Julia Clemente

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the students knowledge, but rather they should reflect, as faithfully as possible, the students reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. Its focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the students knowledge state - especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the students features and a high flexibility for its integration in varied ITSs.

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the students behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the students knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.

Guiding the Evolution of a Multilingual Ontology in a Concrete Setting

Evolving complex artifacts as multilingual ontologies is a difficult activity demanding for the involvement of different roles and for guidelines to drive and coordinate them. We present the methodology and the underlying tool that have been used in the context of the Organic.Lingua project for the collaborative evolution of the multilingual Organic Agriculture ontology. Findings gathered from a quantitative and a qualitative evaluation of the experience are reported, revealing the usefulness of the methodology used in synergy with the tool.

ON-SMMILE: Ontology Network-based Student Model for MultIple Learning Environments

Abstract Currently, many educational researchers focus on the extraction of information about the learning progress to properly assist students. We present ON-SMMILE, a student-centered and flexible student model which is represented as an ontology network combining information related to (i) students and their knowledge state, (ii) assessments that rely on rubrics and different types of objectives, (iii) units of learning and (iv) information resources previously employed as support for the student model in intelligent virtual environment for training/instruction and here extended. The aim of this work is to design and build methodologically, throughout ontological engineering, the ON-SMMILE model to be used as support of future works closely linked to supervision of students learning as competence-based recommender system. For this purpose, our model is designed as a set of ontological resources that have been extended, standardized, interrelated and adapted to be used in multiple learning environments. In this paper, we also analyze the available approaches based on instructional design which can be added to ontology network to build the proposed model. As a case study, a chemical experiment in a virtual environment and its instantiation are described in terms of ON-SMMILE.