Lecture Notes in Computer Science- P109 ppt

F. Li et al. (Eds.): ICWL 2008, LNCS 5145, pp. 529–540, 2008. © Springer-Verlag Berlin Heidelberg 2008 A Semiautomatic Content Adaptation Authoring Tool for Mobile Learning Hsuan-Pu Chang 1 , Chun-Chia Wang 2 , Timothy K. Shih 1 , Louis R. Chao 1 , Shu-Wei Yeh 1 , and Chen-Yu Lee 3 1 Department of Computer Science and Information Engineering, Tamkang University, Taiwan 2 Department of Information Management, Technology and Science Institute of Northern Taiwan, Taiwan 3 Digital Education Institute, Institute for Information Industry, Taiwan musicbubu@gmail.com Abstract. A lot of studies about automatic content adaptation have been done and are proposed to overcome the drawbacks of browsing regular content with handheld devices such as pocket PCs and smartphones. But we argue that the total automatic adaptation algorithm designed by an engineer to transform Web Page presentation is still appropriate to be applied on educational content. Therefore, this paper proposes a learning content adaptation tool that provides different adaptation templates to help the author automatically and efficiently reproduce high-quality learning content for specific handhelds. Furthermore, the author will not only be able to preview the adaptation result before publish- ing the course but also be able to adjust the template parameters manually to af- fect the process if they are not satisfied with the current result. Finally the new adapted content can be packaged with original content as a multi-version learn- ing course. 1 Introduction The need to adapt content for use on handhelds has been long recognized 34, and both manual and automatic approaches to implement the content adaptation have been pro- posed. This research 5[6]79 mostly focused on adapting normal Web Pages such as commercial web sites or portal sites. There have been a lot of automatic approaches designed to provide a real time content adaptation system for browsing Web Pages on handhelds. On the other hand, manual adaptation techniques, such as WAP89, have high cost for data producers who are required to maintain multiple versions of the content. We believe that automatic and manual mechanisms are equally important for adapt- ing learning content. Adopting the total automatic approach for adapting content with- out supervision by educators or authors may lead to the adapted result becomes unpre- dictable and unexpected even results possibly lose its educational essence. Contrarily, requiring a teacher to manually reproduce and maintain courses for a variety of mobile learning platforms is not intelligent. Because it does exist, an optimal adaptation ap- proach that can perfectly satisfy various adaptation requirements, as well as existing learning material should never be adapted due to their educational characteristics or 530 H P. Chang et al. author’s will. So, the main purposes of our project 1 are specifically listed as follows, (1) we propose a web-based content adaptation tool that uses templates to automatically and efficiently adapt content for mobile learning; (2) through adjusting the template’s pa- rameters, users can easily manipulate the process and change the result if they are dis- satisfied; (3) the tool allows user to preview the adapted content and decide whether it is appropriate to read on handhelds. 2 Related Works The proposed authoring tool allows authors to produce the adapted content for the sake of appropriately displaying it on specific mobile learning platforms. After finish- ing the editing phase, the author has alternative way to package the new adapted con- tent: creating another totally independent course package or packaging the adapted content associated with the original content package. Despite what kinds of packaging way to handle the adapted content, the package formats are following the international e-learning standard: ADL SCORM 1 and IMS Common Cartridge 2. 2.1 IMS Common Cartridge Package File Structure The diagram in figure 1 shows the overall layout of the common cartridge package interchange file structure. z imsmanifest.xml The standard IMS manifest file that is a mandatory XML file describing the package itself. In the absence of this file, the package is not an IMS package Fig. 1. Common Cartridge package interchange file 1 This paper is supported by the Digital education Institute, Institute for Information Industry, Taiwan , R.O.C., under grant number #96-EC-17-A-02-R7-0808. A Semiautomatic Content Adaptation Authoring Tool for Mobile Learning 531 and cannot be processed. It is required the name be kept, as above, in all lowercase letters. z Learning Application Object Resources These are resources that describe the attributes of a particular Learning Ap- plication Object. Examples include SCORM packages, QTI files and discus- sions top descriptors. The information will generally be parsed on input and transformed into internal data structures in the Learning Management System (LMS). z Web Content Resources These represent standard web content types such as HTML files, images, movies etc. Two main scopes for web content resources are supported: Car- tridge scoped web content and Learning Application Object scoped web con- tent. These web content resources can be organized into directories in the package interchange file and the directories will be included in the importing LMS to ensure relative links between web content continue to work. z Using Directory in Package Interchange File File system directory can be used to organize content within the package in- terchange file. It is required that the resources specific to a given Learning Application Object are packaged in a distinct directory in the package inter- change file. 2.2 Related Literatures Adaptation is a well-studied topic in mobile and pervasive computing for years [10][11]. Hwang et al. proposed a transcoding framework [12] that represents a Web page as a modified tree structure to efficiently analyze and transcode pages. It is based on html syntax analysis and structure-aware techniques that intend to make complex Web pages accessible and reflect the relative importance of Web components during the transcoding process. The design guidelines for such PDA devices are also introduced and discussed in [13]. These guidelines can be classified according to which aspect of the Web me- dia they are related: software/hardware, content and its organization, or aesthetic and layout. On the other hand, refer to textual web content summarization [14], the methods for summarizing are introduced to handle the textual Web pages and HTML forms. A Web page is separated into text units that can each be hidden or partially displayed. Six