  Enhancing the expressiveness of human teaching is vital for both improving
robots' learning from humans and the human-teaching-robot experience. In this
work, we characterize and test a little-used teaching signal:
\textit{progress}, designed to represent the completion percentage of a task.
We conducted two online studies with 76 crowd-sourced participants and one
public space study with 40 non-expert participants to validate the capability
of this progress signal. We find that progress indicates whether the task is
successfully performed, reflects the degree of task completion, identifies
unproductive but harmless behaviors, and is likely to be more consistent across
participants. Furthermore, our results show that giving progress does not
require extra workload and time. An additional contribution of our work is a
dataset of 40 non-expert demonstrations from the public space study through an
ice cream topping-adding task, which we observe to be multi-policy and
sub-optimal, with sub-optimality not only from teleoperation errors but also
from exploratory actions and attempts. The dataset is available at
\url{https://github.com/TeachingwithProgress/Non-Expert\_Demonstrations}.
