Design of network English autonomous learning education system based on human-computer interaction

Area of Science:

• Computer Science
• Educational Technology
• Artificial Intelligence

Background:

• Human-Computer Interaction (HCI) and information technologies are transforming digital learning environments.
• Network and multimedia technologies are reshaping Autonomous Learning Systems (ALS).
• Improving interactive functions is crucial for enhancing autonomous learning.

Purpose of the Study:

• To introduce an Optimized Deep Learning Network (ODNN) for building an Autonomous English Learning System (AELS).
• To enhance autonomous learning capabilities through improved HCI and ODNN.
• To quantitatively analyze the effectiveness of the ODNN-based AELS compared to classroom-based learning.

Main Methods:

• Developed an Autonomous English Learning System (AELS) using Optimized Deep Learning Network (ODNN).
• Integrated learning and activation functions within the ODNN to assess and train student capabilities.
• Utilized the Japanese-English Bilingual Corpus for system evaluation and assessment questionnaires.
• Employed quantitative analysis to compare HCI-based autonomous learning with classroom-based learning.

Main Results:

• The ODNN-based AELS demonstrated a learning efficiency of 98.51% compared to classroom learning.
• The HCI-based autonomous learning process provided effective guidelines for independent student learning.
• The system successfully improved students' learning abilities and skills.

Conclusions:

• The ODNN-based AELS significantly enhances autonomous English learning efficiency.
• HCI integration in autonomous learning systems offers a superior alternative to traditional classroom settings.
• The developed system shows high potential for improving language acquisition through technology.