 1 [less than] Time 3). Frequency of using statistical packages showed significant increase between Time 1 and Time 2. However, the decrease between Time 2 and Time 3 canceled out the overall increase between Time 1 and Time 3 Crime 1 [less than] Time 2 ; Time 2 = Time 3; Time 1 = Time 3).

Change in Computer Attitudes Over Time

A significant time effect was found in both attitudinal variables. No significant cohort effect or time by cohort interaction effect was found. A summary of mean scores and Tukey pairwise post hoc tests of computer attitudes can be found in Table 2. Both perceived computer comfort and perceived computer usefulness showed significant change between Time 1 and Time 3. For perceived computer comfort, increase between Time 1 and Time 2 was not significant but increase between Time 2 and Time 3 was (Time 1 = Time 2 [less than] Time 3). For perceived computer usefulness, neither the increase between Time 1 and Time 2 nor the increase between Time 2 and Time 3 was significant (Time 1 = Time 2; Time 2 = Time 3; Time 1 [less than] Time 3). Change in the two computer attitude variables did not quite follow the change pattern of frequency of using computer technology.

Change in Perceived Self-Efficacy Over Time

A significant time effect was found in perceived self-efficacy with all six selected computer technologies. Mean scores of all six variables increased significantly between Time 1 and Time 3. From Time 1 to Time 2, four variables a showed significant increase, while from Time 2 to Time 3, only two indicated a significant increase. Table 3 summarizes the mean scores and result of Tukey post hoc comparisons. For perceived self-efficacy with word processing, all three post hoc comparisons were significant (Time 1 [less than] Time 2 [less than] Time 3). For perceived self-efficacy with e-mail, spreadsheets, and CD-ROM databases, significant change was found between Time 1 and Time 2, but not between Time 2 and Time 3 (Time 1 [less than] Time 2 = Time 3). For perceived self-efficacy with database management software, no significant change was found between Time 1 and Time 2, and between Time 2 and Time 3 (Time 1 = Time 2; Time 2 = Time 3; Time 1 [less than] Time 3). For frequency of using statistical packages, ch ange between Time 1 and Time 2 was not significant, but change between Time 2 and Time 3 was (Time 1 = Time 2 [less than] Time 3).

Cohort Difference Across Time

A significant cohort effect was found in the frequency of using e-mail and perceived self-efficacy with word processing. Separated cohort mean and standard deviation scores of frequency of using e-mail are presented in Table 4 and Table 5. Cohort 2 (Mean=3.59; out of 5 possible) showed a higher averaged frequency of use in e-mail than Cohort 1 (Mean=3.12). Cohort 2 (Mean=37.61; out of 40 possible) also had a higher level of perceived self-efficacy with word processing than Cohort 1 (Mean=36.09). A significant cohort effect, as well as a significant time by cohort interaction effect, were found in perceived self-efficacy with e-mail. The analysis of simple main effects for both factors is summarized in Table 6. Both cohorts showed a significant increase in perceived self-efficacy with e-mail between Time 1 and Time 2, and between Time 1 and Time 3, but not between Time 2 and Time 3 (Time 1 [less than] Time 2 = Time 3). Cohort 2 appeared to have a higher level of perceived self-efficacy with e-mail than Cohort 1 across time, but the cohort difference decreased over time.

DISCUSSION

Change Over Time

Between Time 1 and Time 2, nine variables changed significantly and five variables did not. These five variables were:

* Frequency of using database management software.

* Perceived computer comfort.

* Perceived computer usefulness.

* Perceived self-efficacy with database management software.

* Perceived self-efficacy with statistical packages.

It was likely that the increases in use and self-efficacy with some of these technologies were related to course exposure and class assignments. For example, basic word processing and e-mail skills were introduced in the short computing tool class sessions. Using word processing, e-mail, and spreadsheet programs was part of the introductory instructional technology course requirements. Cohort 2 also received training on library CD-ROM database searching. Since database management software and statistical packages were never introduced in the three short-class sessions or the required introductory instructional technology course, it is not surprising that no significant increase related to them was found. However, the significant increase in frequency of using statistical packages might be due to the possibility that Psychology and Math Education majors often used some statistical packages within their subject area studies. On the other hand, the increase of perceived computer comfort and perceived computer u sefulness at Time 2 was not statistically significant. It might be because the majority of preservice teachers in this study initially had positive computer attitudes.

Between Time 2 and Time 3, only a proportion of prospective teachers took an elective instructional computing course. It is hard to determine the course effect on the study sample. While most variables did not show significant change, four variables did. They were:

* Frequency of using word processing.

* Perceived computer comfort.

* Perceived self-efficacy with word processing.

* Perceived self-efficacy with statistical packages.

