Academic Quality Management System Audit Using Artificial Intelligence Techniques

Abstract

Quality management systems are a challenge for higher education centers. Nowadays, there are different management systems, for instance: quality, environmental, information security, etc. that can be applied over education centers, but to implement all of them is not a guarantee of education quality because the educational process is very complex. However, a few years ago the Quality Management Systems for higher education centers are taking importance especially in Europe and North America, although in Latin America is an unexplored field. Higher education centers quality is a very complex problem because it is difficult to measure the quality since there are a lot of academic processes as enrollment, matriculation, teaching-learning with a lot of stakeholders as students, teachers, authorities even society; in a lot of locations as campuses, buildings, laboratories with different resources. Each process generates a lot of records and documentation. This information has a varied nature and it is present at a structured and no-structured form. In this context, artificial intelligence techniques can help us to analyze and management knowledge. Our work presents a new approach to audit academic information with machine learning and information retrieval. In our experiments, we used information about syllabus, grades, assessments and online content from a Latin American University. We conclude that using artificial intelligence techniques minimize the decision support time, it allows full data analysis instead of a data sample and it finds out patterns never seen in the case study university.