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The Journal of Higher Education
ISSN: 0022-1546 (Print) 1538-4640 (Online) Journal homepage: http://www.tandfonline.com/loi/uhej20
College Students' Computer Use
Linnda R. Caporael
To cite this article: Linnda R. Caporael (1985) College Students' Computer Use, The Journal of
Higher Education, 56:2, 172-188, DOI: 10.1080/00221546.1985.11777084
To link to this article: http://dx.doi.org/10.1080/00221546.1985.11777084
Published online: 01 Nov 2016.
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j-E
Linnda R. Caporael
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College Students' Computer Use
"A computer for every student!" is the rallying
cry for the new alliance between computers and education. Several
colleges have announced that students will be required to purchase
computers; other colleges have developed incentive plans to encourage purchases. Beyond the predictable financial expenditures, the
impact of computers on traditional student life is uncertain. Skeptics
expect the present wave of enthusiasm to crash on the shoals of unfulfilled expectations remembered by veterans of computerized instruction [9]. Advocates prophesy "profound impacts" (if the software barrier is surmounted, if there are adequate networks, if there are enough
terminals for everyone, if the faculty use them, and so on). And bewildered hoi polloi, with perhaps one or two bouts of computing under
their belts, might wonder about the excitement, concluding as did one
frustrated student given a computer, "It's a 'do-what-l-say' machine,
not a 'do-what-I-mean' machine." Uncertainty about social impacts
is inherent in many new technologies, and this dubiety is especially
applicable to computers. Current sociocultural concerns, the absence
of a shared conception of the computer, and values about the desirability of computers contribute to speculations about their impact [1].
This research was supported by the IBM Corporation. The author gratefully
acknowledges the contributions of Valerie E. Orlop, research associate, and of Daniel
Zuckerman, Laurie Blake, and Glen Culbertson. Special thanks go to James Moss,
director of the Office of Computer Services at Rensselaer; Richard Park, chairman
of the Darrin Scholar Steering Committee; and, most of all, to the students who so
willingly shared their experiences with us.
Linnda R. Caporael is assistant professor oj psychology in the department oj
Science and Technology Studies at Rensselaer Polytechnic Institute.
Journal oj Higher Education, Vol. 56, No.2 (Marchi April 1985)
Copyright Е 1985 by the Ohio State University Press
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Computer Use
173
These predictions, although provocative, tell us more about the present
than about the future.
The first effect of a new device requiring attention and concentration is the displacement of time once used for other activities. In the
case of computers, displacement will depend on the availability of hardware and how its use requires time [3]. Other effects will be a function of how the computer actually is used rather than how it could
be used [2]. The replacement uses of a popular innovation are characterized by familiar forms in a new medium. An example is early
television advertising, which used a format familiar from radio advertising. Emergent uses are characterized by unpredictable, exploratory forms and uses for a new medium, some of which may become
standard usage of the technology. Among many such emergent uses
of computers might be computer conferencing. Research on an innovation is complicated by the reciprocal effects between the new device
and the social milieu into which it is introduced: existing social factors influence how the computer will be used, and the availability of
the computer may change the factors influencing its use. Thus,
although we know there will be emergent uses and effects, specifying
which of the many experimental variations will become standard is
the job of those condemned to study history [5], who confront the
problem of extrapolating from "horseless carriage" the analytic concept of "suburbs" and of foreseeing the demand for multilevel parking
lots. One possible way to detect trends is by local experiments undertaken
to identify questions, hypotheses, and variables for subsequent investigation in other settings. Meta-analysis of many such experiments - for
example, those on the effectiveness of computer-based instruction
[6]- may be the only viable strategy for investigating technologically
induced change.
This research reports one such local experiment on college students'
use of computers. Its purpose was to study time allocation associated
with owning a personal computer, to investigate how students used
computers, and to identify potential educational and social impacts.
The study took advantage of a novel scholarship program that awarded
microcomputers to academically talented students regardless of their
interest in computing. Two comparison groups were recruited, one
composed of academically similar students without computers and the
other composed of students already owning computers at college entry.
