Recommending degree studies according to students' attitudes in high school by means of subgroup discovery
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{NoamanLRV16,
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author = "Amin Y. Noaman and Jose Maria Luna and
Abdul Hamid M. Ragab and Sebastian Ventura",
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title = "Recommending degree studies according to students'
attitudes in high school by means of subgroup
discovery",
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journal = "International Journal of Computational Intelligence
Systems",
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year = "2016",
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volume = "9",
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number = "6",
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pages = "1101--1117",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Subgroup
discovery, recommending degree, students, skills",
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ISSN = "1875-6883",
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URL = "http://www.atlantis-press.com/php/download_paper.php?id=25868751",
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DOI = "doi:10.1080/18756891.2016.1256573",
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size = "17 pages",
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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",
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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
Citations