Analyzing heterogeneous learning logs using the iterative convergence method

This paper presents the use of iterative convergence method in analyzing learning log data from a three year mobile learning project. The authors of the paper propose the use of iterative convergence method as a non-standard student' evaluation method based on lessons' weights. During the project duration a large amount of log data on competitive, collaborative and augmented reality digital lesson use was collected and stored in a proprietary database. The method calculates lessons' weights and students' success in a scenario where there are no prior lessons' weights or students' success known, and where not all students complete the same set of lessons. After the application of the algorithm, the students' success data on the three different types of lessons is compared and correlated. The main implications coming for the analysis is that students have better success in competitive and augmented reality lessons and that there is negative correlation between students' success on competitive and augmented reality lessons on one side and collaborative lessons on another.