Electronic learning (e-learning) continues to grow rapidly but most e- learning technology involves wired infrastructures.
Mobile learning (m-learning) is “any sort of learning that happens when the learner is not at a fixed, predetermined location, or learning that happens when the learner takes advantage of learning opportunities offered by mobile technologies” .
It is believed that the emerging wireless and mobile networks will provide new applications in m-learning. With the rapid evolution of mobile devices such as PDAs, Table PCs and smart phones, pervasive (or ubiquitous) systems are becoming increasingly popular.
Given the rapid use of mobile technologies for facilitating the learning process anywhere and anytime, learners are able to use idle time, for example, when waiting for public transport, in between lectures, and travelling to and from university. This time can therefore be used more efficiently in terms of learning .
The awareness of learning context is important. A learning system should adapt the learning process in response to context change. The main goal for context-aware mobile learning application is to sense the mobile learner’s situation (environment) and respond to it .
Most current learning contents were designed for use with desktop computers and high-speed network connections. They usually contain rich media data such as image, audio, and video.
The widespread problem in e-learning environments is that they cannot offer personalization for the student and that they can only present identical contents to all the learners. Mobile based education is already reaching a large number of learners and it offers a valuable advantage over traditional teaching with the possibility to adapt to individual learners.
This paper presents an m-learning framework. The main objective of this framework is to provide personalization and tackle adaptation using machine learning technique according to obtained user profiles. These user profiles contain users’ preferences, knowledge, cognitive, goals, plans, place and possibly other relevant aspects that are used to provide personalized adaptations.
Martin designed a system for recommending activities for learners; this process is dependent on the learner’s personal attributes, actions and the current context (location, time, available devices) .
Ogata and Yano designed a context-aware language learning support system for learning Japanese polite expressions .
The MOBIlearn project is an interactive model in which data is collected from sensors, and translated to appropriate services.
In Mobile scaffolding-aid-based bird-watching learning system, an outdoor learning system is proposed, meaning that a learner with higher learning efficiency will gain less support from the system .
Ketamo have implemented an m-learning environment (xTask) that adapts to different user devices (PC, PDA and WAP devices) .
Few of the m-learning researchers have tackled the problems of adaptation of learning tasks and personalization of course content based on students’ models, learning styles and strategies .
These issues have been explored within the traditional web-based systems in numerous well-known projects. ELM-ART is an intelligent learning website environment that supports example based programming .
Museum tour guide is another research used mobile devices personal museum expert which is mainly concerned with user location .
User adaptive systems aim to adapt learning content , location and presentation to each individual user’s characteristics or behavior in order to improve the interaction between users and the system.
The process is based on storing and exploiting information about the user. However, users differ in traits such as skills, aptitudes and preferences for processing information, constructing meaning from information, and applying it to real-world situations.
Modelling the users’ behavior is a fundamental mechanism for providing personalization . Depending on the application, user profiles are generated to store information about user preferences, interests, goals, usage data and interactive behavior.
User preference is an important concept to predict user behaviors and make appropriate adaptation actions. Preferences can be explicitly supplied by the user .
A Personalization Engine is usually employed to infer adaptation actions on the basis of identified user characteristics, for retrieving or filtering appropriate content and adapting the content presentation, and to match the navigation support and the interface attributes to the user needs.
Adaptive system uses the knowledge given by learner models to implement the following tasks :
In order to achieve personalized services, we must be able to specify user interests .
This can be done using a machine learning algorithm, which takes a learner’s information for input, then compares and analyses the learner’s need, interest and environment.
Enriched learner profiles enable the system to select between a number of topics and interests to match with the best learning style for that user.
Creating learner profiles allows for much more accurate results, given a sufficiently expressive keyword. Profiles are derived from a common keywords set.
A learner can indicate his interest in a specific domain or even single concept by specifying a value in a predefined range. Precise information allows the system to more accurately support user decision-making.
These changes include the arrangement of icons and items on the learner’s device screen.
In our system we will make a default interface layout with only limited changes to the interface, for example font colour and size.
By using a default interface layout with limited changes to the device screen, problems arising from frequent changing interfaces will be minimised.
Learner Control over the M-learning Application
Learner Control over the M-learning Application
Learners expect benefits from using the adaptation system such as saving of time and efforts.
A mount of learner control/involvement with the adaptation system must be determined from the outset of system development.
Incorrect action by the system may have serious consequences which will discourage the learner from using the system any more.
After consideration of the implications of level of user control, in our framework the system will have full control over the adaptations process with only one option for the learner to disable the entire process.
This decision is made for two reasons:
to give the learner full control over the system assumes that the learner has a certain level of knowledge about the system (which not everyone has); and
This paper has presented a new framework that depicts the process of adapting learning content to satisfy individual learner characteristics by taking into consideration his/her learning style.
We have described the system architecture of our context adaptation based user profile framework and learning style adaptation which is fundamentally grounded on a number of logical layers: context acquisition , information classification , learner model , information extraction , learner profile representation , reasoning and interface .
It is a generic framework for selecting the appropriate learning style for learners based on their learner preferences and contextual features.
The implementation of the system is currently in progress and the effectiveness of the system will be evaluated both quantitatively and qualitatively, using a series of simulations and a small number of human users to work with our system.
We will also extend our tool for incorporating other types of learning objects and/or materials.
The ultimate goal of the framework is to provide a logical structure for the process of adapting learning content to satisfy individual learner characteristics by taking into consideration his/her learning style.
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