MMALL
Multilingual Mobile-assisted Language Learning
Maria Virvou, Efthimios Alepis and Christos Troussas
Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece
Keywords: Mobile-assisted language learning, Mobile learning, Multiple language learning, User modelling, Error
diagnosis, Adaptive learning.
Abstract: Learning multiple foreign languages has become a necessity in modern life since globalization is a
phenomenon responsible for joining different cultures from all over the world. Computer-assisted language
learning tools exist over the last decades and their assistance has been considered as quite beneficial in the
area of education. However, the incorporation of mobile facilities in these tools offers the quite important
facility of time and place independence for the users that are going to use them. Towards addressing the
problem of providing mobile-assisted language learning, in this paper we present a sophisticated educational
system called m-MALL. The m-MALL system has the additional advantage of providing multilingual
support, a facility that is not yet investigated in the related scientific literature.
1 INTRODUCTION
Mobile-Assisted Language Learning (MALL)
represents a very recent research field in the domain
of language learning where the educational process
is assisted or enhanced through the use of handheld
mobile devices. As a result, MALL is a subset of
both Mobile Learning (m-learning) and Computer-
assisted language learning (CALL). MALL has
evolved to support students’ language learning with
the increased use of mobile technologies such as
mobile phones (cellphones), PDAs and devices
known as smartphones. Through MALL, students
have the ability to access language learning
materials, test their knowledge, as well as to
communicate with their teachers and peers at any
time and at any place.
In our century, we have witnessed major
improvements in the areas of transportation and
telecommunications, permitting globalization by
which regional economies, societies, and cultures
have become integrated through a global network of
people. As a result, personal, professional, social,
and economic considerations all point to the
advantages of learning foreign languages (Kurata,
2010). Considering the scientific area of Intelligent
Tutoring Systems (ITSs), there is an increasing
interest in the use of computer-assisted foreign
language instruction. Especially, when these systems
offer the possibility of multiple-language learning at
the same time, the students may further benefit from
this educational process (Virvou et al, 2000). The
need for tutoring systems that may provide user
interface friendliness and also individualized support
to errors via a student model are even greater when
students are taught more than one foreign languages
simultaneously. Student modeling may include
modeling of students’ skills and declarative
knowledge and can perform individualized error
diagnosis of the student. However, in the recent
scientific effort, it is not depicted the
implementation of mobile-assisted language learning
systems, which can support multilingual content in
their domain of knowledge.
In view of the above, in this paper we propose a
multilingual mobile-assisted language learning
system which is the application of MALL in a
multiple language learning environment. The
prototype system combines an attractive multimedia
interface and adaptivity to individual student needs
in mobile learning. The communication between the
system and its potential users as students is
accomplished through the use of web services.
The paper is organized as follows. Firstly, we
present the related work, concerning CALL systems,
MALL systems and mobile learning in section 2. In
section 3, we discuss our system’s architecture.
Following, in section 4, we present a description of
129
Virvou M., Alepis E. and Troussas C.
MMALLMultilingual Mobile-assisted Language Learning.
DOI: 10.5220/0004459401290135
In Proceedings of the First International Symposium on Business Modeling and Software Design (BMSD 2011), pages 129-135
ISBN: 978-989-8425-68-3
Copyright
c
 2011 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

our system, namely a general overview accompanied
by screenshots of the system. Finally, in section 5,
we come up with a discussion about the usability of
the resulting system and we present our next plans.
2 RELATED WORK
Teaching languages through computer-assisted
approaches is a quite significant field in language
learning. A small number of researchers in the
subject area have been further attracted by mobile-
assisted language learning (MALL) over the last
decade but this interest is rapidly growing. The
majority of scientists, who are interested in these
fields, have been attracted by computer-assisted
language learning (CALL) or even mobile learning
(m-learning). In this section, we try to imprint the
speckle of the scientific progress in assisted
language learning.
2.1 Computer-assisted Language
Learning
AutoTutor is a CALL system, developed by
Graesser et al (2005), which simulates a human tutor
by promoting the conversation and provides
feedback to the learner, pumps him/her for more
information, gives hints, fills missing information
with assertions, identifies and corrects bad answers,
answers learner’s questions and summarizes
answers. Another CALL system is rEcho, which is
developed by Zhou et al (2007), can give relevance
feedbacks through anatomy animation and is based
on deliberate data trained recognition to give error
trend relevant feedbacks. SignMT was implemented
by Ditcharoen et al (2010) to translate
sentences/phrases from different sources in four
steps, which are word transformation, word
constraint, word addiction and word ordering.
