This paper proposes a time-bounded window operation to extract statistical features from raw gaze data captured in a remote teaching experiment and link them with the student’s attention level. Feature selection or dimentionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores.