E-Learning Critical Success Factors in Moroccan Universities

during the Covid-19 Pandemic: Case Moulay Ismail University

Azeddine Zriba, Said Amali

Faculty of Sciences, Moulay Ismail University, Meknes, Morocco

Keywords: E-learning, COVID-19, pandemic, CSF, AHP, TOPSIS

Abstract: The pandemic COVID-19 has significantly disrupted the world, higher Education is among the most

impacted activities when schools and universities remained closed all over the world. In response to the

state of health emergency implemented by Moroccan authorities to minimize the spread of the coronavirus,

the Ministry of Higher Education, Scientific Research and Professional Training have decided to suspend

classroom courses in all educational institutions from 16 March 2020 until further notice.

This paper proposes to identify the critical success factors (CSFs) for the remote learning mode adopted by

the Moroccan universities during the COVID-19 pandemic by using two techniques, the first one is the

multi-criteria Analytic Hierarchy Process (AHP), and the second is Technique for Order Preference by

Similarity to Ideal Solution (TOPSIS). This study was carried out on a sample of 244 teachers and 3877

students who participated in the surveys established by Moulay Ismail University.

1 INTRODUCTION

The pandemic COVID-19 has significantly disrupted

the world, higher Education is among the most

impacted activities when education institutions

remained closed all over the world. As a result of

this situation, Moroccan authorities declared a state

of emergency on 16 March 2020

, and many

precautionary measures have been taken to ensure a

pedagogical continuity, including suspension of the

classroom courses in all public and private schools

and universities, and adopting distance learning

mode, to avoid the spread of the coronavirus

In this pandemic context of the coronavirus

Covid-19, Moulay Ismail University has deployed a

pedagogical continuity plan to continue the training

of its more than 70,000 students through distance

learning.

In this study, we determinate the critical success

factors (CSFs) during the COVID-19 pandemic from

News, Morocco World (19 March 2020). "COVID-19:

Morocco Declares State of Emergency". Morocco

World News. Retrieved 19 March 2020

Courses Suspended in Morocco from March 16 Until

Further Notice". Maghreb Arabe Press. 13 March

2020. Retrieved 14 March 2020.

the perspectives of teachers and students by

evaluating the E-learning experience during the

spring session using multi-criteria decision-making

methods.

This research can help decision-makers in

Moroccan universities to determine the best strategy

to adopt during a crisis by improving the most

critical factors to be taken into consideration when

implementing any remote learning process.

2 RESEARCH CONTEXT:

E-LEARNING IN MOROCCO

Over the last few years, higher Education knows a

new dynamic aimed at improving the quality of

higher Education and modernize its practices by

putting the learner in the center of educational

action, and integrating ICT (Information and

Communication Technology) into Education, as part

of the national strategy. “Maroc Numeric 2013”

To improve performance, quality and

productivity, and harmonize with standards

https://lematin.ma/journal/2012/NTI_Strategie-de-Maroc-

Numeric-2013--un-premier-bilan-globalement-

positif/170317.html

Zriba, A. and Amali, S.

E-Learning Critical Success Factors in Moroccan Universities during the COVID-19 Pandemic : Case Moulay Ismail University.

DOI: 10.5220/0010732300003101

In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021), pages 267-273

ISBN: 978-989-758-559-3

267

international organizations and to make it a vector of

development, the Moroccan education system has

been the subject of numerous reforms and upgrading

programs (Alem , 2012):

 The Education Reform (Law 0100), initiated

in 2002/2003, consists of establishing the

LMD (Bachelor's-Master's-Doctorate) system

(Bologna process 1999). The significant

contribution of this new reform was the

reference to ICT both as an object and as a

learning and governance tool for all

disciplines and higher education institutions.

 The Emergency Program (2009-2012) aims to

increase the reception capacity of universities,

improve the quality of training and promote

scientific and technical research. This program

emphasizes the continuing Education of

teachers and requires the university to acquire

a Digital Work Environment (ENT) and a

strategy for integrating educational

technologies in university-wide training.

 Maroc Numeric 2013 is a national strategy,

aiming to position Morocco as a regional

technological hub and to insert it into the

global knowledge economy through its

companies and universities. At the university

level, it consists of supporting them in

equipment and teacher training.

 All these initiatives and efforts have made ICT

a vital component as an object and a learning

tool in the education system.

