English for Specific Purposes Needs in Vocational Higher Education: Uncovering Urgency, Variance, and Skill Priorities Through Listening and Reading Data

English for Specific Purposes Needs in Vocational Higher Education: Uncovering Urgency, Variance, and Skill Priorities Through Listening and Reading Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article English for Specific Purposes Needs in Vocational Higher Education: Uncovering Urgency, Variance, and Skill Priorities Through Listening and Reading Data I Wayan Eka Dian Rahmanu, Gyöngyvér Molnár This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7269299/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study investigates receptive English language proficiency across six academic departments including Business Administration, Accounting, Tourism, Electrical Engineering, Mechanical Engineering, and Civil Engineering at the State Polytechnic of Bali, Indonesia. Using the Test of English for International Communication (TOEIC) Listening and Reading scores from 1,618 students, the research aims to identify urgent needs for foundational English for Specific Purposes (ESP) instruction, assess score consistency within departments, and determine which skill, listening and reading, presents the greater overall need. Quantitative analyses were conducted using SPSS, with descriptive statistics, standard deviation, interquartile range, and ANOVA supporting cross-departmental comparisons. Findings reveal that students from business-oriented departments outperformed to those in the technical programs, with Mechanical and Civil Engineering showing the lowest mean scores in both Listening and Reading. Mechanical Engineering also displayed the most consistent scores at the lowest performance level, which indicates a uniformly weak language foundation. Tourism and Electrical Engineering, on the other hand, exhibited the widest performance variability, suggesting a need for differentiated instruction. Importantly, Reading emerged as the most consistently weak skill across all departments, positioning it as a potential focus for pre-specialisation ESP modules. While offering valuable insights, the study is limited by its exclusive focus on receptive skills and reliance on quantitative TOEIC data. Future research should incorporate productive language skills and qualitative measures to capture motivational, contextual, and strategic learning factors. Overall, the findings inform curriculum development for vocational higher education by highlighting specific language needs across disciplines and guiding targeted ESP interventions. Listening reading vocational education English for specific purposes Figures Figure 1 Figure 2 Figure 3 Figure 13 Figure 14 Introduction Within tertiary education, English for Specific Purposes (ESP) has become an essential pedagogical strategy (Chan, 2019 ). This is particularly true in vocational and technical institutions such as polytechnics, where proficiency in discipline-specific language fundamentally underpins both academic achievement (Coxhead et al., 2019 ) and preparedness for the workforce (Al Hilali & McKinley, 2021 ). As pivotal institutions for Technical and Vocational Education and Training (TVET), educators in polytechnics equip students with sector-specific practical skills through the use of technology, for example, multimodality including audiovisual and auditory combination (Naef et al., 2022 ), gamification (Zuo et al., 2025 ) and Virtual reality (VR) (Smith et al., 2020 ). This training necessitates discipline-targeted English competencies, enabling students to comprehend technical documentation, adhere to safety protocol, follow complex procedures, and communicate effectively in professional settings (Isaksen et al., 2025 ). Despite ESP's acknowledged significance, effectively diagnosing and prioritising needs within polytechnics' inherently diverse student departments remains challenging (Gaffas, 2019 ). Substantial variation in prior English proficiency and educational backgrounds among students creates considerable within and between departments disparities (Lasekan, 2024 ). Although Needs Analysis (NA) is fundamental to ESP program design (Huang & Yu, 2023 ), conventional methods frequently emphasise perceived needs or target-situation analyse (Liu et al., 2011 ). This risks neglecting diagnostic data, for example, general English proficiency, that exposes immediate foundational deficiencies hindering progress before specialised skill development (Nateghian, 2024 ). Moreover, polytechnic NA research often prioritises speaking and writing skills (Akay et al., 2025 ; L. Jiang et al., 2024 ), resulting in scarce empirical insights into discipline-specific listening and reading comprehension gaps (Liu, 2020 ). While ESP is a cornerstone of language instruction in vocational and technical education, current approaches to NA in vocational education tend to emphasise productive skills. The previous study conducted by Bashori et al. ( 2022 ) focused on the speaking skills study through the use of technology. Although this study demonstrated the enhancement of learners' speaking abilities, it did not address learners' needs in receptive skills, particularly listening and reading. Similarly, an experimental study was conducted by Hong et al. ( 2022 ) explored oral proficiency in English tourist guides. However, the study overlooked the importance of receptive skills like listening comprehension, which are significant in real-life tourist settings (Stadler et al., 2025 ). This issue is also evident in research on writing skills, where previous studies focused solely on writing production, without considering the role of receptive or supporting language skills. The empirical study organised by Jiang et al. ( 2024 ) exposed the vocational learners’ argumentative writing skills. However, the study did not explore how reading comprehension results in a limited understanding of the learners’ performance in ESP settings. The limited use of diagnostic tools to assess general English proficiency can reduce foundational weaknesses that must be addressed before specialised instruction can be effective (Hua & Beverton, 2013 ). Consequently, there is a need for more robust, data-driven NA models that give equal attention to receptive skills, reading and listening skills, and provide a more accurate basis for developing ESP classroom in the vocational context. Accordingly, the research is guided by the following questions: RQ (1) Which departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) demonstrate the most urgent need for foundational ESP intervention in Listening and Reading comprehension? RQ (2) Which departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) exhibit the most consistent (low standard deviation/interquartile range) and the most diverse (high standard deviation/interquartile range) Listening or Reading scores among their students? RQ (3) Which specific skill (Listening or Reading) presents as the greater area of need across the majority of departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering), suggesting a potential core focus for foundational ESP modules before specialisation? Previous Study A previous study conducted by Park et al. ( 2020 ) explored reading and listening scores through the TOEIC test at the universities in Korea. The study examined the relationships at the different levels between the reading and the listening scores. In their study, 11.328 TOEIC test scores were analysed. One of the results was that the students had more difficulty with the reading component than the listening on the TOEIC test. In contrast with other studies conducted by Nakamura ( 2018 ) and Kamiya ( 2024 ) at different universities in Japan, they found different results. There were 48 freshmen who enrolled in English for general purposes (EGP) involved in the study from Nakamura ( 2018 ). TOEIC mock tests were used as benchmarks to establish a baseline study. During the study, they found that the reading test score was higher than the listening score. Similarly, Kamiya ( 2024 ), who involved 128 Japanese freshmen and high school seniors learners of English aged 18 or 19, participated in this study (118 females, 10 males), explored that the mean of the reading test score was higher than the listening test score. Methodology Participants The participants in this study consisted of 1.618 students enrolled at the State Polytechnic of Bali, Indonesia. These students were drawn from six different academic departments, representing both service-oriented and technical disciplines. The departmental distribution was as follows: 285 from Business Administration, 305 from Accounting, 376 from Tourism, 276 from Electrical Engineering, 184 from Mechanical Engineering, and 192 from Civil Engineering (see Fig. 1 ). This diverse representation allowed for meaningful comparisons across academic programs with varying linguistic and professional demands, providing a comprehensive overview of receptive English proficiency within the polytechnic context. Instrument To assess the students’ receptive English skills, this study utilised the Test of English for International Communication (TOEIC) as the primary diagnostic instrument. The TOEIC Listening and Reading test is divided into two sections: Listening Comprehension, which includes 100 questions and ranges from 5 to 495 points, and Reading Comprehension, which also includes 100 questions with the same scoring range. The TOEIC was selected due to its international recognition and its strong alignment with the language demands of professional and vocational environments, making it particularly suitable for evaluating English for Specific Purposes (ESP) proficiency in the context of polytechnic education. Data analysis A series of quantitative analyses were conducted using TOEIC Listening and Reading comprehension scores collected from students across six academic departments: Business Administration, Accounting, Tourism, Electrical Engineering, Mechanical