Big data is an emerging topic, with huge investments in IT and education worlds. Together with awareness of the knowledge discovery and education improvement progresses, big data concept has come with a growing consciousness. There are several educational data mining analysis methods created, evaluated and presented in the literature in this manner. But, how the educational big data for finding “intelligently” valuable results to benefit students, teachers and administrators will be collected, filtered and handled? In this study, a unique data collection, filtering and evaluation framework for educational data mining was designed, evaluated and presented. In this perspective, an IT infrastructure and process monitoring software for gathering client computers’ data of 3 laboratories in a public high school was developed. Time series big data was collected during 5 months from 62 computers with this back stage working software. After filtering this data with eligible methods, resultant data was evaluated with Pearson’s correlation analysis between students’ rate of interactions with computers and their exam grades on laboratory courses. Results showed that students’ computer interaction and their success in the courses are highly correlated.
ABSTRACT. “Learning Analytics” became a buzzword during the hype surrounding the advent of “big data” MOOCs, however, the concept has been around for over two decades. When the first online courses became available it was used as a tool to increase student success in particular courses, frequently combined with the hope of conducting educational research. In recent years, the same term started to be used on the institutional level to increase retention and decrease time-to-degree. These two applications, within particular courses on the one hand and at the institutional level on the other, are at the two extremes of the spectrum of Learning Analytics – and they frequently appear to be worlds apart. The survey describes affordances, theories and approaches in these two categories.