The Relationships Between Computer And

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MAY /JUNE 2001 VOL 42 NO 3 217

The Relationships Between Computer andInformation Technology Use, Selected Learning andPersonal Development Outcomes, and Other

College ExperiencesGeorge D. Kuh Shouping Hu

This study examines the relationships between

student characteristics, student use of computers

and other information technologies (C&IT), the

amount of effort they devote to other college

activities, and self-reported gains in a range

of desirable college outcomes. Based on an

analysis of responses to the College Student

Experiences Questionnaire from 18,344 under-

graduates at 71 four-year colleges and univer-

sities, students appeared to benefit more from

C&IT when they used it frequently and in a

variety of ways. Equally important, using C&IT

was positively related to educational effort with

the effects of C&IT on outcomes of college being

largely mediated through the educational efforts

students put forth.

Computing and information technology (C&IT)is now almost ubiquitous on most college

campuses (Dolence & Norris, 1995; Gilbert,

1996; Green & Gilbert, 1995; West, 1996). The

1998 National Survey of Information Tech-

nology and Higher Education indicated that in

1994 about 8% of postsecondary classes were

using E-mail (Institute for Higher Education

Policy, 1999). By 1998, this percentage jumped

to about 44%. Between 1996 and 1998 the

percentage of classes using Internet resources

increased twofold from 15% to 30%. In the

1980s, only 32% of students reported substantial

progress in becoming familiar with computers

during college. By the mid-1990s this per-

centage had jumped to about 60% (Kuh,

Connolly, & Vesper, 1998). About half of all

institutions have a mandatory student fee to

support information technology that is becoming

increasingly interactive and distributed, allow-

ing users anywhere to access at any time a rich

array of information and resources via the

Internet and other sources (Green, 1996).

Indeed, “nothing before has captured the

imagination and interests of educators simul-

taneously around the globe more than the World

Wide Web” (Owston, 1997, p. 27).

Classrooms linked to global networking

technologies allow students to interact with

peers and master teachers around the world

(Riel, 1993). In 1995, about one third of all

colleges and universities offered distance

education courses and almost a quarter had

degrees that students could complete entirely on

line (Merisotis, 1999). Accredited on-line degree

programs are now available with electronic file

exchange, Internet video conferencing tools,

E-mail office hours, electronic libraries, virtualcafes, whiteboards, digitized movies, voice and

chat tools, debate forums, and student opinion

polls (Fetterman, 1996; Harasim, Hiltz, Teles,

& Turoff, 1995). Through on-line learning

apprenticeships, experts and learners can share

their ideas and pose questions about those of

others, thereby clarifying and extending their

thinking and knowledge. “Used appropriately

in concert with powerful pedagogical ap-

proaches, technology is supposed to enrich

synchronous classroom activities and provide

students with engaging self-paced and asyn-

chronous learning opportunities that enable

students to learn more than they would other-

wise at costs ultimately equal to or below that

of traditional classroom-based instruction” (Kuh

& Vesper, 2001, p. 87).

Thus far, studies examining the effects of

using computer and information technology are

George D. Kuh is Chancellors’ Professor of Higher Education at Indiana University Bloomington. Shouping

Hu is Assistant Professor of Educational Administration and Supervision at Seton Hall University.



218 Journal of College Student Development

Kuh & Hu

in some ways encouraging. For example,

compared with traditional classroom activities,

E-mail discussion, delayed text collaboration

and file sharing, real-time idea brainstorming,

and real-time text and graphics collaboration

appear to enhance productive collaborationamong students and encourage higher levels of

student participation (Alavi, 1994; Bonk,

Medury, & Reynolds, 1994; Oblinger & Maru-

yama, 1996). Both delayed and real-time

conferencing support tend to promote more

frank discussion and equal opportunity among

participants than traditional classroom instruc-

tion (Sproull & Kiesler, 1993). A study of

computer-mediated discussions among under-

graduate education students found that aninstructor’s informal conversational discourse

style together with comments directed to

individual students or to specific statements

made in the conference fostered higher partici-

pation, more complex interactions, and greater

peer-peer interaction than when instructor

comments were posed as either questions or

formal statements to the entire group (Ahern,

Peck, & Laycock, 1992).

Most of what is known about the effects on

achievement of using computing and informa-tion technology is based on student performance

in individual courses. Kulik and Kulik (1991)

found that college students had about a .31

standard deviation advantage in course learning

knowledge over peers who received instruction

without the aid of computers. From their

synthesis of studies of hypermedia and hypertext

use, Dillon and Gabbard (1998) found only

small and nonsignificant differences between the

learning gains of students using hypermedia andhypertext and those exposed to traditional forms

of instruction.

Two recent studies examined the impact of

computing on outcomes other than achievement

and content-specific knowledge across multiple

institutions. Kuh and Vesper (2001) reported

that after controlling for such factors as college

grades, age, gender, hours worked per week,

parents’ education, and educational aspirations,

students’ self-reported gains in becoming

familiar with computers were highly correlatedwith self-assessed gains in a variety of other

areas such as independent learning, writing

clearly, and problem solving. Flowers, Pasca-

rella, and Pierson (2000) also controlled for

many of the same potentially confounding

influences but also included precollege cognitive

development and motivation. They found thatcomputer and E-mail use had only trivial and

nonsignificant effects on four standardized

cognitive measures: end of first year composite

cognitive development, reading comprehension,

mathematics, and critical thinking. At the same

time the use of computers and E-mail signi-

ficantly affected the cognitive growth of students

attending 2-year colleges. It was not clear why

information technology had a greater effect on

two-year college students than their counterpartsat 4-year institutions.

