providing personalized learning content to mobile users

IADIS International Conference Mobile Learning 2010

PROVIDING PERSONALIZED LEARNING CONTENT TO MOBILE USERS Glaroudis Dimitrios, Manitsaris Athanasios and Kotini Isabella Department of Applied Informatics University of Macedonia

ABSTRACT The continuously increasing demand for enhanced remote and mobile services render as essential the adaptation of educational material in these requirements. Although educational web sites are used as supporting learning tools for students, there is still weakness to easily incorporate current learning services for mobile users. The main topic addressed in this paper is to propose an architecture aiming to reduce mobile users’ effort when navigating in the educational portal and drive them to desired educational material, while gaining knowledge without spatial and temporal constraints. After tracking mobile users’ preferences from preceding log records while visiting the portal and exploiting the semantics of the learning content, an online system recommends the LMS’ web pages with similar conceptual content. KEYWORDS Mobile browsing, personalization, recommendations

1. INTRODUCTION The evolution of Internet and multimedia services has lead to interesting applications in education. A significant number of institutions support direct network learning and provide the educational material such as a formal educational environment. However, knowledge acquisition presupposes the physical presence of learners in predefined places at specific times, along with the existence of computers and Internet access. Thus, being obliged to participate in an educational activity or owning a computer with a reliable Internet connection can probably be important restrictions for learners. The scenario of educators who cannot always attend the conducted lectures in the campus or access the educational content through a desktop computer motivated us to propose a system architecture providing personalized learning content to mobile learners. Mobile learning gains attention by researchers as mobile communication technologies can overcome time or territorial restrictions, preparing learners for more flexible training environments. Trifonova [11] defined mobile learning as e-learning that can be transmitted via portable appliances, and more concretely via each appliance that has small size, is autonomous, accompanies the user each moment and can be used as a mean of transmitting or accepting educational material. Nevertheless, mobile devices have significant restraints when they are used for learning purposes. In his research Sharples [9] named some of them; small screens, significantly browsing latency, lack of continuous Internet connection, and weakness to easily incorporate current learning services for desktop PC. These limitations have high impact on mobile Internet users’ browsing behavior and such users seem to suffer more severely from the problem of undesired outcomes than stationary Internet users do. While the limited screen size forces most mobile browsers to support a linebased navigation, researchers denote that page-to-page navigation is very costly when browsing in general [4] and they advise web developers to reduce the amount of page-to-page navigation. The main solution is to provide a quick way for users to navigate through small screen web pages. Present browsing methods for mobile devices are classified into three main categories [3]: presentation optimization, semantic conversion, and scalable (zooming) methods. Buyukkokten et al. [2] present ideas of extracting semantics from the Web text, yet greatly shortening the length of text. Each text page is broken into a number of text units that can be hidden, partially displayed, fully visible, or summarized. Introducing an approach to the personalization-based optimization of Web interfaces for mobile devices, Hinz et al. [5] uses the ideas of structure adaptation and content adaptation to realize adaptive intelligent user interfaces.

255

ISBN: 978-972-8924-99-7 © 2010 IADIS

Still, for more effective personalization, Mobasher et al. [7] proposed content characteristics and navigation data to be integrated into a Web mining framework and used by machine recommendations uniformly. Besides the fact that recommendations are critical to the success of large websites, there are many ways to define them and the quality of recommenders depends on many factors that are still unknown. As happens in some of the above mentioned works, content’s semantic conversion and personalization are proposed in our approach in order to improve the learners’ browsing usability. However, the recommendation factors and measurement of their quality, which is correlated with the user’s navigational patterns, is also defined. The proposed architecture makes use of both the usage and the educational content data in order to personalize its presentation to mobile users. The innovative contribution of this work lies in identifying mobile users’ preferences when visiting an educational portal, providing feedback to a real time system which recommends the portal’s web pages with similar conceptual content.

2. PROPOSED ARCHITECTURE Since mobile learners do not share the same privileges of qualitative browsing usability as formal students do, the critical point in mobile learning applications is how the knowledge can be easily acquired. Based on the conceptual analysis of the learning content and the users’ navigation history in the portal, our approach aims to enhance mobile users’ web experience by providing personalized educational data to them. If a system relies solely on usage-based records, then valuable information conceptually related to what is finally recommended may be lost. However, after defining and modeling the semantics of the portal and simultaneously collecting mobile users’ navigation patterns, a knowledge system can then recommend personalized and desired knowledge to mobile learners. Therefore, the overall system can analyze the information in web pages visited by the user, locate pages that have similar information and propose their links to the user.

2.1 System Description The system’s architecture is divided in four main phases: mobile user identification, educational content mining, knowledge system development and link suggestion (Figure 1).

