Learning Skill Equivalencies Across Platform Taxonomies

TL;DR

This paper presents a machine learning-based method to automatically identify and link equivalent skills across different digital learning platforms, improving cross-platform assessment and reporting.

Contribution

It introduces six models that use problem content and clickstream data to map skills between diverse taxonomies, enabling scalable crosswalks.

Findings

Achieved an average recall@5 of 0.8 in skill equivalency prediction

Demonstrated effective mapping between fine-grained and coarse-grained taxonomies

Validated methods on three major learning platforms

Abstract

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.