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

The continuous development of Human-Computer Interaction (HCI) and information technologies impact the digital learning environment. The network and multimedia technologies change the Autonomous Learning System (ALS) structure. The learning process uses several techniques; however, the interactive function requires continuous improvement to enhance autonomous learning. Therefore, Optimized Deep Learning Network (ODNN) is introduced to build the Autonomous English Learning System (AELS) in this work. The ODNN system uses the learning and activation functions that derive the student’s learning capabilities and gives the proper training to the student. The HCI-based created autonomous learning process provides the guidelines to the student for making independent learning. The ALS improves the student’s learning ability and skills compared to classroom-based learning. The discussed ODNN-based AELS system effectiveness is evaluated using the Japanese-English Bilingual Corpus with a set of assessment questionaries. Then the HCI-based autonomous English learning is a quantitative analysis with the classroom-based learning. The discussed system is implemented using the Python tool, in which the AELS system ensures 98.51% learning efficiency compared to classroom learning.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.