Artificial Intelligence Tools for Student Learning Assessment in Professional Schools

Abstract: 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-of-information 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