Although research findings to date are

generally promising, a substantial gap remains

in our understanding of the effects of computer

and information technology on student learning

and other educational outcomes (Morrison,

1999). For example, little evidence is available

beyond student performance in individual

classes to determine the effects of different forms

of technology on various aspects of the college

experience including the acquisition of a rangeof desired outcomes of college (Morrison; Hibbs,

1999) or the most efficacious design and use of

these new technologies (Ehrmann, 1995). Nor

is it clear which aspects or forms of computer

and information technology have the greatest

effects for what types of students for what

outcome areas.

In addition, some evidence suggests that the

effects of computing and information technology

use may not be uniform for different types of institutions or students. Institutional affluence,

student ability and socioeconomic status (SES),

and accessibility and use of computing and

information technology appear to be highly

correlated (Gladieux & Swail, 1999). The

greatest learning benefits from using technology

appear to be realized by higher ability students

(Dillon & Gabbard, 1998), which is consistent

with the Flowers et al. (2000) finding that

students with the highest level of precollege

cognitive development benefited the most fromcomputer and information technology use as




Technology Use, Selected Learning, and College Experiences

reflected by end-of-first-year cognitive gains.

Others worry that technology may dehumanize

the educational process and that the quality of

social relations between students and faculty will

deteriorate if, for example, E-mail substitutes

for rather than augments face-to-face inter-actions. Less frequent contact with peers could

mute the development of interpersonal commun-

ication and other skills as students increasingly

rely on information technologies to obtain

information, prepare class assignments, and

communicate with one another and their

teachers (Upcraft, Terenzini, & Kruger, 1999).

Also, computers and information tech-

nology can be a distraction if used primarily for

noneducational or entertainment purposes, suchas downloading and compiling music, playing

games, or communicating with family, friends,

and coworkers (Reisberg, 2000). Using com-

puters for these activities reduces the amount

of time available for students to be engaged in

educationally purposeful activities such as

taking advantage of cultural and performing arts

venues, attending lectures by visiting scholars,

and discussing substantive topics with instruc-

tors and peers—activities that the research

shows contribute to student learning andpersonal development.

The purpose of this study was to examine

the characteristics of student use of C&IT and

to determine the relationships between student

use of C&IT and the amount of effort they put

forth in other college activities and the gains

they make in a range of important college

outcomes. Three research questions guided the

study.

First, what is the nature and frequency of use of various types of C&IT by undergraduates

with different background characteristics and at

different types of colleges and universities? Do

students with certain characteristics or attending

particular types of institutions use different

forms of computer and information technology

more frequently? For example, do students at

private colleges and large research universities

use C&IT more frequently, and are they

therefore advantaged, because they attend an

institution that can afford enough state-of-the-art

hardware to make technology accessible to

virtually all students?

Second, what is the relationship between

using C&IT and the amount of effort students

expend in academic pursuits and engagement

in other educationally purposeful collegeactivities? Are students who use certain forms

of C&IT more or less likely to be engaged in

other meaningful aspects of the college

experience, such conversing with faculty

members and peers face-to-face or participating

in clubs and organizations?

Finally, what is the relationship between

using C&IT and the range of desirable outcomes

of attending college? Is using C&IT a positive,

neutral, or negative influence on overall gainsfrom college or on specific areas as general

education, personal or social qualities, or job-

specific abilities?

METHODS

Data Source and Instrument

The data for this study are from the College

Student Experiences Questionnaire (CSEQ)

research program. The fourth edition of the

CSEQ (Pace & Kuh, 1998) is designed forstudents attending 4-year colleges and univer-

sities and gathers information about students’

background (age, major field, etc.) and their

experiences in three areas. The first area is the

amount of studying, reading, and writing

students do and the time and energy (effort) they

devote to various activities measured by items

contributing to 13 activities scales (Kuh, Vesper,

Connolly, & Pace, 1997). One of these scales,

C&IT, includes nine items describing variousforms and uses of computers and information

technology (Table 1). The response options for

all Activities items are: 1 = never , 2 = occa-

sionally, 3 = often, and 4 = very often. The

second area is student perceptions of the extent

to which their institution’s environment

emphasizes important conditions for learning

and personal development measured by 10

Environment items. The final area is an estimate

of what students think they have gained from

attending college represented by 25 Gains itemsthat load on five factors: general education,




Kuh & Hu

intellectual skills, personal and social develop-

ment, science and technology, vocational

preparation (Kuh et al., 1997). Response options

for the gains items are: 1 = very little, 2 = some,

3 = quite a bit , and 4 = very much.