Engineering, and Civil Engineering. All analyses were conducted using SPSS (version 26), and findings were visualised using bar charts, boxplots, and summary tables to support interpretation and comparative evaluation across departments and language skills. The analyses were conducted using descriptive and inferential statistical techniques to evaluate performance levels, consistency of scores, and comparative skill needs. The primary focus was to determine the extent of foundational ESP support required across departments. The mean and median scores for each department were used as indicators of overall performance, with lower average scores suggesting greater instructional needs. The standard deviation (SD) and interquartile range (IQR) were computed for both Listening and Reading scores across all six departments. Departments with lower SDs and IQRs were identified as having more homogeneous student performance, indicating relatively uniform English proficiency levels. ANOVA analysis was applied to determine whether the differences in Listening and Reading scores across the entire sample were statistically significant. Results Departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) demonstrate the most urgent need for foundational ESP intervention in Listening and Reading comprehension Table 1 ANOVA result of TOEIC Listening and Reading Skills F Sig. Listening 27.799 .000 Reading 23.669 .000 The ANOVA test result in Table 1 revealed a statistically significant difference in listening and reading scores between groups, p < .001. Further investigation, through post hoc comparisons, was conducted to determine which specific departments demonstrate significantly lower performance. Therefore, the urgent need for foundational ESP intervention was analysed. Table 2 Multiple Comparisons for TOEIC Listening Scores by Department Departments Mean Difference Among Departments Std. Error Sig. BA AC -6.799 6.612 .908 TO 7.386 6.303 .850 EE 13.572 6.777 .341 ME 72.811 7.589 .000 CE 30.962 7.493 .001 AC BA 6.799 6.612 .908 TO 14.185 6.184 .197 EE 20.372 6.667 .028 ME 79.611 7.491 .000 CE 37.762 7.393 .000 TO BA -7.386 6.303 .850 AC -14.185 6.184 .197 EE 6.186 6.361 .927 ME 65.426 7.220 .000 CE 23.577 7.118 .012 EE BA -13.572 6.777 .341 AC -20.372 6.667 .028 TO -6.186 6.361 .927 ME 59.239 7.638 .000 CE 17.390 7.542 .192 ME BA -72.811 7.589 .000 AC -79.611 7.491 .000 TO -65.426 7.220 .000 EE -59.239 7.638 .000 CE -41.849 8.279 .000 CE BA -30.962 7.493 .001 AC -37.762 7.393 .000 TO -23.577 7.118 .012 EE -17.390 7.542 .192 ME 41.849 8.279 .000 Note. BA = Business Administration, AC = Accounting, TO = Tourism, EE = Electrical Engineering, ME = Mechanical Engineering, CE = Civil Engineering Post hoc comparisons using the Tukey HSD test were conducted to determine which departments differed significantly in listening comprehension scores. The results indicated that Mechanical Engineering students scored significantly lower in listening than all other departments ( see Table 2 ): Business Administration (M difference = -72.81, p < .001), Accounting (M difference = -79.61, p < .001), Tourism (M difference = -65.43, p < .001), Electrical Engineering (M difference = -59.24, p < .001), and Civil Engineering (M difference = -41.85, p < .001). Similarly, Civil Engineering students also scored significantly lower than Business Administration (M difference = -30.96, p = .001), Accounting (M difference = -37.76, p < .001), and Tourism (M difference = -23.58, p = .012). There were no statistically significant differences between Business Administration, Accounting, Tourism, and Electrical Engineering students, with p values above .05. However, Accounting students performed significantly better than Electrical Engineering students (M difference = 20.37, p = .028). These findings confirm that Mechanical Engineering and Civil Engineering students exhibit the most pressing need for foundational ESP intervention, as they consistently underperformed compared to students in business-related departments and Electrical Engineering. The results underscore the importance of tailoring ESP instruction to meet the distinct language proficiency needs across disciplines. Table 3 Multiple Comparisons for TOEIC Reading Scores by Department Departments Mean Difference Among Departments Std. Error Sig. BA AC -15.681 6.175 .113 TO -9.733 5.887 .563 EE 4.579 6.330 .979 ME 51.961 7.089 .000 CE 19.816 6.998 .053 AC BA 15.681 6.175 .113 TO 5.948 5.776 .908 EE 20.259 6.227 .015 ME 67.642 6.997 .000 CE 35.497 6.905 .000 TO BA 9.733 5.887 .563 AC -5.948 5.776 .908 EE 14.311 5.941 .154 ME 61.693 6.744 .000 CE 29.549 6.649 .000 EE BA -4.579 6.330 .979 AC -20.259 6.227 .015 TO -14.311 5.941 .154 ME 47.382 7.134 .000 CE 15.238 7.044 .256 ME BA -51.961 7.089 .000 AC -67.642 6.997 .000 TO -61.693 6.744 .000 EE -47.382 7.134 .000 CE -32.144 7.733 .000 CE BA -19.816 6.998 .053 AC -35.497 6.905 .000 TO -29.549 6.649 .000 EE -15.238 7.044 .256 ME 32.144 7.733 .000 Note. BA = Business Administration, AC = Accounting, TO = Tourism, EE = Electrical Engineering, ME = Mechanical Engineering, CE = Civil Engineering Regarding the result in Table 3 , post hoc comparisons test revealed several statistically significant differences in Reading comprehension scores among departments. Mechanical Engineering students performed significantly lower than all other departments: Business Administration (M difference = -51.96, p < .001), Accounting (M difference = -67.64, p < .001), Tourism (M difference = -61.69, p < .001), Electrical Engineering (M difference = -47.38, p < .001), and Civil Engineering (M difference = -32.14, p < .001). Civil Engineering students also performed significantly lower than students from Accounting (M difference = -35.50, p < .001) and Tourism (M difference = -29.55, p < .001). However, their difference from Business Administration students was marginally non-significant (p = .053), and their difference from Electrical Engineering students was not statistically significant (p = .256). In addition, Accounting students scored significantly higher than Electrical Engineering students (M difference = 20.26, p = .015). No statistically significant differences were observed between Business Administration, Accounting, Tourism, and Electrical Engineering, indicating relatively comparable performance among students in these departments. These results suggest that Mechanical Engineering students demonstrate the most urgent need for foundational ESP intervention in Reading comprehension, followed by Civil Engineering students. The findings reinforce the need for targeted language support in technical departments, while also recognising the relative strengths in reading skills among business-oriented and service-related disciplines. Departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) exhibit the most consistent (low standard deviation/interquartile range) and the most diverse (high standard deviation/interquartile range) Listening or Reading scores among their students Table 4 Standard Deviation and Interquartile Range of TOEIC Reading and Listening Department Variable Listening Reading SD IQR Levene’s score SD IQR Levene’s score F(5,1612) p F(5,1612) p Accounting (n = 305) 79.293 95 7.395 .000 76.365 95 14.41 000 Business Administration (n = 285) 74.713 100 71.483 85 Civil Engineering (n = 192) 82.001 128 70.790 80 Electrical Engineering (n = 276) 90.182 130 83.349 93 Mechanical Engineering (n = 184 65.862 69 48.687 40 Tourism (n = 376) 82.790 120 82.161 105 Note. SD = standard deviation, IQR = Interquartile Range According to Table 4 , Levene’s test of homogeneity of variance indicated a statistically significant difference in score variability across departments for listening comprehension, F(5, 1612) = 7.395, p < .001. This result suggests that the assumption of equal variances is violated, and that departments differ significantly in how consistent student listening scores are. Among all departments, Mechanical Engineering showed the most consistent performance in listening, with the lowest SD (65.86) and smallest IQR (69). In contrast, Electrical Engineering demonstrated the greatest variability, with the highest SD (90.18) and widest IQR (130), indicating a broader spread of listening scores and a potential need for targeted instructional intervention to standardise listening proficiency within that department. The study also found a statistically significant difference in reading score variability across departments, F(5, 1612) = 14.41, p < .001. This again confirms unequal variances among the groups. In terms of consistency, Mechanical Engineering performed the lowest SD (48.69) and smallest IQR (40), suggesting a more homogeneous level of reading ability among students. On the other hand, Accounting and Tourism showed higher variability, with SDs of 76.37 and 82.16 and IQRs of 95 and 105, respectively. Skill (Listening or Reading) presents as the greater area of need across the majority of departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering), suggesting a potential core focus for foundational ESP modules before specialisation The IQR highlighted variations in score distribution across departments for both Listening and Reading skills. For the Listening component, IQR values ranged from 69 in Mechanical Engineering to 130 in Electrical Engineering, indicating a moderate to substantial dispersion in performance. In comparison, the Reading scores exhibited a generally narrower spread, with IQR values ranging from 40 in Mechanical Engineering to 105 in Tourism. This suggests that Listening scores showed greater variability among students, whereas Reading scores were more consistently distributed within most departments (see Table 4 ). The results also showed that reading is the greater area of need across the majority of departments, indicating a potential core focus for foundational ESP intervention. Specifically, departments such as Mechanical Engineering and Civil Engineering may require targeted support in