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Esta investigación de tipo cualitativo se propone analizar la incidencia del Control Fiscal en el Programa de Alimentación Escolar en el periodo 2008 - 2011 en el Municipio de Santiago de Cali. Este programa constituye una estrategia diseñada por el Gobierno Nacional para fortalecer la política de permanencia en las instituciones educativas oficiales, a través de éste se brinda el suministro de un complemento alimenticio a la población estudiantil en edad escolar. En la realización de esta investigación se interpretó y comprendió el rol desempeñado por cada uno de los actores entrevistados que tuvieron participación en el programa, se conocieron las acciones de control ejercidas a éste por la Contraloría General de Santiago de Cali, se identificaron las acciones que al respecto aplicó el municipio y la opinión de quienes intervenían en las diferentes etapas del programa acerca del accionar de la Contraloría y de cómo estos percibían el ejercicio del mandato constitucional del control fiscal. ; This qualitative research aims to analyze the incidence of Fiscal Control in the School Feeding Program in the period 2008 - 2011 in the Municipality of Santiago de Cali. This program constitutes a strategy designed by the National Government to strengthen the policy of permanence in official educational institutions, through it the provision of a food supplement to the student population of school age is provided. In conducting this research, the role played by each of the interviewed actors who participated in the program was interpreted and understood, the control actions exercised by the General Comptroller of Santiago de Cali were known, the actions that were In this regard, the municipality and the opinion of those who intervened in the different stages of the program applied about the actions of the Comptroller's Office and how they perceived the exercise of the constitutional mandate of fiscal control. ; Magister en Planeación Para el Desarrollo ; http://unidadinvestigacion.usta.edu.co ; Maestría

The amounts of slurry and manure produced each year are steadily rising as a result of an increasing demand for livestock products, which are expected to almost double by 2050 [1]. This two byproducts of farm-activity are commonly used as a fertilizer for crops production. However, their direct disposal may also overcome soils capacity to absorb nutrients in some areas [2], thus giving to rise to health and environmental issues. This demands the use of feasible and efficient waste management technologies that help to limit the impact of these waste ; This project has received funding from the Bio Based Industries Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 668128

Using data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets. Data itself is not new. Data has always been generated and used to inform decision-making. However, most of this was structured and organised, through regular data collections, surveys, etc. What is new, with the invention and dominance of the Internet and the expansion of digital systems across all sectors, is the amount of unstructured data we are generating. This is what we call the digital footprint: the traces that individuals leave behind as they interact with their increasingly digital world. Data analytics is the process where data is collected and analysed in order to identify patterns, make predictions, and inform business decisions. Our capacity to perform increasingly sophisticated analytics is changing the way we make predictions and decisions, with huge potential to improve competitive intelligence. These examples suggest that the actions from data mining and analytics are always automatic, but that is less often the case. Educational Data Mining (EDM) and Learning Analytics (LA) have the potential to make visible data that have heretofore gone unseen, unnoticed, and therefore unactionable. To help further the fields and gain value from their practical applications, the recommendations are that educators and administrators: • Develop a culture of using data for making instructional decisions; • Involve IT departments in planning for data collection and use; • Be smart data consumers who ask critical questions about commercial offerings and create demand for the most useful features and uses; • Start with focused areas where data will help, show success, and then expand to new areas; • Communicate with students and parents about where data come from and how the data are used; • Help align state policies with technical requirements for online learning systems. This report documents the first steps conducted within the SPEET1 ERASMUS+ project. It describes the conceptualization of a practical tool for the application of EDM/LA techniques to currently available academic data. The document is also intended to contextualise the use of Big Data within the academic sector, with special emphasis on the role that student profiles and student clustering do have in support tutoring actions. The report describes the promise of educational data mining (seeking patterns in data across many student actions), learning analytics (applying predictive models that provide actionable information), and visual data analytics (interactive displays of analyzed data) and how they might serve the future of personalized learning and the development and continuous improvement of adaptive systems. How might they operate in an adaptive learning system? What inputs and outputs are to be expected? In the next sections, these questions are addressed by giving a system-level view of how data mining and analytics could improve teaching and learning by creating feedback loops. Finally, the proposal of the key elements that conform a software application that is intended to give support to this academic data analysis is presented. Three different key elements are presented: data, algorithms and application architecture. From one side we should have a minimum data available. The corresponding relational data base structure is presented. This basic data can always be complemented with other available data that may help to decide or/and to explain decisions. Classification algorithms are reviewed and is presented how they can be used for the generation of the student clustering problem. A convenient software architecture will act as an umbrella that connects the previous two parts. The document is intended to be useful for a first understanding of academic data analysis. What we can get and what we do need to do. This is the first of a series of reports that taken all together will provide a complete and consistent view towards the inclusion of data mining as a helping hand in the tutoring action. ; European Union ; Programme: Erasmus+ Project Reference: 2016-1-ES01-KA203-025452 ; info:eu-repo/semantics/draft