2013. Vol.4, No.11, 718-725
Published Online November 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.411102
E-Learning Student Perceptions on Scholarly Persistence in the
21st Century with Social Media in Higher Education
Anna H. Lint
College of Education, Trident University International, Cypress, USA
Received August 23rd, 2013; revised September 23rd, 2013; accepted September 30th, 2013
Copyright © 2013 Anna H. Lint. This is an open access article distributed under the Creative Commons Attribu-
tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
The purpose of this quantitative analytic study is to evaluate and test the theoretical underpinnings of the
Kember (1995) student progress model that examines the direct or indirect effects of student persistence
in e-learning by identifying the relationships between variables such as student perceptions, performance,
cost-benefit analysis, and student persistence. Thomson (1999), Houle (2004), Harlow (2006), and Porta-
Merida (2009) verified the reliability and validity of the theory, yet their results are slightly dissimilar in
the magnitude of influence on student persistence. Former studies indicate that it could be meaningful to
reexamine the variables in more current studies. The online survey in this study explored the relationships
among variables. The population of the sample of this study was 169 students at a public community col-
lege in Maryland that is offering online and hybrid degree programs. The logistic regression and multiple
regression analysis were utilized to analyze the survey data. The findings of this study consistently indi-
cated that negative external attribution was a significant factor for student persistence, degrading the stu-
dent’s work. Simultaneously, individual student grade point average (GPA) and academic integration
were highly correlated to student persistence. The findings of this study convey the current phenomena
and knowledge of e-learning regarding student persistence. Social media has been seen as a potential
problem, but it could also be a solution if it increases social interaction on focused scholarly topics. De-
creasing external attribution and encouraging higher GPA by increasing the academic integration help
students continue to pursue their educational goals.
Keywords: Student Retention; Persistence; Perceptions; E-Learning; Higher Education; GPA; Social
Integration; Academic Integration; External Attribution
Student persistence is a critical issue for both students and
institutions because it affects students’ accomplishment of their
education goals and financially sustains the institution’s own
goals. The purpose of this study is to evaluate and test the theo-
retical underpinnings of Kember’s (1995) student progress
model which examines the direct or indirect effects of student
persistence on their successful completion of community col-
lege level e-learning programs. In the fall of 2006, almost 3.5
million students (nearly 20% of college students in the USA)
were enrolled on at least one online course (Allen & Seaman,
2008; Griffin, 2008). This is compared to the more current in-
formation of over 6.7 million students (nearly 32% of college
students in the USA) taking at least one online course in the fall
of 2011, which increases 570,000 students from 2010 (Allen &
Seaman, 2013). Individual student motivation is an important
factor in evaluating persistence in successful completion of
academic programs (Nichols, 2010).
The theoretical model of student persistence was developed
by Spady (1971). Subsequently, Tinto (1975) modified Spady’s
model regarding dropout behavior in his study. Despite the
validity and impact of the Tinto model, Bean & Metzner (1985)
claim that the Tinto model (1975) is less relevant where social
interactions with peers and faculty are limited to time in class
such as in e-learning programs. The Bean & Metzner (1985)
conceptual model states that non-traditional students are more
affected by the external environment than by the social integra-
tion variables that affect traditional student attrition. Kember
(1995) evolves his dropout model of student persistence by
connecting a model of student progress as it relates to student
social integration, academic integration, external attribution,
and academic incompatibility. Thomson (1999), Houle (2004),
Harlow (2006), and Porta-Merida (2009) verified that the reli-
ability and validity of the theory, yet their results are slightly
dissimilar in the magnitude of influence on student persistence.
Former studies indicate that it could be meaningful to reexam-
ine the variables in more current studies.
This quantitative analytic study is designed to identify the
relationships among student perceptions defined as student
progress factors, student performance, cost-benefits, and stu-
dent persistence through the perspective of Kember’s (1995)
model of student progress. This study demonstrates whether the
Kember model fits with current e-learning practice and findings
from a community college in Maryland, USA.
1) Is there a statistically significant relationship between stu-
dent perceptions of the academic experience a) social integra-
A. H. LINT
tion, b) academic integration, c) external attribution, and d)
academic incompatibility with student persistence (within the
online learning environment at the community college level)?
