Student Perception of Usability: A Metric for Evaluating the Beneﬁt

When Adapting e-Learning to the Needs of Students with Dyslexia

Weam Gaoud Alghabban

1,2 a

and Robert Hendley

1 b

School of Computer Science, University of Birmingham, Birmingham, U.K.

Computer Science Department, Alwajh University College, University of Tabuk, Tabuk, Saudi Arabia

Keywords:

e-Learning, Student Needs, Engagement, Perceived Usability, System Usability Scale, Dyslexia, Arabic, Skill

Level.

Abstract:

Usability is now widely recognised as a critical factor to the success of e-learning systems. A highly usable

e-learning system increases students’ satisfaction and engagement, thereby enhancing learning performance.

However, one challenge in e-learning is poor engagement arising from a “one-size-ﬁts-all” approach that

presents learning content and activities in the same way to all students. Each student has different character-

istics and, therefore, the content should be sensitive to these differences. This study evaluated the students’

perceived level of usability of an e-learning system that matches content to reading skill levels of students with

dyslexia. 41 students rated their perceived usability of an e-learning system using the System Usability Scale

(SUS). Results indicated that when the e-learning system matches content to students’ skill level, students per-

ceive greater usability than when the learning is not matched. There was also a moderate, positive correlation

between perceived usability and learning gain when e-learning was matched to their skill level. Thus, students

assessment of the usability of a system is affected by the degree to which it is suited to their needs. This may

be reﬂected in increased engagement and is associated with higher learning gain.

1 INTRODUCTION

The context of learning is changing radically. The

process of teaching and learning is no longer re-

stricted to a traditional classroom environment, due

to the advent of electronic learning (e-learning) tech-

nologies (Pal and Vanijja, 2020). E-learning, which

refers to the use of electronic technologies in educa-

tional settings, includes the delivery of educational

content via Internet, audio, video and other media

(Ozkan and Koseler, 2009). E-learning has become

a signiﬁcant development in the educational ﬁeld and

has been shown to improve the quality of learning

(Hamidi and Chavoshi, 2018). Although e-learning is

not new, it has not yet been widely used as the primary

mode of instruction (Hidalgo et al., 2020). However,

due to institutional closures precipitated by the Coro-

navirus (COVID-19) pandemic, educational institu-

tions worldwide have dramatically increased their re-

liance on e-learning technologies. Existing litera-

ture suggests students’ perceptions toward e-learning

https://orcid.org/0000-0003-0857-1548

https://orcid.org/0000-0001-7079-3358

is mostly positive (Alqurashi, 2019; Rodrigues et al.,

2019; Valencia-Arias et al., 2019). However, these

studies were conducted when e-learning platforms

merely supplemented traditional classroom teaching.

With the onset of COVID-19, most educational ser-

vices have been delivered electronically. Because ed-

ucators are increasingly reliant upon e-learning tech-

nologies, assessing the effectiveness and perceived

usability of e-learning environments is a critical task

for researchers (H

ok, 2000). If an e-learning system

is not sufﬁciently usable, students will become de-

motivated and retain less material because they have

to focus on system functionality rather than content

(Ardito et al., 2006).

Engagement with course material has a signiﬁcant

impact on students’ learning (Kori et al., 2016). One

challenge in e-learning is to keep students motivated

and engaged (Moubayed et al., 2020). A “one-size-

ﬁts-all” approach that does not consider individual

differences in learning and teaching produces poor

student engagement (Maravanyika et al., 2017). Since

different people learn in different ways, one method

to keep each student engaged is to adapt the content

to their particular preferences and needs in order to

Alghabban, W. and Hendley, R.

Student Perception of Usability: A Metric for Evaluating the Beneﬁt When Adapting e-Learning to the Needs of Students with Dyslexia.

DOI: 10.5220/0010452802070219

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 1, pages 207-219

ISBN: 978-989-758-502-9

207

maximise learning, a process known as adaptive e-

learning (Moubayed et al., 2020). As a result, adap-

tive e-learning systems are becoming increasingly

popular (Hariyanto and K

ohler, 2020). Making

e-learning systems available, effective, and engaging

requires an understanding of the target users (Liaw

et al., 2007). Thus, students’ preferences, cogni-

tive abilities and cultural background should be thor-

oughly considered when designing e-learning systems

(Ardito et al., 2006). In addition, students’ inter-

actions with these systems should be as intuitive as

possible (Ardito et al., 2006), providing ﬂexibility

and adapting to students’ unique traits (Brusilovsky

and Mill

an, 2007). In summary, providing positive

and appropriate user experiences should be one of

the main goals of an e-learning system (Ardito et al.,

2006).

