An overview of aLFanet: an adaptive iLMS based on standards Olga C. Santos1, Carmen Barrera2, Jesús G. Boticario1 aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, c/Juan del Rosal, 16. 28040 Madrid, Spain 1{ocsantos, jgb}@dia.uned.es 2
[email protected]
Abstract. aLFanet (IST-2001-33288) aims to build an adaptive iLMS (intelligent Learning Management System) that provides personalised eLearning based on the combination of different types of adaptation (e.g. learning routes, interactions in services, peer-to-peer collaboration, presentation). It integrates new principles and tools in the fields of Learning Design and Artificial Intelligence, following existing standards in the educational field (IMS-LD, IMS-CP, IEEE-LOM, IMS-LIP, IMS-QTI) and multi-agents systems (FIPA). In this paper we present an overview of the project ongoing research and developments.
1 Introduction Active Learning For Adaptive interNET (aLFanet) is an IST Project funded by the European Commission under the 5th Framework Program (IST-2001-33288) that addresses the problem of effective adaptive learning. The project is the result of the joint effort of four developer partners (Software AG España - SAGE, Universidad Nacional de Educación a Distancia - UNED, Open Universiteit Nederland – OUNL and ACE-Case) and two user partners (Ernst Klett Verlag GmbH and Electricidade de Portugal Mudança e Recursos Humanos S.A.). The key features of aLFanet have been introduced in [4] and [3]. The first reference focuses its attention on the different actors that are involved in the system (i.e. authors, learners, tutors), and how each of them can benefit from it. In this paper, we describe how aLFanet can address self-learning. Thus, we assume that the course is already produced by the author and has been published in aLFanet. aLFanet includes an Authoring Tool developed by ACE-Case that allows authors to generate courses IMS – Learning Design (IMS-LD) compliant. Since we are focusing on self-learning, the tutor is not mandatory. It is undeniable that the figure of the human tutor enriches the performance of the course and can provide valuable feedback for the performance of the adaptation mechanisms used in aLFanet. However, many real learning scenarios do not include the figure of the tutor. In these cases, aLFanet drives users’ interactions based on the combination of different learning routes defined at design-time (IMS-LD) and recommendations based on previous users’ interactions. On the other hand, when a tutor is available, aLFanet reduces the tutor workload by automating
tutoring tasks, e.g., automatic subgrouping of learners based on their interaction profile and by providing meaningful reports based on learners’ interactions. As for the tutor, [5] describes how the tutor can be helped by the system in managing a collaborative task in a web based learning environment like aLFanet. In this paper we focus on how learners can benefit from this adaptive learning environment. As stated in [4], learners in aLFanet are: 1) provided with a learning design based on advanced pedagogical models, which is adapted to the current context and the learners’ particular needs, interests and goals; and 2) supported during the learning process by continuous monitoring the learning behaviour and providing intelligent personalized guidance in the way of adapted recommendations. In [3] we identified three dimensions in which aLFanet can adapt to the user’s preferences, interests and needs. The purpose of the following sections is to describe the adaptation basis of aLFanet and the ongoing progress of the project.
2 Adaptation in aLFanet This section briefly describes aLFanet adaptation basis and focuses on the adaptation dimension worked at aDeNu Research Group [2]. 2.1 Adaptation basis We have worked on the four dimensions of adaptation addressed by aLFanet [3]: 1) Adaptation specified in Learning Design, 2) Adaptation of Presentation (user interface), 3) Adaptation based on users’ Interactions, and 4) Feedback to the author. There are three fundamental sources to provide adaptation in an iLMS (intelligent Learning Management System): 1) learner’s individual differences, 2) specification of learning resources, and 3) the course context. Regarding the first source, the learner’s individual differences are obtained by directly collecting data from the learner about his/her learning styles, preferences, interest level on the course objectives and knowledge level (background and achieved) of the course objectives and stored using an extension of IMS – Learning Information Package (IMS-LIP). To specify the learning resources, the standard IEEE Learning Object Metadata (IEEE-LOM) is used. Finally, the context of the course is built from the analysis of the interactions done by learners in aLFanet. 2.2 Adaptation based on users’ Interactions Adaptation based on users’ interactions focuses on user modeling and collaborative filtering techniques and deals with supporting learners in the optional contents and activities specified by the learning design to work with, the additional material to read, the services available in the LMS to use, the learning experience to share with other fellows, the contributions of fellows to access and/or assess, which fellows of the course to contact to, etc. This implies that it supports user’s interactions with
recommendations derived from other related users by recommending something that have been useful to other learners with similar learned profiles and in closely related learning situations. In [1] we describe in detail how to recommend some learning material to a learner based on implicit collaborative interactions. To acquire the attributes to build the models needed for the adaptation tasks a machine learning multi-agent approach that combines knowledge-based methods and machine learning algorithms in a multi-agent architecture is used [1].
