English Cross-Cultural Competence of College Students Analysis using Hidden Markov Model with Random Forest

Speaking and listening comprehension is the primary objective of language learning communication ability. However, oral expressive abilities are undervalued in accordance with reading and writing competency in English schools throughout the country because of the influence of the test-oriented education approach. This research created the Hidden Markov Model with Random Forest (HMM-RF), a revolutionary form of education for college students, to solve the inadequacies of traditional spoken English training. The HMM captures the linguistic and probabilistic transitions that occur in students' language and behavior over time. Then, with ever-intense accuracy, RF classifies these patterns while handling complex, non-linear interactions. Thus, these HMM-RF hybridization systems include sequential pattern recognition and excellent classification of college students' cross-cultural competence. With an accuracy of 99.98%, the results demonstrated that the suggested HMM-RF method performed better than existing models, such as the Cultural Competence Assessment Model (CCAM), the Gradient Boosting Decision Trees (GBDT), and the Cultural Intelligence Scale (CIS).