ERIC EJ1074636: Studying Online: Student Motivations and Experiences in ALA-Accredited LIS Programs

2015·1.2 MB·English

by  ERIC

Studying Online: Student Motivations and
Experiences in ALA-Accredited LIS Programs
Fatih Oguz
Department of Library and Information Studies, The University of North Carolina at Greensboro.
Email: [email protected]
Clara M. Chu
Mortenson Center for International Library Programs, University of Illinois at Urbana-Champaign.
Email: [email protected]
Anthony S. Chow
Department of Library and Information Studies, The University of North Carolina at Greensboro.
Email: [email protected]
This paper presents a large scale study of online MLIS students (n = 910), who complet-
ed at least one online course and were enrolled in 36 of the 58 ALA-accredited MLIS
programs in Canada and the United States. The results indicate that the typical student
is female, White, lives in an urban setting, and is in her mid-30s. Online students were
found to be quite diverse, with statistically significant differences in their preferences
and satisfaction across five demographic variables: age (generational cohort), employ-
ment status, urban status, commute distance, and program modality. Three motiva-
tions emerged: accommodation, predisposition, and selectivity, which influenced the
respondents to choose online learning. The prevalent issues online MLIS students expe-
rienced were a sense of isolation from peers and instructors, and a lack of professional
development and networking opportunities with peers. The findings have implications
for enhancing MLIS online education including marketing, course offerings, and student
support services.
Introduction In the United States online education
has grown at a much greater pace than has
While online education has grown at overall college enrollment. In fall 2008,
a fast pace in the United States, this over 600,000 graduate students took at
trend is also evident across the globe at least one online course, which constitutes
institutions of higher education (“8 coun- about 14% of all students who took at
tries”, 2012). Due to the evolution of open least one online course in postsecondary
education through the offering of massive degree-granting institutions in the United
open online courses (MOOCs), anyone States (Allen & Seaman, 2010). Likewise,
around the globe with an internet con- the American Library Association (ALA)-
nection is able to freely access MOOCs accredited master’s programs are seeing
(Yuan & Powell, 2013). This phenomenon continuing changes in their enrollment as
in global distance learning (Hanover Re- more students choose to take a hybrid of
search, 2011) is showing that e-learning traditional face-to-face and online cours-
is the fastest growing market in education es or an entirely online degree. In library
which by 2017 is expected to increase by and information science (LIS) programs,
23% (IBIS Capital, 2013). online course offerings have also been
J. of Education for Library and Information Science, Vol. 56, No. 3—(Summer) July 2015
ISSN: 0748-5786 © 2015 Association for Library and Information Science Education 213
doi:10.12783/issn.2328-2967/56/3/4
214 JOURNAL OF EDUCATION FOR LIBRARY AND INFORMATION SCIENCE
steadily increasing as about 76% of all dis- education also appeal to students. It is well
tance LIS courses (n = 2,039)1 during the established that these benefits afforded by
2009–2010 academic year were delivered online education are crucial for students
online (Wallace & Naidoo, 2010b).2 when making a decision to enroll in online
Convenience, flexibility, and afford- courses (Dutton, Dutton, & Perry, 2002;
ability are potential factors that allow Pastore & Carr-Chellman, 2009). External
students to consider and choose online factors including feedback from peers or
education to pursue a master’s degree in instructors, and unavailability of classes in
library and information science (MLIS). face-to-face format may also be motivat-
In the context of this study, the acronym ing factors for students (Pastore & Carr-
MLIS refers to the various graduate de- Chellman, 2009; Scott, 2011).
gree names in the library and information Information and communication tech-
field. In order for MLIS programs to effec- nologies have extended the reach of LIS
tively meet the needs of their online stu- education, which has included blended,
dents, it is important to understand these TV, Telenet, video conferencing, and on-
students and their experiences. This study line delivery (Barron, 1996). Despite the
explored a number of issues to better un- growing body of research addressing dis-
derstand online MLIS students. Who is tance education in LIS programs, larger
a typical online MLIS student? Are they studies are needed to understand fac-
older? Employed? Why did they choose tors that motivate students to take online
to pursue their education online? What are classes and challenges that they encounter
the factors that influence their satisfaction when taking online classes.