Does the relationship statistically significantly vary with re-
spect to student characteristics and learning style?
2) Is there a statistically significant relationship between stu-
dent perceptions of the academic experience a) social integra-
tion, b) academic integration, c) external attribution, and d)
academic incompatibility with student persistence mediated by
student performance defined by GPA?
3) Is there a statistically significant relationship between stu-
dent perceptions of the academic experience a) social integra-
tion, b) academic integration, c) external attribution, and d)
academic incompatibility with student persistence mediated by
The study engages three null hypotheses and three alternative
hypotheses based on three research questions.
Review of the Literature
Persistence in E-Learning
The development of distribution technologies has spurred
many institutions to offer online education (Kay, 2009). Di-
verse factors related to student persistence have been discussed
by researchers in the field. Tinto (1999) argues that higher
educational institutions need to retain existing students. It is
reported that between 20 and 50 percent of online students drop
their studies (Farmer, 2009; “Academic Retention Indicators,”
2005; Wojciechowski & Palmer, 2005). Nichols (2010) points
out that a number of reasons for withdrawal are usually given
by students, including the effectiveness and quality of online
courses (Perantoni, 2010). The highest rate of student with-
drawal is in the first one or two years of college (Barefoot,
2004). An additional tool for the study of persistence is the
identification of predictors for college student achievement and
retention (Davis, 2010).
Relationship of Soci al an d A c ademi c Integration,
External Attribution, and Academic Incompatibility
With the increase in the number of online courses, there is
also an increase in the number of students who do poorly in the
courses or drop out, resulting in a waste of the student’s time
and finances (Angelino, Williams, & Natvig, 2007). Tinto’s
(1993) classic model of student departure provides a solid
foundation of attrition. Students may complete programs at a
higher rate if they feel a connection with their institutions
(Heyman, 2010; Herbert, 2007; Soen & Davidovitch, 2008) and
students who are socially integrated feel less isolated (Senhouse,
2008). Academic integration and social integration positively
affect retention and academic integration positively influences a
grade point average (Woosley, 2009). Due to an increasing
appreciation of the internet environment, there are many di-
verse methods of social integration. Kord (2008) stresses the
possible positive and negative influence of online social net-
working on college students’ academic experiences, and which
will continue to offer more traditional support to students
through a less traditional medium (Heyman, 2010). The inter-
action with faculty and social networking with peers are impor-
tant factors for academic success (Vuong, Brown-Welty, &
Tracz, 2010). If students are kept engaged in their academic
programs (Dizik, 2010), this elicits students’ stronger positive
opinions for e-learning environments (Lei & Gupta, 2010; Rif-
fell & Sibley, 2005).
The Relationship between the Grade Point Average
(GPA) and Persistence
E-learning has become a mainstream educational methodol-
ogy, thus it demands new and hybrid methods for evaluating its
impact (Mandinach, 2005). New methodology for evaluation
can increase the credibility of students’ accomplishments and
persistence. The GPA on student persistence is significantly
related to continued enrollment (French et al., 2003) and stu-
dent retention (Porta-Merida, 2009). Higher levels of social
integration are more likely to be associated with slightly lower
GPAs than academic integration (Woosley, 2009), thus distrac-
tion from social networking may influence student academic
performance (Blashak, 2010). College GPA accounted for 25%
of variance in predicting persistence (Davis, 2010; Weidman,
Theoretical Orientation and Conceptual Framework
Kember’s (1995) student progress model is the fundamental
theory used in this study. Additionally, Tinto’s model (1975)
discusses non-traditional students’ dropout and Bean &
Metzner’s (1985) conceptual model suggests seven variables of
direct, indirect effects, and a possible effect on dropout rates.
Kember et al. (1991) develop the Distance Education Student
Progress (DESP) Inventory and Kember (1995) modifies his
dropout model by connecting a model of student progress
which encompasses student entry characteristics, social integra-
tion, academic integration, external attribution, and academic
incompatibility. This study tests Kember’s (1995) student pro-
gress model considering a strong integration environment and
student background in association with student learning styles,
student performance, and persistence (Figure 1). This concep-
tual framework shows the direction of the operational flow by
presenting various paths among the variables in the model in an
attempt to answer each research question and show how the
detected variables affect Student Persistence.
