Environment Model for Adaptive e-Learning

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The development of Internet and Network enables people to access and get information easier and faster. There are a lot of devices provided for manipulating information, such as desktop, laptop, Pocket PC, and mobile phone. Each device plays a different role depending on individual  needs. Mobile phone is a personalised device, which can be  easily used anywhere to communicate with others, but cannot handle a high  speed and large size of data, while laptop with a bigger size and high  performance, can apply for a high speed and high quality content.  Learning, the process of transferring information from one to others has a long history. E-learning is a type of distance learning where learning  content and data are sent through the Internet. With the combination  between learning and device, one can say we come closer to achieve learning anywhere and anytime.  Adaptive e-learning tries to analyse system to match with individual  needs. It collects learner history, behaviour, etc to construct learner  model and make the system automatically adapt to support user’s need. There is, however, another  approach that does not concentrate  on learner model. It tries to realise the important issue based on digital divide. In Thailand, there is very big  gap between students in urban and rural in accessing Internet, and computer performance. Based on these infrastructures, one issue  becomes important which is how to develop and analyse the appropriate 

Adaptive learning is considered as a learner-centered model. There are a lot of approaches that try to adapt the system based on  earner history, behavior, activity and so on. However, very few researches concentrate on different environment, for example, speed of Internet, and connecting devices. The article mainly considers on the adaptive system based on the environment of each learner. The research designs the system to automatically detect the environment for each learner and transfer the suitable content for his/her environment. It introduces a process on choosing type and format of data for e-Learning system according to the environment. 


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content for each learner’s environment, which ultimately forms the “adaptive environment model”. A system has been designed in this research to  utomatically detect the environment for each learner and  transfer the suitable content for his/ her environment. A process has been  introduced on choosing type and format of data for e-Learning system  according to the student’s environment.  

System Architecture
n order to automatically detect the  environment for each learner and transfer the suitable content for their environments, it is necessary to design the  rchitecture by considering both client and server  side. In the client side, an appropriate LMS will be installed and executed  based on device. There are two main  modules, which are necessary to exist in LMS, Information Requesting Module and Information Executing  Module. Information Requesting Module will receive a request for information from a user and send to server.  nformation  Executing Module will apply the information received from server and execute  it to  user. It is  normally a  part of  LMS. In the  server side, there are three main modules,  Environment Detecting  Module, Infor-mation Selecting Module and Information  Transferring Module. Environment Detecting Module detects  environment for each user. Based on  the  nvironment information, Information  Selecting Module will analyse and select the most appropriate content in the server which matched  with the needs of user. Finally the  content will be transferred to client in Information  ransferring Module.  The article concentrates mainly on Adaptive Environment Module, which is the module that assists the server to send the appropriate information based on the environment.  Adaptive Environment Module  The process on detecting environment is explained here. The server starts with  detecting connec-tion speed. The speed is  detected by calculating average time  per a prepared data. A  data  is  sent  and  the  starting  time and finishing time for calculating  speed connection is  chec-ked there by. This method will enable the server to detect the  speed based on the average size of content. After that, server detects the user’s opera-ting system  and web browser. Finally, it detects the supported file types. The existing  command is applied in PHP for  detecting operating system, web browser and supported file types.  The server classify content into three groups, text-only, picture-and-text,  and full-multimedia. The server detects user environment and match with the three types of content based  on environment information. The server applies the following criteria  for selecting content. 1) Regarding the  onnection speed,  Internet connection could be in many ways such as dial-up  modem, broadband, Wi-Fi, satellite and cell phone via GPRS  and EDGE etc. Range of the connection speed is wide. The  rate is up to connection type ranged from   6Kbit/s-maximum  speed of dial-up modem connection to 160-Gbit/s highest     eed broadband. If a server detects low connection speed,  below 60   bit/second, text-only will be assigned for the users. If a  server detects medium connection speed, at 61-200  Kbits/second, picture-and-text will be assigned for the users. If a  server detects high connection speed, more than 200 Kbits / second, full-multimedia connect will be assigned for the users.   2) Regarding  the device, if a server detects mobile phone device,  picture-and-text will be assigned for the users. Otherwise, fullmultimedia  connect will be  assigned for the users.  3) Regarding the web browser, Mozilla and Firefox cannot fully   support JavaScript, some tags type, such as DHTML, while Internet explore can support this. Moreover the supported plug-in  for each web browser is  ifferent.  After the server knows the environment, the server selects  appropriate learning content from  information stored in an XML file. In  XML file, learning contents are represented in tag format. Each part of  the content refer to three learning  bject for each user group. The system choose  ppropriate learning  object according to the detected learning environment and then send  the learning object to the user.  

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