Associations of Student Characteristics and Course Organization

Factors with Dropping out of University Distance and Online

Learning

Louise Sauvé

, Cathia Papi

, Serge Gérin-Lajoie

and Guillaume Desjardins

Education Department, Université TÉLUQ, 455, Rue du Parvis, GIK 9H6, Québec (QC), Canada

Industrial Relations Department, Université du Québec en Outaouais, 283, boul. Alexandre-Taché,

J8X 3X7, Gatineau (QC), Canada

Keywords: Dropout, University Studies, Courses, Distance and Online Learning, Socio-demographic Variables,

Academic Variables, Environmental Conditions, Pedagogical Organization.

Abstract: Distance and online learning (DOL) is becoming a must for university education in times of pandemic. In this

context, it is important to take into account the factors that can influence students’ perseverance in, or dropout

from, university studies. Not all of these factors are unanimously accepted in DOL research. This study, of

791 undergraduate students in 2018-19 enrolled in a francophone DOL institution, concludes that the factors

influencing dropout from DOL are as much related to the personal characteristics of the students and to their

learning strategies as to the pedagogical design of the course.

1 INTRODUCTION

Distance and Online Learning (DOL), which had

become an important course offering component for

higher education institutions before the current

pandemic, is now a crucial way for their students to

complete their studies. However, research indicates

that the dropout rate for DOL students is higher than

for on-campus students (Fortin, Joanis, & Ragued,

2019).

Longstanding research on university dropouts has

mainly studied on-campus learning, identifying

multiple conditions likely to influence student

attrition, including student-related factors,

environmental factors, and course and program-

related factors (Sauvé, Papi, Gérin-Lajoie, &

Desjardins, 2020).

Research on DOL has identified variables that are

similar to those for on-campus learning but with

effects that differ in importance (Facchin & Boisvert,

2019; Li, & Wong, 2019). Moreover, these studies

often consider only a few of the many dimensions

influencing a student's life and path (McDougall,

2019). No scholar has examined all relevant factors,

although they have recommended doing so when their

results were not significant.

For this study, we hypothesized that the learner's

decision to interrupt his or her DOL studies is the

result of a complex process that cannot be attributed

to a single cause, but rather to a set of factors whose

weights vary according to the learner's

characteristics. Based on this hypothesis, we

formulated the following question: "Are there

associations between students’ background and

academic characteristics, the environment in which

they are studying, their learning strategies, the ways

in which their courses are organized, and their

decision to drop a course or not to re-enroll after two

sessions of undergraduate studies?

To answer this question, we analyzed the

respective associations between students' dropping

out and their socio-demographic characteristics (e.g.,

age, gender, marital status, family situation, mother

tongue, citizenship), academic variables (e.g., study

regime, parental education, distance from home,

disability), environmental characteristics (e.g.,

support from family and friends, financial

and work

situations), learning strategies, and pedagogical

organization of their DOL courses.

We begin this paper with a brief review of the

DOL literature on variables found to be associated

with dropping out of university courses or of school

Sauvé, L., Papi, C., Gérin-Lajoie, S. and Desjardins, G.

Associations of Student Characteristics and Course Organization Factors with Dropping out of University Distance and Online Learning.

DOI: 10.5220/0010442304670474

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 1, pages 467-474

ISBN: 978-989-758-502-9

467

altogether. We then present the methodology and

results of the study.

2 DROPPING OUT OF DOL

Although dropping out of university is an important

issue, data on DOL dropout rates are not consistently

published by Canadian universities. Most studies

indicate that student perseverance is lower in distance

education than in face-to-face learning (Sauvé et al.,

2020).

Reported DOL dropout rates can vary greatly,

depending on the definitions on which they are based

and whether they relate to programs or courses, the

time of data collection, the time period studied, and,

the calculation methods used. As a result, authors

have suggested that the DOL dropout rate varies from

25% to 40% (Bistodeau, & Mottet, 2017; Bonin,

2018).

Similarly, limited data are available on dropout

rates for individual DOL university courses. Xu, &

Jaggars (2011) indicate rates of 8% to 14% while

Paquelin (2016) finds that 9.5% of distance students

drop out, compared to 5.3% of on-campus students.

3 VARIABLES ASSOCIATED

WITH DROPPING OUT

Much of the research on university dropouts uses

socio-demographic and academic variables to

describe study populations. Some studies examine

these variables in relation to dimensions such as

learning strategies, environmental conditions or the

DOL learning environment (Audet, 2008; Lee, Choi,

& Kim, 2013). To better identify associations with

student attrition, we selected the variables that seem

to have the greatest impact on dropping out of DOL,

as follows.

