Analysis of Proposal Texts for STEM Writing Education : A Comparative Study with Report Texts to Propose Teaching and Learning Strategies

This study analyzed the topic structure of proposal texts to establish essential information for writing education of science, technology, engineering, and mathematics university students. The analyzed corpus consists of 36 award-winning proposals (42,625 syllables) from 2023 competitions, categorized by ‘fixed’ or ‘free’ formats. The analysis, which utilized tools such as ‘T-units’ and ‘topic clusters’, compared the characteristics of proposal texts with those of report texts to derive pedagogical implications. The findings revealed that proposal texts follow a ‘Summary-Introduction-Body’ superstructure, with core topics focusing on [Method/Plan] and [Expected Effects] to persuade concerned judges of the idea’s feasibility and outcomes. Based on these findings, four pedagogical implications were derived. First, students should learn about the ‘Summary-Introduction-Body’ structure, which is different from the ‘Introduction-Body-Conclusion’ structure of report texts. Second, writing instruction should focus on concretizing ideas by emphasizing the methods and expected effects. Third, students must practice both phrasal and discursive sentence styles, along with the effective use of visual aids. Finally, the current study revealed that using first-person expressions like ‘I’ and ‘we’ can enhance persuasiveness by highlighting the writer's practical agency.

Publication Details

Published
2025-08-30
DOI
https://doi.org/10.37736/kjlr.2025.08.16.4.10
Primary Topic
Advanced Computational Techniques in Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Proposal Texts for STEM Writing Education : A Comparative Study with Report Texts to Propose Teaching and Learning Strategies

Advanced Computational Techniques in Science and Engineering
article

Analysis of Proposal Texts for STEM Writing Education : A Comparative Study with Report Texts to Propose Teaching and Learning Strategies

article en

Abstract

This study analyzed the topic structure of proposal texts to establish essential information for writing education of science, technology, engineering, and mathematics university students. The analyzed corpus consists of 36 award-winning proposals (42,625 syllables) from 2023 competitions, categorized by ‘fixed’ or ‘free’ formats. The analysis, which utilized tools such as ‘T-units’ and ‘topic clusters’, compared the characteristics of proposal texts with those of report texts to derive pedagogical implications. The findings revealed that proposal texts follow a ‘Summary-Introduction-Body’ superstructure, with core topics focusing on [Method/Plan] and [Expected Effects] to persuade concerned judges of the idea’s feasibility and outcomes. Based on these findings, four pedagogical implications were derived. First, students should learn about the ‘Summary-Introduction-Body’ structure, which is different from the ‘Introduction-Body-Conclusion’ structure of report texts. Second, writing instruction should focus on concretizing ideas by emphasizing the methods and expected effects. Third, students must practice both phrasal and discursive sentence styles, along with the effective use of visual aids. Finally, the current study revealed that using first-person expressions like ‘I’ and ‘we’ can enhance persuasiveness by highlighting the writer's practical agency.

Vol. 16(4)
Quality Education
Openalex Percentile: Top 34%
Advanced Computational Techniques in Science and Engineering
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.