Automated Writing Evaluation (AWE) platforms have become common, but a significant gap remains between automated assessment and expert human feedback. We address this gap by introducing a supervised learning method that uses a multi-component student writing profile (comprising estimated CEFR levels, grammatical error rates, and vocabulary distribution) to align AI scoring with expert human judgements. In the context of an online essay-writing platform for second language learners of English, our model achieves a 36% reduction in RMSE for holistic essay scoring and an 84% improvement in similarity to human-expert annotation of grammatical errors compared to automarker scores (26% and 57% improvement from the best-performing comparable earlier work, by Zaidi et al. (2019) . Furthermore, we demonstrate that the model can predict a student’s final submission profile (CEFR level and grammatical error rate) from earlier drafts and that predictions generalise to a subsequent task, offering new possibilities for automated curriculum planning. Finally, we introduce a visualisation tool that provides educators with clear expert-aligned longitudinal views of student development.

In order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, in a task we refer to as ‘speech-unit (SU) delimitation’. We closely recreate the automatic delimitation system described by Lee and Glass (2012), ‘Sentence detection using multiple annotations’, Proceedings of INTERSPEECH, which combines a prosodic model, language model and speech-unit length model in log-linear fashion. Since state-of-the-art natural language processing (NLP) tools have been developed to deal with written text and its characteristic sentence-like units, SU delimitation helps bridge the gap between ASR and NLP, by normalising spoken data into a more canonical format. Previous work has focused on native speaker recordings; we test the system of Lee and Glass (2012) on non-native speaker (or ‘learner’) data, achieving performance above the state-of-the-art. We also consider alternative evaluation metrics which move away from the idea of a single ‘truth’ in SU delimitation, and frame this work in the context of downstream NLP applications.