OntoSIDES: Ontology-based student progress monitoring on the national evaluation system of French Medical Schools

Abstract

We introduce OntoSIDES, the core of an ontology-based learning management system in Medicine, in which the educational content, the traces of students' activities and the correction of exams are linked and related to items of an official reference program in a unified RDF data model. OntoSIDES is an RDF knowledge base comprised of a lightweight domain ontology that serves as a pivot high-level vocabulary of the query interface with users, and of a dataset made of factual statements relating individual entities to classes and properties of the ontology. Thanks to an automatic mapping-based data materialization and rule-based data saturation, OntoSIDES contains around 8 millions triples to date, and provides an integrated access to useful information for student progress monitoring, using a powerful query language (namely SPARQL) allowing users to express their specific needs of data exploration and analysis. Since we do not expect end-users to master the raw syntax of SPARQL and to express directly complex queries in SPARQL, we have designed a set of parametrized queries that users can customize through a user-friendly interface.

Knowledge modeling: A survey of processes and techniques

“…Liu and Zhang 103 first constructed the ontology framework manually through a seven-step method, and then extracted hierarchical and nonhierarchical concepts from unstructured text data by combining statistics with rules to implement automatic extension of ontology. Palombia et al 104 constructed a rule-rich lightweight ontology by domain experts and then populate the ontology using an Ontology-based Data Access 105 mapping method. Wang et al 83 conducted research on automatic knowledge acquisition based on multiple dictionaries in the Chinese environment combined with manual methodology of METHONTOLOGY, and developed modeling tools for building domain ontology.…”

“…Most semi-automatic ontology development uses machine learning and data mining methods, such as References [103,[106][107][108][109][110][111]115]. In addition to machine learning methods, other semi-automated methods have also been proposed, such as References [83,104,[112][113][114]. Table 5 shows the process of automatic and semi-automatic ontology construction.…”

Impact of panelists’ experience on script concordance test scores of medical students

“…A group of 2312 undergraduate medical students in the 5th year of their medical studies at the Universities of Besançon, Lorraine, Versailles Saint-Quentin, Créteil-Paris Est, Paris Diderot, Paris Descartes, Paris Sorbonne Université, Paris Sud (France) were asked to complete the computer-based SCT as an optional session during a mock national ranking exam [ 14 ]. The SCT exam was administered through the online national evaluation system of French medical school [ 15 ]. At the beginning of the SCT exam, students were asked if they had previously performed a traineeship in cardiology or emergency medicine.…”

A Knowledge Graph Enhanced Learner Model to Predict Outcomes to Questions in the Medical Field

“…OntoSIDES [13] is a knowledge graph that comprises a domain ontology represented in OWL and a set of factual statements about the entities on the SIDES platform, linking them to the ontology classes and properties. Being an RDF graph, it is possible to query OntoSIDES with the standard query language SPARQL.…”

“…To achieve these goals, the approach taken leverages semantic Web models and technologies to enrich and integrate these resources in RDF with OWL ontologies. As part of the SIDES 3.0 project, existing data from the platform, such as annotated questions and students' learning traces, were converted into structured data expressed in RDF using the On-toSIDES OWL ontology [13], and stored in the OntoSIDES knowledge graph.…”