The objective of the research is to develop a methodology to analyse a set of data extracted from a learning management system, in order to implement a dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching–learning processes. The methodology used consisted of analysing 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analysed were number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show that the data analysis was carried out on the (o Konstanz Information Miner (KNIME) data mining analysis platform, and the model was implemented in five phases: requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customised dashboard for teachers to identify observable behavioural patterns that allow them to make decisions to improve the teaching–learning processes of students. Keywords: Analytics, dashboard, KNIME Learning, personalised, teaching

The objective of the research is to develop a methodology to analyze a set of data extracted from a Learning Management System (LMS), in order to implement a Dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching-learning processes. The methodology used consisted of the analysis of 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analyzed were: number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show the data analysis in the KNIME data mining analysis platform, the model was implemented in five phases: Requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customized Dashboard for teachers to identify observable behavioral patterns that allow them to make decisions to improve the teaching-learning processes of students. Keywords: Analytics, dashboard, KNIME Learning,  personalized, teaching.