with an online education? Are there differ- An online course can be defined as a
ences based on demographic factors? Web-based instructional method in which
at least 80% of the instruction occurs via
Literature Review the Internet (Allen & Seaman, 2007). LIS
programs in the United States have been
The primary rationale for program de- offering online classes since the 1990s
livery of online courses in LIS include in- (Small & Paling, 2002). Online classes
creasing access to professional qualifica- offered by a majority of ALA-accredited
tions, removing geographical barriers, and LIS programs have little to no face-to-face
offering independent and more diverse instruction (Bird, Chu, & Oguz, 2011).
life-style oriented courses and learning The earliest statistics issued by the Asso-
opportunities (Islam, Kunifuji, Hayama, ciation for Library and Information Sci-
& Miura, 2011). Students’ motivations for ence Education (ALISE) on online offer-
taking online classes vary. For some, work ings in LIS programs are available from
or family commitments, scheduling con- the 2000–2001 academic year in which
flicts, or physical distance from the cam- about 14% (n = 994) of course offerings
pus may be a determining factor. Positive were online, while over ten years later
prior experiences with online education, (2011–2012 academic year) almost 60%
ability to study at their own pace, broad- of the courses were delivered online as
er selection of courses in online format, shown in Figure 1.
quality of the program, potential savings Research has found that online courses
of time and money on travel, and cost of are not only for the non-traditional student
who often has work or family responsibili-
ties that limit the student’s ability to attend
1The ALISE statistical report compiles data for distance courses,
in which different delivery methods are reported, including online. classes in traditional face-to-face format,
2Information needed to update this data was missing from the most
but also for the residential student (Pas-
recently available ALISE statistical report. Data for “number of
courses” which should appear in Table III-30a Method of Course tore & Carr-Chellman, 2009). The non-
Delivery (this title appeared in the Table of Contents) of the ALISE
traditional student is someone who is not
Statistical Report 2012 was not included.
Studying Online: Student Motivations and Experiences in ALA-Accredited LIS Programs 215
Figure 1. Course Delivery Modality in LIS (Academic Year 2000–2011). Data for Course Delivery
Modalities from Daniel and Saye (2002, 2003, 2005), Saye (2008), Saye and Wallace (2009), Wal-
lace and Naidoo (2010a, 2010b), and Wallace (2012).
a full-time student, straight out of high Although residential students are in-
school, while the residential student is a creasingly enrolling in online courses,
student who generally takes their courses Wyatt (2005) and Dutton et al., (2002)
on campus. found that geographical proximity is a
It has also found that one of the most very important motivation, especially for
important motivations for students to en- those who need to commute to campus. In
roll in an online course is convenience. Re- some cases, students may be motivated by
search has shown a number of attributes their curiosity of the online course format
of convenience, which include not need or the technology-intensive aspect of this
needing to relocate and ease of access to modality (Dyrbye, Cumyn, Day, & Heflin,
content at any time (see Table 1). 2009; Wilde & Epperson, 2006).
Table 1. Attributes of Convenience and Flexibility in Online Education Research.
Attribute Study
1. Ability to complete the course requirements in 1. Mellon & Kester, 2004; Pastore & Carr-Chell-
a setting of the student’s choice. man, 2009.
2. Not needing to relocate. 2. Mellon & Kester, 2004; Wilde & Epperson,
2006; Wyatt, 2005.
3. Ability to keep current employment. 3. Pastore & Carr-Chellman, 2009; Small & Pal-
ing, 2002; Wilde & Epperson, 2006.
4. Ease of access to course content at any time. 4. Dutton et al., 2002; Mellon & Kester, 2004;
Pastore & Carr-Chellman, 2009; Small & Pal-
ing, 2002.
5. Set own schedule and not needing to travel to 5. Dutton et al., 2002; Dyrbye, Cumyn, Day,
campus for instructional purposes. & Heflin, 2009; Fredericksen, Swan, Pelz,
Pickett, & Shea, 1999; Mellon & Kester, 2004;
Pastore & Carr-Chellman, 2009; Small & Pal-
ing, 2002; Wyatt, 2005.
6. Flexibility afforded by temporal and physical 6. Pastore & Carr-Chellman, 2009; Wyatt, 2005.
separation.
