A Digital Reference Room for E-learning

Transcription

1 A Digital Reference Room for E-learning Ru Lu, Feng Shang, Yuichi Goto, and Jingde Cheng Department of Information and Computer Science, Saitama University Saitama, , Japan {luru, frank, gotoh, Abstract This paper addresses Digital Reference Room (DRR) for e-learning, a project that deals with two hot fields of studies for both IT industry and academic institution: digital library and e-learning. Our basic design ideas are to develop a user-friendly digital reference room utilizing a simple but effective architecture, taking advantage of XML and making efficient use of available open source offering. Hence, we adopted native XML database technology instead of traditional RDBMS (Relational Database Management Systems), and Apache's open source offering, Xindice as tools to develop the DRR. This paper describes the architecture and implementation of the digital reference room system for e-learning. Keyword: digital reference room, digital library, e-learning, native XML database 1. Introduction Digital library and e-learning have been two hot separate research and development topics in recent years all over the world. Standard organizations such as ACM (Association for Computing Machinery), DLF (Digital Library Federation) and IEEE (Institute of Electrical and Electronics Engineers), IMS (Global Learning Consortium, Inc.) and ADL (the Advance Distributed Learning Initiative, sponsored by the OSD (Office of the Secretary of Defense of USA)) have published several well known specifications for digital library and e-learning either independently or cooperatively [5, 6, 7]. A digital library means an intentional collection of digital resources assembled, catalogued, indexed, preserved, and presented to serve the needs of people to access it through Internet whenever they want to. E-learning is instructional content or learning experiences delivered or enabled by electronic technologies, and has increasingly come to mean Web-enabled material deployed using the network. Digital library and e-learning system have come to enable people to access and use as a tool or virtual classroom for lifelong learning and study what they want to learn anywhere and anytime. IMS [7] has been developing and promoting open specifications for facilitating online distributed learning activities such as locating and using educational content, tracking learning progress, reporting learning performance and exchanging student records between administrative systems. XML-based specifications supporting learning technologies have been developed by the SCORM initiative and distributed through the ADL (Advanced Distributed Learning) initiative s network [5]. There are two key mechanisms in SCORM, one is the content aggregation model and the other is runtime environment. SCORM provides specifications for building a unified content model and a run-time environment, enable courseware sharing and multiplatform (cross-platform) learning. There is a tendency for the two fields to merge to cope with ever-increasing demands for lifelong learning. Digital library can be considered to be a core part to store and provide contents for e-learning. However, experiences have shown that the production of rich-media contents for web-based courses tends to be extremely cost and time consuming. Therefore, our initial motivation is to develop an efficient digital reference room useful in any e- learning environment for teachers/instructors or their assistants to store their prepared learning contents and for the students/learners to retrieve/extract what they want to learn. Our basic design goal is to develop a generalpurpose, user-friendly digital reference room with a simple but effective architecture. So we adopted native XML database technology and open source offering Xindice after thorough investigation. This paper describes the architecture and implementation for the digital reference room system for e-learning environment, for example, HILBERT system [1], an autonomous evolutionary information system for teaching and learning various logic systems underlying diverse reasoning forms in scientific discovery as well as everyday logical thinking. 2. Requirements for the DRR The DRR under developing should satisfy the following requirements: It should be Multi-OS enabled so that the DRR can be used on major operating systems such as unix, Microsoft Windows, Apple Mac OS and so on.

