Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs

Abstract

Determining when instructor intervention is needed, based on learners' comments and their urgency in massive open online course (MOOC) environments, is a known challenge. To solve this challenge, prior art used autonomous machine learning (ML) models. These models are described as having a "black-box"nature, and their output is incomprehensible to humans. This paper shows how to apply eXplainable Artificial Intelligence (XAI) techniques to interpret a MOOC intervention model for urgent comments detection. As comments were selected from the MOOC course and annotated using human experts, we additionally study the confidence between annotators (annotator agreement confidence), versus an estimate of the class score of making a decision via ML, to support intervention decision. Serendipitously, we show, for the first time, that XAI can be further used to support annotators creating high-quality, gold standard datasets for urgent intervention.