216 JOURNAL OF EDUCATION FOR LIBRARY AND INFORMATION SCIENCE
It has been shown that there are no sig- education experience (Kazmer, Gibson, &
nificant differences between online and Shannon, 2013).
traditional course delivery modalities in The present study focused on gradu-
terms of student achievement (Dutton ate students’ experiences with online
et al., 2002; Means, Toyama, Murphy, education while completing their MLIS
Bakia, & Jones, 2009) and students’ per- degree in an ALA-accredited institution.
ception of academic rigor (Mortagy & Although some LIS programs do not of-
Boghikian-Whitby, 2010; Pastore & Carr- fer online courses, students from such
Chellman, 2009). Differences, however, programs were invited to participate in the
between these modalities in other aspects study as they may have taken online LIS
such as satisfaction, learning, and inter- courses at another program for credit. The
action with their peers have been found. following research questions guided the
Tucker (2001) and Dutton et al. (2002) study:
found that older students were more likely
to prefer distance education because of RQ1: What are the demographic charac-
reasons including family and work com- teristics of students pursuing an online
mitments. Fredericksen, Swan, Pelz, Pick- MLIS education?
ett, and Shea (1999) reported that older RQ2: What factors influence students to
students (36–45 age group) were more sat- select online MLIS coursework?
isfied with online courses and learned the RQ3: What factors are associated with
most online than younger students (16–25 student satisfaction and an online MLIS
age group). Students tend to perceive face- degree?
to-face classes as more engaging in terms RQ4: What issues concern online MLIS
of learning and interaction regardless of students?
their course attendance mode (distance,
face-to-face) (Hagel & Shaw, 2006). Sher Method
(2009) found that student-to-student and
student-to-instructor interactions were The study used an online questionnaire
significantly associated with student learn- (see Appendix A) to gather data from stu-
ing and satisfaction. These interactions dents enrolled in all ALA-accredited LIS
can be related to- and may be impeded programs, which are located in the United
by- the student’s sense of isolation from States, Puerto Rico, and Canada. At the
peers, instructor, and school; lack of tech- time of the research there were 58 LIS
nical and academic support; and limited programs accredited by the ALA (“Al-
opportunities for social interaction and phabetical List of Institutions”, 2013).
professional development (Croft, Dalton, Because of the lack of a suitable sampling
& Grant, 2010; Hara & Kling, 1999; Ka- frame, a non-probability sampling method
zmer, 2007; Muilenburg & Berge, 2005). was employed. The questionnaire was ad-
Students’ experiences with online classes ministered by email in spring of 2012 with
are also informed by their motivations the assistance of the administration of the
as older students have certain constraints LIS programs and their student associa-
including family and work commitments tions. Thirty-six programs from Canada
(Fredericksen et al., 1999; Tucker, 2001); and the United States were represented
such barriers, however, tend to be rated by the 1,038 students who participated
lower by older and male students (Mui- in the study. Respondents who had taken
lenburg & Berge, 2005). The students’ age and completed at least one online course
may in part explain younger students’ dis- constituted the sample (n = 910) that was
satisfaction with online education as age used for analysis and the reporting of the
may serve as an indicator for temporal results. Although a non-probability sam-
proximity to more traditional face-to-face pling strategy was employed to collect
Studying Online: Student Motivations and Experiences in ALA-Accredited LIS Programs 217
the data, no statistically significant differ- Census tracts. Responses from students
ence was detected between demographic (n = 44) attending MLIS programs in
characteristics of respondents in terms of Canada (n = 4) were not assigned an urban
age, gender, and ethnicity with that of cur- status code, therefore excluded in certain
rent MLIS students, reported in the 2012 analyses where urban status code was used.
ALISE Statistical Report (Wallace, 2012), Since the data in question did not show
suggesting the study’s participants reflect- normalcy, non-parametric tests (Pearson
ed a representative sample. Chi-Square, Kruskal Wallis, and Mann-
Whitney U) were applied to determine
Data Instrument and Analysis significance of relationships among both
scalar and categorical variables. The
An 18-item questionnaire was devel- Cronbach’s alpha (α) statistic was used to
oped based on characteristics related to measure internal consistency reliability of
students’ experiences in online courses. statements used for students’ motivations
The questionnaire consisted of two major for taking online courses, and their experi-
sections: demographic information, and ences and satisfaction with online cours-
experiences with online classes and pro- es. The results were summarized using
grams. The statements used to determine exploratory factor analysis, a statistical
students’ motivations to enroll in an on- method for identifying groups of variables
line class (RQ2) were adapted in part from (Field, 2009).
