Willow is a free-text Adaptive Computer Assisted Assessment system, which supports natural language processing and user modeling. In this paper we discuss the benefits coming from extending Willow with recommendations. The approach combines human computer interaction methods to elicit the recommendations with data mining techniques to adjust their definition. Following a scenario-based approach, 12 recommendations were designed and delivered in a large scale evaluation with 377 learners. A statistically significant positive impact was found on indicators dealing with the engagement in the course, the learning effectiveness and efficiency, as well as the knowledge acquisition. We present the overall system functionality, the interaction among the different subsystems involved and some evaluation findings.
In this paper we discuss the mechanism of a recommender system recommending papers for an evolving web-based learning system. Our system is unique in three aspects. The first is that our learning environment can evolve based on the system's observance of learners and their behaviors. Therefore, the fittest papers will survive the natural selections by learners: papers liked by learners will survive. The second is that we introduce a pedagogically layered similarity between items that have been read by learners and candidate items for recommendation, which is different and desirable, since we argue that papers that match a learner's interest might not be pedagogically suitable for him/her. The third significance is that we propose to annotate each paper with temporal sequences of learners' learning behaviors. By doing it, we can maintain the objectivity as well as integrity of the papers. In addition the accumulated sequences of learners can play a key role for a deeper understanding of their knowledge levels/states, which, in turn, provide'justin-time' recommendations to support and encourage e-learning.
In E-learning, there is still the problem of knowing how to ensure an individualized and continuous learner's follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learner's follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. The system can support any learning subject. The success of a case-based reasoning system depends critically on the performance of the retrieval step used and, more specifically, on similarity measure used to retrieve scenarios that are similar to the course of the learner (traces in progress). We propose a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help and guide the learner, the system is equipped with combined virtual and human tutors.
This article presents the description of the objectives, the structure and the functionality of an interactive system intended to focus the teaching on the performance of the student and to resolve problems detected in Internet use for distance learning. This adaption is done through user model acquisition from the data available on the students and user interaction with the system. WebDL, the system we have developed, is the result of an effective combination of techniques used in intelligent tutoring systems, adaptive hypermedia programs and learning apprentice systems for software personalization. The widespread use of the Web in distance learning could help to satisfy the need for information and to mitigate the isolation that characterizes the student in this domain. In order to solve the problems that characterize distance learning on the Web, we have constructed a multiagent architecture that is intended to be adaptable to the user's needs (Boticario & Gaudioso 1999) based *PhD grant from UNED Copyright () 2000, American Association for Artificial Intelligence (www.aaai.org).
In the last years, knowledge technologies have been exploited for self-regulation functionalities inside e-learning systems. The definition of integrated system suitably scaffolding learners to improve their experi- ence is still lacking though. In this work, we propose an innovative Web-based educational environment that sustains metacognitive self-regulated learning processes upon Semantic Web and Social Web methods and technologies.