Figure 1. System architecture

As users visit the web pages of the portal, their navigational data is logged in the server’s log file that resides in the web server supporting the learning management system. However, a prime concern is to track only the so called useful data, which concern the actual web links of the pages that are being accessed, the IP addresses and the kind of clients’ web browsers, and the request times. Τhe server log file is addressed to an application server that performs data filtering using a Java NetBeans environment, which is an open source tool. After the initial data is filtered, the mobile user’s identification is established. In our approach, each user is defined by his IP address and the browser he uses. We consider only IP addresses and browsers that refer to mobile devices. This mode can significantly improve the speed and reliability of our method. Then, the

256

IADIS International Conference Mobile Learning 2010

identification of mobile learners’ navigational patterns is established in the application server by the NetBeans environment. Each user’s filtered data are analyzed and a session identification technique is used so as to reveal his navigational pattern. User session is considered to be a sequence of requests coming from the same user and divided by a predefined period of time (timeout). Finally, each user’s data, serving as the user’s profile, are aggregated and form a knowledge base stored in the database server. The following task is the semantic enrichment of the learning content. Prior to applying semantic conversion techniques, Java Net Beans functions, running in the application server, is used for content preprocessing. These functions immediately parse all the pages in the educational portal and capture the learning content to produce its local copy. This step results in faster and easier content processing. Next, knowledge mining techniques are applied. Knowledge mining uses various recovery information methods (text mining, link mining, screen data selection or removal [6]), without preserving the original web page structure. The choice of a mining method is directly related to the analysis and modeling tool of the resulted knowledge. A text mining method for knowledge extraction has been chosen, as there are many open source tools for ontology creation that easily and efficiently cooperate with simple text files. Only the text information from each web page is extracted and the final information is formatted as a text string. Eventually, every page in the portal is represented by an extracted text record, stored in the database server. After retrieving the extracted text data set from the database server, the application server performs automatic text analysis via open source language technology tools for concepts definition. Specifically, a knowledge mining architecture that supports many languages, the GATE tool [8], is used. Gate tool is software architecture for language engineering and supports many file types, including text files. This architecture uses a java annotation engine, which performs linguistic and syntax analysis of the text documents using open source lexicon databases and then creates rules so as to comment the files with tags. The lexicon databases are supported plugins in the tool. Since the text analysis obeys to grammatical and syntactical rules, the extracted data are separated and grouped according to their meaning. Then the tool annotates and tags the data using XML, resulting to a semantic correlation between the produced content information, and leading to concepts definition. The emerging concepts from each web page are recorded and stored in the database server. The proposed architecture is based on conceptual analysis of the learning material, and an ontology which defines and models the concepts of the educational portal is developed. Since developing ontologies from scratch is a difficult task, automated production of the ontology using open source tools has been chosen. Open source tools (OntoLearn [1], TexttoOnto [10]) support users to develop ontologies from specific data sets, especially from text files. All these tools share the same philosophy of design; concepts definition is the first step, followed by concepts’ recording to a database and then identification of possible relationships between the concepts. The final ontology can be developed by forming the identified relationships as classes with specific attributes. For modeling the concepts and constructing the ontology we use the TexttoOnto ontology tool in our approach, which runs in the application server. TexttoOnto, which works with the Gate tool, retrieves the recorded concepts, and its included machine learning algorithms are applied to distinguish the possible relations among the concepts. The choice of an algorithm (by concept, instance, similarity, subclass of, instance of, relation), defines how the concepts will be related. Then, the tool groups these relations into ontology classes, resulting to the formation of the ontology and the semantically enrichment of the learning content. This approach has the additional advantage that feedback between the knowledge system and the process of concepts definition can be supported. The ontology production tools [8] store indicators for each object in the ontology’s model, revealing the data from which they are emanated and allowing the system to understand why a certain concept or a relation has been created. When changes occur in the text of a particular web page, the system can discover these changes and redefine the concepts or create new ones, without processing all the pages. Then, the ontology can easily be updated, hence leading to increased efficiency of the overall system. After the learning content has been semantically modeled, the evaluation process follows. When the mobile user visits a specific web page in the portal, the knowledge system initially checks which semantic data that page contains. Then, the knowledge system searches for semantic data related to the remaining pages of the educational portal and loads the user’s profile data from the database server. Respectively to the semantic data relationship the system recommends links to pages with similar content. Then, it is critical to present the outcome of the knowledge system, taking into account the limited screen size of mobile devices.

257

ISBN: 978-972-8924-99-7 © 2010 IADIS

Using the basic principles of human computer interaction, a usable mobile application is designed, which presents the suggested links and guide the mobile learner to them with just one click.