Reading, with mean scores 48.34 and 58.05 points lower than their respective Listening scores. An examination of the boxplot shapes further reveals patterns in student performance (see Fig. 2 and Fig. 3 ). The distributions for Mechanical Engineering and Civil Engineering were skewed toward lower scores, with lower medians, shorter upper whiskers, and an increased presence of low outliers. These patterns reinforce the observation that students in these departments generally struggled with listening comprehension. In contrast, the Accounting, Business Administration, and Tourism departments displayed higher medians and broader upper distributions, suggesting that a larger proportion of students in these fields achieved stronger listening scores. Additionally, while several departments, particularly Accounting, Business Administration, and Tourism, featured high outliers indicative of outstanding listening skills, Mechanical Engineering lacked such high-performing individuals. This absence further highlights the consistent challenges faced by Mechanical Engineering students in developing effective listening skills. The boxplot visually reinforced these patterns. Mechanical Engineering displayed a lower median, a compressed IQR, and dense clustering of scores around the lower end of the scale, indicating a systemic issue with Reading comprehension within the department. Civil Engineering also displayed a lower median with a wider dispersion compared to higher-performing departments. In contrast, Accounting and Tourism departments demonstrated higher medians and a broader spread toward higher Reading scores, suggesting stronger performance by a substantial proportion of their students. Furthermore, a considerable number of high outliers were observed in Business Administration, Accounting, and Tourism departments, particularly above 300 points, suggesting the presence of high-achieving individuals. Mechanical Engineering, on the other hand, exhibited almost no high-end outliers, further confirming the overall poor Reading performance within this department. Discussion Students from business-related fields, such as accounting, tourism and business administration, tended to perform better in both listening and reading comprehension. This echoes the previous research conducted by Wang et al., ( 2023 ) in the accounting program, who found that there is a relationship between English language proficiency, accounting knowledge and academic success. Learners believe that English proficiency has the potential to boost their careers in the future. With the continuous global expansion of the tourism industry, the integration of the English language into tourism and hospitality education is gaining increasing significance (Park et al., 2018 ). In terms of business fields, the demand to promote English language skills for employees is important if the firms or companies are keen to expand their operations to other countries (Yamao & Sekiguchi, 2015 ). The students believe that proficiency in the English language is becoming a vital employability skill and a strategic asset for business-related careers. Therefore, they practice and sharpen their English language skills, which results in better scores in listening and reading proficiency. As the previous research conducted in the engineering program, the importance of reading and listening abilities cannot be neglected (Kassim & Ali, 2010 ; Spence & Liu, 2013 ). Engineers also face numerous English communicative challenges in global events, including highly frequent activities, for example, sending emails, reading reports, writing memos, conducting meetings, teleconferences, and presentations (Spence & Liu, 2013 ). However, the current study found that students from engineering disciplines, such as Mechanical Engineering and Civil Engineering, demonstrated lower mean scores, both in listening and reading comprehension. Foundational ESP support for Mechanical Engineering and Civil Engineering cannot be merely additive but must be integrative and intensive. Pre-specialisation modules should prioritise core academic listening and reading micro-skills, for example, more word-based and lexico-grammatical strategies, aural vocabulary, orthographic vocabulary, and listening comprehension (Hamada & Yanagawa, 2024 ; Lee, 2018 ; Lin, 2002 ). In addition, motivation is also needed by the learners to improve their English language learning attitude (Apple et al., 2020 ; Ma & Zhao, 2025 ). Organising ability grouping can be implemented in the classroom because it has a positive effect on learners of lower than average proficiency (Sheppard et al., 2018 ). The analysis of the standard deviation and interquartile range of TOEIC Reading and Listening scores reveals significant differences in the consistency of student performance across departments. Mechanical Engineering stands out as the department with the most consistent performance in both Listening and Reading skills, with the lowest variability in scores. Although Mechanical Engineering students demonstrated the most consistent performance in both Listening and Reading skills, as reflected in the lowest standard deviation and interquartile range, their average scores were the lowest among all departments. This suggests a uniform but overall weak performance in English receptive skills. In contrast, departments such as Tourism and Electrical Engineering exhibit the most diverse scores, particularly in Listening. The high variability in scores in these departments indicates stark disparities in student proficiency levels, with a mix of high achievers and students struggling with foundational skills. This suggests that these departments require urgent, differentiated instruction to address the needs of both low and high performers. For example, Electrical Engineering's high variability in Listening scores suggests the difference in prior language exposure. To address this issue, the teacher or tutor could consider implementing test-taking strategies (Lee, 2018 ). With regard to the reading skills barrier, the implementation of translanguaging pedagogy has the potential to be integrated into the learning practice (Alexis, 2023 ). The suggestion to employ several strategies was also proposed by Park & Kim, ( 2011 ). They believe that the use of using multimedia, computer applications, dialoguing, setting up reading purposes and planning, previewing and determining what to read, connecting prior knowledge and experiences with texts and tasks, and inferring. Likewise, listening ability can be assisted through several learning strategies, for example, note-taking, active learning, comprehension, predictive organisation, critical thinking, resource-based learning, self-management (Palanisamy & Rajasekaran, 2025 ), vocabulary learning (Chujo & Oghigian, 2009 ; He & Loewen, 2022 ; Hsu & Chao, 2024 ) and translation exercise (Čarapić, 2022 ). The results clearly point to reading as the greater area of need across the majority of departments, underscoring its potential as a foundational focus for ESP modules before moving into discipline-specific content. The IQR analysis revealed that while listening scores showed greater variability among students, reading scores were more consistently distributed within most departments, with a generally narrower spread. This consistency in Reading scores, however, is not a positive indicator, as it suggests that students are struggling with Reading comprehension to a similar extent. These findings differ from both Nakamura's (2018) and Kamiya's (2024) studies, which found that the reading test score was higher than the listening score. Foundational reading comprehension is essential for academic success across all fields, particularly in disciplines requiring the interpretation of technical texts, manuals, research articles, and written instructions. Adjusting the method, materials and pace of teaching to the development of students’ language skills (Kim, 2012 ). Participation in the international internship and self-learning activities advantages the TOEIC results (Chang & Utsumi, 2024 ). In terms of reading comprehension on TOEIC, teachers-centred approach including the teacher’s feedback, are important to assist learners’ performance (Busa & Chung, 2024 ). The use of multimodal text can be used to elevate learners’ reading ability (Cahyaningati & Lestari, 2018 ; Stewart et al., 2020 ). Emphasising reading through scaffolded instruction (Ament et al., 2025 ), technical vocabulary training through digital platform (Jiang & Zhao, 2025 ), and extensive reading (Taye & Teshome, 2025 ), can establish a solid linguistic base for students. Pre-specialisation modules must consider the morphological analysis of technical terms (Stoffelsma et al., 2025 ) and syntactic deconstruction of complex sentences (Li et al., 2024 ), before tackling specialised content. Limitation Despite offering valuable insights into receptive English proficiency across departments, this study is subject to several notable limitations that should be acknowledged. First, the exclusive reliance on quantitative data derived from TOEIC Listening and Reading scores restricts the scope of analysis. While these scores provide a standardised measure of receptive language ability, they do not capture the complex interplay of cognitive, affective, and contextual factors that influence student performance. Variables such as learner motivation, prior exposure to English, language learning strategies, access to language-rich environments, and attitudes toward English learning are all important determinants that were not explored in this study. Furthermore, the study's exclusive focus on receptive skills, Listening and Reading, which means that it does not account for productive language competencies such as Speaking and Writing. In ESP contexts, especially within professional and technical disciplines, productive skills are essential for real-world communication tasks such as writing reports, engaging in meetings, delivering presentations, or