How Engineering Students Acquire Technical Vocabulary: An Empirical Study of Effective Learning Strategies

This study examines engineering undergraduates’ vocabulary learning strategies (VLS) to acquire technical vocabulary, a critical skill in today’s globalized engineering landscape. A total of 150 engineering students from ENSAM, Meknes participated by completing an adapted 62-item version of the Vocabulary Learning Questionnaire (VLQ; Gu & Johnson, 1996; Gu, 2018), modified to address the specialized language demands of technical coursework. The instrument demonstrated excellent internal consistency (α = 0.89) and yielded a seven-factor structure corresponding to beliefs about vocabulary, metacognitive strategies, inferencing, dictionary use, note-taking, rehearsal, and activation strategies. Descriptive analyses indicated moderate overall strategy use (M = 4.0, SD = 0.7 on a 7-point scale), with dictionary (M = 4.5, SD = 0.5) and inferencing strategies (M = 4.1, SD = 0.6) being the most frequently reported. Pearson correlations revealed significant associations between technical vocabulary test performance and both dictionary (r = 0.45, p < .01) and inferencing strategies (r = 0.40, p < .01). Multiple regression analysis indicated that these strategies together accounted for 30% of the variance in test scores (R² = 0.30, F(2, 147) = 31.5, p < .001). The findings underscore the importance of targeted strategy instruction in technical curricula and suggest that engineering educators should emphasize activities that enhance contextual inference and precise dictionary use. Implications for engineering education and directions for future research are discussed.

Authors

Institutions

Publication Details

Journal
American Journal of Education and Technology
Published
2025-03-19
DOI
https://doi.org/10.54536/ajet.v4i2.4416
Primary Topic
Educational Assessment and Pedagogy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How Engineering Students Acquire Technical Vocabulary: An Empirical Study of Effective Learning Strategies

Younnes Elkhadiri
American Journal of Education and Technology
Educational Assessment and Pedagogy
article

How Engineering Students Acquire Technical Vocabulary: An Empirical Study of Effective Learning Strategies

Younnes Elkhadiri
article en

Abstract

This study examines engineering undergraduates’ vocabulary learning strategies (VLS) to acquire technical vocabulary, a critical skill in today’s globalized engineering landscape. A total of 150 engineering students from ENSAM, Meknes participated by completing an adapted 62-item version of the Vocabulary Learning Questionnaire (VLQ; Gu & Johnson, 1996; Gu, 2018), modified to address the specialized language demands of technical coursework. The instrument demonstrated excellent internal consistency (α = 0.89) and yielded a seven-factor structure corresponding to beliefs about vocabulary, metacognitive strategies, inferencing, dictionary use, note-taking, rehearsal, and activation strategies. Descriptive analyses indicated moderate overall strategy use (M = 4.0, SD = 0.7 on a 7-point scale), with dictionary (M = 4.5, SD = 0.5) and inferencing strategies (M = 4.1, SD = 0.6) being the most frequently reported. Pearson correlations revealed significant associations between technical vocabulary test performance and both dictionary (r = 0.45, p < .01) and inferencing strategies (r = 0.40, p < .01). Multiple regression analysis indicated that these strategies together accounted for 30% of the variance in test scores (R² = 0.30, F(2, 147) = 31.5, p < .001). The findings underscore the importance of targeted strategy instruction in technical curricula and suggest that engineering educators should emphasize activities that enhance contextual inference and precise dictionary use. Implications for engineering education and directions for future research are discussed.

American Journal of Education and TechnologyVol. 4(2)
Cadi Ayyad University (MA)
Openalex Percentile: Top 95%
Educational Assessment and Pedagogy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.