2 It should provide appropriate GUI (Graphic User Interface) for three types of users: DRR administrators, instructors or teachers, and learners or students. It should provide catalog and index function for all the contents (learning contents or reference material) in the DRR; and once a new content is added to the DRR, the catalog and index should be updated correspondingly. It should provide the administrators with the capability to allow the instructors to upload, modify and update the learning contents or carry out the functions by administrators themselves. It should provide the instructors the right to upload and update the e-learning contents, the right to access the records and achievements and homework of each student and supervise interfaces to the student. It should provide the authenticated students the interface to access the contents available in the DRR, allow the students to select what they want to learn, update the user profile once a student finish a course, and access the scores of each of his/her progress and the comments and advices given by the instructors/teachers. It should provide appropriate interface with LMS (the learning managing system) and UMS (user managing system) so that only those authenticated users are allowed to use the system. EnCal IA Instruction/data buffer User LKB Otter HOL ACL Me R Mo C/S WS UDB LRDB LPDB LCDB FT MR EE Figure 1. A reconfigurable architecture of HILBERT 3. Basic Design Ideas Illustrated in Fig.1 is the reconfigurable architecture of HILBERT [1], where at the bottom layer, there are 4 databases, namely user database UDB, logic reference database LRDB, logic puzzle database LPDB and logic course database LCDB. We would like to integrate the four databases into the digital reference room What to store? The main objects to be stored are contents for e- learning, for example, digital contents of lesson materials, references etc., such as the references logic puzzles and answers for HILBERT. User data such as User profile, ID, password, personal preference, learning achievements (scores and assessment provided by teachers or instructors after each leaning session) are another kind of important data to be stored. For e-learning system, users can be classified into four groups, contents providers, administrators, instructors/teachers, learners/students. For the reference room part, the latter three kind user groups are taken into account. Learning contents log which is created when a new learning content is added to the DRR and every time a content is updated. If the contents are prepared in XML, they can be dealt with X-Collection or X-Update. However, many current available contents for e-learning were not produced in latest XML format. Instead they are available in traditional formats such as plain text (.txt or.tex),still image (.jpg,.gif), Microsoft power point (.ppt) and video (.mpeg,.wav, wmz,.mp3). Of course, these traditional learning materials are valuable asset so we also store them as learning resources, if they can not be managed by XML, we planed to use adaptors make them native XML enabled. Why XML technology? The Extensible Markup Language (XML) is a subset of SGML that is completely described in document. Simple, open,.extensible widely accepted, XML includes a specification for a style language called Extensible Stylesheet language (XSL). We chose XML as the standard format to store and access e-learning contents. XML [7], which is fast becoming a standard format for Internet/intranet data information exchange, serves as an excellent means for representing data to provide an open, Internetbased integration of cross-platform e-learning applications. A few examples of information and data that may be exchanged in an XML format include, learning content catalog records, learning resources or content documents (with corresponding rich presentation and navigation structures), administrative records of learners' progress, Configuration and log files. XML allows separation of style from content. By separating the data from its presentation style, XML provides a flexible model for multimode delivery of content in which different styles can be applied on the same data for different contexts or needs. XML can facilitate learning resource searching since XML structures can be easily parsed or indexed for search purposes.

3 XML has gradually found its popularity in both e- learning and digital library field for its high capacity. So we adopted XML and chose to use native XML database technology to store and access the learning contents. As defined by the XML:DB initiative [3], a native XML database is simply a database for storing and accessing XML data using XML. This is different from a relational database where XML data must by stored as tabular data, accessed using SQL. Relational Database Managing Systems (RDBMS) has traditionally been used to map XML files to available database. At first glance, RDBMS seems like a good way to organize all of your disparate XML data. However, mapping XML documents to relational models is not only difficult, but often results in ugly schemas. So we adopted native XML database instead of a traditional RDBMS. Native XML database understands the structure of the contained XML documents. This feature is important since it allows the documents to be queried and updated using appropriate XML technologies, the first of which is a W3C [4] specification known as XPath. XPath is a language for addressing parts of an XML document, in Xindice XPath queries can be executed at either the document level or the collection level, this means that a query can be run against multiple documents and the result set will contain all matching nodes from all documents in the collection. The Xindice server also supports the creation of indexes on XML documents to speed up commonly used XPath queries. Using XPath it is possible to obtain a set of XML elements contained in a given XML document that conforms to the parameters of the XPath query. 3.3 How to store? We adopted Xindice to store the learning contents. Figure 2. A DRR storage tree Instead of storing each tree node in a separate record, we store whole documents (subtrees or branch or trunk of documents) together in one record. Illustrated in Fig.2 is a XML storage tree, where different collections (items) are stored under the root of the organization. Organizations are description of the navigation structure for the e-learning and resources are a collection of resources that make up the content. Xindice stores all XML documents inside of collections. Thus every XML document must be stored in at least one collection. Collections can be nested and are considered part of an XPath query string, so there is always a root collection. While collections can be used strictly for organizational purposes, Xindice also allows for indexes to be created on collections to increase XPath performance How to retrieve or how to query? Searching or retrieving what one wants requires simple mechanisms, e.g. a single keyword, to limit the flood of hits. More commonly, complex searching approaches using many more keywords are employed, linked as phrases by boolean operators. We adopted Xquery and XPath technology of native XML database. Xindice uses XPath for its query language and XML:DB XUpdate for its update language. We provide an implementation of the XML:DB API for Java development and it is possible to access Xindice. As standards in the XML database area mature Xindice will include support for those that are most important. [8] How to update? Using XPath it is possible to obtain a set of XML elements contained in a given XML document that conforms to the parameters of the XPath query. The second technology of importance is XUpdate as defined by XML:DB. XUpdate makes it possible to update specific elements of an XML document without having to overwrite the entire document. It is extremely useful, especially for very large XML documents. 4. A conceptual architecture of e- learning system from the viewpoint of DRR A three-layer conceptual architecture for e- learning from the viewpoint of DRR is as illustrated in Fig.3 and is comprised of three main components: First, the user layer (depicted in the upper part of Fig.3) is further subdivided into three main user groups: learners/students, instructors/teachers and administrators. Users can access metadata in the DRR via LMS. Interfaces for instructional buffer as well as that for user login etc. are necessary for the

