  Identifying the regions of a learning resource that a learner pays attention
to is crucial for assessing the material's impact and improving its design and
related support systems. Saliency detection in videos addresses the automatic
recognition of attention-drawing regions in single frames. In educational
settings, the recognition of pertinent regions in a video's visual stream can
enhance content accessibility and information retrieval tasks such as video
segmentation, navigation, and summarization. Such advancements can pave the way
for the development of advanced AI-assisted technologies that support learning
with greater efficacy. However, this task becomes particularly challenging for
educational videos due to the combination of unique characteristics such as
text, voice, illustrations, animations, and more. To the best of our knowledge,
there is currently no study that evaluates saliency detection approaches in
educational videos. In this paper, we address this gap by evaluating four
state-of-the-art saliency detection approaches for educational videos. We
reproduce the original studies and explore the replication capabilities for
general-purpose (non-educational) datasets. Then, we investigate the
generalization capabilities of the models and evaluate their performance on
educational videos. We conduct a comprehensive analysis to identify common
failure scenarios and possible areas of improvement. Our experimental results
show that educational videos remain a challenging context for generic video
saliency detection models.