3.1 Socio-demographic Variables

Age. Stone (2017) reports DOL studies in which older

age is correlated with success in distance education,

perhaps due to greater maturity and self-regulation.

Conversely, Prinsloo, Muller, and Du Plessis (2010)

consider students over the age of thirty to be at risk of

dropping out, possibly due to a higher overall

workload. Finally, Bonin (2018) argues that since

persistence decreases with age: younger students

have a better chance of continuing on to graduation.

Gender. Gender seems to be more important than age

or location in the attrition of post-secondary students

(Vogel et al., 2018). More specifically, men drop out

of DOL courses or university studies at greater rates

than women (Bonin, 2018; Paquelin, 2016). Mixed

results regarding gender have also been obtained

(Eliasquevici, Seruffo, & Resque, 2017).

Marital and Family Status. Married or common-

law students with dependent children are more likely

to drop out of their post-secondary DOL programs

than are single students without dependent children

(Bonin, 2018; Shah, & Cheng, 2019).

The variables citizenship and first language

seem not to have been addressed in DOL persistence

research. In one study, however, language or a

language barrier is cited as the main obstacle to

success in courses for immigrants (Stoessel, Ihme,

Barbarino, Fisseler, & Stürmer, 2015).

3.2 Academic Variables

Study Regime. Before the pandemic, DOL students

were generally adults whose living situations

(employment, family commitments, etc.) lead them to

enroll in

only a limited number of courses per year.

Their learning program therefore

extended over a

longer period, which is more conducive to dropping

out of school (Sauvé, Fortin, Landry, & Viger, 2015).

Bonin (2018) concluded that under similar living and

study conditions, taking distance education courses at

the undergraduate level on a part-time basis is

associated with a greater risk of dropping out.

Parents' Level of Education. Since we could not

find any studies on this variable in relation to

dropping out of university-level DOL, we examined

what the research on this variable says in the context

of face-to-face, on-campus learning.

Higher parental education level is correlated with

the likelihood that the student will pursue post-

secondary education. (Murdoch, Kamanzi, & Doray,

2011; Organisation for Economic Co-operation and

Development, 2017). In fact, it seems that this factor

is more of a determinant than family income and

living environment (Murdoch et al., 2011). A

nuanced conclusion is provided by Berger, Motte, and

Parkin (2009), who link parental education level with

family income to explain dropping out of school.

Disability. In 2019-2020, more than 19,000 students

who declared themselves to be disabled were

pursuing studies in Quebec universities (Gagné, &

Bussières, 2019-2020). We have found that university

students with disabilities have a higher DOL dropout

rate than other students (James, Swan, & Daston,

2016; Sauvé, Racette, Bégin, & Mendoza, 2016).

CSEDU 2021 - 13th International Conference on Computer Supported Education

468

Diploma Obtained Before Studying via DOL. The

level of education attained before enrolling in DOL

studies and past higher education experience are

factors that

influence dropout (Yoo, & Huang, 2013).

Fortin, Sauvé, Viger, and Landry (2016) indicate that

distance education students are more likely to drop

out if they have previous university education.

3.3 Environmental Conditions

Conditions in a student’s life have more influence in

DOL than in face-to-face learning, since students

enrolled in DOL are often older, have to balance a

busy schedule that includes work

and family

commitments, and are more often isolated from their

peers and instructors (Park, & Choi, 2009).

Four of these factors are generally examined in

DOL in relation to dropping out of post-secondary

education: (1) family support (Dussarps, 2015;

Kaddouri, De Villiers, Barbier, & Bourgeois, 2006);

(2) support from friends (Dussarps, 2015); (3) the

burden of work (Loisier, 2013; Moore, & Greenland,

2017; Shah, & Cheng, 2019); and (4) financial issues

(Fortin et al., 2016; Vogel et al., 2018).

The results of these studies indicate that the more

a student feels supported by those close to him or her,

the less he or she will be tempted to drop out. As for

employment and hours worked, these variables are

part of a multifactorial phenomenon and are not the

sole cause of dropping out (Sauvé et al., 2015).