3 LITERATURE REVIEW

3.1 Definition of e-Learning

E-learning, also referred to as distance learning,

online learning (Sangra, 2011), virtual learning

(VL)( Bezhovski et al, 2016), Computer-Based

Learning (CBL) (Fenouillet et al, 2006) “is a

conjunction of information and telecommunication

technology (ICT) with educational world” (Grubisic

et al, 2009). (Sun et al, 2008) stated that e-learning is

delivering and transferring educational learning

information through information and communication

technology (ICT). The most essential advantages of

e-learning are increasing teacher/student interactions

and relations between students without location and

time limitations via synchronous and asynchronous

educational network models (Hameed et al, 2008).

According to (Beningo et al, 2000), e-learning is

conducted on the internet, where students can access

lectures online at any place and time as needed, and

allows them the possibility to review the information

many times.

E-learning has two aspects: The first aspect is

related to structural issues (technology, learning

process, learning design), and the other aspect is

related to communicational issues (trainees habits,

skills and communication patterns (Beningo et al,

2000).

3.2 Analytic Hierarchy Process (AHP)

In 1980 Saaty developed the Analytic Hierarchy

Process (AHP) (Saaty, 1988). This technique is used

to manage qualitative and quantitative multi-criteria

elements involving in decision-making behavior.

AHP is one of the most inclusive systems, which

is considered to make decisions with multiple

criteria because this method gives to formulate the

problem as a hierarchical and believe a mixture of

quantitative and qualitative criteria as well

(Taherdoost, 2017).

3.3 Technique for Order Preference by

Similarity to Ideal Solution

(TOPSIS)

The (TOPSIS) method has been developed by

Hwang and Yoon in 1981 (Hwang et al, 1981).

This method is used to choose the best

alternative among a group of alternatives

(Backmann et al, 1981), also, it allows knowing the

distance of both the positive and the negative

alternatives of the ideal solution (Prakash et al,

2015).

3.4 CSF and Types of e-Learning

Systems

The critical success factors are referred to as

“characteristics, conditions, or variables that, when

properly sustained, maintained, or managed, can

have a significant impact on the success of a firm

competing in a particular industry” 16 (Alhabeeb et

al, 2018). The three types of e-learning systems used

in this study are defined in Table 1, to determine the

best type of e-learning system associated with AHP

and the TOPSIS technique during COVID-19. In

Table 2, we defined the factors that were considered

in this paper. Fig1 explain the critical success factor

hierarchy problem discussed in this study based on

the multiple-criteria decision analysis problem

representation.

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Table 1: Types of E-learning systems

e Definition

Face to face

learning

Traditional learning where the course

content and learning material are

taught from teacher to student. This

allows for live interaction between a

learner and an instructor.

(Thai et al 2017) (Young et al, 2014)

Blended

Learning

A mix of traditional and online classes.

(Graham et al, 2013)

(Young et al, 2014)

Synchronou

s Learning

A real-time interaction distance

learning.

(

Rowe, 2019

)

Table 2: definitions and studies related to CSF of e-

learning

Facto

Criteria

Learner’s

dimension

Motivation, learning speed, computer

skill, commitment.

(Bhuasiri et al, 2012)(Anggrainingsih

et al, 2018)

Instructor’s

dimension

Teaching style, instructor attitude to

the student, knowledge of learning

technology.

(Bhuasiri et al, 2012)(Anggrainingsih

et al, 2018

)

Learning

Environment

dimension

Learning management system,

technical infrastructure, design of user

interface, network security.

(Bhuasiri et al, 2012)

(

rainin

sih et al, 2018

)

Course

dimension

Sufficient, updated and understandable

content.

(Bhuasiri et al, 2012)

(

rainin

sih et al, 2018

)

Support

dimension

Providing financial support,

communication tools, help disk

availability, and training.

(Bhuasiri et al, 2012)

(

rainin

sih et al, 2018

)

Figure 1: Hierarchical structure for dimensions and CSFs

of E-learning system.

4 RESEARCH METHODOLOGY

The methodology used in this paper is based on

three parts, a survey, the AHP method, and the

TOPSIS method, a detailed description of each part

will be presented in the next sections.

4.1 Data Collection

This study is based on data collected from two

surveys released by Moulay Ismail University, the

first was for teachers

, and the second was for

students

. Table 3 summarizes the teachers who

participated in the study regarding seniority,

disciplinary field, degrees concerned by E-learning,

and table 4 summarizes the students who

participated regarding gender, place of residence,

faculty, and cycles of studies.