Participants in the two-year study reported their activities in a structured diary [10] for three days each semester and were individually
174
Journal of Higher Education
interviewed after at least the first year of college. The interview data
are discussed separately.
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Time Allocation
Subjects and Procedure
The study is designed around two freshman cohorts: Cohort A
entered in 1981 and Cohort B entered in 1982. Each cohort is divided
into three comparison groups of approximately twenty students per
group. One of these is composed of the scholarship program (SP) students, who were selected for their awards on the basis of high-school
class standing, Scholastic Aptitude Test (SAT) scores, and clinical judgment. A declared major in computer science or an interest in computing were not factors in their selection. In order to compare the
scholarship students with others of similar academic talent, the second
group in each cohort was composed of volunteers from a pool of the
top forty students having similar SAT scores. These students were the
"no personal computer" (NPC) group. And, to be able to compare
the scholarship students with other students who also owned microcomputers, the "own personal computer" (OPC) group was composed
of students who, regardless of their academic standing, were invited
to participate because they owned a personal computer at college entry.
SP and NPC students were initially contacted about the study by letter;
OPC students were recruited by announcements and word of mouth.
The Cohort A-SP students received an Atari 800 microcomputer. The
Cohort B-SP students received an IBM personal computer. Over time,
as the scholarship students learned more about their computers, they
were expected to diverge from the NPC group and become more similar
in computer use to the students owning personal computers at college
entry.
SAT scores were used to test the assumption that SP students and
NPC students were academically similar. Because the sample could
not satisfy the standard analysis of variance assumptions for normality
and homogeneity of variance, the statistical tests are based on random
permutation techniques that empirically generate sampling distributions to assess chance probabilities [4]. Randomization testing also
permitted the use of a very simple and direct test statistic, the difference between the means (DM).
Table 1 shows there was no evidence disconfirming the assumption
that SP students and NPC students were similar (Cohort A: Verbal
SAT, DM=23.2, n.s.; Math SAT, DM=22.6, n.s. Cohort B: Verbal
Computer Use
175
TABLE I
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Means and Difference Tests for High-School SAT Scores
Group
n
Verbal SAT
Math SAT
Cohort A
SP
NPC
OPC
Differences
19
19
17
735.8
712.6
573.7
SP vs. OPC'
755.8
733.2
703.7
SP vs. OPC'
Cohort B
SP
NPC
OPC
Differences
19
25
23
728.5
704.0
568.3
SP vs. OPC'
771.5
749.6
697.5
SP vs. OPC'
NOTES; No significant differences were found between the SP and NPC groups. SP-scholarship program;
NPC-no personal computer; OPC-own personal computer.
*p<O.Ol.
SAT, DM=24.5, n.s.; Math SAT, DM=31.9, n.s.). In both cohorts,
for both types of SAT tests, the SP students did score significantly
higher than the OPC students (Cohort A: Verbal SAT, DM = 162.1,
p<O.OI; Math SAT, DM=52.1, p<O.Ol. Cohort B: Verbal SAT,
DM= 160.2, p<O.OI; Math SAT, DM= 74.0, p<O.OI). The statistical
difference between students on the math scores was not considered
practically significant because the group mean scores are three standard deviations above the normalized mean SAT of 500. The real difference among students lies in their verbal SAT scores. The most frequently reported major in all groups was engineering, followed by computer science, but the curriculum for the first two years is almost identical for all majors.
The study's methodological core was students' reports of their allocation of time on ten mutually exclusive activity categories (for
example, sleep, study, or computer use). Each semester students completed a three-day sequence, excluding weekends, of structured diaries.
(Fall 1981 is an exception because only one day of activities was recorded by Cohort A.) The student indicated his or her activity by drawing a line through fifteen-minute-interva1 markings in the appropriate
category. The student reports were staggererd throughout the semester,
and effects for particular days were assumed to be random. More specific information was requested for some categories, such as computer
use, and was coded into subcategories by the research staff. The coding
categories were developed during pilot work. Confidence in the validity
of students' reports is moderately high though not complete. It is much
higher than it would be for questionnaires.