responding to customer inquiries. Without assessing these dimensions, the study presents only a partial view of students’ overall communicative competence and readiness for workplace communication demands. Conclusion This study examined receptive English proficiency, specifically listening and reading skills, across six academic departments at the State Polytechnic of Bali using TOEIC scores as a diagnostic tool. The findings revealed clear disciplinary differences, with business-related departments (Accounting, Tourism, and Business Administration) generally outperforming their engineering counterparts. In contrast, Mechanical and Civil Engineering students demonstrated the most urgent need for foundational ESP support, highlighting a gap in language preparedness for technical fields. Furthermore, Mechanical Engineering showed the most consistent but lowest performance, indicating uniformly weak proficiency, while departments like Tourism and Electrical Engineering exhibited the widest score variability, suggesting a need for differentiated instruction. Across all departments, Reading emerged as the most consistently weak skill, making it a potential focal point for pre-specialisation ESP modules. This underscores the importance of equipping students with foundational reading strategies to support academic and professional success in their respective fields. Despite these insights, the study is limited by its sole reliance on quantitative data and its focus on receptive skills. Future research should integrate qualitative methods and consider productive language skills to provide a more comprehensive understanding of students’ ESP needs. Moreover, aligning assessments more closely with disciplinary language practices and adopting a longitudinal approach would enhance the applicability of findings. Nevertheless, this study provides a critical first step in identifying departmental language needs, informing curriculum development, and supporting targeted ESP interventions in vocational higher education. Abbreviations TOEIC Test of English for International Communication ESP English for Specific Purposes NA Needs Analysis EGP English for General Purposes SPSS Statistical Package for the Social Sciences SD Standard Deviation IQR Interquartile Range Declarations Ethics approval and consent to participate Ethical approval has been sought from the Ethics Committee of the University of Szeged in accordance with relevant guidelines and regulations. Consent for publication All participants and institutions were informed about the purpose of this research and consented to participate. Funding This research is fully funded by the University of Szeged, Hungary. Author Contribution I Wayan Eka Dian Rahmanu designed the study. He wrote the introduction, chose the research method, elaborated on the results, and explained the data in the discussion session. Gyöngyvér Molnár assisted with the suitable method used in this study. 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Contemporary Clinical Trials Communications , 19 , 100604. https://doi.org/10.1016/j.conctc.2020.100604 Spence, P., & Liu, G.-Z. (2013). Engineering English and the high-tech industry: A case study of an English needs analysis of process integration engineers at a semiconductor manufacturing company in Taiwan. English for Specific Purposes , 32 (2), 97–109. https://doi.org/10.1016/j.esp.2012.11.003 Stadler, R., Bosio, B., & Loderer, M. (2025). Listen to your destination: The use of podcasts in destination marketing. Journal of Destination Marketing & Management , 37 , 101022. https://doi.org/10.1016/j.jdmm.2025.101022 Stewart, J., Batty, A. O., & McLean, S. (2020). Predicting L2 reading proficiency with modalities of vocabulary knowledge: A bootstrapping approach . 2020 conference of the American Association for Applied Linguistics (AAAL). https://doi.org/10.1177/0265532219898380 Stoffelsma, L., Mwinlaaru, I. N., & Spooren, W. (2025). Testing university students’ morphological awareness of English as a second-language. System , 130 , 103626. https://doi.org/10.1016/j.system.2025.103626 Taye, T., & Teshome, G. (2025). The efficacy of extensive reading strategies for enhancing reading comprehension among 4th year EFL students at Mizan Tepi University. Social Sciences & Humanities Open , 11 , 101616. https://doi.org/10.1016/j.ssaho.2025.101616 Wang, H., Schultz, J. L., & Huang, Z. (2023). English language proficiency, prior knowledge, and student success in an international Chinese accounting program. Heliyon , 9 (8). https://doi.org/10.1016/j.heliyon.2023.e18596 Yamao, S., & Sekiguchi, T. (2015). Employee commitment to corporate globalization: The role of English language proficiency and human resource practices. Journal of World Business , 50 (1), 168–179. https://doi.org/10.1016/j.jwb.2014.03.001 Zuo, H., Zhang, M., & Huang, W. (2025). Lifelong learning in vocational education: A game-theoretical exploration of innovation, entrepreneurial spirit, and strategic challenges. Journal of Innovation & Knowledge , 10 (3), 100694. https://doi.org/10.1016/j.jik.2025.100694 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Editor assigned by journal 05 Aug, 2025 Submission checks completed at journal 05 Aug, 2025 First submitted to journal 01 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7269299","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506236270,"identity":"32fe8d28-66dc-49b9-9e14-347bcf9c0405","order_by":0,"name":"I Wayan Eka Dian 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08:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7269299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7269299/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90304338,"identity":"85acd393-de46-4a73-978f-6123222adedb","added_by":"auto","created_at":"2025-09-01 09:14:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSubject-related distribution of the participants in the sample\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/022af35f03cd908039f3f0c3.png"},{"id":90302133,"identity":"ea248062-ce24-47d8-a31b-b9e8c5ddfdad","added_by":"auto","created_at":"2025-09-01 09:06:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Boxplot of Listening Score Among Departments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e 1 = Business Administration, 2 = Accounting, 3 = Tourism, 4 = Electrical Engineering, 5 = Mechanical Engineering, 6 = Civil Engineering\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/54bb93b06222d7d2caa89dba.png"},{"id":90302136,"identity":"616439f2-2249-4e26-a0d6-e519d2685b81","added_by":"auto","created_at":"2025-09-01 09:06:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Boxplot of Reading Score Among Departments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e 1 = Business Administration, 2 = Accounting, 3 = Tourism, 4 = Electrical Engineering, 5 = Mechanical Engineering, 6 = Civil Engineering\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/80bde86eca240543c2e462ff.png"},{"id":90302140,"identity":"94a5b5ca-bb98-4889-bf4c-e79ee56f9aa4","added_by":"auto","created_at":"2025-09-01 09:06:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":6305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Boxplot of Listening Score Among Departments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e 1 = Business Administration, 2 = Accounting, 3 = Tourism, 4 = Electrical Engineering, 5 = Mechanical Engineering, 6 = Civil Engineering\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/723aa05bdc66477c7b8fdacb.png"},{"id":90302144,"identity":"44363f30-5fbd-4e67-90bf-e7658296617c","added_by":"auto","created_at":"2025-09-01 09:06:01","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":6212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Boxplot of Reading Score Among Departments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e 1 = Business Administration, 2 = Accounting, 3 = Tourism, 4 = Electrical Engineering, 5 = Mechanical Engineering, 6 = Civil Engineering\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/5b5ec2a0730f215173e2c259.png"},{"id":90307012,"identity":"981c1107-ca6e-4716-93bb-d8fe843bd5b2","added_by":"auto","created_at":"2025-09-01 09:30:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7269299/v1/e2f577da-9412-44e6-8537-003448469757.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"English for Specific Purposes Needs in Vocational Higher Education: Uncovering Urgency, Variance, and Skill Priorities Through Listening and Reading Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWithin tertiary education, English for Specific Purposes (ESP) has become an essential pedagogical strategy (Chan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is particularly true in vocational and technical institutions such as polytechnics, where proficiency in discipline-specific language fundamentally underpins both academic achievement (Coxhead et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and preparedness for the workforce (Al Hilali \u0026amp; McKinley, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As pivotal institutions for Technical and Vocational Education and Training (TVET), educators in polytechnics equip students with sector-specific practical skills through the use of technology, for example, multimodality including audiovisual and auditory combination (Naef et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), gamification (Zuo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Virtual reality (VR) (Smith et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This training necessitates discipline-targeted English competencies, enabling students to comprehend technical documentation, adhere to safety protocol, follow complex procedures, and communicate effectively in professional settings (Isaksen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite ESP's acknowledged significance, effectively diagnosing and prioritising needs within polytechnics' inherently diverse student departments remains challenging (Gaffas, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Substantial variation in prior English proficiency and educational backgrounds among students creates considerable within and between departments disparities (Lasekan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although Needs Analysis (NA) is fundamental to ESP program design (Huang \u0026amp; Yu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), conventional methods frequently emphasise perceived needs or target-situation analyse (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This risks neglecting diagnostic data, for example, general English proficiency, that exposes immediate foundational deficiencies hindering progress before specialised skill development (Nateghian, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, polytechnic NA research often prioritises speaking and writing skills (Akay et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; L. Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), resulting in scarce empirical insights into discipline-specific listening and reading comprehension gaps (Liu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile ESP is a cornerstone of language instruction in vocational and technical education, current approaches to NA in vocational education tend to emphasise productive skills. The previous study conducted by Bashori et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) focused on the speaking skills study through the use of technology. Although this study demonstrated the enhancement of learners' speaking abilities, it did not address learners' needs in receptive skills, particularly listening and reading. Similarly, an experimental study was conducted by Hong et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explored oral proficiency in English tourist guides. However, the study overlooked the importance of receptive skills like listening comprehension, which are significant in real-life tourist settings (Stadler et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This issue is also evident in research on writing skills, where previous studies focused solely on writing production, without considering the role of receptive or supporting language skills. The empirical study organised by Jiang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) exposed the vocational learners’ argumentative writing skills. However, the study did not explore how reading comprehension results in a limited understanding of the learners’ performance in ESP settings. The limited use of diagnostic tools to assess general English proficiency can reduce foundational weaknesses that must be addressed before specialised instruction can be effective (Hua \u0026amp; Beverton, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consequently, there is a need for more robust, data-driven NA models that give equal attention to receptive skills, reading and listening skills, and provide a more accurate basis for developing ESP classroom in the vocational context. Accordingly, the research is guided by the following questions:\u003c/p\u003e\u003cp\u003eRQ (1) Which departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) demonstrate the most urgent need for foundational ESP intervention in Listening and Reading comprehension?\u003c/p\u003e\u003cp\u003eRQ (2) Which departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) exhibit the most consistent (low standard deviation/interquartile range) and the most diverse (high standard deviation/interquartile range) Listening or Reading scores among their students?\u003c/p\u003e\u003cp\u003eRQ (3) Which specific skill (Listening or Reading) presents as the greater area of need across the majority of departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering), suggesting a potential core focus for foundational ESP modules before specialisation?\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevious Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA previous study conducted by Park et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) explored reading and listening scores through the TOEIC test at the universities in Korea. The study examined the relationships at the different levels between the reading and the listening scores. In their study, 11.328 TOEIC test scores were analysed. One of the results was that the students had more difficulty with the reading component than the listening on the TOEIC test. In contrast with other studies conducted by Nakamura (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Kamiya (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) at different universities in Japan, they found different results. There were 48 freshmen who enrolled in English for general purposes (EGP) involved in the study from Nakamura (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). TOEIC mock tests were used as benchmarks to establish a baseline study. During the study, they found that the reading test score was higher than the listening score. Similarly, Kamiya (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who involved 128 Japanese freshmen and high school seniors learners of English aged 18 or 19, participated in this study (118 females, 10 males), explored that the mean of the reading test score was higher than the listening test score.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe participants in this study consisted of 1.618 students enrolled at the State Polytechnic of Bali, Indonesia. These students were drawn from six different academic departments, representing both service-oriented and technical disciplines. The departmental distribution was as follows: 285 from Business Administration, 305 from Accounting, 376 from Tourism, 276 from Electrical Engineering, 184 from Mechanical Engineering, and 192 from Civil Engineering (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This diverse representation allowed for meaningful comparisons across academic programs with varying linguistic and professional demands, providing a comprehensive overview of receptive English proficiency within the polytechnic context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstrument\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the students’ receptive English skills, this study utilised the Test of English for International Communication (TOEIC) as the primary diagnostic instrument. The TOEIC Listening and Reading test is divided into two sections: Listening Comprehension, which includes 100 questions and ranges from 5 to 495 points, and Reading Comprehension, which also includes 100 questions with the same scoring range. The TOEIC was selected due to its international recognition and its strong alignment with the language demands of professional and vocational environments, making it particularly suitable for evaluating English for Specific Purposes (ESP) proficiency in the context of polytechnic education.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eA series of quantitative analyses were conducted using TOEIC Listening and Reading comprehension scores collected from students across six academic departments: Business Administration, Accounting, Tourism, Electrical Engineering, Mechanical Engineering, and Civil Engineering. All analyses were conducted using SPSS (version 26), and findings were visualised using bar charts, boxplots, and summary tables to support interpretation and comparative evaluation across departments and language skills. The analyses were conducted using descriptive and inferential statistical techniques to evaluate performance levels, consistency of scores, and comparative skill needs. The primary focus was to determine the extent of foundational ESP support required across departments. The mean and median scores for each department were used as indicators of overall performance, with lower average scores suggesting greater instructional needs. The standard deviation (SD) and interquartile range (IQR) were computed for both Listening and Reading scores across all six departments. Departments with lower SDs and IQRs were identified as having more homogeneous student performance, indicating relatively uniform English proficiency levels. ANOVA analysis was applied to determine whether the differences in Listening and Reading scores across the entire sample were statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDepartments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) demonstrate the most urgent need for foundational ESP intervention in Listening and Reading comprehension\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eANOVA result of TOEIC Listening and Reading\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkills\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe ANOVA test result in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed a statistically significant difference in listening and reading scores between groups, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Further investigation, through post hoc comparisons, was conducted to determine which specific departments demonstrate significantly lower performance. Therefore, the urgent need for foundational ESP intervention was analysed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eMultiple Comparisons for TOEIC Listening Scores by Department\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDepartments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Difference Among Departments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.850\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.850\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-14.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.927\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-13.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-20.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.927\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-72.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-79.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-65.