Table 3: Teachers demographic data

Frequency Percentage

Seniority Less than 5 years 54 22%

From 5 to 10

years

48 20%

From 11 to 20

years

32 13%

More to 20 years 110 45%

disciplinary

field

Sciences and

techniques

34%

Legal sciences 17 7%

Economic

Science and

Business

Administration

Letters and

human sciences

30%

Engineering

Sciences

16%

Educational

Sciences

degrees

concerned

by E-

learning

Technological

University

Degree

Bachelor 187

54%

Master 105

30%

Engineering

degree

ENCG degree 2

https://questionnaire.umi.ac.ma/index.php/179221

https://questionnaire.umi.ac.ma/index.php/747239

E-Learning Critical Success Factors in Moroccan Universities during the COVID-19 Pandemic : Case Moulay Ismail University

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Table 4: Students demographic data

Frequency Percentage

Gender

Male

1898 49%

Female

1979 51%

Place of

residence

Rural area

1060 27%

Urban area

2817 73%

Faculty /

school

FLSH

412 11%

FSJES

611 16%

624 16%

FST

476 12%

233 6%

ENSAM

744 19%

ENS

132 3%

EST

217 6%

ENCG

2 0%

No response

426 11%

Cycle of

studies

Bac + 2

299 8%

Bac + 3

1813 47%

Bac + 5

1258 32%

Other

457 12%

No response

50 1%

4.2 The Analytic Hierarchy Process

To use the AHP method, the following steps are

applied (Saaty, 1988) (Alqahtani, 2020):

1. Completing the pairwise comparison matrix

Table 5 using the ratings in the table 6, by

evaluating every two criteria at a time in terms

of their relative importance. The diagonal of

the matrix contains only values of 1.

Table 5: Pairwise comparison matrix

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Instructional

Design

dimension

Support

dimension

Students

dimension

1 1/2 1 1 1

Instructors

dimension

2 1 3 2 2

Learning

Environment

dimension

1 1/3 1 1/2 1/3

Course

dimension

1 1/2 2 1 1/3

Support

dimension

1 1/2 3 3 1

Table 6: Analytic Hierarchy Process (AHP) ratings

Verbal Judgment of

Preference

Numerical Rating

3 Extremel

referre

2 Stron

referre

1 E

uall

referre

2. Calculating the criteria weight, by creating a

normalized comparison matrix where each

value in the matrix is divided by the sum of

its column table 7.

Table 7: Normalized matrix

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Instructional

Design

dimension

Support

dimension

Students

dimensio

0,166 0,166 0,100 0,142 0,217

Instructors

dimensio

0,330 0,357 0,300 0,285 0,434

Learning

dimensio

0,166 0,119 0,100 0,070 0,070

Course

dimensio

0,166 0,166 0,200 0,142 0,070

Support

dimensio

0,166 0,166 0,300 0,428 0,217

3. Determine the average priority vector by

averaging across the rows, the sum of all

elements in priority vector is 1. The priority

vector shows relative weights among the

things that we compare, as shown in table 8.

Table 8: priority vectors

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Instructional

Design

dimension

Support

dimension

0,158 0,341 0,105 0,141 0,255

4.3 The Technique for Order

Preference by Similarity to Ideal

Solution

The TOPSIS process is carried out by applying the

following steps as defined on (Alqahtani, 2020)

(Sunardi, 2019):

1. Form the matrix expressed as follows:

D =





















mnmmm

XXXA







222213

112112

211

......

Where:

Ai = ith alternative project and Xij= the

numerical outcome of the ith alternative project

compared to the jth criterion.

Table 9 is the result of the TOPSIS matrix.

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



Table 9: TOPSIS matrix



Students

dimension

Instructors

dimension

Learning

Environment

dimension

Course

dimension

Support

dimension

Face to Face

Learning

3 3 1 3 1

Blended Learning

2 3 3 2 2

Synchronous

Learning

2 2 1 2 2

2. The normalized matrix is obtained by

applying the following formula :

Table 10 shows the TOPSIS normalize matrix.

Table 10: TOPSIS normalize matrix

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Course

dimension

Support

dimension

Face to

Face

Learning

0,727606875 0,639602149 0,301511345 0,727606875 0,33333333

Blended

Learning

0,48507125 0,639602149 0,904534034 0,48507125 0,66666667

Synchron

ous

Learning

0,48507125 0,426401433 0,301511345 0,48507125 0,66666667

3. During this step, we construct the weighted

normalize decision matrix by multiplying the

normalized decision matrix by its relative

weights, the result is shown in table 11. The

following formula is applied to calculate the

weighted normalized value V

= W

Table 11: weighted normalize decision matrix

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Course

dimension

Support

dimension

Face to

Face

Learning

0,115107408 0,218232253 0,031658691 0,108267903 0,08513333

Blended

Learning

0,076738272 0,218232253 0,094976074 0,072178602 0,17026667

Synchrono

Learning

0,076738272 0,145488169 0,031658691 0,072178602 0,17026667

4. Define both the ideal best and ideal worst

value

= (max v

)

= (min v

)

Table 12 shows the TOPSIS positive and

negative ideal solutions.