The relationships among student perceptions, course per-
formance, cost-benefit analysis, and student persistence, asso-
ciated with student characteristics and learning styles, were
detailed. The extended research questions and the hypotheses
relevant to the research questions were developed from gaps in
the scholarly literature. The researcher exercised a post-posi-
tivist worldview to identify and assess the causes that influence
outcomes (Creswell, 2009).
Study Samplin g and Popul at ion
The target participants of this study were 800 community
college students who were taking online or hybrid courses from
a community college for fall 200 in Maryland. All 800 were 1
Open Access 719
A. H. LINT
invited to take an online survey for this study. Of the 800 stu-
dents and 169 students participated in the survey, which was
21.1% return rate to the survey. This study had a set of four
independent variables and two covariates; the minimum re-
quired sample size for a group is 97 (α = .05) as indicated by
Cohen power (1992, 1988) analysis.
Students who enrolled for the fall 2010 online or hybrid ses-
sion were invited to participate and needed to respond to the
invitation to indicate their consent to be surveyed. The survey
explored the relationship among student demographic charac-
teristics, student perceptions (Distance Education Student Pro-
gress) refined into four factors, course performance, cost-bene-
fits, and student persistence in association with student learning
styles at a public community college in Maryland to verify
Kember’s (1995) model in relation to this environment. The
students’ privacy was carefully protected and the students’
name and identification were not asked. The online survey was
operated via a virtual platform, Survey Monkey. The survey
link was sent to 800 students via the Survey Monkey website
and the link for the survey was uniquely tied to each individual
student. This allowed customization of the survey approach to
individual participants, such as being able to send a second and
final email to students who did not participate on the first and
second survey attempt.
The data were logged and assessed, with data from respon-
dents who did not meet the criteria for the study being removed
to provide consistency and accuracy. The dataset was exported
to Microsoft Excel and filtered to ensure a good fit against the
defined research questions. Second level filtering provided
detail in relation to the exogenous variables of student charac-
teristics: age, gender, class delivery mode, major, work envi-
ronment, marital status, and online course experience, and stu-
dent learning styles. Finally the refined dataset was imported
into a Statistical Package for the Social Science (SPSS) 16.0
database to allow calculation of the mean and standard devia-
tion for each variable and create correlation matrices.
A survey comprised of three sections (Appendix A of Lint,
2011) was used as an instrument to answer the research ques-
tions to support definition of the hypotheses in this study. In
Section I, variables of age, gender, major, delivery mode, work
environment, marital status, e-learning experience, and learning
styles (items 1 - 8) were explored. Student Performance (GPA),
Cost-Benefit analysis (items 10 - 12), and Student Persistence
(items 13, 14, & 16) measured by intent to continue enrollment
or transfer or graduation were investigated in Section II. The
intent to withdraw or not to continue enrollment in the next
semester was used to measure the reliability of the negative
aspect of student persistence (item 15). Section III of the survey
used the DESP inventory.
Distance Education Student Progress (DESP)
The DESP inventory delves into four factors of social inte-
gration, academic integration, external attribution, and aca-
demic incompatibility. The DESP inventory was developed by
Kember et al. (1995, 1994, &1991) and modified to use of 64
items for this study as described in Lint study (2011). Higher
scores are connected to higher progress factors in this study
because of opposite arrangement of the 5-point Likert scale.
Permission to use the DESP inventory was purchased from the
“Copyright Clearance Center”.
Student Online Academic Persistence (SOAP)
Section II of the survey employed items 22, 23, and 25 of
Strevy’s (2009) SOAP inventory for the cost-benefit analysis
and item 40 of the SOAP for student persistence. Cost-Benefit
analysis (items 10-12 of Section II) and modified Student Per-
sistence (items 14, 15 of Section II). Permission to use the
SOAP inventory items was granted by Dr. Strevy.