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-59.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-41.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-30.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-37.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-23.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-17.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e BA\u0026thinsp;=\u0026thinsp;Business Administration, AC\u0026thinsp;=\u0026thinsp;Accounting, TO\u0026thinsp;=\u0026thinsp;Tourism, EE\u0026thinsp;=\u0026thinsp;Electrical Engineering, ME\u0026thinsp;=\u0026thinsp;Mechanical Engineering, CE\u0026thinsp;=\u0026thinsp;Civil Engineering\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePost hoc comparisons using the Tukey HSD test were conducted to determine which departments differed significantly in listening comprehension scores. The results indicated that Mechanical Engineering students scored significantly lower in listening than all other departments (\u003cb\u003esee\u003c/b\u003e Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Business Administration (M difference = -72.81, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Accounting (M difference = -79.61, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Tourism (M difference = -65.43, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Electrical Engineering (M difference = -59.24, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Civil Engineering (M difference = -41.85, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Similarly, Civil Engineering students also scored significantly lower than Business Administration (M difference = -30.96, p\u0026thinsp;=\u0026thinsp;.001), Accounting (M difference = -37.76, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Tourism (M difference = -23.58, p\u0026thinsp;=\u0026thinsp;.012). There were no statistically significant differences between Business Administration, Accounting, Tourism, and Electrical Engineering students, with p values above .05. However, Accounting students performed significantly better than Electrical Engineering students (M difference\u0026thinsp;=\u0026thinsp;20.37, p\u0026thinsp;=\u0026thinsp;.028). These findings confirm that Mechanical Engineering and Civil Engineering students exhibit the most pressing need for foundational ESP intervention, as they consistently underperformed compared to students in business-related departments and Electrical Engineering. The results underscore the importance of tailoring ESP instruction to meet the distinct language proficiency needs across disciplines.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eMultiple Comparisons for TOEIC Reading Scores by Department\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDepartments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003cp\u003eAmong Departments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-15.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-9.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-20.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-14.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-51.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-67.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-61.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-47.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-32.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-19.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-35.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-29.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-15.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e BA\u0026thinsp;=\u0026thinsp;Business Administration, AC\u0026thinsp;=\u0026thinsp;Accounting, TO\u0026thinsp;=\u0026thinsp;Tourism, EE\u0026thinsp;=\u0026thinsp;Electrical Engineering, ME\u0026thinsp;=\u0026thinsp;Mechanical Engineering, CE\u0026thinsp;=\u0026thinsp;Civil Engineering\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding the result in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, post hoc comparisons test revealed several statistically significant differences in Reading comprehension scores among departments. Mechanical Engineering students performed significantly lower than all other departments: Business Administration (M difference = -51.96, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Accounting (M difference = -67.64, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Tourism (M difference = -61.69, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), Electrical Engineering (M difference = -47.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Civil Engineering (M difference = -32.14, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Civil Engineering students also performed significantly lower than students from Accounting (M difference = -35.50, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Tourism (M difference = -29.55, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, their difference from Business Administration students was marginally non-significant (p\u0026thinsp;=\u0026thinsp;.053), and their difference from Electrical Engineering students was not statistically significant (p\u0026thinsp;=\u0026thinsp;.256). In addition, Accounting students scored significantly higher than Electrical Engineering students (M difference\u0026thinsp;=\u0026thinsp;20.26, p\u0026thinsp;=\u0026thinsp;.015). No statistically significant differences were observed between Business Administration, Accounting, Tourism, and Electrical Engineering, indicating relatively comparable performance among students in these departments. These results suggest that Mechanical Engineering students demonstrate the most urgent need for foundational ESP intervention in Reading comprehension, followed by Civil Engineering students. The findings reinforce the need for targeted language support in technical departments, while also recognising the relative strengths in reading skills among business-oriented and service-related disciplines.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDepartments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering) exhibit the most consistent (low standard deviation/interquartile range) and the most diverse (high standard deviation/interquartile range) Listening or Reading scores among their students\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eStandard Deviation and Interquartile Range of TOEIC Reading and Listening\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eListening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e\u003cp\u003eReading\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eIQR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eLevene\u0026rsquo;s\u003c/p\u003e\u003cp\u003escore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eIQR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eLevene\u0026rsquo;s\u003c/p\u003e\u003cp\u003escore\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF(5,1612)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eF(5,1612)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccounting\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;305)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e7.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c11\" namest=\"c10\" rowspan=\"6\"\u003e\u003cp\u003e14.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness Administration\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;285)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e71.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCivil Engineering\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;192)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e70.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectrical Engineering\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;276)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e83.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical Engineering\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;184\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e48.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTourism\u003c/p\u003e\u003cp\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;376)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e82.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote.\u003c/em\u003e SD\u0026thinsp;=\u0026thinsp;standard deviation, IQR\u0026thinsp;=\u0026thinsp;Interquartile Range\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Levene\u0026rsquo;s test of homogeneity of variance indicated a statistically significant difference in score variability across departments for listening comprehension, F(5, 1612)\u0026thinsp;=\u0026thinsp;7.395, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This result suggests that the assumption of equal variances is violated, and that departments differ significantly in how consistent student listening scores are. Among all departments, Mechanical Engineering showed the most consistent performance in listening, with the lowest SD (65.86) and smallest IQR (69). In contrast, Electrical Engineering demonstrated the greatest variability, with the highest SD (90.18) and widest IQR (130), indicating a broader spread of listening scores and a potential need for targeted instructional intervention to standardise listening proficiency within that department. The study also found a statistically significant difference in reading score variability across departments, F(5, 1612)\u0026thinsp;=\u0026thinsp;14.41, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This again confirms unequal variances among the groups. In terms of consistency, Mechanical Engineering performed the lowest SD (48.69) and smallest IQR (40), suggesting a more homogeneous level of reading ability among students. On the other hand, Accounting and Tourism showed higher variability, with SDs of 76.37 and 82.16 and IQRs of 95 and 105, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSkill (Listening or Reading) presents as