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Journal of Higher Education
For clarity in presentation, the reporting categories were lumped
a priori into four analytic categories: personal care (sleeping, eating,
chores,); social activities (socializing with friends, entertainment, club
meetings, sports); academic activities (attending class, studying from
books); and computer activities (any computer activity on any computer system). Three qualifications are in order. First, the overall
strategy was to explore not to confirm the hypothesis. Second, the
research was correlational: differences between groups might be associated with computer ownership but might not necessarily be caused
by computer ownership. Third, the extent to which results might be
generalizable is a matter of common sense not statistical inference.
The setting for this study, Rensselaer Polytechnic Institute (RPI), is
considerably more technical than a liberal arts setting.
Results
General activities. Data for each cohort were analyzed separately.
Table 2 shows the means and test results for differences among the
groups' activities. Cohort A data are based on students' average times
for four semesters; Cohort B data are based on two semesters. As one
would expect, data on the NPC students were stable over both cohorts,
as were data for all groups on the personal care variable. But the expected similarity between OPC groups in the two cohorts was not
obtained. SP students also differed between cohorts, a fact not completely surprising because different microcomputers were awarded in
the two years of the scholarship program. The pattern for the allocation of time to computer use was similar for both cohorts, albeit Cohort
B-OPC students engaged in less computing than did the correspondTABLE 2
Means and Differences for Activities
Academic
Computing
n
Personal Care
Social
Cohort A
SP
NPC
OPC
Differences
21
22
21
10.4
10.8
10.3
5.1
4.6
5.1
5.6
7.2
5.9
SP vs. NPC"
1.5
0.6
2.0
SP vs. NPC"
Cohort B
SP
NPC
OPC
Di ff erences
19
25
24
10.5
11.0
10.3
3.6
4.7
4.0
SP vs. NPC'
7.4
7.2
6.9
1.7
0.5
1.4
SP vs. NPC"
Group
NOTES: Results for Cohort A are based on four semesters, Cohort B on two semesters. SP - scholarship
program; NPC - no personal computer; OPC - own personal computer.
иp<O.05. ииp<O.Ol.
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Computer Use
177
ing Cohort A group. For both cohorts, SP students allocated significantly more time to computing than did NPC students, but SP students
showed no significant differences when compared to OPC students.
This finding was the case not only for overall computing data but also
for the data on the first semester of the freshman year. Within each
cohort, there were no significant differences in time allotted to the
four activities between the SP and OPC students. SP students in Cohort
A, however, did spend less time on academic activities than did the
academically similar NPC students; but in Cohort B, SP students spent
less time on social activities than did NPC students.
One possible interpretation of the overall pattern is that the significance of owning a computer changed between cohorts largely as a function of the diffusion of computing throughout our culture. The data,
our knowledge of the research participants, and conversations with
older students owning computers suggest that in 1981 (before the IBM
personal computer was available), few students owned personal computers. Those who did were highly committed to and experienced with
computing, and student owners participated in a tightly knit "computer culture." By the following year, students less committed and
experienced with computers owned them, often more from a commitment to competing academically than from a commitment to computing. The new accessibility of computing at a microcomputer lab also
diversified members of the "computing culture," reducing potential
social obligations. This analysis suggests that entering students owning computers in the two years had different priorities for allocating
their time.
We took a closer look at the relationship between the report categories and ownership of a microcomputer. Across cohorts, students
who owned computers showed a small but significant inverse correlation for time spent studying (excluding computer use) and computing
(Spearman's rho=-0.21, n=85, p<0.05), but they showed no correlation between computing and socializing. Students who did not own
computers showed an inverse correlation between socializing and computing (Spearman's rho = -0.42, n = 47, p< 0.01), but they showed no
correlation between computing and studying.
