Models for automatic learner engagement estimation
Year: 2018
Type: article
Abstract: Automatic estimation of student engagement can help computer-based learning systems adapt to individual learners. Linear models trained on Gabor features established cutting-edge yet sub-human accuracy on this task, while Convolutional neural networks (CNNs) heavily overfit to the dataset's few subjects. We found that transfer learning enabled linear ridge regression to leverage CNN features learned for image recognition and face re-identification tasks. Our best model achieved a four-fold cross-validated correlation of r=0.581, significantly outperforming the state-of-the-art r=0.522. Our information strength metric correlated with model accuracy (FaceNet, r=0.755; ImageNet, r=0.077), inviting further study of feature utility prediction.
Source: Digital WPI
Author: Eli Sanborn Skeggs
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2947633381