Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms to extract relevant information predicting student retention at various levels, using higher education data and specifying the relevant variables involved in the modeling. Then, we utilize this information to help the process of knowledge discovery. We predict student retention at each of three levels during their first, second, and third years of study, obtaining models with an accuracy that exceeds 80% in all scenarios. These models allow us to adequately predict the level when dropout occurs. Among the machine learning algorithms used in this work are: decision trees, k-nearest neighbors, logistic regression, naive Bayes, random forest, and support vector machines, of which the random forest technique performs the best. We detect that secondary educational score and the community poverty index are important predictive variables, which have not been previously reported in educational studies of this type. The dropout assessment at various levels reported here is valid for higher education institutions around the world with similar conditions to the Chilean case, where dropout rates affect the efficiency of such institutions. Having the ability to predict dropout based on student’s data enables these institutions to take preventative measures, avoiding the dropouts. In the case study, balancing the majority and minority classes improves the performance of the algorithms.

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In this article we propose a collaborative logistics framework for a Port Logistics Chain (PLC) based on the principles of Supply Chain Management (SCM) that rely on stakeholders integration and collaboration, providing a reference model for the inland coordination of the PLC. A comprehensive literature review was conducted, analyzing several cases in which SCM practices have been implemented as well as studies related to port development, governance, coordination and best practices associated. This background information was used to identify current gaps in logistics management practices and potential scopes of intervention within the PLC to suggest a redesign process and configure new structures under a collaborative scheme, following the guidelines of SCM.

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