Sociocultural variables are likely to account for the pattern of time
use across groups, but the intraindividual data suggest that personal
access is associated with a predictable shift in the use of time, a result
consistent with Condry and Keith's [3] prediction that time displacement effects depend on hardware availability. There are many possible reasons for the shift, among them that owning a computer gener-
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Journal of Higher Education
ates a new fit or adaptation between the student and his or her environment. If this interpretation is correct, we might view students
owning computers as living in a new domain, different from the domain
of students without computers. Nevertheless, there is still insufficient
reason to assume that this new domain is final (in the sense that television, for example, has displaced time from reading) rather than transitory.
Computing activity. Students' reports of their computing activities,
collapsed across cohorts, were coded into the five categories shown
in Figure 1. ("Other" includes miscellaneous activities such as tinkering with hardware or software and sending messages. Also, the breakdown of the computing category was not done for Cohort A in Fall
1981 because one day of data is insufficient for coding into subcategories.) The data are presented in terms of the means for the groups
3.0 ......- - - - - - - - - - - - - - - ,
DTI SP
2.5
~NPC
III OPC
2.0
><
Q)
"C
c 1.5
Q)
E
t-
1.0
0.5
HW
G
PP
WP
OTH
Activities
FIG. 1. Computing Activities of Students. The time index is the average number
of hours over a three-day period. Group identifiers are SP - scholarship program;
NPC - no personal computer; OPC - own personal computer. Activity identifiers are
HW - homework; G - games; PP - personal projects (excludes required classwork);
WP - wordprocessing; OTH - other activities.
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Computer Use
179
over a three-day period. Overall, the largest category of use is for homework in computing classes. There were no significant differences in
the number of computer classes taken by students until the second
semester of the sophomore year when NPC students took fewer classes
than did the other groups in Cohort A. After homework, the next most
frequent category was games. OPC students account for the most game
playing, as well as the greater portion of activity in the other categories.
Different computer systems - that is, the mainframe, the personal
computer linked via a modem to the mainframe, and the personal computer as a stand-alone system - are clearly associated with different
activities. Careful inspection of Figure 2 indicates that, whether or
not students own computers, they use the mainframe primarily for
homework and they use it at almost identical levels. The extra time
allocated to computing by students owning microcomputers occurs
on configurations involving the personal computer. The data suggest
3.0....-----------------,
ITIIDsp
2.5
~NPC
RoPC
2.0
><
C1l
1:'
c
C1l
1.5
E
~
1.0
0.5
HOMEWORK
0.0
HOMEWORK
GAMES
L-L_.L.-......L.---l........._"'-----'----I........._L...-....L---a........
MTS
Linked
Systems
PC
FIG. 2. Primary Uses for Different Computer Configurations. The white area
indicates the time index for the primary activity on the system and the hatched area
represents all other activities. Group identifiers are SP - scholarship program; NPCno personal computer; OPC - own personal computer.
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180
Journal of Higher Education
that microcomputing will not automatically reduce reliance on the
mainframe system. On the contrary, microcomputing appears to be
associated with higher usage of the mainframe system, with the microcomputer used as a remote terminal improving access to the campus
computer. Students do virtually no homework at all on the stand-alone
system. The reasons are fairly obvious - most computer courses use
languages that are not readily available on most microcomputers, and
even when those languages are available, the debugging routines for
programming are not.
Grades. Grades were not correlated with computing - nor for that
matter with studying. They did correlate with SAT scores (Spearman's
rho = 0.30, n = 118, p < 0.01), an unsurprising finding since the SAT
is designed to predict academic performance.
Student Interviews
Individual interviews were conducted with 118 participating students
(67 freshmen and 51 sophomores) in Spring 1983. The interview
schedule consisted of open-ended questions on various topics covering educational and social issues associated with computing. Responses
were coded after the interviews were completed. The interviews were
preliminary and exploratory; their primary purpose was the identification of variables and issues for future investigation. Because of the
small number of students in each of the six groups and of the openended structure of the interview, data were collapsed across groups
for analysis.
