Instructional Toolkit for Outcome-Based Instruction on English Grammar of Thai EFL Students in the Thonburi District in Bangkok

Abstract

The purposes of this research were to a) find out how well the new instructional toolkit works at improving students’ English grammar skills in verb tense structures, b) investigate the effects of Thai EFL students’ learning achievement in utilizing an instructional toolkit on the acquisition of English grammar, c) investigate Thai EFL undergraduate students’ knowledge retention after teaching using an instructional toolkit, d) study relationships among study levels, English proficiency levels, and learning achievement of Thai EFL students on the acquisition of English grammar before and after utilizing an instructional toolkit, and e) explore the Thai EFL students’ perceptions regarding the instructional toolkit in terms of design, retention development, and implementation. The sample was categorized into two groups: 100 primary students and 100 secondary students. All students were studying in public school in the Thonburi district in Bangkok, Thailand, using multilevel group research design. The instruments were a) an instructional toolkit, b) achievement tests, c) a questionnaire, and d) interviews. The data were collected before, during, and after conducting research. The data were analysed quantitatively, using SPSS to find out the frequency, mean (M), and standard deviation (SD) of the participants’ perceptions regarding use of the instructional toolkit, and qualitatively using content analysis. The research revealed that a vast majority (95%) of students and teachers acknowledged the toolkit’s efficiency and efficacy, with significant improvements noted post-intervention. Additionally, the toolkit effectively enhanced knowledge retention in verb tense structures across different educational levels, showing particular resonance with higher-grade students and those at intermediate proficiency. However, despite appreciation for its user-friendly design and retention-enhancing features, there was a clear recommendation for the inclusion of more diverse real-world examples in future iterations.