Examinations of the validity of self-reportedinformation (Lowman & Williams, 1987; Pace,

1985; Pike, 1999, 1995) such as that obtained

using the CSEQ indicate that they are generally

valid under five conditions: (a) when the

information requested is known to the respon-

dents, (b) the questions are phrased clearly and

unambiguously (Laing, Sawyer, & Noble, 1988),

(c) the questions refer to recent activities

(Converse & Presser, 1989); (d) the respondents

think the questions merit a serious and thought-ful response, and (e) answering the questions

does not threaten, embarrass, or violate the

privacy of the respondent or encourage the

respondent to respond in socially desirable ways

(Bradburn & Sudman, 1988). CSEQ items

satisfy all these conditions. The questionnaire

requires that students reflect on what they are

putting into and getting out of their college

experience. The items are clearly worded, well-

defined, and have high face validity. The nature

of the questions refers to common experiencesof students during the current school year,

typically a reference period of about 6 months

or less. The format of most response options is

a simple rating scale that helps students to

accurately recall and record the requested

information, thereby minimizing this as a

possible source of error. The estimate of gains

items ask students how much they think their

college or university experience contributed to

their own growth and development. In this sensethe progress that students say they make is a

value-added judgment (Pace, 1990b). Responses

to gains items have been shown to be generally

consistent with other evidence, such as results

from achievement tests (Brandt, 1958; DeNisi

& Shaw, 1977; Hansford & Hattie, 1982;

Lowman & Williams, 1987; Pace, 1985; Pike,

1995). Pike found that student reports to gains

items from the CSEQ were highly correlated

with relevant achievement test scores and

concluded that self-reports of progress could beused as proxies for achievement test results if

there was a high correspondence between the

content of the criterion variable and proxy

indicator. Based on their review of the major

college student research instruments, Ewell and

Jones (1996) concluded that the CSEQ has

excellent psychometric properties and high tomoderate potential for assessing student

behavior associated with college outcomes.

Additional psychometric properties of the CSEQ

are described in Kuh et al. (1997).

Sample

The sample is composed of 18,344 under-

graduates from 71 four-year colleges and

universities who completed the 4th edition of

the CSEQ in 1998 and 1999. The schoolsinclude 21 research universities (RU), 9 doctoral

universities (DU), 22 comprehensive colleges

and universities (CCU), 8 selective liberal arts

colleges (SLA), and 11 general liberal arts

colleges (GLA) as classified by the Carnegie

Foundation for the Advancement of Teaching

(1994). Although the mix of schools reflects the

diversity and complexity of 4-year colleges and

universities, the CSEQ data base is essentially

a convenience sample in the institutions that use

the instrument administer it in different waysand for different reasons. Women (63%),

traditional-age students (92%), first-year

students (48%), and students from private

colleges were overrepresented compared with

the national profile of undergraduates attending

4-year colleges and universities. About 77% of

the sample were White students, 8% Asian

Americans, 6% African Americans, 6% Ameri-

can Indians and students from other back-

grounds and 4% Latinos. Also, more than half of the students were majoring in a preprofes-

sional area, 17% in math and science, 10% in

social science, and 8% in humanities. Almost

one fifth (19%) had majors from two or more

of the major field categories. Descriptive

statistics on student use of nine C&IT items

on the instrument, C&IT total scores, EFFORT-

SUM, GAINSUM, and five gain factors are

reported in Table 1.

VariablesBecause socioeconomic status (SES) and student





TABLE 1.

Descriptive Statistics of C&IT Items, Overall C&IT Score, EFFORTSUM,GAINSUM, and Five Gain Factors

VARIABLESM

SD

1. Used computer or word processor for paper 3.72 0.61

2. Used E-mail to communicate with class 3.41 0.92

3. Used computer tutorial to learn material 1.88 1.00

4. Joined in electronic class discussions 1.71 1.00

5. Searched Internet for course material 3.16 0.92

6. Retrieved off-campus library materials 1.80 1.01

7. Made visual displays with computer 2.36 1.06

8. Used a computer to analyze data 1.95 1.03

9. Developed Web page, multimedia presentation 1.63 0.94

C&IT Overall Score 21.61 5.19

EFFORTSUM (Sum of activities itemsexcluding C&IT scale) 235.68 39.92

GAINSUM (Sum of gain items excluding C&IT item 63.48 12.34

General education 14.16 3.83

Personal development 14.42 3.37

Science and technology 6.97 2.53

Vocational preparation 8.17 2.13

Intellectual development 22.80 4.71

Note. N = 18,344

ability are highly correlated and affect college

outcomes (Pascarella & Terenzini, 1991), twocontrol variables were created, student SES and

academic preparation. SES was represented by

level of parents’ education and the amount

parents contributed to college costs. This

estimate of SES is a far from robust measure of

SES, but it is the best approximation possible

from the variables included on the CSEQ.

Academic preparation is the sum of student self-

reported grades and educational aspirations. In

addition, institutional selectivity and control

(public, private) were also controlled in all

analyses with the selectivity measures taken

from Barron’s Profiles of American Colleges

(1996). Student gender, race and ethnicity, majorfield, institutional type, and year in college were

coded as dummy variables. The variables were

coded as follows:

• Sex (0 = women, 1 = men);

• Age (0 = traditional-age students under

age 24, 1 = students 24 and older);

• Race or ethnicity was coded as a set of

dummy variables: Asian Americans,

African Americans, Latinos, Whites, andOther Ethnicity (American Indians and




Kuh & Hu

others), with Whites as the omitted

reference group;

• SES (the sum of parent education where

1 = neither parent a college graduate,

2 = one parent a college graduate, and

3 = both parents college graduates and

amount parents contribute to college costs

where 1 = none to 6 = all or nearly all);

• Academic preparation (the sum of grades

where 5 = A and 1 = C, C– or lower; and

educational aspirations where 2 = Expects

to pursue an advanced degree after

college and 1 = Does not expect to pursue

an advanced degree);

