  Explanations are pervasive in our lives. Mostly, they occur in dialogical
form where an {\em explainer} discusses a concept or phenomenon of interest
with an {\em explainee}. Leaving the explainee with a clear understanding is
not straightforward due to the knowledge gap between the two participants.
Previous research looked at the interaction of explanation moves, dialogue
acts, and topics in successful dialogues with expert explainers. However,
daily-life explanations often fail, raising the question of what makes a
dialogue successful. In this work, we study explanation dialogues in terms of
the interactions between the explainer and explainee and how they correlate
with the quality of explanations in terms of a successful understanding on the
explainee's side. In particular, we first construct a corpus of 399 dialogues
from the Reddit forum {\em Explain Like I am Five} and annotate it for
interaction flows and explanation quality. We then analyze the interaction
flows, comparing them to those appearing in expert dialogues. Finally, we
encode the interaction flows using two language models that can handle long
inputs, and we provide empirical evidence for the effectiveness boost gained
through the encoding in predicting the success of explanation dialogues.
