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  <front>
    <article-meta>
      <title-group>
        <article-title>The Role of Strategic Financial Management in Enhancing Corporate Value and Competitiveness in the Digital Economy</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib contrib-type="person">
          <name>
            <surname>Ahmad</surname>
            <given-names>Israr</given-names>
          </name>
          <email>chaudhryisrar@gmail.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution>Universiti Sains Malaysia</institution>
        <country>Malaysia</country>
      </aff>
      <history>
        <date date-type="received" iso-8601-date="2023-06-08">
          <day>08</day>
          <month>06</month>
          <year>2023</year>
        </date>
        <date data-type="published" iso-8601-date="2024-02-10">
          <day>10</day>
          <month>02</month>
          <year>2024</year>
        </date>
      </history>
    </article-meta>
  </front>
  
  
<body id="body">
    <sec id="sec-1">
      <title>Introduction</title>
      <p id="_paragraph-2">In the Kingdom of Saudi Arabia, English is taught as a foreign language (EFL). University students are typically required to participate in a Preparatory Year Program (PYP), designed to bridge the gap between secondary and tertiary education. Since English is the medium of instruction (EMI) in Saudi higher education, the PYP aims to develop students' language skills to enable them to comprehend and use specialized academic discourse relevant to their fields of study.</p>
      <p id="_paragraph-3">To ensure students receive instruction appropriate to their language proficiency, the PYP administers placement tests developed by the National Center for Assessment NCA, 2018). These assessments identify students whose English proficiency falls below the B2 level of the Common European Framework of Reference for Languages (CEFR). Based on their results, students are enrolled in level-specific and major-specific courses (e.g., engineering, science, medicine). For instance, engineering students are introduced to the genres and registers typical of technical disciplines, while medical-track students learn to analyze case studies and compose clinical reports. These courses are offered separately to male and female students, with curricula tailored to meet the academic demands of each college.</p>
      <p id="_paragraph-4">While these courses are both level-specific and discipline-aligned, factors such as student proficiency, gender, and chosen major significantly influence the delivery and effectiveness of the curriculum. Although the NCA’s placement test and in-house formative and summative assessments offer valuable insights into students' overall English proficiency, they do not reveal how these independent variables interact to affect learning outcomes.</p>
      <p id="_paragraph-5">Some studies have shown that female students tend to perform slightly better than male students in language tests, often producing syntactically longer and more complex sentences (James, 2010; Lahuerta Martínez, 2018). Other research has found that female students are generally more proficient and more willing to communicate in English than their male counterparts (S. H. Huang &amp; K. J. Tsai, 2024). Additionally, the academic discipline in which students are engaged appears to influence language acquisition; students exposed to content-integrated tasks grounded in real-world scenarios often achieve better language learning outcomes (Juan-Garau &amp; Jacob, 2015).</p>
      <p id="_paragraph-6">While these studies have examined the individual effects of gender or major on language proficiency, fewer have investigated how these factors interact, particularly in EFL contexts like Saudi Arabia’s. Furthermore, most existing research relies on perception-based surveys or small-scale classroom observations, with limited use of authentic, large-scale institutional assessment data. This study lies in its empirical analysis of real test performance data to explore the combined effects of gender and academic major on English language proficiency across multiple proficiency levels. By examining test scores from the English Placement Test (EPT) and the Preparatory Year Program Tests (PYPT), this research aims to provide a more nuanced understanding of learner variation in a gender-segregated, EMI context, contributing to more equitable and data-informed pedagogical strategies in Saudi higher education.</p>
      <p id="_paragraph-7">This study analyzes English Placement Test (EPT) and Preparatory Year Program Tests (PYPT) scores across three proficiency levels: beginner, intermediate, and advanced. The PYPTs are level-specific, periodic, and summative assessments designed and administered in-house by the Department of English Language (DEL). In light of the research problem, the following questions are investigated:</p>
      <p id="paragraph-0c2fefeb00ab38671794d0cf12a77844">
        <bold id="bold-0ce9bf3b1065798a36ec9f33d219ca26">Research Questions</bold>
      </p>
      <list list-type="order" id="list-ebfc5d19890c49a59e53c241c6d6bae3">
        <list-item>
          <p>
            <italic id="_italic-1">What is the degree of the relationship between the EPT scores and the preparatory program tests?</italic>