• Major field (humanities, mathematics andsciences, social sciences, preprofessional,

and students in two or more major fields,

with preprofessional omitted as reference

group);

• Institutional type (RU, DU, CCU, SLA,

GLA with RU omitted as reference

group);

• Institutional control (0 = public, 1 =

private);

• Institutional selectivity (6 = most com-

petitive, 5 = highly competitive, 4 = very

competitive, 3 = competitive, 2 = less

competitive, and 1 = not competitive);

• Year in college (first year, sophomore,

junior, and senior, with first year omitted

as reference group);

• Number of term credit hours (1 = 6 or

fewer, 2 = 7 to 11, 3 = 12 to 14, 4 = 15 to

16, and 5 = 17 or more);

• Hours per week devoted to studying and

preparing for class (1 = 5 or fewer, 2 = 6

to10, 3 = 11 to15, 4 = 16 to 20, 5 = 21 to

25, 6 = 26 to 30, and 7 = more than 30);

• Hours per week working on campus or off

campus (1 = none, 2 = 1 to 10, 3 = 11 to

20, 4 = 21 to 30, 5 = 31 to 40, and

6 = more than 40);

• Overall C&IT score (the sum of individual C&IT item scores). These

items on the fourth edition of CSEQ are

presented in Table 1.

• EFFORTSUM (sum of all Activities item

scores excluding C&IT scale items);

• Gain factor scores (sum of Gain itemscores excluding C&IT item contributing

to the general education, intellectual

skills, personal and social development,

science and technology, and vocational

preparation Gain factors) (Appendix B);

• GAINSUM (sum of all Gain items

excluding C&IT item).

Data Analysis

The data analysis followed a four-step process.

First we calculated and examined the descriptive

statistics (unadjusted means and standard

deviations) for the sample and students’

responses to the CSEQ C&IT score and other

scales and factors. Then we used multiple

regression to determine how student charac-

teristics, institutional characteristics, and other

student experiences during college were related

to students’ overall use of C&IT. Next, we

conducted a series of multiple regressions toexamine the influence of overall use of C&IT

and the use of various forms of C&IT on the

total amount of effort students devoted to

other college activities excluding C&IT effort

(EFFORTSUM). Finally, a series of two-step

multiple regressions was used to examine the

total (gross) and direct (net) effects of C&IT on

student overall gains (GAINSUM) excluding the

C&IT gain item score and the scores from the

five Gain factors (general education, personaldevelopment, science and technology, vocational

preparation, and intellectual development).

Therefore, in this latter set of regressions

EFFORTSUM was treated as a mediating

variable between C&IT and GAINSUM and

gain factors (Pascarella & Terenzini, 1991;

Wolfle, 1980).

The psychometric properties for C&IT scale

are acceptable, with a reliability alpha of .784,

item intercorrelations ranging from .102 to .642,

and item-total score correlations ranging from.406 to .735. The moderate magnitudes of the





item intercorrelations reduced the threat of

multicollinearity and permitted the use of the

individual C&IT items in the multiple regres-

sion analyses.

RESULTS

The three most frequent C&IT activities were

using a computer for word processing, using E-

mail to communicate with an instructor or

classmates, and searching the Internet for course

material (Table 1). The three least frequent

C&IT activities were developing a Web page or

multimedia presentation, participating in class

discussions via an electronic medium, and using

a computer tutorial.Table 2 presents the unadjusted means of

overall and various forms of C&IT use. Men

used C&IT overall slightly more frequently than

women and also preferred more advanced forms

of C&IT. Women opted more often for word

processing and E-mail, with men more fre-

quently using visual displays, data analysis, and

multimedia presentation options. Older students

used C&IT less frequently than younger

(traditional-age) students (unadjusted means

were 20.49 and 21.71 respectively), though thedifferences were due primarily to the more

common forms of C&IT such as word processing

and E-mail. Students majoring in mathematics

and the sciences used most forms of C&IT more

frequently than did their counterparts in the

humanities, social sciences, and those who were

majoring in two or more areas. Seniors were

more likely to use the less common forms of

C&IT, such as to visually display information,

analyze data, and develop multimedia pre-sentations.

Table 3 shows the regression results of the

analysis of student characteristics, institutional

characteristics, and student overall use of C&IT.

Generally consistent with the results from the

analyses on unadjusted means in Table 2, men

used C&IT overall slightly more frequently than

women. Older students used C&IT less fre-

quently than younger (traditional-age) students.

Students from higher SES backgrounds were

more likely to use C&IT more frequently.However, compared with Whites, students from

other race and ethnicity had no significant

differences in the overall use of C&IT. Student

academic preparation was not related to overall

C&IT use. C&IT use also differed by major field

and institutional type and in predictable ways.

Compared with students in preprofessionalfields, students majoring in mathematics and the

sciences used C&IT more frequently, and

students in the humanities, social sciences, and

those who were majoring in two or more areas

used C&IT less frequently. Indeed, humanities

majors used C&IT the least. Students at research

universities had higher overall C&IT scores

compared with their counterparts attending

other types of institutions. Sector also made a

difference as students at private schools hadhigher overall C&IT scores than students at

state-assisted institutions.