the greater area of need across the majority of departments (e.g. business administration, accounting, tourism, electrical engineering, mechanical engineering, and civil engineering), suggesting a potential core focus for foundational ESP modules before specialisation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe IQR highlighted variations in score distribution across departments for both Listening and Reading skills. For the Listening component, IQR values ranged from 69 in Mechanical Engineering to 130 in Electrical Engineering, indicating a moderate to substantial dispersion in performance. In comparison, the Reading scores exhibited a generally narrower spread, with IQR values ranging from 40 in Mechanical Engineering to 105 in Tourism. This suggests that Listening scores showed greater variability among students, whereas Reading scores were more consistently distributed within most departments (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results also showed that reading is the greater area of need across the majority of departments, indicating a potential core focus for foundational ESP intervention. Specifically, departments such as Mechanical Engineering and Civil Engineering may require targeted support in Reading, with mean scores 48.34 and 58.05 points lower than their respective Listening scores. An examination of the boxplot shapes further reveals patterns in student performance (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe distributions for Mechanical Engineering and Civil Engineering were skewed toward lower scores, with lower medians, shorter upper whiskers, and an increased presence of low outliers. These patterns reinforce the observation that students in these departments generally struggled with listening comprehension. In contrast, the Accounting, Business Administration, and Tourism departments displayed higher medians and broader upper distributions, suggesting that a larger proportion of students in these fields achieved stronger listening scores. Additionally, while several departments, particularly Accounting, Business Administration, and Tourism, featured high outliers indicative of outstanding listening skills, Mechanical Engineering lacked such high-performing individuals. This absence further highlights the consistent challenges faced by Mechanical Engineering students in developing effective listening skills.\u003c/p\u003e\u003cp\u003eThe boxplot visually reinforced these patterns. Mechanical Engineering displayed a lower median, a compressed IQR, and dense clustering of scores around the lower end of the scale, indicating a systemic issue with Reading comprehension within the department. Civil Engineering also displayed a lower median with a wider dispersion compared to higher-performing departments. In contrast, Accounting and Tourism departments demonstrated higher medians and a broader spread toward higher Reading scores, suggesting stronger performance by a substantial proportion of their students. Furthermore, a considerable number of high outliers were observed in Business Administration, Accounting, and Tourism departments, particularly above 300 points, suggesting the presence of high-achieving individuals. Mechanical Engineering, on the other hand, exhibited almost no high-end outliers, further confirming the overall poor Reading performance within this department.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudents from business-related fields, such as accounting, tourism and business administration, tended to perform better in both listening and reading comprehension. This echoes the previous research conducted by Wang et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in the accounting program, who found that there is a relationship between English language proficiency, accounting knowledge and academic success. Learners believe that English proficiency has the potential to boost their careers in the future. With the continuous global expansion of the tourism industry, the integration of the English language into tourism and hospitality education is gaining increasing significance (Park et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In terms of business fields, the demand to promote English language skills for employees is important if the firms or companies are keen to expand their operations to other countries (Yamao \u0026amp; Sekiguchi, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The students believe that proficiency in the English language is becoming a vital employability skill and a strategic asset for business-related careers. Therefore, they practice and sharpen their English language skills, which results in better scores in listening and reading proficiency.\u003c/p\u003e\u003cp\u003eAs the previous research conducted in the engineering program, the importance of reading and listening abilities cannot be neglected (Kassim \u0026amp; Ali, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Spence \u0026amp; Liu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Engineers also face numerous English communicative challenges in global events, including highly frequent activities, for example, sending emails, reading reports, writing memos, conducting meetings, teleconferences, and presentations (Spence \u0026amp; Liu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, the current study found that students from engineering disciplines, such as Mechanical Engineering and Civil Engineering, demonstrated lower mean scores, both in listening and reading comprehension. Foundational ESP support for Mechanical Engineering and Civil Engineering cannot be merely additive but must be integrative and intensive. Pre-specialisation modules should prioritise core academic listening and reading micro-skills, for example, more word-based and lexico-grammatical strategies, aural vocabulary, orthographic vocabulary, and listening comprehension (Hamada \u0026amp; Yanagawa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lin, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In addition, motivation is also needed by the learners to improve their English language learning attitude (Apple et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ma \u0026amp; Zhao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Organising ability grouping can be implemented in the classroom because it has a positive effect on learners of lower than average proficiency (Sheppard et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis of the standard deviation and interquartile range of TOEIC Reading and Listening scores reveals significant differences in the consistency of student performance across departments. Mechanical Engineering stands out as the department with the most consistent performance in both Listening and Reading skills, with the lowest variability in scores. Although Mechanical Engineering students demonstrated the most consistent performance in both Listening and Reading skills, as reflected in the lowest standard deviation and interquartile range, their average scores were the lowest among all departments. This suggests a uniform but overall weak performance in English receptive skills. In contrast, departments such as Tourism and Electrical Engineering exhibit the most diverse scores, particularly in Listening. The high variability in scores in these departments indicates stark disparities in student proficiency levels, with a mix of high achievers and students struggling with foundational skills. This suggests that these departments require urgent, differentiated instruction to address the needs of both low and high performers. For example, Electrical Engineering's high variability in Listening scores suggests the difference in prior language exposure.\u003c/p\u003e\u003cp\u003eTo address this issue, the teacher or tutor could consider implementing test-taking strategies (Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With regard to the reading skills barrier, the implementation of translanguaging pedagogy has the potential to be integrated into the learning practice (Alexis, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The suggestion to employ several strategies was also proposed by Park \u0026amp; Kim, (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). They believe that the use of using multimedia, computer applications, dialoguing, setting up reading purposes and planning, previewing and determining what to read, connecting prior knowledge and experiences with texts and tasks, and inferring. Likewise, listening ability can be assisted through several learning strategies, for example, note-taking, active learning, comprehension, predictive organisation, critical thinking, resource-based learning, self-management (Palanisamy \u0026amp; Rajasekaran, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), vocabulary learning (Chujo \u0026amp; Oghigian, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; He \u0026amp; Loewen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hsu \u0026amp; Chao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and translation exercise (Čarapić, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results clearly point to reading as the greater area of need across the majority of departments, underscoring its potential as a foundational focus for ESP modules before moving into discipline-specific content. The IQR analysis revealed that while listening scores showed greater variability among students, reading scores were more consistently distributed within most departments, with a generally narrower spread. This consistency in Reading scores, however, is not a positive indicator, as it suggests that students are struggling with Reading comprehension to a similar extent. These findings differ from both Nakamura's (2018) and Kamiya's (2024) studies, which found that the reading test score was higher than the listening score. Foundational reading comprehension is essential for academic success across all fields, particularly in disciplines