One critical component of user experience is us-

ability (Diefenbach et al., 2014) and, especially, a

student’s perception of the usability. Usability refers

to how easily and efﬁciently end users can accom-

plish their goals within a system (Nielsen, 1993). Us-

ability is therefore a key attribute of software quality

and is as important as security, robustness and per-

formance (Harrati et al., 2016). In the context of e-

learning, a good user experience leads to better en-

gagement and satisfaction, which in turn increases the

likelihood it will help students achieve learning goals.

All students, regardless of their orientation, experi-

ence or background, should be able to readily utilise

e-learning tools to achieve their goals (Harrati et al.,

2016).

E-learning systems have been evaluated from vari-

ous perspectives such as evaluating students’ satisfac-

tion (Alghabban and Hendley, 2020b), engagement

and interaction (Moubayed et al., 2020), and learn-

ing quality and effectiveness (Alghabban and Hend-

ley, 2020a; Rodrigues et al., 2019). However; there is

a lack of research on the examination of the usability

of e-learning (Pal and Vanijja, 2020). Because stu-

dents are key users of e-learning environments (Li-

mayem and Cheung, 2008), their needs and differ-

ences must be the main focus of usability practice

in the e-learning environment (Ardito et al., 2006).

However, recent research by Rodrigues et al. (Ro-

drigues et al., 2019) shows that usability studies of

e-learning largely ignore the perspectives of students.

This is a crucial shortcoming, as poor usability may

reduce students’ motivation to learn.

Another underexamined issue in e-learning is the

sensitivity of content to students’ needs and difﬁ-

culties (Zaharias, 2009). Prior studies have shown

students are more satisﬁed when content is effec-

tively presented, well-organised, ﬂexible and useful

(Holsapple and Lee-Post, 2006). However, accord-

ing to a recent literature review by Gunesekera et al.

(Gunesekera et al., 2019), little research has exam-

ined student-content interaction in the context of e-

learning. This is especially true in the context of spe-

cial needs education, which is the domain in which

this work is focused. Therefore, there is a need to

evaluate the perceived level of usability of e-learning

from the perspective of students when content sup-

ports their needs. Accordingly, the aims of this re-

search are:

1. To evaluate the perceived level of usability of

an e-learning system when matching content to

students’ needs. The evaluation was done using

a standard usability assessment tool (the System

Usability Scale (SUS) questionnaire).

2. To determine whether there is a relationship be-

tween the perceived level of usability and learning

gain when matching content to students’ needs.

The remainder of this paper is structured as fol-

lows. Section 2 introduces the related work. Sec-

tion 3 describes the research methodology. Results

are presented in Section 4, followed by the discussion

in Section 5. Finally, the conclusion and further work

are presented in Section 6.

2 RELATED WORK

In this section we explore three key aspects: e-

learning; assessing student engagement in current re-

search, and ﬁnally, dyslexia and the difﬁculties expe-

rienced in learning and the current state of the art in

evaluating the perceived usability of e-learning sys-

tems of students with dyslexia.

2.1 e-Learning

E-learning has expanded rapidly in all academic in-

stitutions; however, there is little consensus on what

e-learning means. A systematic literature review by

Rodrigues et al. (Rodrigues et al., 2019) deﬁned e-

learning as “an innovative web-based system based

on digital technologies and other forms of educational

materials whose primary goal is to provide students

with a personalised, learner-centered, open, enjoyable

and interactive learning environment supporting and

enhancing the learning processes” (p. 95). Much re-

search emphasizes the beneﬁts of e-learning systems.

The expansion of location- and time-independent ed-

ucation and high-speed Internet access have allowed

educational institutions to increasingly use e-learning

technologies (Ozkan and Koseler, 2009).

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208

However, one challenge in e-learning systems is a

“one-size-ﬁts-all” approach that does not consider in-

dividual differences in learning and teaching (Allen

et al., 2004). Since different people learn in dif-

ferent ways, it is important to match the content

to the student’s particular preferences and needs in

order to maximize and speed up the learning pro-

cess (Moubayed et al., 2020). As a result, adaptive

e-learning systems are becoming increasingly pop-

ular, which provides new ﬂexibility and suitability

to the peculiarities of the students’ speciﬁc needs

(Hariyanto and K

ohler, 2020).

