vocabulary-assessment
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- PaperTESOL Quarterly21 Jul 2026
Receptive Meaning‐Recall Knowledge of Derivational Morphology Among EFL Learners: A Case for L1 ‐Population‐Specific Assessment
Mastoor Al‐Kaboody, Geoffrey G. Pinchbeck, Joseph P. Vitta, Christopher Nicklin et al.
Arabic-speaking EFL learners consistently perform better on base word meaning-recall than on derived forms, with proficiency level moderating this gap but not eliminating substantial knowledge deficits. These findings support a (f)lemma-based approach to vocabulary assessment and instruction for low-to-intermediate proficiency Arabic-speaking learners, highlighting the need for explicit teaching of derivational morphology.
Original abstract
Vocabulary knowledge plays a crucial role in L2 proficiency, significantly impacting learners' reading comprehension. Although extensive research has explored whether knowledge of base words extends to recognition of derivational forms, results are inconsistent, especially among learners from non‐Indo‐European linguistic backgrounds. This study investigates the relationship between base word knowledge and comprehension of derivational forms among Arabic‐speaking EFL learners, examining how this relationship varies with proficiency. Using a meaning‐recall vocabulary test consisting of 30 base words and 46 derivational forms, data from 108 learners across four Common European Framework of Reference (CEFR) levels (Below A1 to B1+) were analyzed through logistic mixed effects modeling. Results indicated that learners consistently performed better with base forms than derivations, with proficiency significantly moderating this relationship. Higher proficiency learners showed increased accuracy, but substantial knowledge gaps persisted across all proficiency levels, highlighting the complexity of derivational morphology for Arabic‐speaking learners. These findings support a (f)lemma‐based approach in vocabulary assessment and instruction for low‐ to intermediate‐proficiency Arabic‐speaking learners, emphasizing the need for explicit attention to derivational morphology in curriculum design and instructional practices.
- PaperarXiv — Language & NLP (cs.CL)9 May 2026
Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling
Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tsamarah Rana Nugraha, Ahmad Cahyono Adi et al.
Proposes Context-Aligned Contrastive Regression, combining Ridge regression ensemble with cross-view context and ordinal soft contrastive learning to improve lexical difficulty prediction across different L1 backgrounds. Experiments show improved cross-lingual alignment, ordinal structure capture, and reduced model bias, leading to more stable performance.
Original abstract
Lexical difficulty prediction is a fundamental problem in language learning and readability assessment, requiring models to estimate word difficulty across different first-language (L1) backgrounds. However, existing approaches rely on regression-only training with scalar supervision, which does not explicitly structure the representation space, limiting their ability to capture cross-lingual alignment and ordinal difficulty. To mitigate these issues, we propose Context-Aligned Contrastive Regression, which integrates Ridge regression ensemble with two complementary objectives, i.e., Cross-View Context and Ordinal Soft Contrastive Learning. Experiments on three L1 datasets show that (i) contrastive objectives improve cross-lingual representation alignment while preserving language-specific nuances, (ii) the learned representations capture the ordinal structure of lexical difficulty, and (iii) the ensemble effectively mitigates systematic biases of individual models, leading to more stable performance across difficulty levels.
- PaperETS Research Report Series13 Mar 2026
Toward an Automatic Method for Generating Topical Vocabulary Test Forms for Specific Reading Passages
Michael Flor, Zuowei Wang, Paul Deane, Tenaha O'Reilly
The K-tool automatically generates topical vocabulary tests to measure students' background knowledge for specific reading passages. It detects the topic of a text and produces vocabulary items with high topic association and distractor words with low association. Designed for native English-speaking middle and high school students, the system aims to predict reading comprehension readiness.
Original abstract
Background knowledge is typically needed for successful comprehension of topical and domain-specific reading passages, such as in the STEM domains. However, there are few automated measures of student knowledge that can be readily deployed and scored in time to make predictions on whether a given student will likely be able to understand a specific content-area text. In this research report, we present our effort in developing the K-tool, an automated system for generating topical vocabulary tests that measure students’ background knowledge related to a specific text. The system automatically detects the topic of a given text and produces topical vocabulary items based on their relationship with the topic. This information is used to automatically generate background knowledge forms that contain words that are highly related to the topic and words that share similar features but do not share high associations to the topic. Prior research has indicated that performance on such tasks can help determine whether a student is likely to understand a particular text based on their knowledge state. The described system is intended for use with middle and high school student populations of native speakers of English. It is designed to handle single reading passages and is not dependent on any corpus or text collection. In this report, we describe the system architecture and present an initial evaluation of the system outputs. Suggested citation: Flor, M., Wang, Z., Deane, P., & O’Reilly, T. (2025). Toward an automatic method for generatingtopical vocabulary test forms for specific reading passages (Research Report No. RR-26-02). ETS.