3.4 Learning Strategies

Various studies point out that self-regulatory learning

strategies are more important in distance and online

learning than in face-to-face courses, since DOL

students face many challenges not experienced when

they are physically present on campus. For example,

they must learn to manage conditions such as

asynchronous classes, written rather than verbal

discussions, and isolation from other students and

instructors, mastering new strategies and skills that

are appropriate to their virtual learning environment

(Wan, Compeau, & Haggerty, 2012). Students who

use the weakest learning strategies are most at risk of

dropping out of school (Kizilcec, Perez-Sanagustin,

& Maldonado, 2017).

Self-regulation is a key concept that refers to the

process by which the learner activates and supports

cognition, affects, and behaviors that are oriented

toward the achievement of learning goals (Schunk &

Zimmerman, 2012). For example, managing

resources such as time, the study environment, effort,

help-seeking, and peer learning, plays a particularly

important role in student retention (Mottet, & Rouissi,

2013).

Based on Zimmerman's (2000) cyclical model of

self-regulation, which is the most widely used one in

studies of post-secondary DOL, we group 56 learning

strategies into three phases: planning,

performance,

and reflection. These strategies have been identified

in the

literature as potentially influencing student

persistence (Sauvé et al., 2020). For example, a

student who is not confident in his or her ability to use

effective study strategies, who feels unable to do well

on exams and assignments, or feels unable to meet

deadlines for the submission of assignments, will be

more likely to drop out of school.

3.5 Pedagogical Organization

Following Allen and Seaman (2017, p. 41), we define

DOL as "teaching that uses one or more technologies

to deliver instruction to students who are separated

from the instructor and to support regular and

substantial interaction between students and the

instructor in a synchronous or asynchronous manner.”

Studies on the impact of DOL pedagogical

organization on dropout rates are still rare and

inconsistent (Sauvé et al., 2020). In addition to not

precisely defining the types of pedagogical

organization studied, these projects are often case

studies or student satisfaction surveys (Deschryver, &

Lebrun, 2014). Moreover, they take little or no

account of possible differences in students' personal

characteristics (Loisier, 2013; Xu, & Jaggars, 2013).

To identify types of pedagogical organization in

the DOL courses under study, we examined the

instructional design that structures and organizes the

online learning environment, making available to the

learner and to the facilitator, tutor, or teacher the

technological and human resources necessary for

learning (Sauvé, 2019). This design is generally based

on a pedagogical scenario (Pernin, & Lejeune, 2004)

that varies according to the components made

available to the learner: technological tools,

pedagogical treatment (the educational approach as

well as formative and/ or summative evaluation),

navigational tools (contextual help, guides), study

tools (e.g., a path set out in modules or weekly

sessions), and supervision methods (e.g., types of

interactions and communication tools). We used these

components to build an analysis grid with the

objective of defining course models or archetypes for

comparative analyses.

Associations of Student Characteristics and Course Organization Factors with Dropping out of University Distance and Online Learning

469

4 METHODOLOGY

This exploratory study was carried out in the context

of distance and online undergraduate studies in the

province of Quebec, Canada. Quantitative data were

collected using: (1) a 25-item online questionnaire to

collect data on students' personal characteristics and

their perception of the course organization and

learning supervision offered in their DOL courses;

and (2) a course analysis grid to determine the course

models used in the DOL courses taken by

respondents. This grid made it possible to quantify the

technological tools, the pedagogical components of

the course, the learning activities in the course

sessions or modules, the navigation and learning aids,

and the courses’ learning support modes.

4.1 Sample

In order to establish our population, we selected

students enrolled in 19 DOL courses offered by a

francophone distance education institution in Quebec,

Canada. These courses were chosen based on three

criteria: (1) inclusion of at least three different

disciplinary fields (education, human sciences and

languages, and administrative sciences); (2) the

number of students per course (from 312 to 900

annual enrollments); and (3) the variability in the

course failure rates, ranging from 4.35% to 26.51%,

and dropout rates, varying between 4.3% and 26.35%

according to departmental data. A total of 3,578

students were solicited over four sessions of study in

2018 and 2019.

4.2 Research Questions and Analyses

Analyses were carried out to answer the following

questions: (1) Are socio-demographic, academic, and

environmental variables (family, finance, work)

associated with dropping out of a course and not re-

enrolling after two university sessions of an

undergraduate program? (2) Is the relationship

between socio-demographic and academic variables,

environmental conditions and the use of learning

strategies associated with dropping a course and not

re-enrolling after two university sessions of an

undergraduate program? (3) Is the relationship

between socio-demographic and academic variables,

environmental conditions, and modes of pedagogical

organization associated with course dropout and non-

re-enrolment after two sessions of undergraduate

university studies?

Various statistical models were used, depending

on their purpose in relation to the study questions.

All

analyses used an alpha of 5% (α =.05). Analysis were

carried out between categories in relation to the 25

socio-demographic, academic and environmental

variables in order to identify the variables that

influence drop-out. A two-step cluster analysis was

used to group the variables into categories that were

internally consistent, yet different from each other.

Analyses included independent samples t-tests and

univariate ANOVA for more than two groups. Chi-

square analyses was employed on variables for

learning strategies and course types. Post-hoc

analyses were also conducted with the Tukey test.

5 RESULTS

5.1 Description of the Study Sample

Of the 791 students who responded to the

questionnaire, 77.9% were female. Similarly, 46.1%

of respondents were 25-34

years old, 28.4% were

between 35-44 years old, 11.6% were over 45 years

old and 13.8% were under 25 years of age. In

addition, 71.2% lived in a couple (married or

common-law). French was the first language for the

vast majority (91.4%) of respondents.

Academically, 82.6% of respondents were

enrolled in part-time study, while 56.3% were

enrolled in a 30-credit certificate program. More than

half of the sample (54%) were in their first year of

university study. A total of 71.4% of students had

earned a post-secondary diploma or degree before

beginning their studies in DOL. Finally, 53.5% of the

respondents' mothers and 49% of their fathers had a

high school or vocational diploma, while 23.2% of

the students noted that their father had a university

degree, compared to 19.5% for their mother.

In terms of finances, 54.5% of respondents

reported working to pay for their education, while

22.3% reported financing their education through

loans and 18.3% through grants. Students rated their

financial situation as excellent (10.4%), good (38.7%)

or acceptable (43.4%).

Of the 791 respondents, 16.9% dropped out of

their course during the data collection semester, and

10% of these did not re-enroll after two study

sessions.

An analysis of the representativeness of the

sample, carried out on the 25 socio-demographic,

academic and environmental variables used,

confirmed that our sample was representative of the

student population except for two variables (gender

and study regime). These were found to be minor in

explaining students’ propensity to drop out.

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470

5.2 Students' Socio-demographic and

Academic Variables

Certain variables can be grouped into factors when

we attempt to explain the student's propensity to drop

out of a course. Seven factors account for 47.58% of

a student's propensity to drop out of a course.

Subsequent analyses were conducted on these seven

factors to examine their meaning. Three of the seven

factors indicate that a student is more likely to drop a

course if: 1) his or her first language is English and

he or she is enrolled in a short program; 2) he or she

is a common-law partner or lives with both parents;

and 3) his or her mother has no schooling, and the

father has a vocational diploma or the mother has only

completed elementary school and the father has no

schooling.

Similarly, an analysis was done to explain the

tendency of students to re-enrol or not after two

sessions. Seven factors explain up

to 66.75% of the

propensity of students to withdraw after two

consecutive sessions. Subsequent analyses were

conducted on these seven factors to examine their

meaning. Five of the seven variables indicate that a

student is at risk of not re-enrolling after two

semesters of study: 1) financing of the student’s

studies is based primarily on loans and grants or on

loans and working more than 21 hours per week;

2) parental education: a) the mother has a vocational

training diploma and the father has no schooling,

b) the mother has completed high school and the

father has an undergraduate university degree, or c)

the mother has no schooling and the father has

completed high school; 3) the student is a woman and

considers her financial situation "acceptable" or

"unacceptable"; 4) the student is a permanent resident

student with an undergraduate university degree

whose education is financed from personal savings;

5) the student's place of residence is located 31 to 45

minutes from the place of instruction.

5.3 Learning Strategies

By grouping learning strategies according to

Zimmerman's (2000) typology of self-regulation, a

single-factor ANOVA analysis identified differences

between certain student socio-demographic and

academic variables and the likelihood of using or not

using these same strategies. For foresight strategies,

only marital status was found to be statistically

significant. For performance strategies, family status,

gender, marital status, maternal education, distance,

parental funding of education, dyslexia, and physical

disability were found to be statistically significant.

For reflection strategies, marital status, language, and

financial judgment were found to be statistically

significant.