In the context of e-learning systems, adaptation is

deﬁned as a procedure for tailoring the educational

environment to learners’ differences (Brusilovsky,

2012) with the aim of improving learning outcome

(Maravanyika et al., 2017). Different student char-

acteristics can be considered. For example, PERSO

adapts course material based on students’ media

preferences and their knowledge (Chorﬁ and Jemni,

2004). Other studies have attempted to incorporate

learning styles as a parameter for the adaptation of

the learning process. For example, both (Sihomb-

ing et al., 2020) and (Alshammari et al., 2016) adapt

based on students’ learning style. Another character-

istic is students’ personality, as reported in (Ghaban

and Hendley, 2018).

2.2 Evaluating Student Engagement

The multidisciplinary nature of e-learning systems

leads scholars from different ﬁelds to evaluate the ef-

fectiveness of these systems. Some researchers have

focused on evaluating e-learning systems from a tech-

nical perspective (Islas et al., 2007). Meanwhile, oth-

ers have evaluated these systems from the human-

factor perspective, taking into account students’ satis-

faction (Alghabban and Hendley, 2020b), experience

(Gilbert et al., 2007), determining e-learning mate-

rials’ effectiveness (Douglas and Vyver, 2004), and

engagement and motivation (Moubayed et al., 2020;

Ghaban and Hendley, 2018).

As part of these evaluation metrics, engagement

with course material has a signiﬁcant impact on stu-

dents’ learning (Kori et al., 2016). Student engage-

ment is deﬁned as the student’s ongoing effort spent

on the learning process to achieve learning goals

(Coates, 2006). That is, student engagement is com-

prised of three categories (Connell et al., 1995): be-

havioral such as sustained concentration in learning,

effort and persistent learning; emotional such as ex-

citement and interest in learning; and psychological

such as independence and challenge preferences (Ap-

pleton et al., 2006).

With the emergence of e-learning, the focus of

earlier research was mainly on how to improve the

learning performance and achievement of students.

However, lately more studies have focused on stu-

dents’ engagement when interacting with e-learning

systems (Ghaban and Hendley, 2018; Lee et al.,

2019; Alshammari, 2019). According to Carini et al.

(Carini et al., 2006), if the engagement level of stu-

dents increases, this may serve as a strong predictor of

improved performance and achievement. Therefore,

engaging more with an e-learning system, can lead

the student to use it for longer and also to learn more

effectively, which in turn can enhance the students’

academic performance. Hence, increasing a student’s

engagement with their e-learning environment is an

important objective.

Many approaches are used by researchers to mea-

sure engagement. These include self-report, teacher

expert knowledge (Lloyd et al., 2007) and interviews

and observation (Fredricks and McColskey, 2012).

However, these methods take time and resources and

can be difﬁcult to implement without inﬂuencing the

outcome. Therefore, different methods are often used

to measure engagement indirectly. For instance, Gha-

ban and Hendley (Ghaban and Hendley, 2018) found

that students who are more motivated and engaged,

will use the e-learning system for longer (They do this

by measuring the drop out rate as a proxy for engage-

ment) and that this, in turn, leads to improved learn-

ing outcomes. Kangas et al. (Kangas et al., 2017)

found that students’ satisfaction is an important in-

dicator of their engagement. That is, if a student is

more engaged, then his/her interest in and enjoyment

of learning is increased too. Also, if a student is more

engaged and motivated, then their perception of the

system’s level of usability will be higher (Ardito et al.,

2006; Zaharias and Poylymenakou, 2009).

In this work we measure students assessment of

the usability of the e-learning system and then as-

sess: i) whether this varies when the system is adapted

to their needs, and ii) whether their learning gain is

higher when their perception of the quality of the sys-

tem is higher. We argue that this is an effective way

to assess the beneﬁt of adaptation.

2.3 Dyslexia

The usage of e-learning systems is a growing research

area for children with speciﬁc learning disabilities.

One of the most carefully studied and most common

of childhood learning disabilities is dyslexia (Ziegler

et al., 2003). It is identiﬁed in 80% of the learn-

ing disabilities population (Lerner, 1989). Dyslexia,

which is called ’reading disorder’, has been deﬁned by

Student Perception of Usability: A Metric for Evaluating the Beneﬁt When Adapting e-Learning to the Needs of Students with Dyslexia

209

the main international classiﬁcation, ICD-10 (WHO,

1992) as ”The main feature of this disorder is a spe-

ciﬁc and signiﬁcant impairment in the development

of reading skills, which is not solely accounted for

by mental age, visual acuity problems, or inadequate

schooling” (p. 245). It may affect the following

skills: reading word recognition, reading comprehen-

sion skill, the performance of tasks requiring read-

ing and oral reading skill (WHO, 1992). The reading

performance of children with dyslexia is signiﬁcantly

below the expected level on the basis of age, intelli-

gence, and school placement (Lyon et al., 2003).

