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A Platform-Independent Tracking and Monitoring Toolkit

Issues concerning students involved with online learning paths, that need to be faced by e-Tutors on their day-to-day activity, most often than not fall into known pedagogical patterns – that are problems and difficulties already occurred in the past and dealt with. These pedagogical patterns belong to e-Tutors’ know-how and experience and their resolution are frequently a matter of activating routine processes or giving pre-factored answers; nevertheless statistical data indicates that these issues consume a considerable slice of tutors’ time. While a portion of the scientific community is still devoting much effort in developing artificial tutoring systems – by deploying AI/MAS-enabled technologies – the solution being investigated by our team focuses on enhancing alreadyavailable, open source LMS by implementing a generalpurpose tracking and monitoring toolkit able to support eTutors in recognizing and dealing with pedagogical patterns stored into a decentralised Knowledge Base. The system architecture is designed to house multiple platforms (only one adapter interface needs to be written for each LMS) and is able to perform real-time, as well as scheduled, data collection by means of Jade-based agents and schedulers. Information obtained from the processed data is then returned to the platform via web services and specific interfaces (instant messaging chatbot). The first deployed prototype is currently being experimented in adult higher education learning paths and is able to track student activity, forum readings and writings and offers a basic chat-based help interface. Our aim is to turn a standard LMS into a knowledge aggregator where information about its users, its contents and interactions between the two can be mined via Knowledge Services; resulting data could then be used to refine users’ and groups’ profiles, to monitor learners’ deviance from expected learning path, and ultimately to adjust the applied pedagogical model.