Prediction tools for student learning assessment in professional schools

• Author(s): Almeida, Paulo Sérgio ; Novais, Paulo ; Neves, José
• Date: 2008
• Persistent ID: http://hdl.handle.net/1822/19092
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Artificial intelligence; Multi-valued extended logic programming; Quality of information
• Document Type: Conference Object
• Language: English

Description

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.