Compared to the first-year students, seniors

used C&IT more frequently, although students

in other years of college had no significant

difference in the overall use of C&IT. Also,

students who took more credit hours, studied

more (class preparation), and worked on campus

were more likely to use C&IT more frequently.

At the same time working off campus was not

negatively related to C&IT use.To better understand the relationship

between C&IT and the college student experi-

ence we first examined the effects of C&IT on

EFFORTSUM (the sum of all CSEQ activity

items excluding C&IT items) and then on gains

from college. To accurately answer whether

C&IT influenced gains, we determined both the

net effects and the gross effects of C&IT on

gains. Table 4 shows the results of the effects

of C&IT overall scores and individual itemscores on EFFORTSUM, and the overall effects

and direct effects of C&IT overall scores and

individual item scores on GAINSUM (the sum

of all gains items) and the five Gain factor

scores. In this analysis, the C&IT overall score

and individual item scores can have both net

and indirect effects on GAINSUM and the Gain

factors. EFFORTSUM is, therefore, a mediating

variable for GAINSUM and the Gain factor

scores where C&IT affects EFFORTSUM, which

in turn affects the different Gain variables(Pascarella & Terenzini, 1991; Wolfle, 1980).




Kuh & Hu

TABLE 2.

Unadjusted Means for C&IT Item Scores and Overall C&IT Score

Item C&IT

VARIABLES 1 2 3 4 5 6 7 8 9 Score

Sex

Men 3.68 3.28 1.93 1.76 3.14 1.88 2.48 2.17 1.81 22.13

Women 3.75 3.48 1.84 1.69 3.17 1.75 2.28 1.82 1.52 21.32

Age

Nontraditional 3.54 2.70 1.85 1.59 2.99 1.91 2.34 1.94 1.62 20.49

Traditional 3.74 3.47 1.88 1.72 3.17 1.79 2.36 1.95 1.63 21.71

RACE OR ETHNICITY

American Indians

and Other 3.70 3.32 1.94 1.80 3.09 1.94 2.36 2.02 1.66 21.84

Asians or PacificIslanders 3.66 3.43 1.96 1.90 3.12 1.86 2.49 2.09 1.83 22.34

AfricanAmericans 3.73 3.23 1.86 1.77 3.12 1.90 2.30 1.90 1.60 21.41

Latinos 3.66 3.32 1.76 1.66 3.09 1.85 2.25 1.90 1.61 21.11

Whites 3.73 3.43 1.87 1.69 3.17 1.78 2.35 1.94 1.61 21.56

MAJOR FIELD

Humanities 3.67 3.25 1.67 1.62 2.95 1.77 1.94 1.49 1.47 19.85

Math and

Sciences 3.70 3.46 2.03 1.72 3.16 1.84 2.68 2.31 1.79 22.70SocialSciences 3.76 3.36 1.76 1.66 3.11 1.81 2.12 1.87 1.44 20.90

Two or moremajors 3.72 3.39 1.90 1.73 3.19 1.80 2.36 1.94 1.65 21.68

Preprofessional 3.76 3.48 1.84 1.73 3.17 1.78 2.35 1.90 1.60 21.61

INSTITUTIONAL TYPE

DU 3.71 3.32 1.91 1.89 3.15 1.84 2.30 1.94 1.61 21.67

CCU 3.69 3.24 1.78 1.57 3.13 1.82 2.30 1.90 1.57 21.01

SLA 3.85 3.71 1.86 1.54 3.20 1.85 2.20 1.90 1.55 21.86

GLA 3.75 3.40 1.81 1.71 3.14 1.91 2.49 2.07 1.58 21.69

RU 3.74 3.60 2.01 1.87 3.19 1.72 2.43 2.00 1.73 22.29

INSTITUTIONAL CONTROL

Private 3.79 3.56 1.85 1.71 3.20 1.91 2.42 2.02 1.63 22.03

Public 3.69 3.36 1.89 1.71 3.14 1.75 2.33 1.92 1.63 21.42

YEAR IN COLLEGE

Sophomore 3.68 3.42 1.86 1.64 3.11 1.75 2.33 1.91 1.60 21.30

Junior 3.65 3.27 1.82 1.66 3.10 1.76 2.38 2.00 1.65 21.29

Senior 3.74 3.22 1.81 1.77 3.19 1.92 2.58 2.16 1.81 22.20

First 3.76 3.53 1.92 1.74 3.18 1.79 2.27 1.87 1.56 21.61

Note. C&IT score is the sum of individual C&IT item scores; C&IT items 1 to 9 as in Table 1.





As is evident from Table 4, a large part of the

influence on gains of using C&IT was mediated

by EFFORTSUM.

The C&IT overall score positively influ-

enced the amount of effort that students

expended on other educationally purposefulactivities (EFFORTSUM). This reinforced that

student use of C&IT may indirectly influence

student gains mediated by EFFORTSUM. As

demonstrated in Table 4, the C&IT overall score

had significant and positive gross effects on all

gain outcome measures (GAINSUM) and each

of the five Gain factors. However, although the

C&IT overall score had net positive effects on

gains in science and technology, vocational

preparation, and intellectual development, it hada net negative effect on general education.

Apparently, EFFORTSUM mediated a large

portion of the gross effects of C&IT overall score

on gain measures.

Table 4 also includes the results from the

analyses of the individual C&IT item effects on

EFFORTSUM, GAINSUM and five gain factors.

