of the courses, so as to personalize the t-learning ... T-learning, personalization, recommender systems. 1. ... Our recommender system is a software agent that.
Solutions for Personalized T-learning Martín López-Nores, José J. Pazos-Arias, Yolanda Blanco-Fernández, Marta Rey-López, Jorge García-Duque, Belén Barragáns-Martínez, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo, Alberto Gil-Solla, Manuel Ramos-Cabrer Department of Telematic Engineering University of Vigo, 36310, Spain {mlnores, jose, yolanda, mrey, jgd, belen, avilas, rebeca, agil, mramos}@det.uvigo.es
Abstract In a t-learning environment, it does not seem the most adequate option to leave the discovery of services in a pull model where it is the user, on his own initiative, who starts looking for interesting courses by navigating through the Electronic Programming Guides. This paper motivates an alternative model that pursues an adequate targeting of the courses, so as to personalize the t-learning experience.
that automatically makes suggestions about courses that the user may find interesting. Thus, whenever a suitable course is found, a blinking button appears at the top-right corner of the screen, warning the user that there are interactive learning services available, possibly related to his interests; pressing the button will display a list of those services.
Key Words T-learning, personalization, recommender systems.
Our recommender system is a software agent that runs as a resident program on the user's set-top box -its design and functionality are analogous to those we presented in (Blanco-Fernández et al. 2004(a)) for the AVATAR system, which is involved with recommending TV programs in general. Applying different inference strategies, the agent identifies potentially interesting contents by matching the following sources of information (see Fig. 1):
1. Introduction The more than 50 years of analogue TV have consolidated a fundamentally passive attitude of TV users, and also a conception of television as an entertainment medium. This has led to a fundamental distinction between those who access distance learning services through an Internetenabled computer (e-learning), and those who access through Interactive TV (t-learning). Whereas the former get involved in educational activities purposefully, the latter may be attracted into education by means of entertaining activities related to the things they find interesting, which can be guessed, for example, from the kind of TV programs they watch. This way, in a t-learning environment, it does not seem adequate to leave the discovery of services in a pull model where it is the user, on his own initiative, who starts looking for interesting courses by navigating through the EPGs (Electronic Programming Guides). Even if the users made conscious attempts to finding interesting courses, they would easily get disoriented and not manage to find the most appealing contents when faced with a growing educational offer (Ghaneh 2004). In response to that, we propose an alternative model, which pursues an adequate targeting of the courses to personalize the t-learning experience. This approach is based on a recommender system
2. A Recommender Agent for T-learning
• The descriptions of the courses being offered, including details such as the themes they cover, the kind of users they are targeted to, the level of difficulty, etc. • A user profile with relevant personal details such as age, occupation, etc. • A record of watching habits, captured by details about the program the user is watching and the programs he has watched in the past. • A record of previous learning activities, with information about the courses that the user has been to, his results on previous evaluation tests, etc. For the descriptions of the courses, we use the Learning Object Metadata (LOM) specification (IEEE Learning Technology Standards Committee 2002), which provides a very rich data model for the description of educational material. The management of user profiles and details about TV programs relies on the TV-Anytime specifications (TV-Anytime Forum 2001), with a few extensions needed for better expressiveness. Finally, the
learning experience is tracked with the IMS Learner Information Package (LIP) specification (IMS Global Learning 2001), which defines a normalized way to store any information about a user that is relevant from an educational point of view.
publications in the recommendation of general TV programs (Blanco-Fernández et al. 2004(b)). Besides, we are initiating research on the assembly of courses à la carte, that is, the automatic aggregation of contents to build courses tailored to the users' interests. Among others, our proposal exploits the semantic richness of LOM descriptions and the TV-Anytime segmentations of audiovisual contents. Achievements in this area may cause a severe reduction in development costs, entailing new business opportunities in the creation of quality contents and detailed metadata descriptions.
4. Acknowledgements This work is being partially supported by the TSI2004-03667 project of the R+D Spanish plan.
References:
Figure 1: A recommender agent for t-learning In elaborating suggestions, the recommender agent takes into account both the courses that are being transmitted at a given moment and those which are scheduled to being transmitted in the future -this is known from the data available in the EPGs. If the user selected one course that is not currently available, the recommender agent would take note of that, so as to automatically record it when it is transmitted (obviously, this operation as a Personal Video Recorder only makes sense if the set-top box has local storage devices). Furthermore, combining LIP with LOM allows making access to a course dependent on having proved some knowledge on related ones, which is a way to make the user become engaged with the courses. Thus, if the user does not meet the requirements of a course, that is not offered by the recommender agent, regardless of how much it could match his interests -on the contrary, the agent recommends the courses that the user has to pass first, provided that they are still available.
Blanco-Fernández, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., Barragáns-Martínez, B. and López-Nores, M., 2004(a): “A multi-agent open architecture for a TV recommender system: A case study using a Bayesian strategy”. In Proceedings of the Sixth IEEE International Symposium on Multimedia Software Engineering, Miami (FL), USA. Blanco-Fernández, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., Barragáns-Martínez, B., López-Nores, M., García-Duque, J., FernándezVilas, A. and Díaz-Redondo, R. P., 2004(b): “AVATAR: An advanced multi-agent recommender system of personalized TV contents by semantic reasoning”. In Proceedings of the Fifth International Conference on Web Information Systems Engineering, Brisbane, Australia. Ghaneh, M., 2004: “System model for t-learning application based on home servers (PDR)”. In Broadcast technology, Vol. 19, Tokyo: NHK Science & Technical Research Laboratories, Japan Broadcasting Corporation. IEEE Learning Technology Standards Committee, 2002: “IEEE Standard for Learning Object Metadata”. IEEE Standard 1484.12.1.
3. The Current State of Research Currently, we have a working prototype of our recommender agent, which identifies interesting contents applying a Bayesian inference strategy. Work is now focusing on achieving a better targeting by implementing semantic reasoning techniques; this advances in parallel with our
IMS Global Learning, 2001: “IMS Learner Information Packaging Information Model v1.0”. http://www.imsglobal.org/profiles. TV-Anytime Forum, 2001: “TV-Anytime Specification Series: S-3 on Metadata”. http://www.tv-anytime.org.