In other research, teacher education students were found to have more experience using computers for word processing than for other purposes (Hunt & Bohlin, 1993; Mowrer-Popiel, Pollard, & Pollard, 1994). In this study, prospective teachers' frequency of using word processing and perceived self-efficacy with word processing continued to increase significantly across time. It also appeared that, as the frequency of using one type of technology stopped increasing, the associated perceived self-efficacy level stopped increasing as well. The only notable exception was the significant increase in perceived self-efficacy with statistical packages between Time 2 and Time 3. This might be related to the previous significant increase in use between Time 1 and Time 2, or perhaps other factors. One thing to note is that the significant increase in perceived self-efficacy with statistical packages was caused by only a small number of prospective teachers (possibly psychology and math education majors) as the mean scores were small and the standard deviations were relatively large. Delcourt and Kinzie (1993) suggested, "experience with computer technologies, either through a course or through frequent use, is a critical area for examination in the study of attitudes and self-efficacy" (p. 40). Nevertheless, course exposure to, and frequent use of, computer technology may exert a more direct impact on the development of perceived self-efficacy than on overall change in attitudes. The significant increase in prospective teachers' perceived computer comfort was not found until after Time 2, and the significant increase in perceived computer usefulness was not detected until Time 3.

Cohort Difference

The authors expected that Cohort 1 would not be statistically different from Cohort 2 since no significant cohort differences in age, gender, and ethnic background were found. However, statistical analyses showed that Cohort 2 had a higher frequency of using e-mail and higher levels of perceived self-efficacy with word processing and e-mail than Cohort 1 across time. Although both cohorts appeared to have similar computer training, Cohort 2 entered the teacher education program a year later than Cohort 1. With the rapid advancement of technology, it was likely that some events or changes in the environment might affect the training outcomes. As mentioned previously, in Fall 1990, Cohort 1 was introduced to the e-mail system on the Prime computers. However, in Fall 1991, Cohort 2 switched to the Virginia's Public Electronic Network (VA-PEN) system, which had a better user interface. This difference might have contributed to the cohort differences found in the frequency of using e-mail and the perceived self-e fficacy with email.

Instructional Implications

These changes suggested a positive effect from the computer training that these prospective teachers received. The training experience would include three short computing tool class sessions, one required instructional technology course, a possible elective instructional computing course, and the educational environment that UVA's teacher education program provided. This would include the Faculty Modeling in Methods courses or prospective teachers' learning with a practicing teacher during the teaching internship. Nevertheless, prospective teachers' class experience with the three short class sessions and a mandatory course was strongly reflected in the measurements taken at Time 2. The development of computer attitudes and self-efficacy with computer technology requires time. The cumulative effect of computers may also play a role to the development. Certain limits prevented this longitudinal study from addressing the causal relationship between selected variables. The educational intervention experience co uld not be controlled.

When examining the change patterns, frequency of use and perceived self-efficacy with a type of computer technology appeared to be related. Word processing and e-mail were used by prospective teachers most frequently. Thus, prospective teachers' perceived self-efficacy with these two technologies was also higher. As database management software and statistical packages were used least frequently, the perceived self-efficacy levels turned out to be the lowest as well. This kind of contrast in change pattern suggests that if spreadsheets and database management have subject-specific instructional applications (such as math and social science), prospective teachers need to be exposed to them and encouraged to use them more frequently. This may also apply to those innovative technologies like multimedia project, hypermedia, distance education, and online learning available through the Web.