Dutton et al. (2002), Scott (2011), and
Wilde and Epperson (2006). The state- Results
ments used to assess student satisfaction
with programmatic services and their The results of the study allow us to
experiences while taking online classes answer each of the study’s four major
were developed from a review of the re- research questions. The data suggest a
search conducted by Dutton et al. (2002), number of trends in demography, student
Fredericksen et al., (1999), and Kazmer considerations when choosing an entirely
(2007). online program, satisfaction of such stu-
Residential zip code information was dents with online education, and student
collected in order to calculate commute experiences while taking online classes.
distance of each participant from a pro-
gram’s main campus. The Google Maps What are the demographic
application program interface (API) was characteristics of students pursuing
used to calculate commute distance based an online MLIS education?
on student-supplied zip code. Commute
distance was calculated from the popula- Although a non-probability sampling
tion centroid of the origin residential zip method was used to disseminate the sur-
code area (student’s zip code) to the popu- vey, no statistically significant differ-
lation centroid of the destination zip code ences were detected for age, gender, and
area (main campus zip code). Analysis of race between data used in this study and
commute distance results revealed no out- student data reported in the most recent
liers in the sample (n = 910). Student resi- ALISE Statistical Report (Wallace, 2012).
dential zip codes were mapped to Rural The majority of survey participants were
Urban Commuting Area codes (“Rural Ur- female (84.5%), White (87.5%), and lived
ban Commuting,” n.d.) to determine their in urban areas (91.1%) (see Table 2). Al-
rural/urban resident status while attend- most half of the students (49%) attending a
ing school. RUCA codes use measures of partially or entirely online MLIS program
population density, urbanization, and daily were of Generation X, with the mean age
commuting to classify the United States being 34.3 (see Table 2).
218 JOURNAL OF EDUCATION FOR LIBRARY AND INFORMATION SCIENCE
The majority of respondents who were and partially online students (U = 82567,
taking online courses were employed p < 0.001). Students who were attending
(83%), were attending a partially online entirely online programs were significant-
program (55.1%), or resided (56.3%) ly older (x = 36, SD = 10) than those (x =
within 50 miles commute distance from 33, SD = 9.5) who attended a partially on-
the main campus. line program (p < 0.001). In terms of gen-
A statistically significant difference was erational cohort differences, a large major-
detected in terms of age between entirely ity of younger Generation Y students were
Table 2. Respondent Demographic Information.
Frequency Percentage
Generational Cohort /Age* (n = 909, x = 34.3, SD = 9.8)
Gen Y (under 29) 345 38
Gen X (29–47) 445 49
Baby Boomers (over 47) 119 13
Gender (n = 907)
Male 135 14.9
Female 766 84.5
Other 6 0.6
Race / Ethnicity (n = 896)
White (Non-Hispanic) 784 87.5
Black or African American 30 3.3
Hispanic or Latino 26 2.9
Multiracial 26 2.9
Asian, Asian-American, or Pacific Islander 23 2.6
American Indian or Native Alaskan 7 0.8
Employment Status (n = 903)
Full-Time 388 43
Part-Time 365 40.4
Unemployed 150 16.6
Metro Status** (n = 832)
Urban 758 91.1
Large Rural 41 4.9
Small Rural 24 2.9
Isolated 9 1.1
Program Modality (n = 910)
Entirely Online 409 44.9
Partially Online 501 55.1
Commute Distance* (n = 886, x = 270, SD = 592)
0–50 miles 499 56.3
51–100 miles 85 9.6
101–200 miles 98 11.1
201–400 miles 68 7.7
> 400 miles 136 15.3
*The data were originally collected or calculated as continuous data.
**Respondents from MLIS Programs in Canada were not included.
Studying Online: Student Motivations and Experiences in ALA-Accredited LIS Programs 219
Figure 2. Participation in Online Education by Generational Cohort (n = 909, p < 0.001).
attending partially online programs as op- White and Non-White3 because of small
posed to their older peers who attended an sample sizes in categories other than White
entirely online program (p < 0.001). for statistical analysis purposes. Race/eth-
The average commute distance of the nicity, however, did not have a statistically
respondents was 270 miles, however, en- significant association with generational
tirely online students lived significantly cohort, employment status, urban status,
(p < 0.001) farther from main campus with commute distance, and program modality.
an average distance of 463 miles (SD =
748) than those who were in partially on- What factors influence students to select
line programs (x = 118 miles, SD = 364). online MLIS coursework?
Gen Y students tended to reside closer to
campus (x = 165 miles) than Gen X (x = Student motivation for taking online
327 miles) and Baby Boomers (x = 367 coursework varied. Students enrolled in
miles). A majority of students who had partially online programs have access to
full-time employment were attending an traditional and blended courses. In some
entirely online program while a majority cases, certain courses in such MLIS
of students who had part-time employ- programs may be offered online-only,
ment were attending a partially online pro- which in turn, limits the students’ ability
gram (p < 0.01). to choose an alternative delivery mode.
