Machine learning (ML) modelling techniques for mobile technology-integrated vocabulary learning on Chinese universities EFL students’ adoption

Abstract

Studying new vocabulary is an important goal for language students, as is making the learning of vocabulary a student-centered process. Of course, the goal of investigating what variables affect people’s willingness to embrace new technologies is to better adopt such technologies. The present study will thus look at the elements that have an impact on Chinese EFL college students’ adoption and usage of mobile technology-integrated vocabulary acquisition as a means to promote more learner-centric education. To better forecast human behavior, we successfully included the following elements from the technology adoption literature into the model: attitude toward change brought on by technology usage; attitude toward technology; desire; financial ramifications; aims; past behavior; perceived consistency; positive expected feelings; visibility; and so on. Furthermore, the characteristics, all of which are external factors that describe the traits of optimal technology design, are included in the development of Unified Theory of Acceptance and Use of Technology (UTAUT) using a machine-learning. All these elements help UTAUT progress. Among its many uses, ML-based modeling may be put to good use in enhancing pre-existing explanatory statistical models (UTAUT) by identifying and analyzing hidden patterns and correlations between their many aspects. This is possible because ML-based modeling may enhance traditional statistical explanations (UTAUT).