Students with dyslexia face other difﬁculties such

as high avoidance, frustration and poor concentration

(Oga and Haron, 2012). Additionally, they frequently

suffer from poor school attendance, problems with so-

cial adjustment and academic failure (WHO, 1992).

They also have varied needs, abilities and characteris-

tics, which needs to consider individually. Therefore,

it is argued e-learning systems should be responsive

to these differences (Brusilovsky and Mill

an, 2007)

rather than treating all students uniformly. One fea-

ture that may be considered is dyslexia type as re-

ported in (Alghabban and Hendley, 2020a) and learn-

ing style (Benmarrakchi et al., 2017b).

Students with dyslexia can become both easily

engaged and disengaged with their learning content.

They may engage with the games (Vasalou et al.,

2017) and collaborative learning environments (Pang

and Jen, 2018), and easily disengaged with too much

learning content (Baker and Rossi, 2013). Their lack

of engagement becomes one of the main causes that

affect their academic performance (Sahari and Johari,

2012). Therefore, it is important to measure students’

engagement in order to be able to retain their attention

and therefore to improve their learning performance.

Many e-learning systems have been designed for

students with dyslexia. Al-Ghurair and Alnaqi (Al-

Ghurair and Alnaqi, 2019) aimed to enhance short-

term memory of students with dyslexia by develop-

ing a story theme in a game-based application. The

game’s usability was evaluated by experts who ob-

served children using the application and children’s

opinions were taken after using that system. Similar

work has been done by Aljojo et al. (Aljojo et al.,

2018) where they assist children with dyslexia in pro-

nouncing Arabic letters using a puzzle game-based

system. The children evaluated the system’s usabil-

ity in terms of learnability, efﬁciency, memorability,

errors and satisfaction. Their ﬁndings were not clear

nor properly discussed. Further, Vasalou et al. (Vasa-

lou et al., 2017) aimed to enhance spelling, word de-

coding, and the ﬂuency of students with dyslexia (in

English) by developing a game-based learning appli-

cation.

Burac and Cruz (Burac and Cruz, 2020) devel-

oped a mobile application which implements a text-

to-speech feature to facilitate reading among children

with dyslexia in English. The overall usability of the

application was assessed by teachers by using a us-

ability questionnaire that measured effectiveness, ef-

ﬁciency, and quality of support, ease of learning and

satisfaction. Results showed that the usability of the

application was excellent. Similarly, Aldabaybah and

Jusoh (Aldabaybah and Jusoh, 2018) developed an as-

sistive technology for dyslexia by empirically identi-

fying a set of accessibility features related to the use

of menus, colours, navigation and feedback.

Despite all these works, some gaps are evident.

First, many systems fail to account for the variations

in abilities and skills among individuals with dyslexia

(Al-Ghurair and Alnaqi, 2019; Aljojo et al., 2018;

Burac and Cruz, 2020; Vasalou et al., 2017). As

a result, students with dyslexia using these systems

may exhibit lower engagement and satisfaction (Al-

ghabban and Hendley, 2020b). Second, much re-

search has utilised teacher evaluations of e-learning

systems, ignoring the perspective of the students (Bu-

rac and Cruz, 2020; Aldabaybah and Jusoh, 2018;

Aljojo et al., 2018). Thus, whether matching con-

tent to reading skill level improves perceived usability

of e-learning for students with dyslexia remains un-

known. The urgency of this question is exacerbated

by the COVID-19 pandemic, which has obstructed ac-

cess to traditional special needs provision.

To address these gaps, this research aims to eval-

uate the perception of the usability of an e-learning

system by students with dyslexia and to compare this

across two conditions: i) when it is matched to their

needs (speciﬁcally their reading skill level) and ii)

when it is not.

3 METHODOLOGY

An empirical study was conducted to evaluate the per-

ceived level of usability when matching e-learning

content to students’ characteristics, speciﬁcally read-

ing skill level among Arabic children with dyslexia.

Details about the study’s hypotheses, the proposed

e-learning system, data collection and experimental

procedure are presented in following sections.

3.1 Study Hypotheses and Variables

For this study, two hypotheses were formulated:

1. Matching learning content to reading skill level of

students with dyslexia achieves signiﬁcantly bet-

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210

ter perceived level of usability compared to non-

matched content.

2. There is a positive correlation between the per-

ceived level of usability and learning gain when

matching learning content to the reading skill

level of students with dyslexia.

Students with dyslexia in this study were divided

into two groups. Students in the experimental group

interacted with a matched version of the e-learning

system that matches content to each student’s reading

skill level. Students in the control group interacted

with a standard version of the system with identical

layout and interface to the matched version but with-

out matching the content to their reading skill level.

