Students are increasingly using online materials to learn new subjects or to supplement their learning process in educational institutions. Issues regarding gender bias have been raised in the context of formal education and some measures have been proposed to mitigate them. In our previous work, we investigate the perceived gender bias in YouTube using manually annotations for detecting the narrators' perceived gender in educational videos. In this work, our goal is to evaluate the perceived gender bias in online education by exploiting an automated annotations. The automated pipeline has already proposed in a recent paper, thus in this paper we only share our empirical results with important findings. Our results show that educational videos are biased towards the male and STEM-related videos are more biased than their NON-STEM counterparts.

翻译：学生越来越多地利用在线材料学习新的科目或补充教育机构的学习过程,在正规教育中提出了有关性别偏见的问题,并提议了一些措施来减轻这些问题。在以往的工作中,我们利用人工说明调查YouTube中存在的性别偏见,以探测教育录像中旁白者的性别观念。在这项工作中,我们的目标是利用自动说明来评价在线教育中存在的性别偏见。自动化编审管道已在近期的一份文件中提出,因此在本文件中我们只分享我们的经验结果和重要的调查结果。我们的结果显示,教育录像偏向男性和与STEM有关的录像,比非STEM的录像更偏向男性和STEM。