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Although modeled after human\u2013human learning and teaching, existing RLfD methods make robots act as passive observers without the feedback of their learning statuses in the demonstration gathering stage. To facilitate a more transparent teaching process, we propose two mechanisms of\n            <jats:italic>Learning Engagement<\/jats:italic>\n            , Z2O-Mode and D2O-Mode, to dynamically adapt robots\u2019 attentional and behavioral engagement expressions to their actual learning status. Through an online user experiment with 48 participants, we find that, compared with two baselines, the two kinds of\n            <jats:italic>Learning Engagement<\/jats:italic>\n            can lead to users\u2019 more accurate mental models of the robot\u2019s learning progress, more positive perceptions of the robot, and better teaching experience. 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