ICECOCS2020 Paper : Data mining techniques for employability:Systematic literature review
Автор: Aniss Moumen
Загружено: 2020-11-08
Просмотров: 494
The professional integration of young
university graduates remains the main concern of all
universities. As a result, these universities make
enormous efforts to prepare laureates adapted to the job
market. Therefore, it is considered appropriate to know
the profiles of students employable in the labour market.
Several models have been implemented to predict
profiles acceptable by employers. In this paper, we
present a Systematic Literature Review (SLR), from
2005 to 2019, about data mining (DM) techniques used
to analyze and predict the professional integration of
students and young graduates. We used several scientific
databases and repositories: Scopus, WoS, IEEE,
Springer, ScienceDirect and ACM to extract and analyze
157 references, with Zotero and NVIVO. After a metaanalyse,
we focus on works that use and compare DM
techniques. As a result, we found that the most famous
DM techniques in our context are: SVM, Naïve Bayes,
PCA, Logistic Regression, k-means algorithm, KNN,
decision tree, Neural networks, text mining and Item
Response Theory. From this SLR and according to the
accuracy, we conclude that logistic regression, decision
trees, ANN, Random Forest, Text-IRT are the best DM
techniques for employability studies.
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