Table 12: the ideal best and worst values

Students

dimension

Instructors

dimension

Learning

Environment

dimension

Course

dimension

Support

dimension

Face to

Face

Learning

0,115107408 0,218232253 0,031658691 0,108267903 0,08513333

Blended

Learning

0,076738272 0,218232253 0,094976074 0,072178602 0,17026667

Synchrono

Learning

0,076738272 0,145488169 0,031658691 0,072178602 0,17026667

(best

value)

0,115107408 0,218232253 0,094976074 0,108267903 0,17026667

(worst

value)

0,076738272 0,145488169 0,031658691 0,072178602 0,08513333

5. Calculating the Euclidean distance from

ideal best and worst value Table 13, by

applying the following formula :

(5)

(6)

(2)

(3)

(4)















jij















jij

(1)

E-Learning Critical Success Factors in Moroccan Universities during the COVID-19 Pandemic : Case Moulay Ismail University

271

6. Calculating the performance score P table

13, by using the following formula :

Table 13: Euclidean distance from best and worst value

and performance score

+ S

Face to

Face

Learning

0,106097952 0,08981275 0,195910702 0,458437182

Blended

Learning

0,05267474 0,128640885 0,181315625 0,70948593

Synchronou

s Learning

0,10988822 0,085133333 0,195021554 0,436532946

7. The last step is ranking the order of

preference alternatives. Table 14 shows the

result of the ranking of alternatives.

Table 14: ranking the alternatives

P Rank

Blended

Learning

0,70948593 1

Face to Face

Learnin

0,458437182 2

Synchronous

Learnin

0,436532946 3

5 RESULTS AND DISCUSSION

The principal goal of this paper is to identify and

analyze the critical success factors of the E-learning

system deployed during the COVID-19 pandemic.

We used the AHP and TOPSIS methods to analyze

the data collected from two surveys. The findings

revealed, after calculating every factor’s weight, that

instructor dimension (0,341), support dimension

(0,225), student’s dimension (0,158), course

dimension (0,141), and learning environment

dimension (0,105) table 8 were the most critical

success factors influenced the E-learning process

during the COVID-19 pandemic.

According to the ranking obtained the most

important factors that influencing the remote

learning success are:

 The instructor’s knowledge of technology:

given the importance of this factor, it is

necessary to implement a training program

for teachers allowing them to integrate

educational technologies into their teaching

practices.

 Learning environment: This includes the

learning management system, networking,

technical infrastructure, and other facilities.

Decision-makers have to ensure the quality

of this infrastructure by allocating an

adequate amount of financial and human

resources.

On the other hand, and as a result of applying the

TOPSIS method, we find that Blended Learning

appears to be the best decision alternative for the

universities when adopting a distance learning mode

during the COVID-19 pandemic with a total weight

of 0,709. In the second position, we find face to face

learning mode with a total weight of 0,458, and

Synchronous learning mode, which was considered

to be the third position with a total weight of 0,436

as showing in table 14.

6 CONCLUSION

The universal pandemic Covid-19 in 2020 has

helped propel the remote teaching practices of

Moroccan universities as in other countries to an

unprecedented level. Indeed, the university’s

pedagogical continuity has been ensured in record

time thanks to an up-to-date technological

infrastructure of the various university components.

Distance Education has proven to be effective in

meeting the needs of learners in terms of knowledge

acquisition and further study in confinement periods.

This study makes a helpful contribution to better

understanding the factors that might impact the

adoption and success of e-Learning, and the results

found to provide useful information to the decisions

makers in the universities in their process of

implementing and adopting e-Learning mode in

Education.

REFERENCES

Alem N, (2012), numéro 5,Plates-formes d’enseignement

à distance dans l’enseignement supérieur, modes

d’appropriation et standardisation des usages,

frantice.net.

