Ensuring the integrity and interoperability of educational usage and social data through Caliper framework to support competency-assessment

Abstract

The acquisition of knowledge is no longer enough to succeed in a society characterized by a constant change and high levels of uncertainty. Accordingly, universities have increasingly emphasized skills and competencies as central elements of students' development. However, the assessment of these competencies is not an easy task. The availability of data that learners generate in educational application offer great potential to study how learning takes place, and thus, to gather evidences for competency-assessment. The lack of interoperability and the decentralization of those educational applications poses a challenge to exploit those trace data. To face these problems we have designed and developed SCALA (Scalable Competence Assessment through a Learning Analytics approach), an analytics system that integrates usage (how the user interacts with resources and platforms) and social (how students and teachers interact among them) trace data to support competency assessment. In this paper, we are going to focus in how we have Extracted, Transformed and Loaded (ETL) those heterogeneous data sources in a single data model following the Caliper framework for ensuring the integrity and interoperability. Finally, we show some initial results of the graphs and learning analytics techniques to support the competency-based assessment.