Advancing the Evaluation of Oral Fluency in English for Specific Classrooms: Harnessing Natural Language Processing Tools for Enhanced Assessment

A crucial component of language learning and teaching is evaluating students’ speaking abilities. Natural language processing (NLP) techniques have been employed recently in language assessment to automate the evaluation process and produce more impartial and reliable findings. In this study, we offer a speaking evaluation tool based on Natural Language Processing (NLP) that assesses a learner’s speaking ability in real-time using cutting-edge algorithms. The instrument is altered to assess the fundamental facet of speaking skills - Fluency. As a result of the tool’s immediate feedback, learners may pinpoint their areas of weakness and focus on honing their language abilities. The usefulness of the instrument was assessed through an intervention with a sample size of 30 students of the post-graduate students of a college in Pune, India. Python libraries, including random and re, were utilized to implement the algorithm. Data preprocessing involved accurate transcription of videos using an online tool and manual checking for corrections. Despite acknowledging limitations, such as potential biases in manually inserted hesitation markers, the study serves as a pivotal step toward automated fluency assessment, presenting exciting prospects for NLP and language education advancements.