Human-Like Active Learning: Machines Simulating the Human Learning Process

TL;DR

This paper demonstrates that human-like active learning significantly improves machine learning performance, confirmed through experiments comparing active and passive learning in both humans and machines.

Contribution

It introduces a novel approach of imitating human active learning in machines and empirically confirms its effectiveness in enhancing learning outcomes.

Findings

Active learning outperforms passive learning in humans.

Machine experiments based on human active learning show better results.

Empirical confirmation of active learning's positive effect on machine performance.

Abstract

Although the use of active learning to increase learners' engagement has recently been introduced in a variety of methods, empirical experiments are lacking. In this study, we attempted to align two experiments in order to (1) make a hypothesis for machine and (2) empirically confirm the effect of active learning on learning. In Experiment 1, we compared the effect of a passive form of learning to active form of learning. The results showed that active learning had a greater learning outcomes than passive learning. In the machine experiment based on the human result, we imitated the human active learning as a form of knowledge distillation. The active learning framework performed better than the passive learning framework. In the end, we showed not only that we can make build better machine training framework through the human experiment result, but also empirically confirm the result of human experiment through imitated machine experiments; human-like active learning have crucial effect on learning performance.