# Learning Skill Equivalencies Across Platform Taxonomies

arXiv: https://arxiv.org/abs/2102.09377 · HTML: https://mlsanity.com/paper/2102.09377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [Data-Mining Textual Responses to Uncover Misconception Patterns](https://mlsanity.com/paper/1703.08544/) (2017)
- [Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance](https://mlsanity.com/paper/1803.09535/) (2018)
- [Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation](https://mlsanity.com/paper/1904.11798/) (2019)
- [Learning Cognitive Models using Neural Networks](https://mlsanity.com/paper/1806.08065/) (2018)

## Built on by (most cited first, 2 of 2)

- [Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions](https://mlsanity.com/paper/2405.20526/) (2024)
- [Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability](https://mlsanity.com/paper/2606.01365/) (2026)

## Abstract (arXiv)

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.