Decoding Short Discrete Texts: An Empirical Study of Distractor Mitigation and Visual-Textual Synthesis in L2 Listening Assessments

Abstract

This research examines the diagnostic efficacy of the Cambridge Preliminary English Test (PET) Listening Part 1 in evaluating the cognitive processing subskills of undergraduate technical students. Utilizing a quantitative descriptive research design, empirical data was extracted from a seven-item discrete-text instrument applied to a cohort of over 60 students. The investigation evaluates four foundational pedagogical objectives: extracting concrete factual information, synthesizing visualtextual inputs, distinguishing key details amidst competing options, and decoding negative filters or conversational repairs. Statistical insights derived from ELT Data on Listening Skills. The collected data expose deep disparities in student performance across these distinct subskills. While students exhibited high proficiency in visual-textual mapping and basic factual extraction when the text was linear—achieving an 82.1% accuracy rate on Q6—their performance dropped sharply during complex cognitive tasks. Specifically, the cohort struggled with distractor rejection and conversational "mind-changes," where accuracy collapsed to 22.4% on Q1 and 20.9% on Q3. These metrics reveal that semantic and phonological distractors introduce an intense cognitive load that overpowers regular bottom-up text processing. This research proves that generic audio exposure is insufficient for technical students operating under standardized frameworks. Ultimately, the study provides curriculum designers with critical guidelines to embed targeted, objective-based listening tasks within Technical English Communication courses, successfully moving learners away from passive word-matching toward active, strategic information filtering