All individual C&IT activities had statistically

significant and positive effects on EFFORT-

SUM, with one exception: developing a web

page or multimedia presentation. Again, thissuggests that individual C&IT activities may

affect student gains directly and indirectly by

influencing EFFORTSUM. However, the results

indicate that different C&IT activities had

different effects on outcomes represented by the

overall gains measure (GAINSUM) and the five

gains factors.

Using a computer or word processor to

prepare papers had a positive net effect on

intellectual development but a negative net effecton science and technology. However, this type

of C&IT activity had positive gross effects on

GAINSUM, general education, personal devel-

opment, and intellectual development. Using E-

mail to communicate with an instructor or other

students had a positive net effect on personal

development but a negative net effect on general

education as well as gross positive effects on

personal development and intellectual devel-

opment. Using a computer tutorial to learn

materials had positive net effects on science andtechnology and vocational preparation as well

TABLE 3.

Standardized Coefficients of StudentCharacteristics and Other Predictors on

C&IT Overall Score

VARIABLES Beta

Men .074*

(Women)

Nontraditional –.057*

(Traditional)

American Indians and Other .018

Asians or Pacific Islanders .012

African Americans .005

Latinos –.018

(Whites)

SES .059*

Academic preparation .000

Humanities –.110*

Math and sciences .032*

Social sciences –.053*

Two or more majors –.035*

(Preprofessional)

DU –.034*

CCU –.132*

SLA –.060*

GLA –.110*

(RU)

Private .105*

(Public)

Selectivity –.026

Sophomore .002

Junior .010

Senior .086*

(First-year student)

Number of term credit hours .067*

Hours on academic work .123*

On-campus work .053*

Off-campus work .000

Adjusted R 2 .076*

Note . The omitted reference group in parenthesis.

* p < .001.




Kuh & Hu

TABLE 4.

Standardized Coefficients of Overall C&IT Score and Individual Itemson EFFORTSUM, GAINSUM, and Gain Factors

Gen Per Sci & Voc IntelEFFORTSUM GAINSUM Ed a Dev Tech Prep Dev

Model 1: C&IT Overall Score

C&IT Overall Score .470* .013 –.062* .006 .049* .029* .095*

(.470)* (.291)* (.186)* (.219)* (.233)* (.177)* (.314)*

Full Model Adjusted R 2 .316* .385* .301* .233* .276* .194* .303*

Model 2: Individual C&IT Items

1. Used computer or word

processor for paper .057* .012 .023 .011 –.039* –.002 .039*

(.057)* (.046)* (.053)* (.037)* (–.017) (.017) (.066)*

2. Used E-mail tocommunicate with class .042* –.007 –.042* .043* –.024 .004 .017

(.042)* (.018) (–.019) (.062)* (–.007) (.018) (.037)*

3. Used computer tutorialto learn material .088* .011 –.005 .019 .036* .022* –.002

(.088)* (.064)* (.041)* (.059)* (.070)* (.051)* (.040)*

4. Joined in electronicclass discussions .089* .001 .014 .008 –.007 –.010 –.003

(.089)* (.054)* (.060)* (.048)* (.028)* (.019) (.038)*

5. Searched Internet forcourse material .118* .020 –.004 .028* –.002 .027* .049*

(.118)* (.090)* (.058)* (.082)* (.044)* (.065)* (.105)*

6. Retrieved off-campuslibrary materials .157* –.026* .014 –.041* .010 –.036* –.039*

(.157)* (.067)* (.096)* (.030)* (.051)* (.014) (.034)*

7. Made visual displayswith computer .085* .007 –.051* –.003 .048* .021 .042*

(.085)* (.058)* (–.006) (.036)* (.081)* (.048)* (.081)*

8. Used a computerto analyze data .108* .015 –.046* –.007 .069* .013 .046*

(.108)* (.079)* (.011) (.042)* (.111)* (.048)* (.096)*

9. Developed Web page,multimedia presentation .015 –.014 .012 –.037* –.025* –.002 .005

(.015) (–.005) (.020) (–.030)* (–.019) (.003) (.012)

Full Model Adjusted R 2 .325* .386* .305* .238* .284* .196* .309*

Notes .Full regression models included controls for all the independent variables in Table 3. The upper lineindicates the net effects and the lower line (in parentheses) indicates the gross effects of the C&IT OverallScore and individual items on outcome variables.

a Gen Ed = General Education; Per Dev = Personal Development; Sci & Tech = Science and Technology; Voc

Prep = Vocational Preparation; Intel Dev = Intellectual Development

* p < .001.





as a positive gross effect on GAINSUM and all

five gain factors. Participating in electronic class

discussions had no significant net effects on any

gain measures, but it did have a positive gross

effect on GAINSUM and four of the five gain

factors, with the exception of vocationalpreparation. Searching the Internet for informa-

tion had positive net effects on personal

development, vocational preparation, and

intellectual development, and a positive gross

effect on GAINSUM and all five gain factors.

Retrieving off-campus library materials had

negative net effects on GAINSUM, personal

development, vocational preparation, and

intellectual development; however, it had a

positive gross effect on GAINSUM and all gainfactors except for vocational preparation.