Learning to Use a Computer
Students' reports of computing experience before college were consistent with the possibility, suggested by the time allocation data, that
differences between cohorts were related to the diffusion of computing
through our culture. Only twelve students (10 percent) had no prior
computing experience, almost all of them 1981 freshmen. Forty-three
percent of the students reported having at least one high-school computing course, and 27 percent reported three or more. The remainder
took college credit courses in high school. The most frequently reported activity was "playing around" with the computer, followed by
programming.
Twenty students (17 percent) felt that their high-school experience
had not been helpful in learning to use the mainframe at Rensselaer.
The remainder gave assorted reasons for why their high-school experience had been useful: they learned to think logically, operating sys-
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Computer Use
181
tems were similar, or they felt comfortable with computers as a result
of their experience. Students reported, in order of preference, a variety
of strategies for learning to use the mainframe system. The most frequently mentioned was trial and error (75 percent), followed by taking
a class (55 percent), asking people (43 percent), reading a short primer
distributed by the Office of Computing Services at RPI (42 percent),
and reading the documentation manuals (29 percent). Based on the
order in which students mentioned the strategies used (e.g., taking a
class was the most frequent strategy in the first position), a profile
of the sequence of strategies was constructed. Beginning with the first
position, these were taking a class, trial and error, asking people, and
reading the documentation manuals. It is widely acknowledged that
reference manuals are very difficult to understand, and this fact is
the likeliest explanation for their relatively infrequent use.
Students were specifically asked if people or books were more important to them in learning to use the computer: 53 percent thought people
were more important, and 47 percent thought books were more important. We suspected that students' responses might be related to how
sophisticated they were vis-a-vis computing. Knowledge of Assembler
(a common but cryptic programming language) was used as a proxy
variable for computer sophistication, and student responses were regrouped according to this variable. Among students knowing Assembler (n = 44),61 percent preferred books and 39 percent preferred
people. The percentages were exactly reversed for the students ignorant
of Assembler (n = 70): 61 percent preferred people and 39 percent preferred books. The reasons given for both preferences were the sameeither people, or books, were faster, easier, and more specific. The
likeliest interpretation for these results is that novices learn from other
people who are able to infer the question being asked even if it is
phrased in nontechnical terms, while sophisticated users, who generally have command of the technical terms, are able to consult a manual.
Living with a Microcomputer
Seventy-six students had microcomputers in their dorms or apartments. Over half, 57 percent, kept the computer on a desk in their
room, and 34 percent reported that this arrangement created a space
problem. Among students reporting other locations for their computers
(table, shelf, and so on), 15 percent reported a problem. There were
six students who stored their computers. Ninety percent of the students claimed to take security precautions to protect their computers,
but almost all of them also state they would take the same precautions
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Journal of Higher Education
to protect the contents of their room if they had no computer. The
upshot appears to be that students take no special security precautions for their computers.
Interestingly, about half (48 percent) of the students reported the
computer helped them to make friends. It attracted other students to
their room and helped them meet people with a common interest. Only
five students (four of them in the OPC groups) reported that they
wouldn't let others use their microcomputer. Among students who
would share, 51 percent reported that specific friends used the microcomputer on a regular basis. About a third of the students owning
their own computers (SP and OPC) and half of the NPC students used
other people's computers. In general, it appears that student-owned
computers tend to be a shared resource. The predominant activities
among owner-users and friend-users were reported to be games and
remote terminal use, but projects, wordprocessing, calculations, and
graphics were also mentioned. The computer was used as a break from
studying (activity unspecified) for an average of 3.6 hours per week.
Personal Computers as Remote Terminals
About half the students (58) had modems, devices that allowed them
to link with the campus mainframe system. Students report that their
microcomputers are used in this way for an average of 8.5 hours per
week. This time includes use by the students and by any others whom
they allow to use their computers. (Data from the time diaries indicate that research participants use their computers for mainframe access
an average of 3.0 hours per week.) But only two students reported
that tying up the telephone line created an inconvenience. Thirty-nine
students had problems linking to the mainframe. The most frequently
mentioned difficulty was telephone line trouble, followed by inadequate software, and busy or no-answer signals on the mainframe telephone. Also mentioned were problems with the students' own equipment and the cost of the phone call.
