Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics

摘要

Learning mathematics has always been (and still is) a major issue. Many students fail to understand the basic concepts and/or are unable to apply them. These students end up moving to other subject areas or simply dropping out. One of the major reasons for this problem is the fact that the educational system is only prepared to apply standardized teaching methods that do not respect or fit the individual characteristics of each student. This paper presents the OPERA learning adaptive system that provides the foundations for further mathematics learning while addressing the diversity of the users/learners. OPERA collects learner interaction data to monitor the learning process in an active and contextualized way and to identify the users' difficulties and achieved knowledge in each stage. Based on the data analysis, OPERA then reorganizes the sequence of contents and provides the precise information needed to progress which makes learning much more efficient.