Integrating and visualizing learner and social data to elicit higher-order indicators in SCALA dashboard

The assessment of competencies is a difficult task; on one hand due to its subjective nature, and, on the other one, because of the difficulties to make it scalable and simple. Since ICT are becoming increasingly important learning mediating tools, data stored in learning tools could yield a wealth of information that could serve as an indicator to measure students’ progress and the development of competencies. However, the lack of data interoperability among different educational applications imposes a challenge to data mining and analytics that rely on diverse and distributed data. Besides, these educational technologies do neither usually provide a statistics module in which the teacher can obtain specific reports about students’ performance, nor visualization tools to summarize student usage data. In response to this weakness, and based on the limitations encountered in existing tools, we have developed an integrated and extensible web tool called SCALA (Scalable Competency Assessment through a Learning Analytics approach) that not only shows but also mines using analytics techniques for the discovery of student patterns and metric relations in web-based educational systems.