2019
DOI: 10.1007/s10100-019-00636-x
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Knowledge accelerator by transversal competences and multivariate adaptive regression splines

Abstract: Transversal competences constitute a set of the knowledge, skills, and attitudes required for various positions and in different professions. Such competences include: entrepreneurship, teamwork, creativity, and communicativeness; they are increasingly listed by employers in different countries as the key requirements in the labor market. The article presents the model of accelerating the process of acquiring transversal competences, developed based on the analysis of data collected in four countries of the Eu… Show more

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Cited by 29 publications
(13 citation statements)
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“…This methodology allows the analysis of nonlinear relationships and complex interactions [ 98 , 99 , 100 , 101 ] between the dependent variables and a series of built spline functions on different intervals of a given independent variable [ 98 ]. According to Koc and Bozdogan [ 102 ], MARS supposes an extension of the original CART models (“Classification and Regression Trees”) [ 103 ], whose non-parametric character makes it much more flexible than the strictly linear models [ 98 , 101 , 104 , 105 , 106 , 107 , 108 ], as well as more efficient when detecting “outliers” in any data sample [ 109 ]. Alreja et al [ 104 ] argued that the main difference between MARS methodology and the linear regression “classical” models stems from the fact that they can unify continuous and categorical data under the same analytical scheme, and they are much easier to interpret and understand.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This methodology allows the analysis of nonlinear relationships and complex interactions [ 98 , 99 , 100 , 101 ] between the dependent variables and a series of built spline functions on different intervals of a given independent variable [ 98 ]. According to Koc and Bozdogan [ 102 ], MARS supposes an extension of the original CART models (“Classification and Regression Trees”) [ 103 ], whose non-parametric character makes it much more flexible than the strictly linear models [ 98 , 101 , 104 , 105 , 106 , 107 , 108 ], as well as more efficient when detecting “outliers” in any data sample [ 109 ]. Alreja et al [ 104 ] argued that the main difference between MARS methodology and the linear regression “classical” models stems from the fact that they can unify continuous and categorical data under the same analytical scheme, and they are much easier to interpret and understand.…”
Section: Methodsmentioning
confidence: 99%
“…MARS can be focused within the new scientific paradigm [ 115 ] of the “data driven-modeling” [ 100 , 116 , 117 ], one of the foundations of machine learning techniques, being defined on a bi-objective algorithm (elaborated from a “two-stage process”) [ 118 ] in which two different phases are distinguished [ 108 , 109 , 111 , 119 , 120 ]: forward selection and backward deletion. Formally, following Koc and Bozdogan [ 102 ] and Zhang and Goh [ 100 ], its working-schema can be defined from Y , the output or objective-dependent variable response, and , a matrix of j input variables (predictors), assuming that the data are generated under an “unknown and true model”.…”
Section: Methodsmentioning
confidence: 99%
“…Its acquisition is influenced by different variables (e.g., number of students and activities, among others). These variables can accelerate knowledge acquisition in students, therefore their analysis is necessary [12]. General or oblique skills are acquired in the learning process as an educational aspect.…”
Section: Universities and Transversal Competencesmentioning
confidence: 99%
“…An unexpected gap is a solution to finance and human resources management through operational research. We would like to introduce publications Savku and Weber (2018) and Graczyk-Kucharska et al (2020) as the pioneering works. The first article added the problem of optimal consumption problem from cash flow with delay and regimes.…”
Section: Computer Sciencementioning
confidence: 99%