Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., misspelling counts, parser outputs, n-gram language model scores). We also present a new publicly available dataset of learner sentences judged for grammaticality on an ordinal scale. In evaluations, we compare our system to the one from Post (2011) and find that our approach yields state-of-the-art performance.

This paper describes HASbot, an automated text scoring and real‐time feedback system designed to support student revision of scientific arguments. Students submit open‐ended text responses to explain how their data support claims and how the limitations of their data affect the uncertainty of their explanations. HASbot automatically scores these text responses and returns the scores with feedback to students. Data were collected from 343 middle‐ and high‐school students taught by nine teachers across seven states in the United States. A mixed methods design was applied to investigate (a) how students’ utilization of HASbot impacted their development of uncertainty‐infused scientific arguments; (b) how students used feedback to revise their arguments, and (c) how the current design of HASbot supported or hindered students’ revisions. Paired sample t tests indicate that students made significant gains from pretest to posttest in uncertainty‐infused scientific argumentation, ES = 1.52 SD, p < 0.001. Linear regression analysis results indicate that students' HASbot use significantly contributed to their posttest performance on uncertainty‐infused scientific argumentation while gender, English language learner status, and prior computer experience did not. From the analysis of videos, we identified several affordances and limitations of HASbot.

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This study describes an approach for modeling the discourse coherence of spontaneous spoken responses in the context of automated assessment of non-native speech. Although the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spontaneous spoken language, little prior research has been done to assess a speaker's coherence in the context of automated speech scoring. To address this, we first present a corpus of spoken responses drawn from an assessment of English proficiency that has been annotated for discourse coherence. When adding these discourse annotations as features to an automated speech scoring system, the accuracy in predicting human proficiency scores is improved by 7.8% relative, thus demonstrating the effectiveness of including coherence information in the task of automated scoring of spontaneous speech. We further investigate the use of two different sets of features to automatically model the coherence quality of spontaneous speech, including a set of features originally designed to measure text complexity and a set of surface-based features describing the speaker's use of nouns, pronouns, conjunctions, and discourse connectives in the spoken response. Additional experiments demonstrate that an automated speech scoring system can benefit from coherence scores that are generated automatically using these feature sets.

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