Content area
This study examined the effectiveness of online English learning among college students by integrating data from multiple learning behavior sources. The research revealed a bidirectional regulatory relationship between device environment and cognitive strategies, clarified the critical threshold for the regulation of metacognitive strategies, and improved language learning theory systems. A hybrid modeling technology was developed based on Dynamic Time Warping - Long Short Term Memory (DTW-LSTM), a 42-dimensional feature matrix was constructed, and a federated learning framework designed that enhanced data processing and analysis capabilities. Regarding teaching practice, an online diagnostic system was developed based on the behavior pattern recognition algorithm. Additionally, an adaptive feedback mechanism to support personalized teaching was designed according to the prediction model.
Details
Cognitive Processes;
Academic Achievement;
Language Acquisition;
Instructional Effectiveness;
Student Motivation;
Language Proficiency;
Educational Environment;
Learner Engagement;
Educational Strategies;
Algorithms;
Educational Quality;
Language Skills;
Influence of Technology;
Distance Education;
Learning Strategies;
Educational Technology;
College Students;
Student Needs;
Accuracy;
Electronic Learning;
Data Analysis;
Investment;
Data Processing;
Educational Facilities Improvement
Behavior;
Pattern recognition;
College students;
Student participation;
Learning theory;
Machine learning;
Bidirectionality;
Metacognition;
Distance learning;
Teachers;
Colleges & universities;
Learning;
Data processing;
Prediction models;
Online instruction;
Technology Acceptance Model;
Learning analytics;
Learning theories;
Self-efficacy;
Algorithms;
Cognitive strategies;
Speech;
Short term memory;
Education;
Internet;
Accuracy;
Language acquisition;
Feedback;
School environment;
Matrices;
Teaching methods;
Federated learning;
Emotions;
Students;
Skills;
Adaptive algorithms;
Second language learning;
Educational objectives;
Design;
Diagnostic systems