Data Analysis and Presentation of Results
The Examined Variables in This Study Are Listed
Independent Variables: Social integration, Academic inte-
gration, External attributions, and Academic incompatibil-
Mediator variables: Student Performance and Cost-benefit
Covariate: Student Characteristics and Student learning
A. H. LINT
Dependent Variable: Student Persistence (three scores:
measured by the intent to continue enrollment in the next
semester, Q13; intent to continue enrollment including
transfer to another institution or graduation, Adjusted Q13
(Ad. Q13); and extent of intent to continue enrollment in
the next semester, Q14).
All 800 eligible online and hybrid class students at a public
community college in Maryland were invited to take an online
survey for this study in the fall of 2010. The return rate was
21.1%. Of the 800 students, 169 students participated in the
survey. The majority of the sample was female (78.4%) while
male participation was 21.6%. The majority of the sample was
single/divorce (66.0%) and the rest (34.0%) were married. The
majority of the sample was taking online classes (75.3%) and
the rest (24.7%) were taking hybrid classes. The average num-
ber of the online course experiences was 3.43.
Bivariat e Analyses
Academic integration and external attribution and student
persistence Q13 were significantly correlated. External attribu-
tion and student persistence Ad. Q13 were significantly corre-
lated. Social integration, academic integration, and external
attribution were significantly correlated with student persis-
tence Q14. External attribution and academic incompatibility
and cost-benefit analysis were significantly correlated. GPA
and student persistence Q13, Ad. Q13, and Q14 were signifi-
cantly correlated. Age and Q13, and prior online experience
and student persistence Q13 and Q14 were significantly corre-
Multivariate Analyses of Student Persistence
The two main analyses of this study used logistic regression
analysis and multiple regression analysis to examine the rela-
tionships among student progress factors and student persis-
tence. To answer the first research question, this study em-
ployed logistic regression analysis for dichotomous variables
Q13 and Ad. Q13 to verify the relationships among student
perceptions, student characteristics, and learning styles and
student persistence. For Q14, this study used the multiple re-
gression analysis to identify the level of relationship.
For the question of Q13, the intent of enrollment for online
course next semester, “Yes” was coded as 1, “No” was coded
as 0. The independent variables were social integration, aca-
demic integration, external attribution, and academic incom-
patibility. External attribution was significant predictor for
predicating student persistence by a factor of .159. Academic
incompatibility also was significant predictor for predicating
student persistence by a factor of .796. Ad. Q13 was re-coded
from “No” to “Yes” if participants answered the reason to with-
draw the next online courses as “transfer and graduation”. For
Ad. Q13, the model was significantly reliable (chi-square =
10.416, df = 4, p = .034, p < .05). Overall 75.0% of predications
were accurate, and 96.6% of predications for the student per-
sistence were accurate. External attribution (OR = .213) was
significant predictors for predicating student persistence. As
shown in Table 1, the null hypothesis of “there is no relation-
ship between student perceptions and student persistence Q13”
Logistic regression analyses for student perceptions and student persis-
tence (Q13 & Ad. Q13).
Q13 Ad. Q13
OR 95% CI OR 95% CI
Integration 2.106[.868, 6.110] .971 [.378, 2.494]
Integration 3.225[.956, 10.892] 1.78 [.506, 6.269]
Attribution .159** [.047, .545] .213* [.060, .763]
Incompatibility 3.796*[1.259, 11.408] 2.763 [.895, 8.531]
Constant 0.011 2.065
Note: OR = odds ration; CI = confidence interval; *p < .05; **p < .01.
was partially rejected. External attribution and academic in-
compatibility were the predicators for student persistence of
intent to enroll. For Ad. Q13, only external attribution was the
significant predicators for student persistence of intent to enroll.
In Table 2, the outcome between student perceptions and
student persistence of extent of intent to enroll was analyzed by
multiple regression analysis. The result of multiple regression
showed that 14.1% of variance could be explained by F(4,114)
= 5.824, p = .000, p < .01. The model was significant. As
shown in Table 2, only academic integration was a significant
predicator. For the second question of research Question 1,
prior online experience and external attribution were significant
predictors for predicating student persistence. Marital status,
auditory learning style, and external attribution were significant
predictors for predicating student persistence.