In addition, a large portion of students Eleven statements were used to assess
who were unemployed at the time of data student motivation including availability,
collection were attending an entirely on- broader selection of courses online, past
line program. Although a large majority of experience, personal circumstances (e.g.,
respondents lived in urban areas, almost health), and conflict with work schedule.
all of the partially online students (96.7%) The Cronbach’s alpha statistic was esti-
resided in an urban setting compared to mated as 0.69 indicating an acceptable
84.5% of entirely online students who re- level of internal consistency.
sided in urban areas (p < 0.001). Thirty- The results of exploratory factor analy-
two (32) LIS programs from the United sis identified three factors with an eigen-
States that were represented in this study
were located in urban areas as per RUCA 3The use of the term Non-White is not to privilege identity based
codes. on a White majority but to try to use a referent that encapsulates
the notion of ethno-racial minority, minority, people of color, etc.
Race/ethnicity data were reclassified as on which it is also difficult to reach a consensus.
220 JOURNAL OF EDUCATION FOR LIBRARY AND INFORMATION SCIENCE
value higher than one. These three factors
Table 3. Motivations for Taking
explained 54.01% of the total variance
Online Courses by Generational
in the students’ choosing to take online
Cohort (n = 494).
coursework. In this analysis, the KMO
measure was 0.808, suggesting enough of Generational Mean
a satisfactory factor analysis to proceed, Cohort n Rank
and the Bartlett’s test of sphericity being
Gen Y 222 205.64
statistically significant (p < 0.001). These
Accommodation* Gen X 221 273.38
results suggest that factor analysis was an
Baby Boomers 51 317.61
appropriate technique for summarizing the
Gen Y 222 258.94
data. Principal component factor analysis
of student motivation (n = 489, α = 0.69) Predisposition Gen X 221 236.50
revealed three factors: accommodation, Baby Boomers 51 245.37
predisposition, and selectivity. Gen Y 222 236.70
Accommodation refers to convenience Selectivity Gen X 221 258.85
and flexibility offered by online classes Baby Boomers 51 245.33
to allow the student to take classes or to
*p < 0.001.
create a schedule that fits the student’s
lifestyle and other priorities. This includes er; therefore pairwise comparisons were
the flexibility to enroll in an online course needed. Pairwise comparisons of genera-
when the student’s ability to enroll in tra- tional cohort groups by accommodation
ditional courses was limited by certain re- indicated that the differences were signifi-
sponsibilities or concerns. Accommodation cant between each pair of generational co-
explained 24.1% of the variance in the stu- hort. Accommodation as a motivation was
dents’ choosing to take online coursework. statistically more important for older stu-
Predisposition refers to the student’s posi- dents than their younger peers (p < 0.001).
tive perception of online classes, reached Statistically significant associations
on their own or from recommendations. were also detected between students’
Predisposition represents a more inten- employment status and motivations of
tional motivation to enroll in an online accommodation (p < 0.001) and predis-
course when there were no limiting issues position (p < 0.05) as shown in Table 4.
for the student and explained 17.6% of the Pairwise comparisons of employment
variance. Selectivity describes a student’s statuses by these two motivations were
choice/motivation to take online classes conducted to investigate which pairs of
due to the limitation of the only available employment status categories differed
format for classes, or in contrast a broader significantly. There was no significant dif-
selection of classes available online. This ference in terms of importance of accom-
factor explained 12.3% of the variance. modation between students who had part-
To assess whether these motivations time employment and were unemployed.
were related to demographic variables Accommodation, however, was signifi-
used in this study, the Kruskal-Wallis test cantly more important for those who had
was conducted. None of the motivations full-time employment than for those who
were found to be significantly associated have either part-time employment or were
with race/ethnicity. Accommodation was, unemployed. There was no significant dif-
however, found to have a statistically ference in terms of predisposition between
significant association with generational students who had full-time and part-time
cohort as shown in Table 3. The Kruskal- employment. Moreover, predisposition
Wallis test is an omnibus test statistic and was significantly less important for those
does not indicate which specific groups who were unemployed than for those who
are significantly different than each oth- had full- or part-time employment.
