Supporting Teachers to Retrieve and Select Learning Objects for Personalized Courses in the Moodle_LS Environment Carla Limongelli, Alfonso Miola
Filippo Sciarrone, Marco Temperini
Dept. of Computer Science and Automation Roma Tre University Rome, Italy {limongel, miola}@dia.uniroma3.it
Dept. of Computer and System Science Sapienza University Rome, Italy {
[email protected],
[email protected]}
Abstract — In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standardcompliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context. Keywords - Learning; Systems, Environments and Architectures; Adaptive, Personalized and Context-Aware Technology-enhanced Learning & Education
I.
INTRODUCTION
Nowadays, the request for distance learning is surging, thanks to the new internet-based technologies: open source Learning Management Systems (LMSs), such as Moodle1 or ATutor 2 , and Web-based Educational Systems (WES), either generic (such as LecompS [7]) or AI-based (such as BLITS [9] or HyperCase [10]) empower instructional designer and learner with convenient and augmented instructional opportunities, and support an increasing demand for them. At the same time, the development and application of standards for e-learning is originating a wide availability of Learning Objects Repositories (LORs) on the internet, such as MERLOT, CNX and WISC-ONLINE, providing a growing portfolio of structured learning material. Moreover, the process of building a new course is a critical and hard task to accomplish in any case: setting the right learning goals on a particular domain, building a new concept map, building or re-adapting old LOs and delivering the course in a suitable LMS. Consequently, one standing problem in the area of web-based e-learning is how to support instructional designers capability to retrieve and select effectively and efficiently learning materials, appropriate for their educational purposes, by also speeding up the overall course building process considerably, from the concept map building to the LOs delivery (see [2], [1] and [5] for an overview on this topic). Here we present a 1 http://www.moodle.org 2 http://atutor.ca
module to support instructional designers to build new courses by speeding up the process of retrieving new didactic material, i.e., LOs from LORs, in order to use, share and re-use them for personalized courses as well. This module acts as a functional interface to LORs, running in the Moodle_LS LMS, an enhanced version of the standard Moodle LMS [8]. The added value of our approach to the LO retrieval problem is threefold. First our module is embedded in the Moodle_LS environment: one doesn’t need to exit from the didactic environment to retrieve LOs, surfing through different LORs, each of them having different rules for retrieval. Second, our module allows selecting and tagging those LOs that were found suitable for the personalized course by the instructional designer. Third, the actual management of a LOR, derived by the retrieval activities, in the LMS itself: teachers can search for and share SCOs with others who had entered them in the system, or simply reuse those already imported for another course or module. A focal point of our work is that the instructional designer is supported in real time: the learning material can be retrieved and added to the new course while building each LO. Besides, all the selected learning material will be stored in a local data warehouse, ready to be locally shared with other instructional designers. The paper is structured as follows. Section 2 shows some related work; Section 3 reports on the functional architecture of the system. In Section 4 a session of use of the proposed module is shown; then conclusions are drawn. II.
SEARCHING LOS FROM LORS: SYSTEMS AND METHODS
In the last years, due to the pace of growth of distance education, a lot of LORs have been posted in the Internet by public and private institutions in order to share didactic material among private and public instruction communities. Also, many approaches and standards are being proposed to overcome the interoperability problem: each LOR presents a particular way of storing LOs, making it difficult interoperability and exchange of teaching materials between different actors who would like to use it to speed up the preparation of new courses. This need becomes harder if the course should be a personalized one, where different versions of LOs are needed, taking into account student learning styles, as in the Moodle_LS system [8]. Our proposal aims to provide teachers with a homogeneous
environment embedded in the Moodle_LS system, where one can build her/his new course, starting from her/his didactic needs, in turn expressed by queries submitted to LORs available through the system. A. Learning Object Repositories In our experiments we have used some prominent LORs, among those available to the instructional community, such as MERLOT. (http://www.merlot.org) This is probably the most well known LOR. It is a centralized stand alone repository containing metadata only and pointing to objects located at remote locations. In addition to providing search and categorization, MERLOT allows for peer review service supported by communities of experts in different subject areas, containing more than 30,000 LOs, continuously updated. CONNEXIONS. (http://www.cnx.or) Is one of the most popular open education sites in the world. Its more than 17,000 learning objects or modules and over 1,000 collections (textbooks, journal articles, etc.) are used by over 2 million people per month. LOs are tagged in XML. WISC-ONLINE. (http://www.wisconline.com) Is a digital library containing over than 20,000 Web-based LOs. The digital library of LOs has been developed primarily by faculty members of the Wisconsin Technical College System (WTCS) and multimedia technicians. Among other available LO repositories is also ICOPER (http://www.icoper.org/) that is capable of storing both learning resources and metadata records. It also provides sharing of assessment resources [4]. B. Sharing and Reusing Learning Resources in Learning Management Systems. Here we report some works in the literature dealing with LORs using the Moodle LMS. Moodle_LS system is an enhanced version of the famous Moodle LMS, providing teachers with the capability of building personalized courses. Sharing Map. It is a Moodle plug-in for easy searching and sharing [3]. This will be the second step after the one we propose in this paper: after having retrieved LOs from LORs, this material should be shared among teachers as well. DOOR. (http://door.sourceforge.net/index.html) Digital Open Object Repository is an Open Source software for creating learning objects repositories. With DOOR one can store digital content in the form of LOs. III.
