Modelo para la predicción de la deserción de estudiantes de pregrado, basado en técnicas de minería de datos

The main objective of this research project is to create a model for the prediction of undergraduate student desertion at the Universidad de la Costa - CUC, based on the analysis of different socioeconomic and academic factors. The study required the execution of a series of phases: characterization, experimentation, development and evaluation. During the characterization phase, a dataset was constructed, based on the compilation of demographic, cultural, social, family, educational, socioeconomic status and psychological profile data of each student, for the periods between 2013-1 and 2018-2. Such information was collected from the registration forms that students fill out when they enter the institution, a total of 1,606 unique student records were collected. During the experimental phase, different machine learning techniques were evaluated for the categories: Bayesian networks, support vector machines, and decision trees. The algorithm with which the best hit rate was obtained was Random forest (from the decision tree category), with an accuracy of 84.8%. In the development phase, the model was integrated into an application that allows us to predict whether a student or a group of students will drop out or not. Finally, in the evaluation phase, the application was subjected to different types of tests to evaluate both the functionality of the graphic interface with the final user and the success rate in terms of desertion prediction, the results have coincided with the precision obtained in the experimental phase.El objetivo principal de este proyecto de investigación es crear un modelo para la predicción de la deserción de estudiantes de pregrado en la Universidad de la Costa - CUC, a partir del análisis de diferentes factores socioeconómicos y académicos. El estudio requirió de la ejecución de una serie de fases: caracterización, experimentación, desarrollo y evaluación. Durante la fase de caracterización se construyó un conjunto de datos (dataset), a partir de la compilación de los datos demográficos, culturales, sociales, familiares, educativos, estatus socioeconómico y perfil psicológico de cada estudiante, de los periodos comprendidos entre 2013-1 y 2018-2. Tal información fue recopilada a partir de los formatos de inscripción que diligencian los estudiantes cuando ingresan a la institución, un total de 1.606 registros únicos de estudiantes fueron recopilados. Durante la fase de experimentación se evaluaron distintas técnicas de aprendizaje automático (Machine Learning) de las categorías: redes bayesianas, máquinas de soporte vectorial y árboles de decisiones. El algoritmo con el cual se obtuvo la mejor tasa de aciertos fue Random forest (de la categoría árboles de decisión), con una exactitud del 84.8%. En la fase de desarrollo se integró el modelo a una aplicación que permite predecir si un estudiante o un grupo de ellos desertará o no. Por último, en la fase de evaluación se sometió la aplicación a diferentes tipos de pruebas para valorar tanto la funcionalidad de la interface gráfica con el usuario final como la tasa de aciertos en cuanto a la predicción de la deserción, los resultados han coincidido con la precisión obtenida en la fase experimental

Toward Intelligent Reconfiguration of RPL Networks using Supervised Learning

International audienceDesigning scalable and energy-efficient routing protocols for IoT low power networks is a particularly challenging problem. The IETF ROLL Working Group has defined and standardized an IPv6 routing protocol for IoT low power networks called RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) [1]. This protocol builds and maintains dynamic routes among network devices based on various objective functions (OFs) that exploit different network metrics for parent node selection (e.g., ETX-based [2], Energy-based [3]), etc.). With such OFs, RPL organizes the network topology as a Destination Oriented Directed Acyclic Graph (DODAG). However, the performance of RPL may be affected by frequent network topology changes, which may be caused by different factors like node battery depletion, link quality degradation, etc. Indeed, in such situations, the OF functions do not guarantee optimal maintenance of the RPL tree. To address this issue, this paper describes how Supervised Learning can be leveraged to improve RPL performance and energy efficiency by mitigating RPL DODAG instability when the network conditions, used by the RPL's OF functions, change frequently. We use an offline supervised learning to provide the optimal value of the transmission range (the maximal distance to which a node can send its data to another one) that mitigates the instability of the RPL network, and hence minimizes the energy consumption. The preliminary simulation results show that our proposal can improve network performance and increase network lifetime