Details
Poster
Presenter(s)
![Ahsan Raza Khan Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/63312.jpg?h=2c352d20&itok=p_fvjAcB)
Display Name
Ahsan Raza Khan
- Affiliation
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AffiliationUniversity of Glasgow
- Country
Abstract
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.