Disadvantages of using the modem were coded separately from the
problems students experienced with them. The former is very dependent
on the availability of alternative computer systems, and at Rensselaer
students have access to "smart" public terminals with a range of desirable capabilities. The disadvantages students report can easily be summarized - a microcomputer with a standard telephone link is not a
smart terminal. The most frequently mentioned disadvantages of using
microcomputers as remote terminals included the lack of full-screen
editing, slow speed when linked, limited screen size, and no program-
Computer Use
183
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mable function keys, scrolling, or page buffer. In general, the problems
and disadvantages experienced by students are associated with the limitations of linking over a telephone modem (e.g., lack of full-screen
editing) as well as the limitations of the equipment they own (e.g.,
limited screen size).
Student Projects
Thirty-two students reported undertaking special projects above and
beyond their classroom responsibilities. Seven were unrelated to computing (e.g., historical research, radio construction) or used computers
for calculations only. Students who did not own computers did not
report computer projects on any system: only computer owners
engaged in computer projects. The computer projects covered a wide
range of topics and varied considerably in their sophistication. Several students were working on game programs, several others were
working on a microprocessor-based controller for a student-run space
telescope project. Examples of other projects were a program for helping cerebral palsy victims communicate, software development for
doctors, terminal emulators, modifications of existing programs, and
developing small demonstration progams (e.g., one that matches
people's interests).
Project initiation and development is largely a social activity with
peers: two-thirds of the students' projects were undertaken with friends.
In general, it appears that students who report working alone are
engaged in very sophisticated projects (e.g., a two- and three-dimensional imaging program for the IBM personal computer or a Lisp
interpreter); whereas the collective projects seem to have a greater
range. Only three students, all in the OPC groups, reported approaching professors to initiate a project. The initial data on student projects
is promising, especially considering that the students were freshmen
and sophomores. It is likely that, as students mature in their academic
careers, more of them will initiate projects of wider scope and complexity.
Computing and Classwork
Only three students reported having absolutely no courses for which
they used any computer. Computers were used in noncomputing
courses (e.g., science or engineering) by 60 percent of the respondents.
Overall, sophomores (Cohort A), on average, had taken about five
college courses where computers were used, and the freshmen had taken
an average of three. The highest number of reports were for computer
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Journal of Higher Education
science courses (114) followed by science (usually physics), of which
there were 58 reports. Interestingly, there were twice as many reports
(23) of computers being used in humanities and social science courses
than in engineering courses (11 reports). It is difficult to categorize
the classwork activities associated with computing because of the overlap of tasks. With this qualification in mind, student responses were
grouped as follows: class projects (86 percent), textprocessing (46 percent), calculating homework problems (20 percent), and plotting (17
percent).
More details were requested about the particular activity of writing
papers for class. Sixty-seven students reported one or more assigned
papers. Of the eighty-four papers written, 31 percent were typewritten,
25 percent were textprocessed on the mainframe, 23 percent were textprocessed on a microcomputer, 19 percent were handwritten, and 2
percent were textprocessed on a microcomputer and printed on the
mainframe system. The infrequency of the last category captures a
central frustration of the computer age - incompatibility. Ideally, one
should be able to prepare the final version of a paper on a personal
computer and duplicate the completed version on a mainframe so it
can be copied on a high quality printer. But this capability is not available at Rensselaer, and its closest approximation involves mastering
two wordprocessing systems.
One of the few closed questions of the interview concerned the systems used for different activities, the latter derived from McCredie's
Campus Computing Strategies [7]. They represent a panoply of potential uses for a computer in a university environment. Students were
shown an activity-by-system matrix and asked to check every applicable cell. The matrix and percentage of students indicating use is
shown in Table 3. In general, student activity seems high in all categories, and they engaged in a wider range of activities than those taught
in the classroom. Our impression is that knowledge about these activities is spread through a student network.