Another computer-based program on second
language acquisition is Diglot Reader, which was
developed by Christensen et al (2007) and is used in
a way that students may read a native language text
with second language vocabulary and grammatical
structures increasingly embedded within the text.
TAGARELA is an individualized instruction
program, implemented by Amaral et al (2007),
which analyzes student input for different activities
and provides individual feedback. Finally, VIRGE,
developed by Katsionis and Virvou (2008), works as
an adventure virtual reality game but it has
educational content as well and supports
personalized learning based on a student modeling
component.
2.2 Mobile Learning
Mobile learning is a general aspect of assited
language learning and focuses on learning across
contexts and learning with mobile devices. Jeng et al
(2010) conducted an investigation of add-on impact
of mobile applications in learning strategies. They
surveyed recent researches including context
awareness, pedagogical strategy-enhanced learning
scenarios, as well as collaborative and socially
networked mobile learning. Through their review
study, essential characteristics of mobile learning
were identified and discussed. Frohberg and
Schwabe (2009) note in their critical analysis that
mobile learning can best provide support for
learning in context. There, learners are asked to
apply knowledge and not just consume it.
Furthermore, mobile learning should provide
instruments to provoke deep reflection,
communication and cooperation. Kuo and Huang
(2009) propose an authoring tool named Mobile E-
learning Authoring Tool (MEAT) to produce
adaptable learning contents and test items. In
addition, the visualized course organization tool has
also been provided to teachers to organize their
teaching courses. Moreover, Motiwalla (2005)
proposes a project which explores the extension of
e-learning into wireless/handheld (W/H) computing
devices with the help of a mobile learning (m-
learning) framework. This framework provides the
requirements to develop m-learning applications that
can be used to complement classroom or distance
learning. A prototype application was developed to
link W/H devices to three course websites. Gu et al
(2011) make an effort to provide learning in
informal settings through mobile. In order to learn
how to develop usable learning content for lifelong
learners on the move, a set of design principles from
both pedagogical and usability concerns was
identified. Next, a pilot system, based on the design
principles, was developed to implement two
prototype lessons. Finally, Mobile Author is an
authoring tool, which was implemented by Virvou
and Alepis (2005) and allows instructors to create
and administer data-bases, concerning characteristics
of students, of the domain to be taught and of tests
and homework, through any computer or mobile
phone.
2.3 Mobile-assisted Language Learning
Mobile-assisted language learning has evolved to
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130

support students’ language learning with the
increased use of mobile technologies such as mobile
phones and other devices. With MALL, students are
able to access language learning materials and
communicate with their teachers and peers. Chang
and Hsu (2011) introduce a computer-assisted
language-learning system for use on PDAs, which
integrates an instant translation mode, an instant
translation annotation mode and an instant multi-
users shared translation annotation function in order
to support a synchronously intensive reading course
in the normal classroom. Zervas and Sampson
(2010) propose an IEEE Learning Object Metadata
(LOM) Application Profile that can be used for
tagging educational resources suitable for Language
Learning and supported by mobile and wireless
devices. Fotouhi-Ghazvini (2009) et al also occupied
with mobile-assisted language learning. Their paper
concludes that using m-learning within the informal
framework of learning provides a ubiquitous tool
that can powerfully help adult learners and students
in Iran during their continuous lifelong learning.
Uther et al (2005) present a mobile adaptive
computer-assisted language learning (MAC)
software aimed to help Japanese-English speakers in
perceptually distinguishing the non-native /r/ vs. /l/
English phonemic contrast with a view to improving
their own English pronunciation. Sandberg et al
(2011) conducted a research concerning the way that
mobile learning may affect learning performance.
Three groups participated in their study on the added
value of mobile technology for learning English as a
second language for primary school students.