4 administrator/instructor to upload or write and modify or update data/contents into a database and for the learner to extract what they selected through the LMS. Of course users also can access externally distributed educational objects or assets through common Web and streaming media protocols. Details about this area are omitted here for brevity because this paper deals mainly with DRR. Second, the LMS in the middle layer of Fig. 3 can be regarded as the operating system for e-learning. It provides search and retrieval access for end users and administrators to the metadata store (i.e. the database layer). At a minimum, it automates the administrator of training events; handing course schedules and registration; delivering learning content; facilitating communication among learners and between learners and instructors; and tracking and reporting on learners progress and test scores. It is designed to handle courses from multiple instructors. For administrators, the application also presents interfaces for maintenance functions (i.e. providing quality control of metadata records and managing particular functions of the learning managing or contents managing applications). While the resource management module or CMS is therefore responsible for the storage, retrieval, and querying on the catalog database. 5. Implementation The contents to be stored in the DRR can be classified into different collections by the learning system administrators or instructors according to their features and aims of use. Here we do not consider the issue of automatic classification according to semantics etc. On the other hand, once a category or class of a collection is created in the DRR, it should be automatic indexed, e.g., written into the catalogue. For example, we can save HILBERT as one collection if we have e-learning courses in other subject such as Chemistry or Physics. If you are establishing a DRR only for HILBERT, then you can make HILBERT as root collection and learning systems such as EnCal [2], IA (intelligent adviser), LKB (logic knowledge-base system), and some automated tools for theorem proving and proof checking such as ACL, OTTER, HOL as subcollection; another sub-collection can be UDB (User database), under which are sub-directory of three groups of users: adminstrators, instructors and learners and so on. The tree-branch relation will be kept by native XML database using Xindice. Needless to say, the tree-branch relation must be reflected to the CDB (Catalogue/index database) once a collection or sub- (sub-sub) collection is added to the database. Xindice's proprietary Collection Manager service is much more powerful than the XML:DB Collection Management service. Figure 4. Steps to manage the XML contents in DRR Figure 3. A conceptual architecture of e-learning from the viewpoint of DRR. Third, the metadata storage layer or DRR layer at the bottom comprise of at least UDB and contents database system CDB, and for HILBERT, CDB is comprised of LRDB, LPDB and LCDB. The CMS module (above the content databases) allows the administrator or instructors to define one or more projects each representing a logical organization resources. Fig.4 shows a procedure for managing the XML contents in a DRR. Considering the case when an instructor or an administrator wants to update/modify an available content in XML, for instance, logic puzzles for HILBERT, he or she can do in following steps: Convert the XML document to a Java object model using an XML parser and DOM (Document Object Model). Using the DOM API, we will create our own a Java object model. For HILBERT system, an XML LRDB (logic reference database) document, for example, is converted to a Java object model for

5 logic reference database. The DOM API is used to read in information from an XML document. DOM can also be used to change this information, and can be used to generate an XML document. There is an easier way of getting around using DOM for modifying and saving the XML data; working with the Java object model. The contents of the logic reference database are stored in the object model and this model is used to add, edit or delete information. Generate an XML document from a Java object model. The Java object model for the logic reference database is saved as an XML document. 6. Concluding Remarks We have presented our basic design ideas for our ongoing project on a digital reference room for e- learning. We adopted native XML database and Xindice to implement the three layers architecture of digital reference room so that it is cross-platform enabled and multi-os interoperable. Future works include further personalization of UDB to enable high efficient e-learning and full test and evaluation of the under-developing digital reference room. Acknowledgments The authors are grateful for all the other members of Cheng laboratory for their kindness in discussing on the project and for their valuable comments. References [1] J. Cheng, N. Akimoto, Y. Goto, M. Koide, K. Nanashima, and S. Nara, HILBERT: An autonomous evolutionary information system for teaching and learning logic, Proceedings of CBLIS03, [2] J. Cheng, EnCal: An automated forward deduction system for general-purpose Entailment Calculus, in N. Terashima and E. Altman (Eds.), Advanced IT Tools, Proc. IFIP World Conference on IT Tools, IFIP 96 14th World Computer Congress, pp , CHAPMAN & HALL, [3] XML:DB initiative, [4] W3C, [5] IEEE Standard for Learning Object Metadata June [6] IMS Content Packaging Specification v1.1.3, 2003, [7] SCORM, [8] Xindice,