Enhanced hybrid neural network for automated essay scoring

<jats:title>Abstract</jats:title><jats:p>In an online learning system, the automatic scoring of an essay is key to providing immediate feedback on essays submitted by students. To the best of our knowledge, existing approaches ignore the multidimensional and heterogeneous characteristics of essays or rely too heavily on the manual creation of features; therefore, a more comprehensive method of scoring essays is required. To address this issue, this paper proposes an enhanced hybrid neural network for automated essay scoring that extracts and fuses the linguistic, semantic, and structural attributes of an essay to achieve a comprehensive representation. Specifically, linguistic attributes include not only lexical features extracted from the words of an essay but also syntactic features obtained from sentences and syntax trees. Semantic attributes include the dynamic textual semantic representation and topic similarity obtained by the text encoder. We also considered the structural attributes. The text encoder provides the overall structural representation, while the sentence similarity matrix provides the two spatial features of connectivity and aggregation. Finally, we fused the three attributes and six features to achieve a more objective and comprehensive automatic scoring. We found that our model improves the Kappa index by an average of 1.4% over the current best model when tested against four state‐of‐the‐art models using eight public data sets.</jats:p>