However, after a thorough investigation in the
related scientific literature, we came up with the
result that teaching multiple languages through an
integrated tutoring system via mobile is an approach
that was not investigated before. For this reason, we
implemented a prototype system, which incorporates
user modeling and error diagnosis components,
while teaching multiple languages through mobile
devices. Our system offers the possibilities of e-
learning and distance education as it operates in
mobile devises, but also it offers interactivity and
adaptivity to individual students needs. The resulting
system includes all the standard attributes of an ITS
and complies with its architecture, which consists of
the domain knowledge, the student modeler, the
advice generator and the user interface (Virvou and
Alepis, 2005). A novelty of the system lies in the
multilingual component and in the error diagnosis
process, which is carried out through the m-MALL.
The system provides estimation of the learner’s
proficiency in the domain as well as his/her
proneness to commit errors. The facility of
individualized error diagnosis is particularly
important for students, who can benefit from advice
tailored to their problems.
3 GENERAL ARCHITECTURE
OF THE SYSTEM
In this section, we describe the overall functionality
and features of m-MALL. The architecture of m-
MALL consists of the main educational application,
a student modelling mechanism, a web service, a
database and finally an educational application
installed locally in each user’s mobile device. The
web service is responsible for transferring the
available information from the main system’s
database to the mobile application. The database is
divided in two logical partitions. One part of the
database is used to store educational data and
another part is used to store data related to user
modelling, namely student profile information and
error diagnosis data. Accordingly, the database is
used to store user models and user personal profiles
for each individual user that uses and interacts with
the system, as well as stereotypic information about
user profiles. Each user’s initial profile is updated
while s/he uses the educational system. The system’s
general 4-tier architecture is illustrated in figure 1.
Correspondingly, the student modelling
mechanism consists of two sub-mechanisms. One
mechanism that is responsible for each student’s
model and another mechanism that reasons about
Multilanguage errors (figure 2). Each student’s
profile takes initial values by combining the
student’s personal information (such as age, gender
and educational level) with pre-stored stereotypic
information. Consequently, the student model is
adapted according to the students’ performance
while using the educational application. The
Multilanguage error diagnosis mechanism tries to
find possible reasons about student errors in a
Multilanguage domain of knowledge. As a next step,
these errors are categorized in terms on five pre-
defined categories of errors.
Error diagnosis in the student’s domain of
knowledge is accomplished by recognizing errors
and trying to associate them with one of the
following five categories of errors:
1. Article and pronoun mistakes
For example the user may have used “a” instead of
“an” or “he” instead of “we”.
2. Spelling mistakes
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131

Figure 1: Architecture of m-MALL.
Figure 2: The Student modelling mechanism.
A spelling mistake is a result of letter redundancy,
letter missing or interchange of two neighbouring
letters.
3. Verb mistakes
Verb mistakes occur when the user has typed
another person than the correct one, for example s/he
may have typed “I has” instead of “I have”.
4. Unanswered questions
The user may have no idea about what s/he should
write and leave the question unanswered. That
means that s/he has lack in theory.
5. Language Confusion
The resulting system is a multilingual learning
system, which means that a student may learn two or
more languages at the same time. However, there is
the possibility of student’s getting confused,
concerning the proper use of an article or verb.
In order to successfully recognize one or more of the
fore mentioned categories of errors, m-MALL
incorporates two algorithmic approaches, as
illustrated in figure 3. The first algorithm tries to
find string similarities by matching a student’s given
“exact” wrong answer with the systems correct
stored answer. If string matching occurs in a high
percentage the system decides whether the mistake
lies between categories 1-4. Correspondingly, using
the second algorithm, the system also tries to find
meaning similarities between the given and the
correct answer by translating these two answers to
the system’s available supported languages. As an
example, the student may have used “I am” instead
of “Je suis”, which is the French equivalent.
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Figure 3: Error diagnosis mechanism.
A matter of great importance is the existence of a
long term user model for each student. The system
includes also a form, which keeps information about
the student’s progress in the three languages, the
total grade in each one of the three languages and all
the results of the tests. Students may benefit from
viewing their own student models. For this reason,
this form can be presented to students so that they
stay aware of their advance of knowledge. M-MALL
programs may promote noticing (focus on form) that
will result in the improvement of students’ existing
grammatical knowledge. This can be accomplished
by evaluating the students’ performance through
several tests.
