In our longitudinal research, we have been working towards an adaptive learning system automatically detecting student engagement as a higher-order user state in real-time. The labeled data necessary for supervised learning can be obtained through labeling conducted by human experts. Using multiple labelers to label collected data and obtaining agreement among different labelers on same samples of data is critical to train final engagement model accurately. Addressing these challenges, we developed a rigorous labeling process (HELP) specific to educational context with multi-faceted labels and multiple expert labelers....
Authors
Sinem Aslan
Sinem Emine Mete
Related Content
Integrating Real-Time and Batch Processing in a Polystore
This paper describes a stream processing engine called S-Store and its role in the BigDAWG polystore. Fundamentally, S-Store acts as...
Learning to Propose Objects
We present an approach for highly accurate bottom-up object segmentation. Given an image, the approach rapidly generates a set of...
Named Entity Recognition on Twitter for Turkish using...
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as...
Dense Monocular Depth Estimation in Complex Dynamic Scenes
We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene....