  Assessment and reporting of skills is a central feature of many digital
learning platforms. With students often using multiple platforms,
cross-platform assessment has emerged as a new challenge. While technologies
such as Learning Tools Interoperability (LTI) have enabled communication
between platforms, reconciling the different skill taxonomies they employ has
not been solved at scale. In this paper, we introduce and evaluate a
methodology for finding and linking equivalent skills between platforms by
utilizing problem content as well as the platform's clickstream data. We
propose six models to represent skills as continuous real-valued vectors and
leverage machine translation to map between skill spaces. The methods are
tested on three digital learning platforms: ASSISTments, Khan Academy, and
Cognitive Tutor. Our results demonstrate reasonable accuracy in skill
equivalency prediction from a fine-grained taxonomy to a coarse-grained one,
achieving an average recall@5 of 0.8 between the three platforms. Our skill
translation approach has implications for aiding in the tedious, manual process
of taxonomy to taxonomy mapping work, also called crosswalks, within the
tutoring as well as standardized testing worlds.