requiring the interpretation of technical texts, manuals, research articles, and written instructions. Adjusting the method, materials and pace of teaching to the development of students\u0026rsquo; language skills (Kim, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Participation in the international internship and self-learning activities advantages the TOEIC results (Chang \u0026amp; Utsumi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In terms of reading comprehension on TOEIC, teachers-centred approach including the teacher\u0026rsquo;s feedback, are important to assist learners\u0026rsquo; performance (Busa \u0026amp; Chung, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The use of multimodal text can be used to elevate learners\u0026rsquo; reading ability (Cahyaningati \u0026amp; Lestari, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Emphasising reading through scaffolded instruction (Ament et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), technical vocabulary training through digital platform (Jiang \u0026amp; Zhao, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and extensive reading (Taye \u0026amp; Teshome, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), can establish a solid linguistic base for students. Pre-specialisation modules must consider the morphological analysis of technical terms (Stoffelsma et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and syntactic deconstruction of complex sentences (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), before tackling specialised content.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite offering valuable insights into receptive English proficiency across departments, this study is subject to several notable limitations that should be acknowledged. First, the exclusive reliance on quantitative data derived from TOEIC Listening and Reading scores restricts the scope of analysis. While these scores provide a standardised measure of receptive language ability, they do not capture the complex interplay of cognitive, affective, and contextual factors that influence student performance. Variables such as learner motivation, prior exposure to English, language learning strategies, access to language-rich environments, and attitudes toward English learning are all important determinants that were not explored in this study.\u003c/p\u003e\u003cp\u003eFurthermore, the study's exclusive focus on receptive skills, Listening and Reading, which means that it does not account for productive language competencies such as Speaking and Writing. In ESP contexts, especially within professional and technical disciplines, productive skills are essential for real-world communication tasks such as writing reports, engaging in meetings, delivering presentations, or responding to customer inquiries. Without assessing these dimensions, the study presents only a partial view of students\u0026rsquo; overall communicative competence and readiness for workplace communication demands.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined receptive English proficiency, specifically listening and reading skills, across six academic departments at the State Polytechnic of Bali using TOEIC scores as a diagnostic tool. The findings revealed clear disciplinary differences, with business-related departments (Accounting, Tourism, and Business Administration) generally outperforming their engineering counterparts. In contrast, Mechanical and Civil Engineering students demonstrated the most urgent need for foundational ESP support, highlighting a gap in language preparedness for technical fields.\u003c/p\u003e\u003cp\u003eFurthermore, Mechanical Engineering showed the most consistent but lowest performance, indicating uniformly weak proficiency, while departments like Tourism and Electrical Engineering exhibited the widest score variability, suggesting a need for differentiated instruction. Across all departments, Reading emerged as the most consistently weak skill, making it a potential focal point for pre-specialisation ESP modules. This underscores the importance of equipping students with foundational reading strategies to support academic and professional success in their respective fields.\u003c/p\u003e\u003cp\u003eDespite these insights, the study is limited by its sole reliance on quantitative data and its focus on receptive skills. Future research should integrate qualitative methods and consider productive language skills to provide a more comprehensive understanding of students\u0026rsquo; ESP needs. Moreover, aligning assessments more closely with disciplinary language practices and adopting a longitudinal approach would enhance the applicability of findings. Nevertheless, this study provides a critical first step in identifying departmental language needs, informing curriculum development, and supporting targeted ESP interventions in vocational higher education.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTOEIC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTest of English for International Communication\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eESP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnglish for Specific Purposes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeeds Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEGP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnglish for General Purposes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval has been sought from the Ethics Committee of the University of Szeged in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants and institutions were informed about the purpose of this research and consented to participate.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is fully funded by the University of Szeged, Hungary.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI Wayan Eka Dian Rahmanu designed the study. He wrote the introduction, chose the research method, elaborated on the results, and explained the data in the discussion session. Gy\u0026ouml;ngyv\u0026eacute;r Moln\u0026aacute;r assisted with the suitable method used in this study. She provided feedback on the research method and discussion structure of the study. She also contributed to choosing suitable references to underpin the results of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkay, E., Yılmaz, B., \u0026amp; Y\u0026uuml;nc\u0026uuml;, H. R. (2025). A CLIL-based model proposal for a solution to English-speaking anxiety of gastronomy and culinary arts students. \u003cem\u003eJournal of Hospitality, Leisure, Sport \u0026amp; Tourism Education\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e, 100559. https://doi.org/10.1016/j.jhlste.2025.100559\u003c/li\u003e\n\u003cli\u003eAl Hilali, T. S., \u0026amp; McKinley, J. (2021). 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Using the Test of English for International Communication (TOEIC) Listening and Reading scores from 1,618 students, the research aims to identify urgent needs for foundational English for Specific Purposes (ESP) instruction, assess score consistency within departments, and determine which skill, listening and reading, presents the greater overall need. Quantitative analyses were conducted using SPSS, with descriptive statistics, standard deviation, interquartile range, and ANOVA supporting cross-departmental comparisons. Findings reveal that students from business-oriented departments outperformed to those in the technical programs, with Mechanical and Civil Engineering showing the lowest mean scores in both Listening and Reading. Mechanical Engineering also displayed the most consistent scores at the lowest performance level, which indicates a uniformly weak language foundation. Tourism and Electrical Engineering, on the other hand, exhibited the widest performance variability, suggesting a need for differentiated instruction. Importantly, Reading emerged as the most consistently weak skill across all departments, positioning it as a potential focus for pre-specialisation ESP modules. While offering valuable insights, the study is limited by its exclusive focus on receptive skills and reliance on quantitative TOEIC data. Future research should incorporate productive language skills and qualitative measures to capture motivational, contextual, and strategic learning factors. Overall, the findings inform curriculum development for vocational higher education by highlighting specific language needs across disciplines and guiding targeted ESP interventions.\u003c/p\u003e","manuscriptTitle":"English for Specific Purposes Needs in Vocational Higher Education: Uncovering Urgency, Variance, and Skill Priorities Through Listening and Reading Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:05:56","doi":"10.21203/rs.3.rs-7269299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-14T05:02:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262202610835639636855090696167283084729","date":"2025-08-25T01:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T07:53:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T07:40:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T07:39:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Language Testing in Asia","date":"2025-08-01T08:43:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"language-testing-in-asia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ltia","sideBox":"Learn more about [Language Testing in Asia](http://languagetestingasia.springeropen.com)","snPcode":"40468","submissionUrl":"https://submission.springernature.com/new-submission/40468/3","title":"Language Testing in Asia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34ceae3f-1784-41d8-9ab9-06967debf802","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T09:05:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 09:05:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7269299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7269299","identity":"rs-7269299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}