The perceived level of usability and learning gain

were the main dependent variables measured in this

study. Additionally, this study investigated whether

there was a correlation between the perceived usabil-

ity and learning gain. The effect of other adaptations

and the effect on learning gain are reported elsewhere.

3.2 e-Learning System

The Reading Enhancing System for Dyslexia (RESD)

was designed in order to support the evaluation of the

perceived usability. The RESD system is a dynamic,

web-based e-learning system for Arabic children with

dyslexia in elementary schools. It provides different

word recognition exercises, as word recognition is a

strong predictor of reading ﬂuency and improves stu-

dents’ ability to comprehend novel passages (Lo et al.,

2011). There are two versions of the RESD system

to support the experimental conditions. The matched

version matches training content of exercises to the

reading skill level of each student, while the standard

version ﬁxes the content to suit all reading skill lev-

els. Both versions of the system are identical in lay-

out; the only difference is the provided content in the

exercises.

The system trained students by providing a num-

ber of different word reading recognition activities.

In total, there were six training sessions, each with 20

activities. The content of training sessions was de-

rived from the study school curriculum and targeted

three main reading skills. These skills are the fun-

damental literacy skills that serve as building blocks

for more advanced skills and are presented in Table 1.

The design of matching content in the RESD system

is presented in Figure 1. The system relies on a re-

liable, ofﬂine reading skill level diagnostic tool, as

described in section 3.3. Students accessed training

content matching their reading skill level, according

to the algorithm presented in Figure 2. For example,

if a student’s reading skill required them to master

reading letters with short vowels, then the exercises

assessed and reinforced this skill.

Figure 1: Design of matching content based upon dyslexia

reading skill level in the RESD system.

A screenshot of a training activity of the system is

presented in Figure 3. As shown in Figure 3, the ques-

tion was presented at the top of the screen in written

form and accompanied by audio. The target word was

missing and denoted as dots inside a rectangular box

under the query, and its audio had a question mark

icon beside it. The student listened to the query and

selected the correct target word among three choices,

of which only one was correct. If the student chooses

the correct response, the interface will provide spo-

ken and written praise. If the student chooses an in-

correct response, negative written and spoken feed-

back was provided. Feedback on training progress is

provided as a progress bar at the top of the screen

as shown in Figure 3. Because motivation increases

conﬁdence and self-esteem of students with dyslexia

(Benmarrakchi et al., 2017a), motivational messages

were presented after completing every seven activi-

ties, as shown in Figure 4.

The training sessions’ layout was consistent for

all reading skills, keeping the question, target word,

audio icon, and answer choices in the same place. The

user interface followed best design practices in terms

of font, type, colour, and background colour for e-

learning systems targeting Arabic dyslexia (AlRowais

et al., 2013) and used encouraging spoken feedback.

Student Perception of Usability: A Metric for Evaluating the Beneﬁt When Adapting e-Learning to the Needs of Students with Dyslexia

211

Table 1: The reading skills and content represented in the RESD system.

No. Reading skill Content

Reading letters

with short vowels

Reading letters with fat-ha /a/

Reading letters with kasra /i/

Reading letters with damma /u/

Reading words

with consonant

sections

Reading one consonant section in 2-letters words

(consonant section in the end of a word)

Reading one consonant section in 3-letters words

(consonant section could be in the middle or the end of a word)

Reading two consonant sections in 4-letters words

(consonant sections are in the middle and end of a word)

Reading words

with short vowels

and consonant

sections

Reading 3-letters words with fat-ha /a/

Reading 3-letters words with fat-ha /a/ and kasra /i/

Reading 3-letters words with fat-ha /a/ and damma /u/

Reading 3-letters words with fat-ha /a/, kasra /i/ and damma /u/

Reading 4-letters words with all short vowels and consonant sections

Figure 2: The proposed content matching algorithm based

upon reading skill level.

3.3 Data Collection

Data was gathered by using three tools: reading skill

level diagnostic tests, pre- and post-tests, and the SUS

questionnaire.

Reading skill level diagnostic tests consisted of

three subtests: (1) letter reading with short vowels, (2)

Figure 3: A screenshot of the RESD system.

Figure 4: A motivational message.

word reading with consonant sections and (3) word

reading with short vowels and consonant sections.