          </p>
        </list-item>
        <list-item>
          <p>
            <italic id="_italic-2">Is there a statistically significant difference in the EPT scores across gender, proficiency levels, and major variables?</italic>
          </p>
        </list-item>
        <list-item>
          <p>
            <italic id="_italic-3">Is there a statistically significant difference in the final test scores of the preparatory program across gender, proficiency levels, and major? </italic>
          </p>
        </list-item>
        <list-item>
          <p>
            <italic id="_italic-4">Is there a statistically significant difference in the preparatory program final test sub-domain scores across gender, proficiency levels, and major variables?</italic>
          </p>
        </list-item>
      </list>
    </sec>
    <sec id="sec-2">
      <title>Literature Review</title>
      <p id="_paragraph-8">Several studies have emphasized the role of individual factors such as gender and academic major in shaping students’ language proficiency. For instance, J. F Lee (2023) found that female students consistently outperformed male students in speaking and writing proficiency, although the gap was not highly significant. In another study, M. Perrone-Bertolotti, C. Pichat, J. F Bas, Le, A. Baciu, and M. Baciu (2011) observed that females were more efficient at translating speech sounds into visual representations and read faster than males. This cognitive efficiency was attributed to greater involvement of the inferior frontal cortex (Broca’s area) in females, suggesting a potential biological advantage in language processing. While there is no conclusive evidence that such processing advantages apply uniformly across all cultural and linguistic contexts, these findings point to the possibility of a biological edge in females when processing textual data. However, it remains unclear how such advantages manifest in EFL environments, particularly when the target language is learned after the so-called "critical period" (around the age of 11–14). This study seeks to examine the performance of Preparatory Year Program (PYP) students under standardized testing conditions, focusing on the interaction effects of gender and academic major on students’ language proficiency.</p>
      <p id="paragraph-3ec5925c3774b4653e600166da53b207">
        <bold id="bold-b75934eebe42e7cdb568678039a4f66d">Gender and Language Proficiency</bold>
      </p>
      <p id="_paragraph-9">Recent research has consistently shown that females tend to score higher than males on language proficiency assessments. James (2010) reported that female students scored significantly higher in language usage, reading comprehension, and sentence construction. Similarly, on standardized language tests such as IELTS and TOEFL iBT, females also outperformed males (James, 2010, p. 388). These results suggest that females are generally more proficient language users, at least in the context of placement testing.</p>
      <p id="_paragraph-10">Some scholars have cautioned that gender could act as a "potential source of measurement error" (Bachman, 1990, pp. 162–163), indicating its significant influence on test outcomes. It has also been observed that female students use a wider range of language learning strategies compared to their male counterparts (Green &amp; Oxford, 1995; Psaltou-Joycey, Sougari, Agathopoulou and Alexiou, 2014; Goh &amp; Foong, 1997). However, Milla and Gutiérrez-Mangado (2019) found that these strategic differences tend to diminish as students' proficiency levels increase.</p>
      <p id="_paragraph-11">While female students often demonstrate stronger language proficiency, this advantage does not necessarily translate to superior performance across all academic contexts. In general, females tend to excel in arts and humanities, while males report higher self-confidence in mathematics (G. Huang, 2013; Asio, 2019). Stoeger, Almulhim and Ziegler (2022) examined the gender achievement gap in Saudi Arabia and found that female students consistently outperformed males academically. They also possessed and utilized more educational and learning capital. However, within the context of this study—where male and female students have access to the same curriculum and resources—outcomes may differ.</p>
      <p id="_paragraph-12">Although gender is considered a significant factor in language learning strategies, socio-cultural theorists argue that language proficiency is not inherently gender-determined (Hyland, 2007). Instead, various factors influence test performance, including the quality of education at the secondary and tertiary levels (Woodfield, Earl-Novell, &amp; Solomon, 2005), personality traits that align with certain test formats (Bishop, Bulloc, Martin and Thompson, 1997), perceptions of test fairness, and levels of test anxiety (Woodfield et al., 2005).</p>
      <p id="paragraph-4f248683c39e878b5633cda098344a76">
        <bold id="bold-adb8b2418c00b59c8c9e2ce14b657eb4">Academic Major and Language Performance</bold>
      </p>
