Author(s):
Almeida, Paulo Sérgio 
; Novais, Paulo ; Costa, Eduardo ; Rodrigues, Manuel ; Neves, José
Date: 2008
Persistent ID: http://hdl.handle.net/1822/19090
Origin: RepositóriUM - Universidade do Minho
Subject(s): Artificial intelligence; Multi-valued extended logic programming; MOODLE
Description
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.