These tests met the requirements of the reliable and

standardised tests approved by the Ministry of Edu-

cation in Saudi Arabia (SA) for students with learn-

ing difﬁculties (Bukhari et al., 2016). The skill level

test for letter reading tested the student’s ability to

accurately recognise letters (spelling) in three-letter

words with short vowels. The skill level test for words

reading with consonant sections, included a list of ten

vowelised words with consonant sections within two

and three letters-words. The skill level test for word

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212

reading with short vowels and consonant sections in-

cluded a list of ten vowelised words used to assess the

students’ level of this skill. The words differed in the

number of letters and using the combination of short

vowels and consonants. For each reading skill level

test, each participant was asked to read these words

aloud to detect his or her ability to spell letters cor-

rectly (as in the ﬁrst test) and to read the words cor-

rectly (as in the second and third tests).

Pre- and post-tests are commonly used to derive

learning gain, or changes in learning outcomes that

have been achieved after a speciﬁc intervention (Pick-

ering, 2017). The pre-test contained 15 different vow-

elised words from the curriculum and was validated

by special educational experts. The pre-test met the

standardised test requirements for students with learn-

ing difﬁculties (Bukhari et al., 2016). The pre-test

was used at the beginning of the study to assess ini-

tial reading performance of students and participants

were divided into two groups, balanced by the pre-

test scores. The post-test, which is the same as the

pre-test, is used upon completion of the intervention

to measure learning gain. Learning gain is calculated

by subtracting the score of the pre-test from the score

of the post-test. Using the same tests in this study was

the best choice to allow a precise comparison of read-

ing abilities (Bonacina et al., 2015).

SUS was used in this study to assess children’ per-

ception of usability for the following reasons. The

SUS tool is one of the most widely used instruments

for Human-Computer Interaction (HCI) researchers

to evaluate the perceived usability of systems (Harrati

et al., 2016). It has a high degree of reliability and

validity (Bangor et al., 2008), and can be adapted for

various contexts (Peres et al., 2013). It is a valid in-

strument to compare the usability of two or more sys-

tems (Peres et al., 2013). With SUS, reliable results

are evident even with small samples (Tullis and Stet-

son, 2004). It has also been adapted to evaluate the

usability by children (Alghabban and Hendley, 2020a;

Putnam et al., 2020) and also used as a standard us-

ability tool for Arabic users with a high degree of re-

liability (AlGhannam et al., 2018).

The SUS includes 10 mixed-tone items. The odd

numbered items have a positive tone, and the even

number a negative tone. All items are on a ﬁve-point

Likert scale from strongly disagree (1) to strongly

agree (5). SUS uses alternative positive and negative

questions.

Several pictorial elements are used to convey the

meaning of the Likert scale for children (Read, 2012;

Putnam et al., 2020). A widely-used visual represen-

tation in academia is the Smileyometer (Read, 2012),

as shown in Figure 5. In this study, the Likert scale

used by SUS were converted to Smileyometers, since

a visual representation of the Likert scale of agree-

ment matches children’s cognitive abilities (Alghab-

ban and Hendley, 2020a; Putnam et al., 2020).

Figure 5: The Smileyometer (Read, 2012).

3.4 Procedure

The study was subject to ethical approval by Uni-

versity of Birmingham, and for each child explicit

consent was obtained from parents/guardians and

schools, prior to study participation. All children

were selected from elementary schools in SA.

The procedure timetable is presented in Table 2.

In one session, children were welcomed and intro-

duced to the study’s objectives. Then, their demo-

graphic information (age, grade) was collected and

reading skill level diagnostic tests, including the read-

ing pre-test, were administered. Subsequently, chil-

dren were divided into two groups: the experimental

group and the control group. The two groups had sim-

ilar distribution of age, grade and prior reading per-

formance assessed by the pre-test. The experimental

group used the matched version of the RESD, and the

control group used the standard version. The exper-

iment took place in person, in a quiet room, within

each child’s school. Each child individually inter-

acted with the system. Six training sessions were con-

ducted, two sessions each week, each one lasted for

30 minutes.

At the end of the total training sessions, the post-

test and SUS were administered.

4 RESULTS

4.1 Participants

The participants were all female students, aged 7-

10 years at different elementary schools in SA. They

were all ofﬁcially diagnosed with dyslexia. Forty-one

students with a mean age of 8.4 years (Grade 2 to 4)

were included in this study. All participants had pre-

vious experience with electronic devices. Due to the

customary separation of males and females in SA ed-

ucational organizations, researchers did not have ac-

cess to male students. This also has an advantage by

Student Perception of Usability: A Metric for Evaluating the Beneﬁt When Adapting e-Learning to the Needs of Students with Dyslexia

213

Table 2: Procedure timetable.

