  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.
