Detection of cheating by decimation algorithm

TL;DR

This paper extends item response theory to detect cheating students using a greedy inference algorithm, demonstrating its effectiveness with limited training data and sparse interaction assumptions.

Contribution

It introduces a greedy algorithm for inferring cheating in exams, outperforming standard sparse interaction inference methods in Boltzmann machine learning.

Findings

Greedy algorithm performs well with limited training data.

Sparse interactions are key to detecting cheating effectively.

The method outperforms standard inference approaches in several aspects.

Abstract

We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, the greedy algorithm that we employed in the present study exhibits good performance with a few number of training data. The key point is the sparseness of the interactions in our problem in the context of the Boltzmann machine learning: the existence of cheating students is expected to be very rare (possibly even in real world). We compare a standard approach to infer the sparse interactions in the Boltzmann machine learning to our greedy algorithm and we find the latter to be superior in several aspects.