Week # Session Activity

1 Pre-starting

Overview of the study, collecting demographic information,

conducting reading skill level tests, pre-test

Training sessions

Training session 1 and 2

3 Training session 3 and 4

4 Training session 5 and 6

4 Learning gain, SUS Post-test, SUS

reducing variances between participants.

All participants were native Arabic speakers and

the study was conducted in Arabic. The sample char-

acteristics are presented in Figure 6. All of the partic-

ipants completed the experiment.

4.2 The Effect of Skill Level Content

Matching on Perceived Level of

Usability

The experimental sample includes 20 students in the

experimental group and 21 in the control group. The

groups were balanced for prior level of reading per-

formance (p-value for pre-test = 0.711 > 0.05) and

age (p = 0.808 > 0.05). Analyses were performed in

IBM SPSS.

First, the perceived level of usability was calcu-

lated for both matched and standard variants of the

RESD system. The usability scores for the matched

version of the system based on reading skill level (M

= 96.25, SD = 3.39) and the standard version (M =

84.76, SD = 12.09) were acceptable since their av-

erage score is greater than 70 (Bangor et al., 2008).

Thus, both versions of the system were assessed as us-

able and valuable in the learning process, and the stu-

dents with dyslexia were generally satisﬁed and found

them easy to use.

The two versions of the system (matched and stan-

dard) were also compared to get a deep insight into

their usability and to investigate whether the provi-

sion of matching content has any impact on usabil-

ity. Because data were not normally distributed, an

independent sample Mann-Whitney U test was con-

ducted to compare the two conditions. Distributions

of the usability scores for both versions were simi-

lar, as assessed by visual inspection. The results in-

dicated that there was a statistically signiﬁcant differ-

ence between the overall usability score of the two

versions, U = 358, p = 0.0001. Therefore, the ﬁrst

hypothesis is conﬁrmed, and it can be concluded that

the matched version of the e-learning system based

on reading skill level of students with dyslexia yields

signiﬁcantly higher levels of perceived usability than

a standard version.

When investigating differences among individual

items on the SUS tool, we found, for instance, that

children using the matched version provided higher

ratings to item 8 (“I found the website was very con-

venient to use”) than those using the standard version.

Thus, e-learning systems that detect and respond to

students’ characteristics elicit higher user satisfaction,

offer greater academic support, and enhance engage-

ment and satisfaction.

4.3 Perceived Level of Usability and

Learning Gain Correlation

A Spearman’s rank-order correlation was run to as-

sess the relationship between learning gain and per-

ceived level of usability of the experimental and con-

trol groups. The relationship between these two vari-

ables is monotonic, as assessed by visual inspection

of a scatterplot.

Among participants in the experimental group,

there was a moderate positive correlation between

perceived usability and learning gain, r = 0.517, p

= 0.02 < 0.05. By contrast, usability and learning

gain were unrelated among participants in the con-

trol group, r = -0.364, p = 0.105 > 0.05. Therefore,

usability was associated with greater learning gain

among students using the matched e-learning mech-

anism.

5 DISCUSSION

This work aimed to ﬁll a research gap in evaluating

the perceived level of usability of an e-learning sys-

tem that matches the needs of students with dyslexia.

This work contrasts with other research on e-learning

that does not consider individual differences in learn-

ing among students with dyslexia (Al-Ghurair and Al-

naqi, 2019; Aljojo et al., 2018; Burac and Cruz, 2020;

Vasalou et al., 2017) or relied on teacher reports of

usability rather than student perception (Burac and

Cruz, 2020; Aljojo et al., 2018; Aldabaybah and Ju-

soh, 2018). Students have the most inﬂuential inter-

actions with the content (Gunesekera et al., 2019),

CSEDU 2021 - 13th International Conference on Computer Supported Education

214

Figure 6: The experimental sample characteristics.

and so evaluating their perception of the usability and

their engagement, from their perspective, is an impor-

tant aspect to ensure the effectiveness and usefulness

of these tools (Plata and Alado, 2014).

Although both versions of the system had the

same user interface, this work suggests both factors

can be enhanced by programs which match students’

abilities and needs. The ﬁndings of this work are con-

sistent with previous research (Alghabban and Hend-

ley, 2020a; Alshammari et al., 2016) that shows stu-

dents engage with and enjoy products more when

they target their speciﬁc skills, needs, and attributes

and that those products are perceived as more usable.

Such satisfaction precipitates improved learning per-

formance by allowing students to focus on their learn-

ing rather than the functionality of the system (Or-

fanou et al., 2015).

