Utilizing Text Mining Technology to Enhance English Learners' Vocabulary

This study investigates the efficacy of text-mining technology in enhancing the acquisition of vocabulary by learners of English. This research chose 50 participants who made use of a text-mining-based vocabulary learning system for personalization using adaptive recommendations. Participants received pre-and post-test measures to determine vocabulary improvement, and the average increase in the post-test scores was extremely huge at 17.8 points. Statistical analysis included a paired t-test that showed the enormity of the impact of the intervention, with t = 22.47 and p < 0.001, supported by an immense effect size at Cohen's d = 3.18. Relevant feedback in a qualitative review underlined how the system provided the learners with relevant and engaging learning material adapted to their proficiency level and their interests. While most traditional approaches to feedback and adaptive pathways remain static, this text-mining system adds a new dimension by dynamically guiding students through individualized paths of learning toward greater participation and deeper retention. The educational implication of such a development will be to incorporate text-mining technology into the curriculum, which will enrich learning experiences for language learners and improve educational outcomes significantly, using learning to individualize learning with the stimulation of learner autonomy. Limitations include sample size and duration, so further research with larger and more diverse participant cohorts over extended periods is needed to confirm the long-term efficacy and generalizability of findings. This study brings insight into how advanced technology may best optimize a language learning environment by providing information on pedagogically sound strategies for enhancing vocabulary acquisition and wider language proficiency development.