      <p id="_paragraph-13">Academic major refers to the specialization students pursue in their undergraduate studies. According to Becher and Trowler (2001), academic disciplines can be broadly categorized into “hard” and “soft” fields, each with either theoretical/pure or applied orientations. For example, disciplines like physics and mathematics are considered hard–pure, while medicine and engineering fall under hard–applied. In contrast, sociology and anthropology are examples of soft–pure disciplines, whereas education and English language teaching (ELT) are soft–applied (Lau &amp; Gardner, 2019).</p>
      <p id="_paragraph-14">In Saudi Arabia, this disciplinary distinction is often rephrased in terms of compliance: "highly compliant" disciplines typically refer to STEM fields, while "low compliant" disciplines encompass the humanities and social sciences (Friedrich, Prøitz, &amp; Stensaker, 2016, p. 879). High-compliance disciplines tend to project concrete and measurable learning outcomes (Laird, Shoup, Kuh &amp; Schwarz, 2008), whereas arts and humanities are more abstract and less aligned with standardized qualification frameworks (Friedrich et al., 2016).</p>
      <p id="_paragraph-15">This distinction between technicality and abstraction has been widely discussed in language education (Wignell, 1998). Disciplinary discourses, whether logical or ideational, and their modes of expression can significantly influence students’ language use over time. Exposure to diverse academic registers and genres has linguistic consequences that may affect proficiency development. To better understand how gender and academic major influence students' language performance across specific sub-domains, this study posed the research questions outlined earlier.</p>
      <p id="_paragraph-16">The impact of language proficiency on academic performance also varies by discipline. For example, language plays a more central role in the social sciences than in technical fields like engineering (S. Y. Lee, 2024). Although limited research has examined the direct interaction between disciplinary background and language proficiency, existing studies indicate that language skills are crucial to academic success. Sánchez-Pérez (2021) suggests that language proficiency is a key factor in acquiring disciplinary knowledge. In light of these insights, this study compares student performance across multiple testing conditions. The following section outlines the methodology used.</p>
    </sec>
    <sec id="sec-3">
      <title>Methodology</title>
      <p id="_paragraph-17">This study compared the performance of 2,913 Preparatory Year Program (PYP) students under two assessment conditions. The sample included all PYP students enrolled during one academic year. First, we examined whether there was a correlation between results on the Preparatory Year Program Tests (PYPT) and the English Placement Test (EPT). The second objective was to quantitatively investigate the effects of gender, proficiency level, and academic major on students’ performance in both tests. The third research question focused on the relationship between independent variables and the results of the PYPT. Finally, we aimed to explore whether sub-domain scores varied across the different variables.</p>
      <p id="_paragraph-18">All students in the study took the EPT administered by the National Center for Assessment (NCA) and four internally developed, periodically administered PYPTs (Test 1, Test 2, Test 3, and Final Test). Based on their EPT scores, students were categorized into level-specific groups, considering both gender and major. The students were admitted into four academic majors: engineering, science, health-1 (medicine, dentistry, pharmacy and applied medical sciences), and health-2 (nursing and public health). Within each major, students were further classified into three English proficiency levels: beginner, intermediate, and advanced. Table 1 presents the frequency distribution of students across the variables—gender, proficiency level, and academic major.</p>
      <table-wrap id="tbl1">
        <label>Table 1</label>
        <caption>
          <title>Frequency distribution of students across the variables.</title>
          <p id="_paragraph-20"/>
        </caption>
        <table id="_table-1">
          <tbody>
            <tr id="table-row-fcc9fe13634830e9c7a9076c4fc3e618">
              <th id="e22dd4e89615476d12dcce939cd0aa26">
                <bold id="_bold-2">Variables</bold>
              </th>
              <th id="ffab0654ed5e164f7dd4a4be0c3f7e9d">
                <bold id="_bold-3">Gender</bold>
              </th>
              <th id="4e28bf662a3cb4e952f3d37ce37c9c17">
                <bold id="_bold-4">Frequencies</bold>
              </th>
              <th id="43aebbc91058893532f678879fbe612b">
                <bold id="_bold-5">Percentages (%)</bold>
              </th>
            </tr>
            <tr id="table-row-7d3db5e067593d321ce49671016553ff">
              <td id="88d4c83a78c078ba7d79f6748f3ea0c1" rowspan="2">Gender</td>
              <td id="5217708a1a35324ab34477c58c2bb823">Male</td>
              <td id="17349b65c3bbbb771c2c1b52b1900d07">1368</td>
              <td id="139ea75928044c1e4d4fab8bd2179ae4">47</td>
            </tr>
            <tr id="table-row-a14a19c767bf0a7061d02ca0bf5835d3">
              <td id="4c57abbf0483d988797390956214c5a8">Female</td>
              <td id="9a58624341025c542ef0f7638726ebfb">1545</td>
              <td id="e809ac817af1bc9a11fd1f05d43ea93d">53</td>
            </tr>
