Abstract
Students must face up to decision making situations since early ages, in order to keep on the chase of some professional competences. However, the suitability of people in jobs or studies is not only based on their preferences. Other factors are involved: capacities, skills, social attitudes, etc. that must be taken into account to successfully decide (Salgado, 1996). Many countries have created a so-called advisor figure, whose role is to guide them in decisions regarding their academic future. Advisors consider different criteria and indicators in their task, the key one being the student’s marks. Marks mean much more than a simple assessment: they indicate not only knowledge, but also skills, preferences about fields, attitudes, etc. Advisors generally are required to guide support several hundreds of students and hence analyze large amounts of information. The aim of this contribution is to support them in their task of guiding students by means of Decision Support systems (DSS) that uses students’ marks and provides linguistic information about the choices that students can make.
TopIntroduction
Students must face up to decision making situations since early ages, in order to keep on the chase of some professional competences. However, the suitability of people in jobs or studies is not only based on their preferences. Other factors are involved: capacities, skills, social attitudes, etc. that must be taken into account to successfully decide (Salgado, 1996).
Many countries have created a so-called advisor figure, whose role is to guide them in decisions regarding their academic future. Advisors consider different criteria and indicators in their task, the key one being the student’s marks. Marks mean much more than a simple assessment: they indicate not only knowledge, but also skills, preferences about fields, attitudes, etc.
Advisors generally are required to guide support several hundreds of students and hence analyze large amounts of information. The aim of this contribution is to support them in their task of guiding students by means of Decision Support systems (DSS) that uses students’ marks and provides linguistic information about the choices that students can make.
We found out that the analysis of huge amount of the data by advisors to make recommendations follows a similar scheme to personalized marketing in Internet accomplished by Recommender Systems (RS) (Adomavicius, 2005; Resnick, 1997). These systems offer recommendations to users according to their preference profiles, guiding them through search spaces to find the most suitable items for their needs.
Due to the complementarity between the necessities of Academic Orientation and the facilities of RS, we have develop OrieB, a Web-DSS for Academic Orientation based on a Collaborative RS (CRS) for supporting advisors in their student guidance task. RS usually compute numerical degrees to rank the best items to be recommended. But in Academic Orientation we consider more appropriate the use of linguistic values (Martínez, 2007; Zadeh, 1975) for supporting advisor’s tasks in order to manage the vagueness and uncertainty inherent to the problem, rather than precise numerical values that may mislead students, because predictions are just approximations.
Key Terms in this Chapter
Advisor: Person who is in charge of performing the Academic Orientation task.
Academic Profile: Set of subjects (core, module and/or elective) and modules chosen by a student.
Decision Support System: Information system that supports users in decision-making activities.
Collaborative Filtering: Process of filtering for information using techniques involving collaboration among multiple users, data sources, etc.
Module: Branch of knowledge which groups several module subjects in order to allow students specialize in an area.
Subject: An area of knowledge studied in school, college or university.
Academic Orientation: Task that guides students to choose a suitable Academic Profile.