Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency.

如今，计算智能辅助的教育质量自主评估已成为智慧教育管理领域越来越普遍的关注点。由于教育质量评估是一个复杂的过程，存在多种异质因素，因此利用简单的信息模态和标准进行有效的评估仍然具有挑战性。为了解决这个问题，本文引入了多尺度深度学习，并提出了一种新颖的数据驱动的教育质量自主评估框架。具体来说，选择异构指标的四个方面作为基本指标，并制定扩张的卷积神经网络结构来进行多尺度特征提取。然后采用结构方程进行多元表征并输出最终的评估结果。最后，对现实的教育运营数据进行了一些模拟，以评估所提出的自主评估框架的效率。从结果中可以推断出两个方面的发现。一方面，多种指标的多源融合对于自主教育评估很有意义。另一方面，多尺度深度学习可以对评估效率提供一些有益的提升。