The rapid evolution of mobile and wireless technologies has created a new dimension of modern people's lifestyles; it facilitates their daily activities and summaries distances between them, and allowed them to do several tasks whenever they want and wherever they go. When these technologies started to be used in conjunction with learning a new paradigm has been emerged, it's about mobile learning. Since its emergence it has been raised a lot of attention by researchers whose attempt to propose approaches that address limitations of mobile learning environment. A promising technology which can reduce most of these limits is used in this paper which is mobile agent technology. This paper seeks to provide an agent-based approach for mobile learning systems using jade-LEAP platform.
Mobile learning has emerged as an "anytime anywhere learning". Therefore, learning content and services must be always available and delivered to the learner whenever he wants and wherever he goes. However, mobile learning environment has a number of constraints which may hinder mobile learning applications designers to reach this potential. These constraints are related to the limitations of the mobile devices themselves which have reduced processing power, low memory capability, limited battery life and display capability. However, these limitations are reduced at present, since the exponential growth of mobile devices and adoption of the computer capabilities in those devices. Other limitations are related to the wireless networks which have high latency and transmission delays, and low bandwidth especially with considerable number of users, as a result the size of data exchanged should be optimized. Moreover, wireless link may not be available in permanent way, in addition to the expensive and fragile network connections which creates problems for services designed to operate with fast and reliable and continuously open connection. The other side, mobile agents are a promising solution that can reduce problems mentioned above; furthermore they facilitate introducing automatic and dynamically adaptive learning methods. Thus, we propose an agent based approach for an e ective mobile learning systems using jade-LEAP platform. The remainder of this paper is organized as follows. First, we present an overview of jade-LEAP platform. Second, we describe in detail our proposal. Finally, our conclusion and future work is given. 2
JADE-LEAP (Lightweight and Extensible Agent Platform) is an extension of
JADE platform that can be deployed not only on PCs and servers, but also
on lightweight resource devices such as Java enabled mobile phones. In order
to achieve this, JADE-LEAP can be shaped in di erent ways corresponding to
the two con gurations of the Java Micro Edition and the Android Dalvik Java
Virtual Machine: [
These versions provide the same APIs to developers thus o ering a homogeneous
layer over a diversity of devices and types of network, except the midp's version
which have some unsupported features compared with the other versions of
jadeLEAP. [
This execution mode is very useful for our work because it use less memory and need less processing power on the mobile device, since the Front-End is de nitely more lightweight than a complete container. Furthermore, it allows us to let the intensive processing tasks to the remote server and let the mobile device. It has the advantage of minimizing the bandwidth and optimizes wireless connection to the main container, since all communications with the Main container required to join the platform are performer by the Back End and therefore they are not carried out over the wireless link. Thus, the bootstrap phase is much faster. In our work we attempt to implement the Jade-LEAP in mobile learning environment and bene t with the advantages of the split execution mode mentioned above, which addresses some limits of the mobile learning environment such as: low bandwidth.
There are several multi-agent platforms for mobile devices such as The
MobiAgent [
We are proposing a multi-agent architecture for implementing mobile learning system which supports context-awareness and adaptive learning content using jade-LEAP platform. In our proposal we used agents to bene t of their advantages such as autonomous, reactive, proactive and social. The other side, we need to reduce wireless network problem by the use of mobile agents through the wireless connections to the mobile devices. The detailed description of these agents is articulated below: 1. Interface Agent: it is a stationary agent which have several tasks: { It performs the authentication of the new learner, and checks user authorization by verifying the password. { It acts as a communication point between learners' devices and the system. { Send requests to Jade-LEAP platform to create and send mobile agents to the learner device. { It informs the Supervisor Agent to update or store information concerning the learner pro le. 2. Sensor Agent: we called it sensor because it sense the learning environment and react accordingly to changes. This mobile agent has a role of monitoring and tracking the learner in his learning process and save his behavior and relevant data about it.
{ Send information about the device's features (memory size, processing power, available connectivity, communication costs, bandwidth, and battery level) to be saved in the context device features database. { Send observation about the learner; the duration of learning a course, concentration level (how often he interrupted by an external event such as a call or a message, navigation behavior, etc), how often he check the help page, duration between two connection to the system, and then send a report to the system when the student is disconnected. { Save the current learner location and send a request to the system contains the current learner location when the learner changes it to update context data base and to adapt course content to the user location. 3. Tutor Agent: A mobile agent that manages the course delivery to the remote learner. The main tasks of the tutor agent are: { Carry and manage the adaptive course material based on the learning style of the student. { It saves the pause point of the learner when he logout, and start from this point when learner login. { It insures the display of services and learning content according to the user preferences and device capabilities, in collaboration with the sensor agent. { Bring the test content to the learner and retrieve his answers to the adaptation module which calculate and send him his note. 4. Context-aware Agent: Context-aware Adaptation Agent consists of Context analyzer module and context adaptation module. Context analyzer module charges of analyze the information sent by the sensor agent and lter it to extract data related to the context, it receives periodically data from sensor agent, then it models this data and classify it according to its priority to be treated e ectively by the context adaptation module, it send user pro le information and context information to the supervisor agent who associates it to the context features and to the learner pro le.
Context adaptation module use the information retrieved by context
analyzer module and apply it. For example, if the user has a limited bandwidth
connection, then we must reduce multimedia content, and in the worse case
we can replace it with text. On the basis of the present context, context
adaptation agent predicts the future context and performs appropriate
activity. For the previous example, it will transmit only data with small size.
Finally, context adaptation module transmits context into adaptation
module via the supervisor agent, which in turn save the learning context and
incorporates it with adaptable learning content.
5. Supervisor Agent: It is a supervisor agent which has the role of monitoring
the functionality of the system. It considered as a mediator between the
system modules and it coordinate between them. It is the only agent who
has the ability to change and update data in the learning object repositories
(context features, learner pro les), with the help of interface agent which
request it to create a new learner pro le and informs it about data changed
in the learner context.
6. Adaptation Agent: Since learners have di erent learning styles and devices
have di erent characteristics, it has been necessary of personalized learning
content. This task is realized by the adaptation agent, which consists of two
modules; learning styles adaptation module and learning content adaptation
module. These two modules coordinate between them, that is, learning style
adaptation module matches the appropriate learning objects according to
the learner style to be chosen later by the learning content adaptation
module who manages the knowledge about courses and teaching strategies, and
packaging the course material and tests according to the user pro le and
device pro le.
7. J2ME Application: The Java 2 Micro Edition was, at the time, quickly
becoming a de facto standard to develop mobile client-based applications [
In this paper we have described our proposed context-aware and adaptive learning system for Mobile Learning using mobile agent technology, which considered as promising solution in mobile learning systems, it may facilitate introducing automatic and dynamically adaptive learning for e ective mobile learning systems. We are currently designing the system prototype which will be implemented using JADE-LEAP platform.