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               Summary of Tukey Post Hoc Tests for the Time
                       Effect of Frequency of Using
                      Selected Computer Technologies
Frequency of Use     Time                    Tukey Post Hoc Test
                      n    1        2    3       [1&2.sup.q]
Word processing       86
       M                  3.44 [a] 3.91 4.22        6.71 [*]
       SD                 0.88     0.73 0.71
E-mail                85
       M                  2.14     4.02 3.89       20.80 [*]
       SD                 1.37     0.79 0.82
Spreadsheets
       M              85  1.42     2.12 2.09       10.00 [*]
       SD                 1.37     0.79 0.82
Database mgmt.        83
       M                  1.23     1.40 1.51        2.83
       SD                 0.61     0.85 0.90
Statistical packages  84
       M                  1.04     1.26 1.20        4.40 [*]
       SD                 0.19     0.78 0.56
CD-ROM databases      85
       M                  1.12     2.45 2.28       19.00 [*]
       SD                 0.39     0.92 0.88
Frequency of Use
                     [2&3.sup.q] [1&3.sup.q]
Word processing
       M               4.43 [*]    11.14 [*]
       SD
E-mail
       M               1.44        19.44 [*]
       SD
Spreadsheets
       M               0.43         9.57 [*]
       SD
Database mgmt.
       M               1.83         4.67 [*]
       SD
Statistical packages
       M               1.20         3.20
       SD
CD-ROM databases
       M               2.43        16.57 [*]
       SD
NOTE. (a.)out of 5 points possible;
Tukey post hoc test [alpha] level = 0.5
                  Summary of Tukey Post Hoc Tests for the
                          Time Effect of Computer
                                 Attitudes
Computer Attitudes       Time                   Tukey Post Hoc Test
                          n     1     2     3       [1&2.sup.q]
Perceived comfort [a]     81
        M                     21.42 22.42 24.85        2.86
        SD                     5.14  5.45  5.24
Perceived usefulness [b]  75
        M                     37.32 38.29 39.45        2.55
        SD                     4.18  5.18  4.16
Computer Attitudes
                         [2&3.sup.q] [1&3.sup.q]
Perceived comfort [a]
        M                   6.94 [*]  9.80 [*]
        SD
Perceived usefulness [b]
        M                   3.05      5.61 [*]
        SD
Note.
(a.)out of 32 possible;
(b.)out of 44 possible;
Tukey post hoc test [alpha] level = 0.5
                  Summary of Tukey Post Hoc Tests for the
                  Time Effect of Perceived Self-Efficacy
                    with Selected Computer Technologies
Perceived Self-Efficacy  Time                   Tukey Post Hoc Test
                          n     1     2     3       [1&2.sup.q]
Word processing [a]       86
  M                           34.78 36.98 38.74        6.47 [*]
  SD                           5.91  3.69  2.47
E-mail [b]                84
  M                           20.12 32.89 33.69       20.60 [*]
  SD                          11.01  3.60  3.31
Spreadsheets [c]          83
  M                           14.31 21.71 20.74       13.21 [*]
  SD                           7.12  4.15  5.84
Database mgmt. [d]        81
  M                           10.63 11.54 13.32        1.60
  SD                           5.79  6.07  7.26
Statistical packages [e]  82
  M                            9.29  9.40 11.49        0.24
  SD                           4.84  4.57  5.34
CD-ROM databases [f]      82
  M                            7.83 16.67 15.88       16.68 [*]
  SD                           4.35  5.64  6.38
Perceived Self-Efficacy
                         [2&3.sup.q] [1&3.sup.q]
Word processing [a]
  M                         5.18 [*]   11.65 [*]
  SD
E-mail [b]
  M                         1.29       21.89 [*]
  SD
Spreadsheets [c]
  M                         1.73       11.48 [*]
  SD
Database mgmt. [d]
  M                         3.12        4.72 [*]
  SD
Statistical packages [e]
  M                         4.54 [*]    4.78 [*]
  SD
CD-ROM databases [f]
  M                         1.49       15.19 [*]
  SD

Note.

(a.)out of 40 possible;

(b.)out of 36 possible;

(c.)out of 28 possible;

(d.)out of 28 possible;

(e.)out of 28 possible;

(f.)out of 24 possible

Tukey post hoc test [alpha] level = .05

                Means and Standard Deviations for Frequency
             of Using Electronic Mail of Cohort 1 and Cohort 2
Cohort n  Mean [a]  SD
1      44   3.12   0.98
2      42   3.59   0.96
Note.
(a.)mean score out of 5 possible
         Means and Standard Deviations for Perceived Self-Efficacy
               with Word Processing of Cohort 1 and Cohort 2
Cohort n  Mean [a]  SD
1      44  36.09   4.56
2      42  37.61   3.20
Note.
(a.)mean score out of 40 possible
                     Summary of Separate Analyses for
                      Interpreting the Cohort by Time
                      Interaction Effect of Perceived
                    Self-Efficacy with Electronic Mail
Simple               Time                     F          Tukey Post Hoc Test
Main Effect           n     1     2     3                    [1&2.sup.q]
            Cohort 1  43
               M [a]      15.07 31.77 32.98 166.31 [***]      21.41 [*]
Time           SD          8.43  4.05  3.55
Effect      Cohort 2  41
               M          25.41 34.07 34.44  27.85 [***]       8.93 [*]
               SD         10.99  2.62  2.90
Simple
Main Effect [2&3.sup.q] [1&3.sup.q]
               1.55      22.96 [*]
Time
Effect
               0.38       9.31 [*]
                 Cohort 1 Cohort 2
       Time 1 84
         M       15.07             25.41 4.85 [***]
Cohort Time 2 84
Effect   M       31.77             34.07 3.18 [***]
       Time 3 84
         M       32.98             34.44 2.23 [*]
       t
       Cohort 1 [less than] Cohort 2
Cohort
Effect Cohort 1 [less than] Cohort 2
       Cohort 1 [less than] Cohort 2
Note.
(a.)mean score out of36 possible;
(*.)p[less than].05
(**.)p[less than].01
(***.)p[less than].001
Tukey post hoc test [alpha] level = .05