Opinions on the Future of Computing
Students are ready and waiting for the inclusion of microcomputers
in their college courses: over 95 percent of the students thought microcomputers could be integrated into the classroom. But most students
were unable to respond to a probe about how this could be accomplished. Twenty-three percent volunteered that it was important that
students either individually own microcomputers or have easy access
for self-pacing. Several (12) added that the computer would be a more
Computer Use
185
TABLE 3
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Percentage of Students Indicating System Used for Specific Activity
Activity
PC
PC/MTS
Mrs
Games (84)
Data analysis, calculations, plotting data,
statistical package (82)
Electronic mail (78)
Textprocessing (67)
Information retrieval, library database (55)
Simulations, modeling (36)
Computer controlled experiments (28)
Class evaluations, test-taking (15)
66
18
48
3
53
15
31
12
21
57
59
11
4
6
6
7
2
2
20
38
II
17
23
5
45
37
18
3
13
Other
- - - - - - ------'''--'----'-------------------- - - ---- NOTES: Numbers in parentheses are the percentage of students indicating they had engaged in the activity;
to the central mainframe campus computer.
----
n ~ 118. MTS refers
effective educational tool if more people knew how to use it and started
learning at a younger age. About a quarter of the students recommended lectures in class or special classes on how to use microcomputers for various tasks. Students were much more specific about what
features would be useful to them in a microcomputer. Some of their
responses reflect their experience with their own equipment (e.g.,
having a printer would be useful). Other responses reflect dissatisfaction with circumstances at Rensselaer (e.g., transferring files between
systems). Still others reflect the available technology (e.g., user-friendly
features).
There was little consensus about how the computer would change
education. The 118 students listed over 60 different changes, most positive, many neutral, and very few negative. Only three students thought
computers would replace the classroom, and five listed dehumanizing effects. By and large, student opinions reflected those often read
in the media and were equally contradictory. Some felt there would
be less face-to-face interaction; others felt there would be more student-faculty interaction. Some thought there would be more "number-crunching"; others believed in greater possibilities to learn general
principles and theories rather than do busy-work. Our impression during the interview was that students, like most people, lacked enough
experience with computing on which to base their responses.
General Conclusions
This study's perspective was that the social and educational impact
of computers will depend on the purposes, replacement and emergent,
for which computers will be used once they are widely adopted. Thus,
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186
Journal of Higher Education
conclusions are tentative at best because the data describe a dynamic
process of change and transition rather than a static state where computing is as casual as television watching. By the same token, computing may be diffusing through society as fast or faster than did television, and it is important to generate questions and identify potentially important variables in the early stages and possibly to adopt
strategies that may influence the direction of the computer transition.
The primary uses of the computer are as replacements. At present,
it does seem fair to say that the appropriate analogy for the microcomputer is a typewriter or calculator. These tools contribute to academic productivity but are hardly central features of instructional
activity. Currently, the primary use of the microcomputer is as a replacement for a terminal to the mainframe. As a stand-alone system,
the primary use of the microcomputer is as a game machine, but this
use is quite variable among semesters. Computer games might be classified as an emergent use of computers, and there are [cf. 8] arguments that the structure and requirements of a game may match the
characteristics we would desire in educational software (e.g., challenge,
the requirement to solve a problem, rapid feedback). Nevertheless,
most observers would agree that the potentials of a microcomputer
would be lost if the sole use, emergent or otherwise, was for computer games. The many differences in the microcomputer-owning
group, compared to the relative stability of the nonowning groups,
suggest educational microcomputing is in a transitional stage as students try to integrate the computer into the academic environment.
