Sensor-Free or Sensor-Full: A Comparison of Data Modalities in Multi-Channel Affect Detection

  title={Sensor-Free or Sensor-Full: A Comparison of Data Modalities in Multi-Channel Affect Detection},
  author={Luc Paquette and Jonathan P. Rowe and Ryan Shaun Joazeiro de Baker and Bradford W. Mott and James C. Lester and Jeanine DeFalco and Keith W. Brawner and Robert A. Sottilare and Vasiliki Georgoulas},
Computational models that automatically detect learners’ affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors—which recognize learners’ affective states at run-time using behavior logs and sensor data—have advanced substantially across a range of K-12 and postsecondary education settings. Machine learningbased affect detectors can be developed to utilize several types of data, including software logs, video/audio… CONTINUE READING
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