The site of this study was the College of Education, at a large metropolitan university.
Enrollment at this university approaches 15,000 students, of which over 3,000 are in the College
of Education. The metropolitan area population is over one million. This university educates the
majority of teachers entering the profession in the metro area. Continuing education is provided for
educators working on special certification. Also offered are Master and Ph.D. degree programs. As
a metropolitan university, many of its students are non-traditional. By non-traditional we mean
students who are over the age of 25, working full or part-time, maybe with preschool and/or
school age children of their own, while attending university full or part-time. This provides a
student population with a rich diversity in backgrounds, experiences, and special needs (Merriam
A convenience sample of N
= 54 students was used. These students were enrolled in upper
level classes with a significant computer technology component. The curriculum of one course was
explicitly set forth as computers in education, while the other two contained purposeful use of
ALNs. The sample was composed of: 39 females, 15 males; 2 undergraduates, 52 graduates; 5
Afirican-Americans, 8 Americans, 3 Asian-Americans, 31 Caucasians, 3 Latinos, 2 Multi-
ethnic/Mixed, and 2 other. In describing access to computers, 5 students stated that their only
access was while on the university campus (in student computer labs), 49 students stated that they
use computers with Internet access while off campus.
Data were collected by means of a questionnaire containing 33 items. The majority of
questions were Likert-like items using a scale from “very small extent” to “very great extent.”
Seven question were asked to collect factual information such as gender, ethnicity, class
enrollment, etc. All information used in this analysis was derived from questionnaire data. The
questionnaire was reviewed by two assistant professors in the College of Education. One professor
has significant experience integrating ALNs in education curricula. The other professor has
significant experiencein designing survey instruments. Both offered needed comments and
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suggestions that were included in the questionnaire before it was administered. Three sets of
comparable questions measuring the same trait were asked and reliability coefficients calculated (p
> .7). Internal consistency of the questionnaire was check with Cronbach’s alpha (p> .76).
The questionnaire was administrated during the fifth week of the Fall Semester, 1998.
Through the cooperation of their professors, students in two College of Education courses were
asked to complete the survey at the beginning of a regular class session. Also with the cooperation
of the professor, students in another College of Education course were asked to complete the
survey at their own convenience. Questionnaires were completed anonymously by students
beginning on a Monday and ending on a Thursday. Completion of every questionnaire was done
using Test Pilot by by Malcolm Duncan (e-Mail: wmd@Virtual-Indiana.com).
Test Pilot is a software package for the creation of tests and surveys for delivery and
collection via a web server. Test Pilot’s web server component is 100% Java, meaning its user
interfaceis identical on many different computer platforms(web address of Test Pilot is:
consists of a database of test, tutorial or survey questions and a Web server extension to both
administer the surveys and tests and record, score and retrieve user responses. Responses can be
exported to text files and easily imported into statistical analysis software like SPSS.
With respect to each research hypothesis, data analysis was done using MANOVA on the
independent variable gender.
For all tests, assumptions of MANOVA procedures were satisfied, included Levene’s test
for homogeneity of variances. Effect sizes were calculated and power analysis performed.
Hotelling’s T2was used to test for significance of each MANOVA.
Results of the MANOVA data analysis using Hotelling’s T2are summarized in Table 1
below. No significant effect was found for research hypothesis 1: There are no gender
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differences in students’ perceptions of how difficult/easy it is to use computers in
Asynchronous Learning Networks (ALNs), [F (4,49) = 2.310; p> .05]. No significant effect
was found for research hypothesis 2: There are no gender differences in students’ frequency
of using ALNs., [F (4,49) = 1.603; p> .05]. No significant effect was found for research
hypothesis 3: There are no gender differences in how students’ use of e-mail in connection
with their course work, [F (6,47) = 1.163 p> .05]. No significant effect was found for research
hypothesis 4: There are no gender differences in how students’ view the importance of
ALNs to their course work, [F (2,51) = .578 p> .05].
Discussion, implications, conclusions
The scope of this study does not significantly identify gender differences in the technology
component of the curricula at the studied university. The analysis of each research question using
MANOVA returned results consistent with expectations. This result is both expected and desired.
Teaching to differences is a general curricula goal well defined by Feminists Pedagogues. Most
university student bodies are over 50 percent female and are becoming increasingly ethnically
diverse. Students in the Colleges of Education bring many differences to the classroom. Because
of the importance of these diversity issues, research should continue to insure that gender based
differences are not found in the future as universities increase the ALN component of required
course work. Future studies should look at other curricula areas, and other areas of diversity.
Some areas of importance for future study are; age, socioeconomic background, teaching level,
There is a trend in the results of the data analysis that can point to the need for additional
research and possible remedial actions. Research hypothesis 1 asks about concrete computer use.
Each successive research hypothesis asks about less concrete and more abstract computer use, with
research hypothesis 4 being the most abstract question about computer use (see Figure 2).
Although not statistically significant, this finding indicates that females more than males find the
computer itself more difficult to use, that males may be more practiced using computer technology
than females. Prior research has tended to support this finding (Karma 1994)(Dambrot, 1985)
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