Making visual displays with a computer had

positive net effects on science and technology

and intellectual development but a negative net

effect on general education as well as a positive

gross effect on GAINSUM and all gain factors

except general education. Using a computer to

analyze data had positive net effects on science

and technology and intellectual development but

a negative net effect on general education and

positive gross effects on GAINSUM and all gainfactors but general education. Finally, develop-

ing a Web page and multimedia presentation had

negative effects on personal development and

science and technology; the gross effect on

general education was also negative.

Limitations

This study is limited in several ways. First, the

measures we employed were limited to those

available on the CSEQ. For example, the C&ITitems from the CSEQ are not an exhaustive list

of the computing and information technology

activities that students can use that might affect

their learning in positive or negative ways. For

example instructor-designed use of hypermedia

and hypertext are not specifically mentioned nor

are activities that represent noneducational uses

of C&IT such as surfing the Web or playing

games. Thus, these data do not shed light on

such potential debilitating behaviors associated

with C&IT such as Internet addiction orcocooning (Kandell, 1998). Also, the SES

measure used in this study is not as precise as

one would desire and may not accurately reflect

the underlying construct. Such data might

provide a different view of the nature of the

relationships between C&IT, student effort, and

self-reported gains.Second, this study was based on a con-

venience sample of institutions participating in

the CSEQ research program from a recent 2-

year period. If data from other institutions were

available or a longer period was covered,

perhaps the results would differ. Another

limitation is that the findings related to

institutional type may be skewed due to practices

at certain institutions included in the study. For

example, some of the schools may require allmatriculating students to purchase laptops. At

the same time many schools that are not

represented in the study have state-of-the-art

networks, hardware, and software that provide

unusually rich opportunities for their students

to become familiar with and use information

technology; the experiences of these students is

not captured in the results. Fourth, we employed

measures representing two levels of analysis—

students and institutions. An analytical tech-

nique such as hierarchical linear modeling (Bryk & Raudenbush, 1992) is recommended in such

situations. Finally, as with other studies that use

self-report data the findings may be affected by

response set (Pascarella & Terenzini, 1991) and

the halo effect (Pike, 1999) that create dif-

ficulties in determining whether the outcomes

are really being influenced by the use of C&IT

or whether other variables are intervening.

DISCUSSION

Computer and information technology repre-

sents a substantial investment of university

resources that fortunately seem to be generally

beneficial. The findings from this study show

that using C&IT is related in complex, statis-

tically significant ways to the amount of effort

students devote to educationally purposeful

college activities. Although both positive and

negative net effects on student gains were found,

the gross effects of using C&IT, either inaggregate or individual activities, generally were




Kuh & Hu

positive, though most of the effects on student

gains were mediated through the effort students

expended on other activities. The exception to

this conclusion is the item regarding developing

Web pages and multimedia presentations, which

had a negative gross effect on some gain factors(e.g., personal development and science and

technology). That said, the results of the study

both confirm the popular view that C&IT use

is positively related to college student learning

and personal development and also raises some

questions about the efficacy of certain C&IT

activities.

First, the use of computing and information

technology for word processing and E-mail is

practically universal. Understandably, moretime-consuming activities that require a higher

level advanced knowledge about how to use

C&IT were far less common, such as developing

a Web page or multimedia presentation. We were

somewhat surprised and disappointed that

participating in class discussions using an

electronic medium was one of the less frequently

used forms of C&IT. Students in the humanities

and social sciences were least likely to parti-

cipate in electronic class discussions, whereas

seniors and students at private colleges were themost likely. Encouraging students to engage

more actively in learning through C&IT is

primarily the responsibility of the instructor.

Thus, institutions and instructors can promote

active learning via technology if they con-

sistently use good educational practices such as

clarifying expectations, preparing course

assignments that require active student engage-

ment, teaching students how to appropriately use

the technology, and giving students prompt andaccurate feedback about their contributions

(Chickering & Gamson, 1987).

The differences between institutional types

and C&IT use favored students at research

universities and private colleges and univer-

sities. Their more frequent use of C&IT may be

a function of institutional affluence, where such

institutions have had more funds available to

invest in technology, making it more available

and accessible to most students. Some of these

schools may even require all matriculatingstudents to have a personal computer. At the

same time, institutional selectivity was not

related to overall C&IT use.

Consistent with other studies (Flowers et

al., 2000; Gladieux & Swail, 1999), students

from higher socioeconomic backgrounds ap-

peared to use C&IT more frequently. However,C&IT appears to create a level playing field for

learning for students from different racial and

ethnic backgrounds who have access to it, given

that the use of various forms of C&IT did not

differ to any great extent by race and ethnicity.

Seniors used C&IT the most, including the more

advanced forms (visual displays, data analysis,

multimedia presentations). This is heartening

in the sense that even though most students

(83%) used the Internet for research or home-work in their senior year of high school prior

to matriculating (Higher Education Research

Institute, 1999), most students do not simply

continue doing the same things with technology

that they did in high school but also expand their

use of C&IT.

General education was the only gains

cluster that potentially suffered as a result of

overall C&IT use; E-mail, making visual

displays, and analyzing data with a computer

were the most deleterious influences on generaleducation gains, even after controlling for major

field. It is not immediately obvious why

developing a Web page would have a net

negative effect on personal development or

obtaining materials from an off-campus library

would have net negative effects on GAINSUM,

personal development, vocational preparation,

and intellectual development. Perhaps the nature

of the Web page being developed is unrelated

to academic work, such as a page used to presenta photo album of a recent social event, as

contrasted with a Web page on which one posts

an electronic portfolio or research paper.