According to Student's t-tests, the scores obtained

in the three Zimmerman phases were not statistically

significantly and therefore did not affect course

dropout. However, in terms of non-re-enrolment, the

reflection phase makes a statistically significant

difference. In other words, the more strategies are

reported to be little used by students in the reflection

phase, the more likely they are not to re-enrol after

two consecutive sessions. For example, the more

dissatisfied students are with the quality of their

courses, their academic results, and their decision to

study at university, the more likely they are to drop

out.

5.4 Type of Pedagogical Organization

To identify course types in DOL, we used two-step

cluster analysis to interpret the data from the course

analysis grid. This method made it possible to move

from disparate courses to identify course models or

course archetypes that remain theoretical constructs.

Five course models (clusters) were characterized by a

set of 22 variables likely to influence student attrition,

such as activities for acquiring new knowledge,

carrying out learning exercises, and completing

summative evaluations. It is important to understand

that the 22 variables must be taken as a whole, rather

than individually, to characterize the course models.

The following is a simplified description of the course

models:

 Course Model 1 - Moderately focused on reading

and practical exercises aimed at the acquisition

of knowledge, with some formative assessments.

 Course Model 2 - Very much focused on

knowledge acquisition activities through visits to

external websites and formative evaluation

activities. More moderate on reading activities.

 Course Model 3 - Very focused on knowledge

acquisition activities through reading and

practical exercises. Moderate on web site visiting

and formative evaluation activities.

 Course Model 4 - Very focused on practical

exercises and formative evaluation activities

aimed at knowledge acquisition. Little reading.

 Course Model 5 - Focused on both acquisition

and discovery activities through reading and

formative evaluation activities. Few practical

exercises.

By crossing the socio-demographic, academic and

environmental variables with the five course models,

Associations of Student Characteristics and Course Organization Factors with Dropping out of University Distance and Online Learning

471

we were able to make certain observations regarding

course dropouts.

For example, for a student in Course Model 2,

being single or living alone is associated with a

greater propensity to drop out of the course. In

contrast, being married/ having a common-law

partner or living with a spouse and children is

associated with a lower propensity to drop out of the

course.

Students who rate their financial situation as

"excellent" and "good" are less likely to drop out of a

Model 3 course. In contrast, students who rate their

financial situation as "acceptable" or" unacceptable"

are more likely to drop the course.

For Course Model 4, being single is associated

with a higher propensity to drop out of the course. In

contrast, being married, living with both parents, or

living with a spouse is associated with a lower

propensity to drop out of the course.

Finally, for Course Models 1 and 5, no variables

could be associated with dropping out of a course.

In short, the results of the study indicate that the

design of the course taken by itself does not affect

course dropout, unless it can be linked to students’

personal characteristics and appropriate interventions

to promote perseverance in their studies.

6 DISCUSSION

For our sample of 791 students, socio-demographic

variables largely explained their propensity to drop

out.

These results reiterate the conclusions of Xavier,

and Meneses (2020). Learning strategies did not

seem to be associated with dropping out of the course

but were associated with not re-enrolling. As in James

et al., (2016), analysis of learning strategies in

relation to socio-demographic, academic, and

environmental variables identified certain predictive

variables in the case of students who did not re-enrol

after two sessions of study: financing of studies,

parental education, financial situation, marital status,

and distance of residence from the educational

institution.

The distribution of variables related to online

course design is not random. In fact, the study

identified five types of courses with internally

consistent

and distinct distributions in terms of

instructional organization. The design of these five

course models, when taken out of context, cannot

explain the propensity of students to drop out of the

course, but it does contribute when we control for the

socio-demographic and academic variables of the

sample as outlined by Vogel et al. (2018). For

example, the study found that marital status and

family situation are two factors specific to the student

that are at risk of causing him or her to drop out of a

course, but only in courses that are close to the Type

2 and Type 4 models. In the other course models

(types 1, 3, and 5), these variables do not play a

significant role in explaining dropout.

7 CONCLUSIONS

A growing number of studies conclude that it is

difficult to identify predictive factors without a

holistic view of the problems and obstacles

encountered by those who drop out. It is in this

context that our study examined the interrelationship

of a multitude of factors that can influence dropping

out of a DOL course or not re-enrolling after two

study sessions.

Although this study has limitations, the results

provide an answer to our research questions, namely

that socio-demographic, academic, and

environmental factors that explain dropping out

cannot be analyzed in themselves but should be

considered in relation to learning strategies as well as

the pedagogical design of courses.

Finally, this

research focused on courses in the social and

administrative sciences. It would be interesting and

useful to do a similar study on students of technical

specializations.

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