Looking For A Match: Self-supervised Clustering For Automatic Doubt Matching In e-learning Platforms

TL;DR

This paper introduces a self-supervised, label-agnostic approach for automatic doubt matching in e-learning platforms, significantly reducing resolution time and outperforming supervised methods.

Contribution

It develops a novel custom BYOL framework combining domain-specific augmentation for effective doubt representation learning without labels.

Findings

Custom BYOL improves top-1 matching accuracy by ~6%.

It outperforms supervised learning methods in doubt matching.

Both BYOL-based methods perform on par or better than human labeling.

Abstract

Recently, e-learning platforms have grown as a place where students can post doubts (as a snap taken with smart phones) and get them resolved in minutes. However, the significant increase in the number of student-posted doubts with high variance in quality on these platforms not only presents challenges for teachers' navigation to address them but also increases the resolution time per doubt. Both are not acceptable, as high doubt resolution time hinders the students learning progress. This necessitates ways to automatically identify if there exists a similar doubt in repository and then serve it to the teacher as the plausible solution to validate and communicate with the student. Supervised learning techniques (like Siamese architecture) require labels to identify the matches, which is not feasible as labels are scarce and expensive. In this work, we, thus, developed a label-agnostic doubt matching paradigm based on the representations learnt via self-supervised technique. Building on prior theoretical insights of BYOL (bootstrap your own latent space), we propose custom BYOL which combines domain-specific augmentation with contrastive objective over a varied set of appropriately constructed data views. Results highlighted that, custom BYOL improves the top-1 matching accuracy by approximately 6\% and 5\% as compared to both BYOL and supervised learning instances, respectively. We further show that both BYOL-based learning instances performs either on par or better than human labeling.