Implications

The findings suggest five near-term implications

for policy, practice, and research. First, as Kuh

and Vesper (2001) suggested, all students in all

fields should be encouraged to become proficient

with computers and the other forms of informa-

tion technology available on their campus. C&ITseems to work for all students, though some





students, such as women, appear to be less likely

to use more advanced forms of technology.

Perhaps the manner in which the technology is

presented, described, or formatted has some-

thing to do with this. Some experiments with

different approaches to getting women to engagein using the technology for course-based

assignments may be in order, where appropriate.

A student’s socioeconomic background also

seems to matter, consistent with the findings

from other studies (Dillard & Gabbard, 1998;

Flowers et al., 2000), with those from higher

SES categories being more likely to use most

forms of C&IT. But some other unalterable

background characteristics such as race and

ethnicity do not seem to affect C&IT use or theadvantages that C&IT can provide in terms of

learning and personal development outcomes.

Thus, the so-called “digital divide” in 4-year

colleges and universities does not seem to be a

problem if institutions make C&IT accessible

(as in the case of most of the private colleges

and research universities in this study).

At the same time, students in some fields

do not seem to do much with C&IT other than

use E-mail. For example, C&IT is underused

by students majoring in the humanities andsocial sciences compared with their counterparts

in preprofessional fields and in mathematics and

sciences. Perhaps C&IT is introduced more

reluctantly or at slower rates in such fields as

history and literature where the immediate

utility of technology is not realized. Electronic

discussions of the material introduced in many

of humanities and social science fields would

seem to be an appropriate pedagogical strategy.

Second, because C&IT appears to bepositively related to learning and personal

development in a variety of areas for most

students, public and institutional policies must

ensure that such resources are accessible by all

students at every college and university.

Systematic efforts are needed to monitor the

extent to which various groups of students are

using various forms of C&IT and the effects of

institutional policies and practices on student

access to and use of C&IT. This is an area to

which student development professionals cancontribute by collaborating with institutional

researchers to collect the needed information.

Third, the costs and benefits of C&IT must

be estimated and interpreted in the context of

student learning data and other institutional

priorities (Morrison, 1999; Upcraft et al., 1999).

Productivity has always been difficult to measurein colleges and universities (McKeachie, 1982).

Information technology typically demands

nontrivial amounts of new money or realloca-

tions of campus resources to establish, update,

and upgrade software, hardware, and networks.

Reallocating institutional resources for this

purpose means that other potentially productive

and useful activities cannot be supported. In the

long run, such investments (including the

replacement of out-of-date technology) mayactually cost more than other uses of the funds

when productivity measures are examined

(Massy & Wilger, 1998). The prevailing view,

however, is that technology will generate

economies of scale following what can often be

a relatively large front-end investment. That is,

following the initial investment, the per-student

cost is expected to be relatively low (Massy &

Wilger). At the same time, although “most

successful technology applications have im-

proved net productivity (benefits, adjusted forquality changes, divided by cost), gross produc-

tivity (number of output units divided by cost)

generally declines (Massy & Wilger, p.51).

Thus, the impact of the institutional C&IT

investment on learning must be evaluated

periodically.

Fourth, studies are needed for the effica-

cious ways to promote the effective use of C&IT

by different groups, especially faculty members

and student development staff with instructionalassignments. These challenges include helping

faculty learn how to integrate technology into

their instruction and providing adequate

instruction for how to use the technology by

students and other users (Green, 1999). Pro-

viding new forms of hardware will not neces-

sarily change the way people teach or cause them

to modify course materials to take maximum

advantage of the technology. In part, this is

attributed to the inertia of the system where the

widespread integration of information tech-nology into core institutional processes ought




Kuh & Hu

not be expected in the short term (Gilbert, 1996).

Without significant structural change, the

innovations that technology promises to bring

to teaching and learning are not likely to occur

(Green & Gilbert, 1995).

Finally, additional research is needed todetermine the extent to which C&IT is being

used for noneducational purposes that are largely

distractions and incompatible with the educa-

tional purposes of postsecondary institutions.

This is very important as the positive effects of

C&IT are largely mediated through the amount

of effort students spend in other educational

purposeful activities. Perhaps the amount of time

most students spend in noneducational use of

C&IT is minimal and does not detract sub-stantially from engaging in more important

activities. However, without this information

institutions cannot act intelligently because they

are at a loss to know first whether such a

problem is significant and for whom. Student

affairs staff can play an important role by

systematically observing how much time

students spend using computers for various

purposes (e.g., surfing the web, playing games,

producing academic work).

Conclusion

The results of this study show that students who

benefit most from using C&IT are those who

use it more frequently and in more advanced

ways. Although women and students from lower

SES backgrounds use C&IT less frequently and

benefit less from its use, the effect sizes

associated with these differences are trivial.

Moreover, effort devoted to using C&IT appears

to have generally positive gross effects on the

development of most important outcomes of

college. Equally important, using C&IT is

associated with greater levels of educational

effort with the effects of C&IT on gains being

largely mediated through the other educational

efforts students put forth. Thus, C&IT useappears to have a general salutary influence on

the overall learning environment.

Correspondence concerning this article should be

addressed to George D. Kuh, Center for Post-

secondary Research and Planning, College of

Education, Indiana University, Bloomington, IN

47405; [email protected]





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