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Background: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID-19 pandemic. However, online learning has seen students lose interest and become anxious, which affects learning performance and leads to dropout. Thus, measuring students’ engagement in online learning has become imperative. It is challenging to recognize online learning engagement due to the lack of effective recognition methods and publicly accessible datasets. Methods: This study gathered a large number of online learning videos of students at a normal university. Engagement cues were used to annotate the dataset, which was constructed with three levels of engagement: low engagement, engagement, and high engagement. Then, we introduced a bi-directional long-term recurrent convolutional network (BiLRCN) for online learning engagement recognition in video. Result: An online learning engagement dataset has been constructed. We evaluated six methods using precision and recall, where BiLRCN obtained the best performance. Conclusion: Both category balance and category similarity of the data affect the performance of the results; it is more appropriate to consider learning engagement as a process-based evaluation; learning engagement can provide intervention strategies for teachers from a variety of perspectives and is associated with learning performance. Dataset construction and deep learning methods need to be improved, and learning data management also deserves attention.

Q345 steel was coated by hot dipping into molten pure aluminum and Al-Si baths. The coatings were annealed at 800 and 900 °C for 1–3 h and subsequently oxidized at 900 °C for 15 h in air. The results revealed that the thickness of the intermetallic layer increased with increasing hot-dipping time in the range of 700–750 °C, while it decreased when the hot-dipping aluminizing temperature was 800 °C. As the silicon content in the aluminum bath increased, the thickness of the intermetallic layer decreased, and the intermetallic layer/steel-substrate interface transformed from an irregular morphology into a flat morphology. The hot-dipped Al-2.5Si samples were subjected to annealing; the higher the annealing temperature and longer the annealing time, the faster the transformation of the intermediate phase in the coating. The Fe2Al5 phase was fully transformed into the ductile FeAl phase after the hot-dipped samples annealed at 900 °C for 3 h. When the outermost layer of Q345 steel was the FeAl phase, oxidation resistance of the oxide was the best.

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