Article Preview
TopIntroduction
Evaluating lexical competency within the hybridized educational landscape has become an essential focus for language educators and researchers. The hybrid English as a Foreign Language (EFL) ecosystem in Saudi Arabia leverages digital platforms such as Blackboard and Google Forms to deliver assessments (Al Fraidan, 2025a), reflecting a shift in pedagogical approaches that combine traditional and online modalities. However, the evaluation practices within this evolving landscape remain rooted in conventional methods, lacking the integration of cutting-edge tools such as artificial intelligence (AI)-driven adaptive testing. This shift necessitates a taxonomic exploration of the prevalent vocabulary assessment formats and their alignment with contemporary educational demands.
Research has suggested that the characteristics of a given task, alongside learners’ language ability, can significantly impact their performance (Alshammari, 2022). Empirical studies consistently show that assessment format and design play a pivotal role in influencing language learners’ cognitive engagement and performance (Alshammari, 2022; Khan et al., 2018; Meara, 1996; Milton, 2009; Nation, 1983; Nation, 1990; Nation, 2001; Read, 2000; Schmitt, 2000). The nature of questions, response formats, and task-specific features shape the cognitive strategies learners use (Al Fraidan, 2025b), ultimately affecting how they showcase their lexical knowledge. For instance, multiple-choice vocabulary tests tend to engage recognition skills, while cloze or fill-in-the-blank formats require deeper processing and the active retrieval of word meanings. Similarly, test items featuring synonyms, antonyms, or contextual clues elicit different cognitive strategies from learners, influencing their capacity to demonstrate a comprehensive understanding of vocabulary.
In an educational system that is increasingly incorporating technology, understanding the prevalent vocabulary assessment modalities used is imperative (Harsch & Hartig, 2016; Webb & Sasao, 2013). Such an understanding allows for a thorough analysis of how these formats influence learners’ vocabulary achievement and the strategic approaches they adopt during testing. This becomes even more pressing within Saudi Arabia’s hybridized EFL education system, where vocabulary development is not only a cornerstone of language acquisition but also critical for learners’ overall academic success (Khan et al., 2018).
The Saudi hybridized EFL ecosystem, like other global EFL contexts, places significant emphasis on vocabulary development as an integral part of language learning. Mastery of extensive lexical knowledge is crucial for EFL learners to attain language proficiency and succeed academically (Khan et al., 2018). However, while digital platforms facilitate the administration of tests, the specific types of vocabulary assessments and their implications for student performance and learning strategies have not been comprehensively explored. This represents a critical gap in the literature on language assessment within Saudi Arabia (Almuhammadi, 2020; Alshammari, 2022; Nation, 2001; Read, 2000).
This study seeks to address this gap through an in-depth taxonomic analysis of the vocabulary assessment modalities employed in the Saudi EFL hybrid education system. By exploring these modalities within a structured framework that incorporates digital platforms like Blackboard and Google Forms, the research reveals a dichotomy in assessment practices, primarily categorized into multiple-choice and open-response formats. The study explores the absence of AI-enhanced adaptive testing tools that could optimize these assessments by providing personalized, real-time feedback and accommodating diverse learner profiles.