Learning Analytics and Software Engineering Competences

This paper examines some issues and questions regarding the use of Learning Analytics (LA) in the measurement, collection, analysis, and reporting of data related to Software Engineering (SE) competences obtained by master students at a university. The paper's authors review some results of iSECRET Project with the goal of introducing Data Mining approach for better control of the teaching process based on remote competences evaluation. This shift gave the opportunity to apply 'business intelligence' techniques to educational data. The research focuses on progress and professional standards for accountability systems used in Software Engineering Master Program with an emphasis on professional competences defined by e-CF Framework. The authors pay particular attention to the information infrastructure and mathematical background needed to conduct effective Learning Analytics at SE Master Program. Accordingly, the paper attempts to develop an original mathematical model was done. The approach is based on the pilot remote measurement of the components of competences using SECEIP Portal. The developed technique of using Learning Analytics for measuring the competences at SE Master program may be used for reengineering of Legacy University Management Information Systems and University executive staff training.