Recommending degree studies according to students' attitudes in high school by means of subgroup discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Amin Y. Noaman and Jose Maria Luna and 
                 Abdul Hamid M. Ragab and Sebastian Ventura",
  title =        "Recommending degree studies according to students'
                 attitudes in high school by means of subgroup
  journal =      "International Journal of Computational Intelligence
  year =         "2016",
  volume =       "9",
  number =       "6",
  pages =        "1101--1117",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Subgroup
                 discovery, recommending degree, students, skills",
  ISSN =         "1875-6883",
  URL =          "",
  DOI =          "doi:10.1080/18756891.2016.1256573",
  size =         "17 pages",
  abstract =     "The transition from high school to university is a
                 critical step and many students head toward failure
                 just because their final degree option was not the
                 right choice. Both students preferences and skills play
                 an important role in choosing the degree that best fits
                 them, so an analysis of these attitudes during the high
                 school can minimize the drop out in a posteriori
                 learning period like university. We propose a subgroup
                 discovery algorithm based on grammars to extract
                 itemsets and relationships that represent any type of
                 homogeneity and regularity in data from a supervised
                 context. This supervised context is cornerstone,
                 considering a single item or a set of them as
                 interesting and distinctive. The proposed algorithm
                 supports the students final degree decision by
                 extracting relations among different students' skills
                 and preferences during the high school period. The idea
                 is to be able to provide advices with regard to what is
                 the best degree option for each specific skill and
                 student. In this regard, the use of grammars is
                 essential since it enables subjective and external
                 knowledge to be included during the mining process. The
                 proposed algorithm has been compared against different
                 subgroup discovery algorithms, achieving excellent
                 results. A real-world experimental analysis has been
                 developed at King Abdulaziz University, one of the most
                 important universities in Saudi Arabia, where there is
                 a special interest in introducing models to understand
                 the students' skills to guide them accordingly",
  notes =        "p1105 'Context-free grammar used to represents
                 subgroups' p1114 'GPA values distribution and final
                 degree option'",

Genetic Programming entries for Amin Yousef Mohammad Noaman Jose Maria Luna Abdul Hamid Mohamed Ragab Sebastian Ventura