Assessing Language Proficiency from Eye Movements in Reading
Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences.
['[BOLD] Features', '[BOLD] MET Fixed', '[BOLD] MET Any', '[BOLD] TOEFL Fixed', '[BOLD] TOEFL Any']
We evaluate the ability of EyeScore to capture language proficiency by comparing it against our two external proficiency tests, MET and TOEFL. Similarly to the EyeScore outcomes, the best performance in the Fixed Text regime is obtained using the WFC feature set, with a Pearson’s r of 0.7 and MAE of 3.31 for MET. This result is highly competitive with correlations between different standardized English proficiency tests. On TOEFL, WFC features obtain the strongest MAE of 6.68, while S-Clusters have a higher r coefficient of 0.55.