Learning Instructor Intervention from MOOC Forums: Early Results and Issues

TL;DR

This paper develops a machine learning classifier to predict when MOOC instructors should intervene in forum discussions, improving accuracy by using forum type information and highlighting real-world challenges.

Contribution

Introduces a novel classifier incorporating forum type data to predict instructor intervention, advancing prior methods in MOOC forum moderation.

Findings

Classifier significantly outperforms previous state-of-the-art.

Intervention decision sensitivity varies with instructor preferences.

Real-world validation shows the complexity of intervention decisions.

Abstract

With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of instructor intervention. Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should intervene in a discussion thread or not. By incorporating novel information about a forum's type into the classification process, we improve significantly over the previous state-of-the-art. We show how difficult this decision problem is in the real world by validating against indicative human judgment, and empirically show the problem's sensitivity to instructors' intervention preferences. We conclude this paper with our take on the future research issues in intervention.