To answer the research Question 2, the researcher measured
mediation effect to the relationship among student perceptions
and student persistence. For measuring the meditated effect of
GPA, the researcher applied Baron and Kenny (1986) four steps.
As shown Table 3, after controlling student perceptions, GPA
had a significant relationship with all three student persistence
Q13, Ad. Q13, and Q14. External attribution, academic incom-
patibility, and academic integration were still significant, but
reduced after controlling GPA that implied there was a partial
To answer the research Question 3, the researcher measured
mediation effect by cost-benefit to the relationship among stu-
dent perceptions and student persistence. External attribution
and academic incompatibility had a significant initial relation-
ship with student persistence Q13. After controlling student
perceptions, there was no significant relationship between cost-
benefits and student persistence Q13, Ad. 13. The outcome
indicated that no relationship among student perceptions and
student persistence were influenced significantly by the inclu-
sion of cost-benefit analysis.
Discussion and Suggestions of the Research
The purpose of this study was to evaluate and test the theo-
retical underpinnings of Kember’s (1995) student progress
model in order to examine the direct or indirect effects of stu-
dent persistence in successful completion of community college
level e-learning programs. The model for this study focused on
three measures of student persitent along with mediation of s
Open Access 721
A. H. LINT
Multiple regression analysis for student perceptions and student persistence (Q14).
B β t 95% CI
Constant −.891 −.499 [−4.423, 2.642]
Social Integration .426 .167 1.936 [−.010, .863]
Academic Integration .895 .269** 2.85 [.273, 1.516]
External Attribution −.54 −.187 −1.878 [−1.109, .029]
Academic Incompatibility .445 .15 1.602 [−.105, .996]
Δ F 5.824***
Note: CI = confidence interval; **p < .01; ***p < .001.
Regression analyses for student perceptions and student persistence (Q13) mediated by GPA.
Q13 GPA GPA + Q13 Sobel Test
IVs B SE B SE B SE B p
Social Integration .745 .452 −.18 .166 .851 .469 −1.001 .317
Academic Integration 1.171 .62 .395 .247 1.047 .664 1.364 .173
External Attribution −1.837** .628 .226 .22 −1.930** .648 .956 .34
Academic Incompatibility 1.334* .563 −.135 .213 1.406* .591 −.616 .538
GPA .744** .285
Constant −4.489 3.416 2.049 1.365 −6.646 3.723
Note: Q13 = Dichotomous variable of student persistence; Logistic regression analysis was used; SE = Standard error; B = Regression coefficient; *p < .05; **p < .01.
GPA and cost-benefits on the relationship between student
perceptions and student persistence.
The findings of the study indicated that external attribution
had a significant negative relationship with student persistence
Q13 and Ad. Q13. The findings of the study also indicated that
academic incompatibility and academic integration had a sig-
nificant relationship with student persistence Q13 and Q14,
respectively. External attribution had a significant relationship
with Q13, Ad. Q13, and Q14 after controlling student charac-
teristics. GPA had a partial indirect effect, while cost-benefits
did not have any indirect effect on relationship between student
perceptions and student persistence. Prior online experience
was significant for student persistence Q13 and Q14. Single
and auditory learners were significant for student persistence
Discussion and Suggestions for Research Questi on 1
External attribution was a significant predictor for two meas-
urements of student persistence Q13 and Ad. Q13. External
pressures in a student’s life may prevent a student from finish-
ing a course or a plan of study (Kember, 1995). Based on the
outcome, lowering external attribution should be managed to
increase student persistence. This negative attribution is a dis-
traction, reducing students’ learning time, and so hindering
study. Also, students entering college directly from high school
have grown up surrounded by social networking during their
life. Therefore, if E-Learning institutions replicate that norm to
increase persistence, it is possible to convert this to a positive
influence. Academic incompatibility and academic integration
were also significant predictors for student persistence Q13 and
Q14, respectively. Academic integration can be reinforced to
motivate students, such as improving the quantity and quality
of postings in online discussions (Jiang & Ting, 2000), focused
feedback (Filimban, 2008), and providing tailored student pro-
grams to increase academic integration.