2.2 Experimental Paradigm Using the above techniques, tests have been conducted so as to check the initial results of the proposed architecture. ASDL, meaning Asynchronous and Synchronous Distance Learning system, is a learning management system (LMS) developed in our department, which offers a significant solution to the direction of active attendance of each person involved in educational process. Additionally, ASDL offers a degree of personalization since every registered visitor has the ability to modify his learning environment. The learner can enroll to his/her desired modules, form a diary with his learning activities, and use built-in services for sending emails or reports. Nevertheless, when accessing the ASDL’s web content within a mobile device it is obvious that the relatively small pocket pc’s screen size affects the resulting browsing. It is conducted a number of attempts accessing the ASDL’s educational content using a smart phone (HTC Touch Diamond) and a pocket pc (HP iPAQ 110 Classic Handheld). For Internet navigation purposes, these mobile appliances use the Opera mini browser and the Mobile Internet Explorer. In order to access the first lecture presentation of the Signal and Image Processing course, the mobile user registers in the portal, and follows the one-line navigation path Signal and Image Processing→ Course Documents → SLIDES→ Lecture 1. Then, the user continues browsing in ASDL accessing information about other courses in the portal, visiting the Multimedia Systems course and its relevant material. Till user’s later registration in the portal, the system filters the portal’s log file and successfully identifies the mobile user and his navigation behavior in the portal’s pages. When the learner visits the portal again, the knowledge system defines the concepts included in the visited pages and searches for relative semantic data in the portal. Finally, the system provides to the mobile user the same web page as before (“My courses” web page), but now the page contains an additional click button, labeled “User Favorite Links” (Figure 2).

Figure 2. Mobile use: adapted web page

Figure 3. Mobile use: suggested links

System’s recommendations are provided when the mobile user clicks the “User Favorite Links” button. Then, a separate web page is displayed containing a list of suggested links (Figure 3). This page includes hyperlinks relative to the user’s interests, as expressed before by visiting specific web pages of the portal. Consequently, the learner is directed to desired learning material by selecting the appropriate link. In the above conducted test, the system recommended the links for Lecture 1 and Lecture 2 of Signal and Image Processing course, and the link for lecture 1 of Multimedia Systems course. It is clearly that the number of browsing steps for the mobile user is decreased. Initially, the user has to follow a one-line navigation path containing 4 steps for accessing the desired file (Signal and Image Processing→ Course Documents → SLIDES→ Lecture 1). On the contrary, with our approach the navigation path will be User Favorite Links→ Signal and Image Processing Lecture 1 link, resulting to fewer browsing steps, enhanced usability and much less navigational effort.

258

IADIS International Conference Mobile Learning 2010

3. CONCLUSION This work proposes a system architecture aiming to personalize the presentation of educational content for mobile learners and reduce their effort when navigating to the educational portal. Contrary to other studies, the desired goal is neither adapting the site’s structure nor mobile learner’s sole direction in the educational portal. Our aim is to provide specific information to the mobile learner, directly related to his educational interests, decreasing the line-based navigation’s effort and frustration, and, more important, saving browsing time. Using web content semantics and user navigational information, the recommendations are consistent with learners’ preferred educational content, thus resulting to usable browsing. The architecture’s main advantage is that the recommendation factors, usage and educational content data, are defined and their quality can be measured. Furthermore, privacy issues, which are a main concern in the majority of personalization methods, are avoided, since users’ data collection refers only to the specific educational portal. It is equally important that the mobile learner has no obligation other than visiting the portal, while the toil of the site’s administrator remains insignificant. On the other hand, the proposed approach is designed for educational portals that include mainly text information in their web pages. If text information is scarce, the semantic representation and concepts definition will suffer from confusions and the recommendation system is likely to be inaccurate. Additionally, although the initial results are promising, showing that the mobile user spends less navigational effort, further tests have to be conducted for evaluating user usability and overall browsing time when using the proposed architecture.

REFERENCES Buitelaar Paul et al, 2005, “Ontology Learning from Text: Methods, Applications and Evaluation” IOS Press. Buyukkokten O. et al, 2001, “Text Summarization of Web Pages on Handheld Devices”, Proc. Workshop on Automatic Summarization 2001, Pittsburgh, PA. Chen H.M. and P. Mohapatra, 2003, “A Novel Navigation and Transmission Technique for Mobile Handheld Devices”, Technical CSE-2003-1, UCDAVIS. Dunlop M. D. & Davidson, N., 2000,"Visual information seeking on PDA top devices:” Proceedings of BCS HCI 2000, Sunderland, Volume II, pp. 19-20 Hinz M. et al, 2004, “Personalization-Based Optimization of Web Interfaces for Mobile Devices”, Proc. Mobile HCI’2004 – 6th International Symposium, Glasgow, UK, pp 204–215. Michalski R.S. and Kaufman, K.A., 1998, “Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach,” In Machine Learning and Data Mining: Methods and Applications, Michalski, R.S., Bratko, I. and Kubat, M. (eds.), London, John Wiley & Sons, pp. 71-112 Mobasher et al, 2000,"Integrating Web Usage and Content Mining for More Effective Personalization”. Proc. of the First International Conference on Electronic Commerce and Web Technologies, London, pp 165 - 176. Natural Language Processing Sheffield University. GATE Home, http://gate.ac.uk/. Sharples M. 2000, "The Design of Personal Mobile Technologies for Lifelong Learning", Proceedings of Computers and Education, vol. 34, pp 177-193. Text2Onto. http://ontoware.org/projects/text2onto/ Trifonova A., Ronchetti M., 2003, “Where is Mobile Learning Going?”, Proceedings of the World Conference on Elearning in Corporate, Government, Healthcare, & Higher Education , Phoenix, Arizona, USA, pp. 1794-1801.

259