Artificial intelligence tools for student learning assessment in professional schools

The necessity to maximize the learning success of the students as well as to produce professionals with the right skills to fulfil the market requirements, raises the question of closely following and assessing the learning paths of the students of Professional Schools. To solve at once problems and difficulties that arise during the learning process, we need to develop technologies and tools that allow the monitoring of those paths, if not in real time, at least periodically. Supported on a knowledge base of student features, also called a Student Model, a Student Assessment System must be able to produce diagnosis of student’s learning paths. Given the wide range of students’ learning experiences and behaviours, which implies a wide range of points and values in students’ models, such a tool should have some sort of intelligence. Moreover, that tool must rely on a formal methodology for problem solving to estimate a measure of the quality-ofinformation that branches out from students’ profiles, before trying to diagnose their learning problems. Indeed, this paper presents an approach to design a Diagnosis Module for a Student Assessment System, which is, in fact, a reasoner, in the sense that, presented with a new problem description (a student outline) it produces a solved problem, i.e., a diagnostic of the student learning state. We undertook the problem by selecting the attributes that are meaningful to produce a diagnosis, i.e., biographical, social, economical and cultural data, as well as skills so far achieved, which may drive, as constraints or invariants, the acquisition of new knowledge. Next, we selected the metrics that would allow us to infer the quality of the ongoing learning, i.e., the degree of expertise on the currently attended learning domains. To collect these indicators we used the Moodle e-Learning System. Both, attributes and metrics, make the student model. Finally, we designed a reasoner based on Artificial Intelligence techniques that rely on the Quality-of-Information quantification valuations to foster a Multi-Valued Extended Logic Programming language, a key element in order to produce diagnosis of the student learning paths. Confronted with a new case, i.e., a student model, the reasoner evaluates it in terms of its QI and outputs a diagnostic

Prediction tools for student learning assessment in professional schools

Professional Schools are in need to access technologies and tools that allow the monitoring of a student evolution course, in acquiring a given skill. Furthermore, they need to be able to predict the presentation of the students on a course before they actually sign up, to either provide them with the extra skills required to succeed, or to adapt the course to the students’ level of knowledge. Based on a knowledge base of student features, the Student Model, a Student Prediction System must be able to produce estimates on whether a student will succeed on a particular course. This tool must rely on a formal methodology for problem solving to estimate a measure of the quality-ofinformation that branches out from students’ profiles, before trying to guess their likelihood of success. Indeed, this paper presents an approach to design a Student Prediction System, which is, in fact, a reasoner, in the sense that, presented with a new problem description (a student outline) it produces a solved problem, i.e., a diagnostic of the student potential of success

Incorporación de semántica en plataformas para e-learning

En el área educativa, la Web Semántica provee las ventajas orientadas principalmente a la clasificación del conocimiento en ambientes de aprendizaje, donde es posible incorporar relaciones entre los materiales de las cátedras, preestableciendo así una red de conocimiento apropiada, sin restar independencia en el proceso educativo. La plataforma Moodle para e-learning puede ser enriquecida con semántica y reglas. Este trabajo pretende analizar la posibilidad de introducir ontologías para el análisis de los contenidos educativos accedidos por los usuarios de plataformas Moodle.Red de Universidades con Carreras en Informática (RedUNCI

Incorporación de semántica en plataformas para e-learning

En el área educativa, la Web Semántica provee las ventajas orientadas principalmente a la clasificación del conocimiento en ambientes de aprendizaje, donde es posible incorporar relaciones entre los materiales de las cátedras, preestableciendo así una red de conocimiento apropiada, sin restar independencia en el proceso educativo. La plataforma Moodle para e-learning puede ser enriquecida con semántica y reglas. Este trabajo pretende analizar la posibilidad de introducir ontologías para el análisis de los contenidos educativos accedidos por los usuarios de plataformas Moodle.Red de Universidades con Carreras en Informática (RedUNCI

Incorporación de semántica en plataformas para e-learning

En el área educativa, la Web Semántica provee las ventajas orientadas principalmente a la clasificación del conocimiento en ambientes de aprendizaje, donde es posible incorporar relaciones entre los materiales de las cátedras, preestableciendo así una red de conocimiento apropiada, sin restar independencia en el proceso educativo. La plataforma Moodle para e-learning puede ser enriquecida con semántica y reglas. Este trabajo pretende analizar la posibilidad de introducir ontologías para el análisis de los contenidos educativos accedidos por los usuarios de plataformas Moodle.Red de Universidades con Carreras en Informática (RedUNCI

Empowering Moodle with Rules and Semantics

Abstract. This short paper describes preliminary ideas for empowering e-learning platform Moodle with rules and semantics. Many existing web applications already contain a lot of structured information, which is still not presented in machine-readable way. Extracting this information from an existing e-learning platform may give benefits to course tutors such as more control over course management, advanced reports and filters, reasoning over the course content. We describe how to represent the existing Moodle content in RDF and how to add rules on top of the RDF fact base. A semi-automatic method for rule mining and rule development is discussed