After using covariate of student characteristics and learning
styles, external attribution was a significant predictor for all
three measurements of student persistence, Q13, Ad. Q13, and
Q14. It was evident that student persistence was diverted by
strong negative social impact. Interestingly, social integration
was not a significant predictor for student persistence. In fact,
social integration in e-learning has been highly touted in current
online education arena. Finally, prior online experience and
auditory learning style could affect student persistence. There-
fore, students experienced in online coursework should be nur-
tured, and students new to online coursework may need thor-
ough orientation in online tools and how to build on the suc-
cesses of online study. While learning styles did not seem to
have a major influence on student persistence, auditory learners
showed significance with Q13. This illustrates that embedded
video or audio may increase student persistence.
Discussion and Suggestions for Research Questi on 2
After controlling student perceptions, GPA had a significant
relationship with all three measurements of student persistence,
A. H. LINT
Q13, Ad. Q13, and Q14. After controlling the GPA, external
attribution and academic incompatibility were significant with
student persistence Q13. External attribution and academic
integration were significant with student persistence Ad. Q13
and Q14, respectively. The outcome implied that there was a
partial meditation effect of GPA on the relationship between
student perceptions and student persistence Q13, Ad. Q13, and
Q14. The GPA itself, however, had a direct relationship with
student persistence. The research question two partially rejected
the null hypothesis of meditation of GPA on relationship be-
tween student perceptions and student persistence. This study
did not support the Kember (1995) model regarding the rela-
tionship between student perceptions and GPA, yet supported
the relationship between GPA and student persistence. There
was a statistically significant relationship between the GPA and
student persistence to the next academic year (Davis, 2010). In
this study, the GPA was a direct factor to predict student per-
sistence. Therefore, the leaderships of e-learning colleges need
to encourage students to achieve higher performance with aca-
demic advice and contact.
Discussion and Suggestions for Research Questi on 3
Academic integration and academic incompatibility were sig-
nificant predictors for cost-benefits. After controlling student
perceptions, cost-benefits had a no significant relationship with
student persistence. After controlling cost-benefits, external
attribution and academic incompatibility were still significant
with student persistence Q13 and Q14. There was no meditation
effect of cost-benefits on the relationship between each student
perceptions and student persistence. This study did not support
the Kember (1995) model for the relationship between cost-
benefits and student persistence, yet there was a significant
relationship among academic integration, academic incompati-
bility, and cost-benefits. Students compare the benefits they
expect to receive by attending college to the costs they will
incur (Stuart, 2010). Based on this outcome, e-learning colleges
need to understand student motivation for the education to im-
prove persistence. A more comprehensive understanding of
student motivation as it relates to a student’s decision to persist
is necessary (Savage, 2010).
Implications and Interpretation
The findings of this study show how the current phenomena
of student perceptions reflected on student persistence. Kember
(1995) included the negative sources of external attribution and
academic incompatibility as harmful factors for student persis-
tence. One of the issues with Kember is that it does not take
into account the modern social media phenomena. It can be
extrapolated that the same negative sources of family and ex-
ternal attribution should include social media interaction, be-
cause the interaction is with the same factors that Kember men-
tioned. In this study, external attribution was the major factor to
influence student persistence. Academic incompatibility and
academic integration were significant factors for one score of
student persistence. Overall, lower external attribution contrib-
uted higher student persistence detected by the study results.
The negative attribution distracts students from learning associ-
ated with insufficient time or other factors hindering study.
Lowering external attribution such as the amount of social dis-
tractions between family and peers or time management be-
tween work and study should be managed to increase student
persistence. Cutting edge IT development of online programs at
colleges has been focused on social and academic integration
for guiding and eliciting student motivation that has led to a
successful e-learning environment. E-learning institutions need
to move from reinforcing interaction with students to trans-
forming negative integration to positive integration by devel-
oping recognized friendly approaches with current technology.
Students should be given additional instruction or mentoring on
time management procedures to cut distractions. Reinforcing
the students’ version of normal in an academic environment
will increase student persistence to degree completion.