3 aLFanet architecture aLFanet integrates new principles and tools in the fields of Learning Design and Artificial Intelligence, following and extending, to cope with the adaptivity requirements, existing standards in the educational field1 (IMS-LD, IMS-CP, IEEE-LOM, IMS-LIP, IMS-QTI) and multi-agent systems (FIPA). It has been implemented by a flexible and modular approach that facilitates its development, extensibility and integration of third parties developments that follow the supported standards. In particular, it integrates an IMS Learning Design Engine (CopperCore2) developed as open source by OUNL, a web application for supporting course management, online communities and collaboration (.LRN3) originally developed at the Massachusetts Institute of Technology (MIT) but currently part of an open source project and, thus, extended by UNED to be integrated in aLFanet, an IMS QTI interpreter developed by SAGE and an infrastructure based on a multi-agent architecture where different types of agents interact to provide adapted recommendations to learners, developed by UNED.
4 Results and Future Work As a result of the work performed on the-one-and-a-half year already spent in the project, a first version of aLFanet was delivered in February 2004. This version allows users to access the basic functionality of the running system. Currently the system provides the authoring tool (IMS-LD compliant) and the interactive space that integrates the personal and course working spaces. The latter includes the learning route of learning objects and activities, which are provided to the learner according to the IMS-LD specified by the author and what services of the LMS are to be used in the activities of the course (file storage area, fora, etc.). aLFanet is course oriented. When the learner enters aLFanet, he/she is provided with an integrated view of the contributions of other users to the services belonging to the courses the learner is enrolled. He/she can access the different courses directly or via accessing the contributions done in the services. Once in the course, the activity 1
http://www.imsglobal.org/specifications.cfm http://coppercore.org/ 3 http://dotlrn.org 2
tree defined by the learning design is given to the learner, as well as the learning objects and services to perform the activities. The activity tree depicts the different learning paths which drive user’s interactions according to the learning design. The rest of services, resources and recommendations are provided according to the user’s ongoing progress with the course activities. The recommendations provided so far are explicitly defined. The reason for this is that in the first running version, the goal was to integrate both the learning design and the services available at the interactive space. Thus we can track and relate the interactions taking part in both and use the machine learning multi-agent approach. Right now, we are working on collecting the data that feed the machine learning algorithms and the inference rules of our approach and defining how different types of adaptation tasks are provided based on the models obtained from these data [1]. In these sense we are defining adaptation scenarios to be used by the author when designing courses for aLFanet. This work is going to be integrated in the second version of aLFanet to be delivered by July 2004. An evaluation is being done on the first prototype regarding technical verification and usability. However, once the second version of aLFanet is delivered, an empirical evaluation of the system focusing on validating the effectiveness of the adaptive features by measuring whether they facilitate the learning process is to be started.
References 1. Barrera, C., Santos, O.C., Rodriguez, A. and Boticario, J.G.: Support to Learners based on Implicit Collaborative interactions. To appear in: Proceedings of the Workshop Artificial Intelligence in Computer Supported Collaborative Learning. 16th European Conference on Artificial Intelligence (2004) 2. Hernández, F., Gaudioso, E. and Boticario, J.G. A multiagent approach to obtain open and flexible user models in adaptive learning communities. In Proceedings of the 9th International Conference on User Modelling. Springer Verlag (2003) 3. Santos, O.C., Barrera, C., Gaudioso, E., Boticario, J.G. ‘ALFANET: an adaptive e-learning platform’. In Méndez, A, Mesa, J.A., Mesa, J. (eds): Advances in Technology-Based Education: Toward a Knowledge-Based Society (2003) 1938-1942 4. Santos, O.C., Boticario, J.G., Koper, E.J.R. ‘aLFanet’. In Méndez, A, Mesa, J.A., Mesa, J. (eds): Advances in Technology-Based Education: Toward a Knowledge-Based Society (2003) 2014 5. Santos, O.C., Rodríguez, A., Gaudioso E., Boticario, J.G. ‘Helping the tutor to manage a collaborative task in a web-based learning environment’. In Calvo, R. and M. Grandbastein, M. (eds): AIED2003 Supplementary Proceedings - Volume IV: Intelligent Management Systems. University of Sydney. (2003) 153-162