THE FUNCTIONAL ARCHITECTURE OF THE SYSTEM
In this section we show design issues of the system, basing on a description of the process that guides teachers along the production of a course, or of a part of it. The main goal of our module is the partial or full automation of the process of course building. The basic idea is to develop a
system which, starting from the concept map of the course designed by the teacher, supports teachers to: a) select suitable LOs retrieved from some LORs; b) add metadata; c) store LOs in a local domain repository embedded into the Moodle_LS system, where it should be possible a deeper analysis of the LOs by using an appropriate On-Line Analytical Processing (OLAP) technique [6] useful to reuse them. Once LOs have been filtered, they will be automatically sequenced and delivered by means of the Moodle_LS system. In the following we show the main steps of the aforesaid process: form the concept map to the e-learning course, detailing the part of the process implemented to date: 1. Concept map - The teacher arranges the concept map of the course by defining only prerequisite relations [11]. 2.
Search – The prerequisite concepts defined in the concept map are used as keywords for searching into LORs.
3.
Storage I - All the filtered LOs are gathered into a local temporary database. The teacher looks at them, and labels the most interesting for import into Moodle_LS.
4.
Storage II – The imported LOs are suitably metadated through the Teacher Assistant (TA) module provided in Moodle_LS and stored in the local database.
5.
Personalized Course Generation and delivery – It is natively provided by the Moodle-LS system. IV.
THE SYSTEM AT WORK
The following discussion reports on an experience undertaken in the proposed framework: the teacher is supposed to deal with the definition of a portion of a course, ranging over “number systems” and “data representation”. When the teacher has sketched a concept map for the course (or, as in this case, a portion of it), it can be transferred into Moodle_LS. In this process, mediated by the TA, the metadata needed to import the map elements into the personalization system are also added by the teacher. The result of this work is in the construction of a Learning Objects Graph according to the teacher’s concept map, whose elements are directly usable by the personalization engine for the production of learning paths. Basing on the LOs Graph, the TA runs a search to the LORs selected by the user, among those allowed by the system. The retrieved LOs are then inspected by the teacher in order to select some of them as suitable to be associated to the nodes of the LOs Graph. Consequently, the selected LOs can be automatically transferred into the Moodle sections (Fig. 1). After this process the system contains a set of LOs, suitably implemented and metadated, and personalized courses can be produced. V.
CONCLUSIONS
In this paper we have presented an approach to support the heavy work of LO retrieval and selection from standard
LORs, necessary to deal with personalized and adaptive course environments. We have shown a proof of concept in the framework of a system allowing personalized e-learning in Moodle. Althought the implementation is incomplete, it already allows to manage with the main parts of the processes, from the shaping of searching queries, based on the concept map of the course, to the delivery of real courses based on the retrieved and validated material. The still unimplemented part relates to the possibility of applying further analysis over the LOs gathered in a local datamart, using classical OLAP to refine a local concept analysis of LOs. In terms of future work, we also plan to enrich the approach to retrieval and validation of LOs, through artificial intelligence techniques, applied to manage a Teacher Model, besides the more traditional Student Model. The overall system is currently in progress. REFERENCES M. Arrigoni Neri and M. Colombetti, “Ontology-based learning objects search and courses generation”, Applied Artificial Intelligence, 23(3), 2009, pp. 233-260. [2] B. Di Martino, “Semantic Retrieval of Learning Objects with Schema Matching”, Journal of e-Learning and Knowledge Society, 5(3), 2009, pp. 49-58. [3] Don Hinkelman “Sharing learning objects within a teaching team: How to use Moodle for an in-house repository”, In JALT, The Japan Association for Language Teaching, 33(4), 2009. [4] I. Gutiérrez Rojas, D. Leony, A. Franco, R.M. Crespo, A. Pardo and C. Delgado Kloos, “Management of Assessment [1]
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Figure 1. The personalized course in Moodle.