A niche exists for software to support educational activities: the software does not now exist or is inadequate. The inverse correlation between studying and computing observed in groups owning microcomputers suggests that students are already displacing studying with computer use. Moreover, the data show that the extra time spent on computing is for classwork, although it appears that NPC students, who
largely have the same curriculum as other students during these early
years, are adequately meeting class demands without additional computer time. This finding suggests that a niche for educational computer use is available within the context of students' priorities, but
the software for wider use of computing across the curriculum is not
available. Aside from curricular software, the student interviews clearly
indicate the need for support software. In particular, software that
would improve access to the mainframe and increase compatibility
between software used on different systems is essential. Another area
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Computer Use
187
of support development is for general purpose educational software,
for example, an academic version of a data base management system
that is conducive to recording and searching through notes.
Learning to use a computer has two important components: the
opportunity for trial-and-error learning and the social transfer of
information. Students describe their learning strategies as "playing
around," "tinkering," and "experimenting." Learning is an active
process where students try a command and observe what happens;
they seek out the information they need to know. For the novice,
socially transmitted information is the most efficient way to find some
particular piece of information necessary for this search without requiring that the information be described in technical terms. Students are
not able to describe this process in any greater detail, an inability not
surprising given the shortcomings of memory. A better understanding of the trial-and-error process and the role of other people in the
learning process requires a descriptive research project exclusively
focused on the learner.
Computing is a social activity. Repeatedly, evidence indicated that
the social environment is an enormously important medium for computing activity. Novices reported a high level of dependence on more
knowledgeable students for information about the computer. Students
reported in their interviews a far wider range of activities (e.g., electronic mail) than those assigned in the classroom, and other students
are the most likely source of their knowledge. Two-thirds of the projects
students reported were initiated with friends - and even the most superficial projects are a vehicle to learning more about the computer. Individually owned computers are by no means individually operated computers: microcomputers are a shared resource and a focal point for
showing off one's knowledge, aiding a friend, cooperating on a group
endeavor, and discovering what computers are good for.
The findings suggest that, in addition to asking what the impact
of computing on students will be, we should also ask what the impact
of students on computing will be. Student culture appears to be an
important factor in how microcomputers will be used, and this possibility may be especially so when the machines are not part of the educational efforts of the institution. Student computing projects suggest that students may be a vastly underrated and undeveloped resource
for inventing emergent uses for the computer. Innovative policy strategies might include those that encourage student applications of the
computer (e.g., an equivalent of the school science fair) and increase
188
Journal of Higher Education
the opportunities for learning about computers by increasing the "bandwidth" of the social transmission of knowledge (e.g., providing small,
publicly available microcomputer clusters).
References
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1. Caporael, L. R. "Computers, Prophecy and Experience." Journal oj Social Issues,
in press.
2. Caporael, L. R., and W. Thorngate. "Introduction." Journal oj Social Issues,
in press.
3. Condry, J., and D. Keith. "Educational and Recreational Uses of Computer Technology: Computer Instruction and Video Games." Youth & Society, 15 (1983),
87-112.
4. Edgington, E. S. Randomization Tests. New York: Marcel Dekker, 1980.
5. Fischoff', B. "For Those Condemned to Study the Past: Reflections on Historical
Judgment." In Fa!!ible Judgment in Behavioral Research, New Directions jor
Methodology oj Social and Behavioral Science, No.4, edited by R. Shweder ,
pp. 79-93. San Francisco: Jossey-Bass, 1980.
6. Kulik, J. A., C. Kulik, and P. Cohen. "Effectiveness of Computer Based College Teaching: A Meta-analysis of Findings." Review oj Educational Research,
50 (1980), 525-44.
7. McCredie, J. W. Campus Computing Strategies. Bedford: Digital Press, 1983.
8. Malone, T. W. What Makes Things Fun to Learn? A Study oj Intrinsica!!y Motivating Computer Games. Cognitive and Instructional Science Series, ClS-7,
SSL-8-11. Palo Alto: Xerox Palo Alto Research Center, August 1980.
9. Oettinger, A. G. Run, Computer, Run: The Mythology oj Educational Innovation. New York: Collier, 1969.
10. Robinson, J. How Americans Use Time. New York: Praeger, 1977.
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