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The developed system determines language proficiency by tracking the gaze of ESL test takers. To determine proficiency, the system relies on an eye tracker camera that records gaze patterns of test takers as they read free-form English sentences and subsequent comprehension questions.

The technique includes a machine learning algorithm to process collected gaze patterns and to compare patterns of native and nonnative English speakers. This feature set and comparison establish a baseline for how the system determines English proficiency.

Proficiency scores obtained with the eye tracking test strongly correlate with scores from standardized tests such as the Michigan English Test (MET) and TOEFL. Comparison of eye tracking test results to results from standardized English tests can serve as a test diagnosis and calibration tool as well as a tool for outcome prediction on standardized tests. Studies demonstrate fidelity in the described approach and high consistency for repeat test takers.