A realistic situation, proposed from data generated within the GeOrder Simulator to elicit the statistical reasoning.

Construction and handling of important statistical concepts, such as statistical estimators computing, or organization of data in graphs and tables, are oftenly taught in a strict algorithmic manner. As consequence, students abilities regarding experimentation, data sampling, data collecting, data organization and results interpretation, are limited and their solutions lack of statistical sustain. In this work, in study class consisting in students of the Logistic and Transport Engineering program, it is developed and implemented a realistic problem situation: a sales e-commerce company has to decide, by analyzing received orders, whether to implement its own delivery system or hire a third-party delivery company. The software GeOrder Simulator is employed to generate random data for weight and location of simulated orders and the data are presented to students in tabular and iconic, onto a map geolocation, semiotic representation registers. The research aim of this work, is to analyze, from the perspective of Semiotic Representation Theory, the effect onto the statistical reasoning that is provoked in students when they solve the realistic problem situation with the data generated by the GeOrder Simulator. The results show that the addressing of the realistic problem situation together with the usage of the GeOrder Simulator, elicited the statistical reasoning in the students since it is observed the integration of statistical concepts within their argumentation, by means of the coordination of tabular, graphical and numerical semiotic representation registers.

A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction.

The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand format. Another relevant contribution of this package is a function for the computation of seven different kernels. These are Linear, Polynomial, Sigmoid, Gaussian, Exponential, Arc-Cosine 1 and Arc-Cosine L (with L = 2, 3, … ) and their sparse versions, which allow users to create kernel machines without modifying the statistical machine learning algorithm. It is important to point out that the main contribution of our package resides in the functionality for the computation of the sparse version of seven basic kernels, which is indispensable for reducing computational resources to implement kernel machine learning methods without a significant loss in prediction performance. Performance of the SKM is evaluated in a genome-based prediction framework using both a maize and wheat data set. As such, the use of this package is not restricted to genome prediction problems, and can be used in many different applications.