The proposed architecture of the m-MALL
prototype system gives great flexibility both to the
students who are using the educational language
learning application and to their instructors or
supervisors, since the remote database can be easily
updated and enriched with new knowledge domain
data and user specific information. Furthermore, this
is also the case of the student modelling mechanism
since the algorithms may be independently modified
or changed in order to provide more sophisticated
feedback for the students
4 OVERVIEW OF THE SYSTEM
M-MALL has been developed to operate on the
Android mobile operating system, while as for
future work the authors are planning to provide
implementations for other existing mobile phone
platforms as well. Correspondingly, the system is
programmed using JAVA as a programming
language. This specific programming language is
also compatible with the system’s Object Oriented
structure.
As we can see in figure 4, the m-MALL system
is packaged and installed as an application in an
Android operating smartphone. Each user can run or
stop the educational application by using to start or
to stop m- MALL through his/her mobile device.
However, this mobile application relies on data
stored in m-MALL’s server and transferred to each
interacting user through a web service.
Figure 4: m-AWARE as an application in a smartphone.
Figure 5 illustrates a snapshot of the operating
educational application where each student’s
personal profile may be updated. Personal student
information is used for the student model to
initialize using student stereotypic information
which is stored in the system’s database. According
to each student’s personal profile the system chooses
which parts of the theory are appropriate for the
student’s learning level as well as the difficulty of
each test.
Figures 6 and 7 illustrate snapshots of the
operating educational application, where a student is
completing a “fill in the gaps” exercise and taking
the system’s feedback. More specifically, in figure 6
we may see a student who has to fill in the gaps with
the right word. The questions appear randomly and
are adapted to each student model. It is quite
important to note that the student must have
acquired the knowledge offered by all the lessons as
prerequisites.
Figure 7 illustrates a categorization to a student’s
specific errors. The student can be evaluated and
check where s/he is wrong and what type of mistake
s/he has made. The different colours indicate
different type of errors:
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133

 The red colour in the field means error in articles
or pronouns.
 The green colour means a verb mistake.
 The yellow colour means a spelling mistake.
 The blue colour means confusion with the French
language, while the purple means confusion with the
English language.
 Finally, the grey colour indicates an unanswered
question.
Figure 5: Snapshot of a user editing his/her profile.
Figure 6: A student is taking a test.
Figure 7: A student is viewing his/her errors.
At the same time, the system shows the grades of
the students, along with the exact number of the
errors in each category. The overall interaction
through a mobile device is as much user friendly as
possible in order to achieve high interactivity with
regards to the limited functionalities and technical
specifications of mobile devices in comparison with
personal computers. To this end, part of the most
“demanding” processing of data is carried through
the system’s main server and then transferred to the
mobile device using web services.
5 CONCLUSIONS AND FUTURE
WORK
M-MALL is a multilingual mobile educational
application which combines attractiveness and user-
friendliness that usual desktop applications provide
with the well-known advantages of mobile learning.
It is not only a post-desktop model of human-
computer interaction in which students can
“naturally” interact with the system in order to get
used to electronically supported computer-based
learning, but it promotes the m-mall in a platform
where the student interacts with his/her mobile
phone. In particular, the system incorporates the
student modeling component for each user and
performs error diagnosis. Moreover, the system
keeps each student’s error history in one language
that is already taught and then provides advice in the
tests of the other languages. In order to perform
error diagnosis, the system bears a detailed
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categorization of common student’s mistakes. The
error diagnosis process of the m-MALL system is
especially focused on errors due to confusion of the
other languages of the system, if the student learns
more than one language at the same time.
Furthermore, apart from the friendliness of the user
interface, the system is oriented to offer adaptivity
and dynamic individualization to each user that
interacts with the educational application. All the
available data that are used both for the domain of
knowledge and for the student model are stored and
processed in a server and transferred to each
student’s mobile device through the use of web
services.
It is in our future plans to evaluate m-MALL in
order to examine the degree of its usefulness as an
educational tool for teachers, as well as the degree of
usefulness and user-friendliness for the people who
are going to use the educational system. We are also
planning to extend the functionalities of our system
by incorporating an authoring component into the m-
MALL system in order to help teachers author their
Multilanguage learning lessons. This incorporation
will give the facility to teachers with limited
computer skills to author important modules of the
educational system in a short time and with less
effort.
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