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Intelligent decision support for university application using RIASEC codes

Authors: Silva, João Pedro; Portela, Filipe; Santos, Manuel Filipe; Taveira, Maria do Céu;

Intelligent decision support for university application using RIASEC codes

Abstract

A study performed recently in Portugal showed that there are currently about 320,000 students attending high school. Typically, 51 % of them didn’t know concretely which course to select, five months before the application date. A significant number (about 160,000) of students needed to be guided and informed about the educational provision at the national level. In addition, 25% of the approximately 309,000 students attending Portuguese Public Higher Education (about 77,000) have changed or thought about changing course during their academic career due to some dissatisfaction related to the current course. In order to minimize these difficulties a decision model was outlined. The solution is based on the construction of a tool that, through a carefully prepared questionnaire, will identify what are the best alternatives for students’ application. Key information has been collected from the analysis of several variables in various contexts (e.g. social, economic, personal, and psychological), from scientific studies and from real facts. The resulting model is a weighted model where variables can be set by the user in order to guide the decision making process. Therefore the model is able to adapt to the intrinsic characteristics of each user. This paper focuses in the psychometric capability of the solution (which is unique in this environment) adopting the RIASEC Codes (domains) as the vocational component of the decision models

Country
Portugal
Related Organizations
Keywords

University application process, IASEC Codes, Decision models, Psychometric tests, Higher education, RIASEC codes

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green