Abstract

Formulaic Sequences (FS), common word combinations stored and used as single units, are crucial for achieving oral fluency yet remain underrepresented in pedagogical materials. This study explores this gap by examining teacher and student perceptions of FS and the teaching techniques within an online Egyptian EFL context. Utilizing a mixed-methods approach, data were gathered via teacher interviews (n=4), student questionnaires (n=100), a student focus group (n=4), and classroom observations. Results indicated that teachers unanimously perceived FS as essential building blocks for fluency, accuracy, and learner confidence, and they utilized a spectrum of techniques ranging from explicit instruction to incidental exposure and technology-assisted recycling (e.g., voice notes, ChatGPT). Students highly valued FS for improving their speaking skills and exam performance, often associating their use with achieving a ‘native-like’ ideal, which emerged as a powerful motivational factor. However, students also reported that they had not received much clear instruction on FS in the past, leading to challenges in retention and appropriate use.

A key finding was some students’ avoidance of FS due to fear of being misunderstood by interlocutors. The study underscores the need to bridge the theory-practice divide by advocating for more systematic, context-sensitive FS instruction that moves beyond native-speakerism standards and instead focuses on intelligibility and communicative competence. It also calls for enhanced teacher training on FS instruction, curricular integration, the development of shared instructional frameworks while respecting teacher autonomy, and a balanced approach that combines explicit focus with meaningful communicative practice while leveraging AI for FS reinforcement and recycling.

School

School of Humanities and Social Sciences

Department

Applied Linguistics Department

Degree Name

MA in Teaching English to Speakers of Other Languages

Graduation Date

Winter 1-31-2026

Submission Date

9-15-2025

First Advisor

Mariah Fairley

Committee Member 1

Atta Gebril

Committee Member 2

Daria Mizza

Extent

164 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Approval has been obtained for this item

Disclosure of AI Use

Data/results generation and/or analysis; Data/results visualization

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