different display modes are introduced that utilizes the progressive displaying textual units with keyword extraction and paragraph summarization to gain an over- view of a page. They found that the combination of keywords and summaries pro- vides the most significant improvements in access time and number of required pen actions. Usage-AwaRe Interactive Content Adaptation (UARICA) and Feedback-driven Context Selection (FCS) [15] made adaptation prediction for a user based on the his- tory of the community of users and reflect both the user’s context and content’s usage semantics. Iqbal et al. think optimal adaptation is a challenging problem because it often depends on the usage semantic of content, as well as the context of users 532 H P. Chang et al. (e.g., screen size of device being used, network connectivity, location, etc.) Their works included an automatic techniques, UARICA that allows a user who is unsatis- fied with the adaptation prediction to take control of the adaptation process and make changes until the content is suitably adapted for his/her purpose. Moreover, FCS takes advantages of user interaction to determine those contextual characteristics that have the most impact on the adaptation requirements of an object, and therefore should be the basis of grouping users into communities. 3 Proposed Learning Content Adaptation Models Our proposed adaptation framework is composed of three main adaptation models. First, the textual adaptation model is responsible for handling the complicated textual body that may make users feel confused or lost while reading on a restricted small screen. Precisely speaking, our content adaptation tool will summarize textual body and utilize progressive disclosure presentation to revel the original content. We refer- enced Buyukkokten’s [14] progressive disclosure for text, it combined with keywords and a summary help present the original content incrementally, has the best improve- ment of average I/O expenditure and completion time across all tasks they had experimented. Second, the image adaptation model takes into account the requirement for adapt- ing image size when displaying it on a handheld. Briefly speaking, an image will be automatically shrunk if it is too big to display on a handheld, as well as expanded if it is too small. Finally, the layout adaptation model is able to reorganize the layout of adapted elements properly according to the display ability of different handheld de- vices. But there are a few questions associated with previous descriptions. Exactly which textual body is needed to be summarized? How do we evaluate and decide whether a picture is required to be shrunk or expanded? Consequently, before we continue to detail each adaptation module, the presentation unit (PU) and screen unit (SU) will first need to be introduced. 3.1 Screen Unit (SU) and Presentation Unit (PU) The adaptation process begins by partitioning the content into presentation unites (PUs). A content page will be separated into several PUs, which instead of presenting in the actual HTML, each PU is a rectangle around a section which typically presents a paragraph, list, table, image, etc. Accordingly, each PU is considered as a basic unit of the adaptation process. Because each PU contains various contents, the question is which SU should execute the adaptation process. We will define the other unit, namely screen unit (SU), that helps us to evaluate and decide which PU is required to be adapted. Figure 2 shows the display area size of a PU is varies because it may contain a paragraph or an image. On the contrary, a SU is a virtual rectangle presentation block where the boundary is fixed according to different handhelds displaying ability. Pre- cisely speaking, the size of a SU is matched to correspond to the screen sizes of hand- helds. For example, pocket PC’s and Smartphone’s in which the typical resolution are 240*320 and 176*220 respectively. A Semiautomatic Content Adaptation Authoring Tool for Mobile Learning 533 Fig. 2. Dividing a html-based content page into several PUs The content of each PU will be retrieved then filled into each SU. The main con- cept is that it does not need to adapt a text or image within a PU if it can be entirely displayed within a single SU without additional scrolling. Mathematically, we evalu- ated whether a PU is required to be adapted with a simple formula that calculates a threshold value. We defined a value, textual information density (TID) as follows: TID = number of words in a PU / area of a SU (1) The area of a SU is constant according to which adapted target platform is required by a user. The default value of TID is allowing a PU to present the maximum number of words without additional scrolling. Users are also allowed to adjust the TID, which will affect which PU is required to be adapted. For example, a larger TID allows a good deal of textual information located in a PU without any summarization so that the user may need more necessary scrolling actions for reading. 3.2 Textual Adaptation Model This need for frequent scrolling can seriously degrade the learning efficiency and performance. Providing a simplified overview is another important adaptation guide- line as well. Therefore, for text summarization, we referenced Buyukkokten’s approach to ex- tract keywords from web pages. Their content adaptation approach utilizes keywords and summary sentences to partially represent the original text, then disclose informa- tion progressively as figure 3.1 shows. The main difference is that our adaptation unit is a PU and only when its TID is higher than the chosen threshold value will it trigger the adaptation process. Next, the details of how to extract keywords and summary sentences will be introduced. 3.2.1 Keyword Extraction Keyword extraction from a text body relies on an evaluation of each word’s impor- tance. The importance of a word W is dependent on how often it occurs within the body of text, and how often the word occurs within a larger collection that the text is part of. Intuitively, a word in given text will be considered as the most important one . Learning Application Object scoped web con- tent. These web content resources can be organized into directories in the package interchange file and the directories will be included in the importing. ensure relative links between web content continue to work. z Using Directory in Package Interchange File File system directory can be used to organize content within the package in- terchange. Learning Application Object are packaged in a distinct directory in the package inter- change file. 2.2 Related Literatures Adaptation is a well-studied topic in mobile and pervasive computing