Studying Online: Student Motivations and Experiences in ALA-Accredited LIS Programs 221
A statistically significant association Participants were asked to respond to
was found between students’ metro sta- eight statements about availability of cer-
tus and accommodation as motivation tain services at the institution or program
(p < 0.05). Pairwise comparisons suggest- levels including academic advising, men-
ed that accommodation was significantly toring, and placement services to assess en-
more important for students who were re- tirely online MLIS students’ satisfaction.
siding in large rural areas than those from Overall, students were satisfied with all
urban areas. There was also a statistically services with the highest satisfaction being
significant association between commut- with virtual practica, online lectures, and
ing distance (as groups) and accommoda- professional development opportunities
tion as a motivation (p < 0.01). Pairwise as shown in figure below. The results of
comparisons indicated that accommoda- exploratory factor analysis identified one
tion was significantly less important for factor with an eigenvalue higher than 1.
those who resided within 50-mile radius of The total variance explained was 53.37%
campus and those who lived farther from (KMO = 0.862, p < 0.001, α = 0.87, n =
50 miles. 237). The estimated factor score of the
new variable was used to capture students’
What factors are associated with student satisfaction with availability of certain
satisfaction and an online MLIS degree? services at the institution or program lev-
els. A new binary variable was created by
About half of the participants reported assigning 0 to students with negative fac-
studying in an entirely online program (n = tor scores (dissatisfied), and 1 to students
409 or 44.9%). Of these, 396 reported that with positive factor scores (satisfied) in
they chose an entirely online program be- order to investigate students’ satisfaction
cause of not needing to relocate (90%), the in terms of demographic variables. There
quality of education (89.1%), the ability were no statistically significant differences
to keep current employment (81.6%), the in terms of satisfaction between White and
cost of education (77.5%), and the lack of Non-White students. Although a larger
access to a close-by, on-site (face-to-face) percentage of older students (Gen X and
MLIS program (53.8%). Baby Boomers) appeared to be more satis-
fied than their younger peers (Gen Y) in
entirely online MLIS programs, entirely
online MLIS students’ satisfaction with
Table 4. Motivations for Taking
the availability of certain services in the
Online Courses by Employment
program was not associated with demo-
Status (n = 486).
graphic variables including employment
Employment Mean status, metro status (e.g., urban, rural), and
Status n Rank commute distance.
Full Time 185 305.48
What issues concern online MLIS
Accommodation* Part Time 233 202.03
students?
Unemployed 68 216.96
Full Time 185 255.95
The most prevalent concerns reported
Predisposition** Part Time 233 244.28 by online MLIS students are: (1) a sense
Unemployed 68 206.95 of isolation from peers, (2) a sense of
Full Time 185 238.26 isolation from instructors, (3) lack of
Selectivity Part Time 233 242.35 professional development opportunities,
Unemployed 68 261.70 and (4) lack of networking opportunities
with peers. Students felt well-supported in
*p < 0.001.
terms of technical and academic support.
**p < 0.05.
222 JOURNAL OF EDUCATION FOR LIBRARY AND INFORMATION SCIENCE
Figure 3. Online Student Satisfaction (n = 447).
The results of exploratory factor analy- (U = 89784.5, p < 0.05). In terms of gen-
sis identified one factor with an eigenvalue erational cohort differences, younger Gen
higher than 1, and the total variance ex- Y students had more negative perceptions
plained was 58.35% (KMO = 0.827, p < of experience with online education com-
0.001, α = 0.87, n = 891). The estimated pared to their older peers who had more
factor score of the new variable was used positive experience with online education
to capture students’ experience with on- (p < 0.05). Those who had full-time em-
line education. A new binary variable was ployment had also more positive experi-
created by assigning 0 to students with ence compared to those who had part-time
negative factor scores (negative experi- employment and the difference was statis-
ence), and 1 to students with positive fac- tically significant (p < 0.01) as shown in
tor scores (positive experience) for further Table 5.
analyses. As noted earlier, a large majority of stu-
Race/ethnicity was not found to be sig- dents lived in urban areas. However, those
nificantly associated with students’ experi- who lived in non-urban areas were found
ences with online education. A statistically to have a more positive experience (p <
significant difference, however, was found 0.05) with online education. Similarly,
between age and students’ experience those who lived farther from campus had
Table 5. Experience with Online Education by Employment Status
(n = 885, p < 0.01).
Employment Status
Full-Time Part-Time Unemployed Total
Positive Experience 47.6% (219) 35.0% (161) 17.4% (80) 100.0% (460)
Negative Experience 37.9% (161) 46.4% (197) 15.8% (67) 100.0% (425)