Alqahtani, Y., Rajkhan, A., (2020) E-Learning Critical

Success Factors during the COVID-19 Pandemic: A

Comprehensive Analysis of E-Learning Managerial

Perspectives, Educ. Sci

A Grubisic, S. Stankov, M. Rosic, B. Zitko, (2009),

Controlled experimentreplication in evaluation of e-

learning system s educational influence, Computer &

Education, vo1.53, pp.591-602.

(7)









BML 2021 - INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML’21)

272

Sun, P.,Tsai, R., Finger, G., Chen, Y., (2008), What

drives a successful elearning? An empirical

investigation of the critical factors influencing learner

satisfaction,

Computers & Education, pp. 1183-1202,

vo1.50.

Hameed, S. Badii, A. Cullen, A.J., 25–26 May (2008),

Effective e-learning integration with traditional

learning in a blended learning environment. In

Proceedings of the European and Mediterranean

Conference on Information Systems

, Al BustanRotana,

Dubai, pp. 25–26.

Benigno. V, Trentin, G., (2000), "The evaluation of

online courses",

Journal of Computer Assisted

Learning

. pp. 259-270,vo1.l6

Saaty, T.L., (1988), What is the analytic hierarchy

process? In Mathematical Models for Decision

Support,

Springer, Berlin/Heidelberg, Germany, pp.

109–121.

Taherdoost, H., (2017), Decision Making Using the

Analytic Hierarchy Process (AHP); A Step by Step

Approach, International Journal of Economics and

Management System, IARAS

Hwang, C.L., Yoon, K., (1981), Multiple Attribute

Decision Making: Methods and Applications,

Springer: Berlin/Heidelberg, Germany.

Beckmann, M., Künzi, H.P., Hwang, C.L., Yoon, K.,

(1981), Multiple Attribute Decision Making;

Scientific Research Publishing: Southern California,

CA, USA

., volume 186

Prakash, C., Barua, M.K., (2015), Integration of AHP-

TOPSIS method for prioritizing the solutions of

reverse logistics adoption to overcome its barriers

under fuzzy environment, J. Manuf. Syst. 37, 599–615.

Leidecker, J.K., Bruno, A.V, (1984), Identifying and using

critical success factors, Long Range Plan. 17, 23–32.

Alhabeeb, A., Rowley, J., (2018), E-learning critical

success factors: Comparing perspectives from

academic staff and students,

Comput. Educ, 127, 1–12.

Scholkmann, A., (2017), What I learn is what I like. How

do students in ICT-supported problem-based learning

rate the quality of the learning experience, and how

does it relate to the acquisition of competences?,

Educ. Inf. Technol, 22, 2857–2870.

Graham, C.R., Woodfield, W., Harrison, J.B, (2013), A

framework for institutional adoption and

implementation of blended learning in higher

education,

Internet High Educ, 18, 4–14.

Thai, N., De Wever, B., Valcke, M., (2017)

, The impact of

a flipped classroom design on learning performance in

higher education: Looking for the best "blend" of

lectures and guiding questions with feedback,

Comput

Educ,

107, 113–126.

Sunardi, ., Robo, S. and Trisno, (2019), MADM Model for

Evaluation of Non-permanent Teacher Performance

using Fuzzy AHP and TOPSIS Methods

Proceedings of the International Conferences on

Information System and Technology (CONRIST 2019)

pages 98-104, SCITEPRESS.

Young, T.P., Bailey, C.J., Guptill, M., Thorp, A.W.,

Thomas, T.L., (2014), The flipped classroom: A

modality for mixed asynchronous and synchronous

learning in a residency program,

West. J. Emerg. Med,

15, 938.

Rowe, J.A., (2019), Synchronous and Asynchronous

Learning: How Online Supplemental Instruction

Influences Academic Performance and Predicts

Persistence

. Ph.D. Thesis, Capella University,

Minneapolis, MN, USA

Alhabeeb, A., Rowley, J., (2018), E-learning critical

success factors: Comparing perspectives from

academic staff and students,

Comput. Educ, 127, 1–12.

Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J.J.,

Ciganek, A.P., (2012), Critical success factors for e-

learning indeveloping countries: A comparative

analysis between ICT experts and faculty, Comput

Educ

, 58, 843–855.

Anggrainingsih, R., Umam, M.Z., Setiadi, H., (2018),

Determining e-learning success factor in higher

education based on user perspective using Fuzzy AHP,

MATEC Web Conf, 154, 03011.

Behzadian, M., Otaghsara, S.K., Yazdani, M., Ignatius, J.,

(2012)

, A state-of the-art survey of TOPSIS

applications,

Expert Syst. Appl, 39, 13051–13069.

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