In addition, academic incompatibility predicted one score of
student persistence. This can be attributed to students identify-
ing the problem areas, and using flexible online scheduling
capability to work on the key points that caused the incompati-
bility, tailoring the due. Flexibility shows caring by the institu-
tion and builds more loyalty to the institution by the student.
With the give and take of setting flexible due dates dependent
on external factors the student’s loyalty increases to the point of
having a strong desire for course completion. Loyalty can trans-
late into student persistence. Academic integration was another
predictor for the degree of intent to enroll in the following se-
mester. This implies that academic interaction and peer interac-
tion linking to academic exchange is still a major solution for
student persistence. Instructors must give focused feedback on
course assignments to increase academic integration. The result
of the study indicated that prior online experience could affect
student persistence. M-learning as a new field may enhance
student flexibility and use of time. Finally, other than student
perceptions, GPA was a significant predictor for student persis-
tence. It can be concluded that the managing of online pro-
grams at college level needs to focus students to achieve higher
performance through academic advice and contact.
Limitations and Recommendations for Future
This study mainly focused on the relationship among student
perceptions and student persistence by developing insights
concerning different variables that may affect student persis-
tence, where sample pools were not randomly assigned to the
classes, but were self-selected. The primary scope was to inves-
tigate the relationship between individual traits pertaining to
student persistence, over which the institution typically has no
control. Additionally, the subjects of this study were students
who were taking online and hybrid courses at a community
college. Therefore, caution must be used when desiring to attain
conclusions about other types of students and institutions. In
this case, responses are representative of e-learning students
who are deliberately taking the online courses to meet their
educational goals within the USA community college environ-
ment. However, it is possible that this study can provide a
starting point for understanding what aspects may be gener-
alizable to other e-learning students in other locations or having
other values for their education across a variety of campus con-
This study evaluated and tested Kember’s (1995) student
progress model in order to examine the direct or indirect effects
Open Access 723
A. H. LINT
of student persistence in successful completion of adult e-
learning programs. Social integration has both a slightly nega-
tive effect on GPA as well as a positive effect on retention
(Woosley, 2009). There is an ample social integration because
of current social networking environment. Seventy seven per-
cent of academic leaders rate learning outcomes for online
courses same with or superior to face to face classes (Allen &
Seaman, 2013). With that tendency, the results showed the
negativity of external attribution, which cannot be determined
as the positive of social integration. Furthermore, the majority
of e-learning students have two or more obligations such as
work, family, and study that cause additional external influ-
ences which interfere in persisting with their studies. Lowering
external attribution helps students continue to pursue their edu-
cational goal based on the results of the study. A possible solu-
tion would be for academic institutions to develop social media
platforms to continue study and interaction. Finally, student
performance, GPA, and academic integration were significant
factors for student persistence. These two results connect the
single point of how student performance is an important role for
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Definition of Terms
1) Student perceptions: Kember (1995) identifies these four
constructs (social integration, academic integration, external
attribution, and academic incompatibility) as four elements of
student progress. In this study, it is defined as student percep-
2) Persistence: Three scores of student persistence will be
measured by the intent to continually enroll in the upcoming
semester and elements including the transfer to other institute
and graduation. Hegedorn (2006) defined student persistence by
including transfer to other college and graduation.
3) Cost-benefits analysis: The continual process of weighing
the emotional, fiscal, and social costs against the expected
benefits in order to choose the best option for the student—
continue or drop out (Kember, 1995).
4) Social integration: The extent to which the employer, fam-
ily, and friends support the student’s decision to enroll and
persist in the course and the extent to which they provide moral
support (Houle, 2004).
5) Academic integration: The academic integration encom-
passes all elements of contact between an institution and the
students (Kember, 1995).
6) External Attribution: The negative social integration. The
external causes in the student’s life such as insufficient time,
work, family, friends, social networking, and unexpected events
that might prevent the student from finishing a course or a plan
of study (Kember, 1995).
7) Academic Incompatibility: The academic incompatibility
and course performance will be defined as not receiving a pass-
ing grade in a course.
8) Student Characteristics: Gender, age, preference of deliv-
ery mode, major, work environment, marital status, e-learning
experience, and learning styles.