Improving College Students'English Phoneme Acquisition Performance through FiF Oral Training System-Assisted Blended Learning
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: This study investigated the effectiveness of blended learning using the FiF Oral Training App on English phoneme acquisition specifically fluency, intelligibility, articulation, and rhythm, and explored students'satisfaction and attitudes toward pronunciation learning. A quasi-experimental design was employed with two intact classes of Primary English Education freshmen at Lijiang Normal University: an experimental group (n = 40) using the FiF App and a control group (n = 40) receiving conventional instruction. Both groups were comparable in prior English performance. Quantitative data from pre-and post-tests and pronunciation learning questionnaires were analyzed using descriptive and inferential statistics. The control group showed modest gains: fluency increased from 6.45 to 7.59, intelligibility from 6.21 to 7.35, articulation from 6.34 to 7.26, and rhythm from 6.28 to 7.35 (all at p < 0.001). The experimental group demonstrated superior improvements: fluency from 5.83 to 7.86, intelligibility from 5.76 to 7.66, articulation from 5.90 to 7.83, and rhythm from 5.74 to 7.63. The experimental group outperformed the control group across all measures, with articulation reaching statistical significance (p = 0.022).Both groups reported positive attitudes toward pronunciation learning. However, the experimental group expressed stronger agreement regarding the importance of pronunciation (M = 1.23 vs. 1.41) and perceived greater support from the learning environment (M = 1.35 vs. 1.53). Findings suggest that integrating AI-based tools like the FiF App enhances articulation and fosters positive learner attitudes, indicating the pedagogical value of blending technology with pronunciation instruction.
Source: Forum for Linguistic Studies
Authors: Xiaoyun Wang, Roderick Julian Robillos
Institutions: Lijiang Normal University, Khon Kaen University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4413178418