Furthermore, this work’s ﬁndings yielded a pos-

itive correlation between perceived level of usability

and learning gain when the e-learning system matches

the students’ needs. These results parallel those from

previous studies (Ardito et al., 2006; Zaharias and

Poylymenakou, 2009), which found that student en-

gagement and satisfaction mediated the link between

usability and academic progress. Students recognise

the content that is useful (Holsapple and Lee-Post,

2006) and suits their needs (Alghabban and Hendley,

2020b; Alghabban and Hendley, 2020a; Sihombing

et al., 2020), resulting in higher satisfaction (Alghab-

ban and Hendley, 2020b; Holsapple and Lee-Post,

2006), perceived usability (Alghabban and Hendley,

2020a; Sihombing et al., 2020) and learning perfor-

mance (Alghabban and Hendley, 2020a; Alshammari

et al., 2016).

Because the system’s interfaces and layout were

otherwise identical, these group differences cannot be

attributed to the customization of the user interface.

Thus, students with dyslexia are more satisﬁed with

e-learning platforms when their content and learn-

ing activities are tailored to their reading skill level.

In addition, the results suggest that the skill level of

students is one signiﬁcant characteristic in education

(Essalmi et al., 2010) that should be incorporated in

e-learning systems in order to enhance engagement

and students’ experience. This is in line with class-

room practice where, once the student’s reading level

has been determined, teachers select the most appro-

priate materials for each student (Dolgin, 1975). The

ﬁndings suggest that students notice when content is

tailored to their needs, and that this tailored content

increases perceived course quality. This is consistent

with previous research (Zaharias, 2009) that shows

that focusing on users’ needs makes them more ac-

tive in an e-learning course. Whilst they may not be

able to explicitly assess this match, they are, at least

subconsciously, aware of it, and this will be reﬂected

in their assessment of aspects of the system that do not

change between conditions (Alghabban and Hendley,

2020b).

6 CONCLUSION AND FUTURE

WORK

This research evaluated student perceptions of the us-

ability of an e-learning system and how this changes

when matching content to the needs of students with

dyslexia. We did this for two reasons. First, evaluat-

ing the perceived level of usability of e-learning sys-

tems is a critical task for researchers (H

ok, 2000),

and recently, it has become of great importance due

to increased use of e-learning technologies spurred

by the COVID-19 pandemic (Pal and Vanijja, 2020).

That is, a usable e-learning system leads to better en-

gagement and satisfaction, which in turn increases

the likelihood that it will help students achieve their

learning goals (Alghabban and Hendley, 2020a; Al-

shammari et al., 2016). Second, we believe that this

is an effective metric for assessing the effectiveness

of adaptation based on students’ needs. By compar-

ing two conditions (adapted and non-adapted) we can

measure the change in a student’s attitude to the adap-

tation. This can augment existing metrics (such as

learning gain) to give additional insight into whether

the adaptation is beneﬁcial.

Student Perception of Usability: A Metric for Evaluating the Beneﬁt When Adapting e-Learning to the Needs of Students with Dyslexia

215

An experimental evaluation of the e-learning sys-

tem’s usability was conducted with 41 elementary

school children with dyslexia, and it yielded signif-

icant results. Findings indicated that matching con-

tent to children’s reading skill level results in a higher

level of perceived usability than non-matched content.

Thus, when e-learning meets students’ needs, they re-

port that the tools are more usable and more engaging

and produce greater learning gains.

This research has, however, some limitations. The

research targeted dyslexia in the Arabic language.

The structure and orthography of Arabic are differ-

ent from other languages such as English. There-

fore, the manifestation of dyslexia in Arabic is dif-

ferent from dyslexia in English (Elbeheri and Everatt,

2007). Thus, further investigation is required to check

whether these results can be generalised to other lan-

guages, other age groups and male students. More-

over, further investigation may include other student

characteristics, such as learning style and personality.

Although this research’s ﬁndings are promising,

further exploration of outcomes associated with the

use of e-learning tools is needed. Education through

computer-based platforms is complex, involving stu-

dent self-efﬁcacy, the quality of the learning content,

ease of use (Li et al., 2012), the extent to which the

tools suit students’ different characteristics (Sihomb-

ing et al., 2020), the user interface design, interac-

tivity and engagement (Plata and Alado, 2014). The

perceived level of usability is one element of the over-

all user experience that this research evaluated, while

further research may focus on other aspect of user ex-

perience.

ACKNOWLEDGEMENTS

This work was supported by University of Tabuk,

Tabuk, Saudi Arabia. Special thanks to the Min-

istry of Education in Jeddah, Saudi Arabia for giv-

ing the permission to undertake this study in different

schools. In addition, many thanks to all participating

students and teachers.

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