Design of Blended Teaching Model Based on Emotion Recognition and Language Learning

Area of Science:

• Educational Technology
• Computer Science
• Psychology

Background:

• Internet technology significantly impacts education, shifting students from passive knowledge recipients.
• Traditional education models in China focus on exams, neglecting student interests and active learning.
• Information explosion necessitates evolving learning modes to meet modern educational demands.

Purpose of the Study:

• To establish a blended teaching model combining traditional classrooms with network applications.
• To transform passive learning identities using network big data characteristics.
• To analyze student emotions and language learning results within a blended learning environment.

Main Methods:

• Integration of offline traditional classrooms with online learning platforms.
• Utilization of network big data for learning process analysis.
• Implementation of emotion recognition and language learning result analysis via a network platform.

Main Results:

• The proposed blended teaching model leverages network science and technology for the entire teaching process.
• Emotion recognition evaluation rate of HTMC reached 71.52%.
• Emotion recognition frequency of ETMC reached 73.89%, reflecting emotional changes in blended learning.

Conclusions:

• The blended teaching model effectively transforms passive learning into active engagement.
• Network platforms enhance the teaching process through data analysis and emotion recognition.
• The developed model and its recognition parameters provide valuable insights into student emotional dynamics during blended learning.