Research on Hybrid Intelligent Assessment Model of English Writing Driven by Big Data

Abstract

With the online English writing class in colleges and universities, teachers are faced with the contradiction between massive composition, behavior log processing, and accurate tracking of students' writing ability. Based on the one-semester curriculum data of the author's university, this paper studies an intelligent writing evaluation model that can be implemented, explained, and integrated into the closed-loop teaching under the big data environment. This study designs a multi-loop evaluation framework, constructs models with different feature combinations, and compares the performance of evaluation indicators. Then, it explores the relationship between process behavior and writing progress by combining platform behavior data and verifies the applicability of the model through teacher interviews and manual re-evaluation. On the premise of not adding extra burden to teachers, this paper integrates intelligent assessment into daily writing tasks and provides reproducible technical paths and experiences for continuous formative assessment.