The internal consistency and accuracy of automatically scored written receptive meaning-recall data: A preliminary study

Abstract

Vocableveltest.org is a testing platform on which users can create online self-marking meaning-recall (reading or listening) and form-recall (typing) tests that address a number of limitations of the existing vocabulary level tests and vocabulary size tests. A major limitation of many existing vocabulary tests is the written receptive meaning-recognition (multiple-choice or matching) format which is associated with increased error due to guessing and decreased power to measure the type of vocabulary knowledge suitable for reading practice (McLean et al., 2020, Stewart et al., 2021a, Stoeckel et al., 2021), despite being designed for this purpose (Nation, 2012, Schmitt et al., 2020, Webb et al., 2017). Conversely, scoring meaning-recall tests by hand is labour-intensive, and the internal consistency and accuracy of automatically marked data are unknown. Thus, this study investigated the internal consistency and accuracy of automatically marked responses of 98 words from the fifth 100 most frequent words of English. This study tested for knowledge of high-frequency words as a more robust test of the marking system, as these words possess multiple-meaning senses, making their automatic marking problematic. Furthermore, the predicted limited range of learners’ knowledge of these 98 words was expected to result in data of a low internal consistency. However, the automatically marked data had a high internal consistency (Cronbach’s α = 0.868) and was 98% similar to human marked meaning-recall responses.