  Recent research has demonstrated the capability of physiological signals to
infer both user emotional and attention responses. This presents an opportunity
for leveraging widely available physiological sensors in smartwatches, to
detect real-time emotional cues in users, such as stress and excitement. In
this paper, we introduce SensEmo, a smartwatch-based system designed for
affective learning. SensEmo utilizes multiple physiological sensor data,
including heart rate and galvanic skin response, to recognize a student's
motivation and concentration levels during class. This recognition is
facilitated by a personalized emotion recognition model that predicts emotional
states based on degrees of valence and arousal. With real-time emotion and
attention feedback from students, we design a Markov decision process-based
algorithm to enhance student learning effectiveness and experience by by
offering suggestions to the teacher regarding teaching content and pacing. We
evaluate SensEmo with 22 participants in real-world classroom environments.
Evaluation results show that SensEmo recognizes student emotion with an average
of 88.9% accuracy. More importantly, SensEmo assists students to achieve better
online learning outcomes, e.g., an average of 40.0% higher grades in quizzes,
over the traditional learning without student emotional feedback.