            <tr id="table-row-a33738e71895c3cb368927341915784d">
              <td id="3315dc067f4fe33493594398ca69d28e" rowspan="3">Proficiency Level</td>
              <td id="c163679524f8c9664d7c0ecd8c6e08bb">Beginner</td>
              <td id="9534e37c389e0a29f6d77d49da5603f4">678</td>
              <td id="1471a51fc714274891fb2e7cb82bf7ee">23.3</td>
            </tr>
            <tr id="table-row-58ecaef4c0b81d0d4780a4b4d5f02408">
              <td id="d5b711ba4b8c04fa0ac2033125495db0">Intermediate</td>
              <td id="f65c0f02ad6c455c8e5c02f885f4fd32">1538</td>
              <td id="46d2dd679e60b049a4e97221b4c0423a">52.8</td>
            </tr>
            <tr id="table-row-4f0808fa36f3cb68b79953c2d94ef660">
              <td id="4477aaf69895fa5665bb8711f2dcc693">Advanced</td>
              <td id="78358ff5ee174cc4920c95375d0b05eb">697</td>
              <td id="eb15a040a0cbc9fc712c9469051e147a">23.9</td>
            </tr>
            <tr id="table-row-494a3976ad15706184bb823ae0283e5d">
              <td id="efa7158675f376cd5dd8760a93bec36d" rowspan="4">Major</td>
              <td id="3b2dd6180ef4b1deb328bc189374a3f8">Engineering</td>
              <td id="1902c2c620405f6bd2048e7e11f25993">513</td>
              <td id="b1e36eca0e9f354d45ec4d910a4467fd">17.6</td>
            </tr>
            <tr id="table-row-134428a869fdaa9524bf03f64748b5eb">
              <td id="d834214cf4dd2d6cce2f43d58c3edc75">Science</td>
              <td id="bb4d5d0e577e1322d31d5f72ea1b6289">1344</td>
              <td id="fbd5409ecb089de6348f1e096b5c766c">46.1</td>
            </tr>
            <tr id="table-row-b35779a738a9e7b8d3b4d4ddc41eb4d0">
              <td id="803c3d700788e827137440d75f2ed36a">Health-1</td>
              <td id="0bc5a0ddbb06a0ae87139c25fb9fda4e">776</td>
              <td id="28bf44c575839d835f891c82f529c04b">26.6</td>
            </tr>
            <tr id="table-row-4b9357d5adbdf6ba8d320d1f9ed06c03">
              <td id="d2d2bb37caf388211cd0d040bbb503b4">Health-2</td>
              <td id="e9c9eacea150f7d0fc13b589738c5176">280</td>
              <td id="e1689499cfac820bf5deb9192295dc84">9.6</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-21">Source: Calculated by the author</p>
      <p id="_paragraph-22">The study group included 1,368 males (47%) and 1,545 females (53%). Based on EPT scores, 23.3% of students were classified as beginners (A1 level), 52.8% as intermediate (A2 to B1), and 23.9% as advanced (B1+ and above). In terms of academic majors, 46.1% of the students enrolled in the Science track, followed by Health-1 (26.6%), Engineering (17.6%), and Health-2 or Nursing (9.6%).</p>
      <p id="_paragraph-23">The sampling strategy adopted in this study involved a population-based approach, whereby all students enrolled in the PYP during the academic year 2020-2021 were included in the analysis. This comprehensive inclusion ensured that the dataset reflected the full demographic and academic diversity of the student population, including variations in gender, major, and proficiency level. Such an approach enhanced the internal validity of the study and allowed for robust statistical comparisons without the constraints of sampling bias. While the study was conducted at a single institution, the large sample size (N = 2,913) and full-population coverage offered a strong foundation for analyzing performance trends and generalizing findings within the institutional context. This sampling design was well-suited to the descriptive-comparative aims of the study and supported the use of factorial ANOVA and post-hoc tests to examine subgroup differences.</p>
      <p id="paragraph-3f879510a162541b842da84473ac6674">
        <bold id="bold-5c768f777ca2c11655da81bf0cb7f6b8">Development and Validation of PYP Tests</bold>
      </p>
      <p id="_paragraph-24">To ensure the validity and reliability of the in-house PYPTs, a structured process was followed during test development and validation. For content validity, the Department of English Language (DEL) collaborated with Oxford University Press to develop a bank of multiple-choice and open-ended items, using the Common European Framework of Reference for Languages (CEFR) as the guiding framework (UCLES, 2018; Alderson, 2002; Little, 2007).</p>
      <p id="_paragraph-25">Test item developers—including both native and non-native English speakers—were selected based on their experience in language teaching and assessment. Each item writer completed a 30-hour workshop led by a certified trainer from Oxford University Press and the Oxford Teachers’ Academy (ELAS International, Examination and Assessment Clinic Phase 1 and 2, 2019). Participants were trained in developing listening, reading, and writing test items aligned with CEFR levels. They were given sufficient time and standard rubrics to develop an item bank.</p>
      <p id="_paragraph-26">All submitted items were reviewed by an expert panel comprising members from the DEL, Oxford University Press, and external consultants. Items were evaluated for accuracy, clarity, complexity, and relevance to course content. A blind review was then conducted by a screening team of four to five teachers representing different proficiency levels—including at least one native and one non-native English speaker—to assess inter-rater reliability. Items were rated for alignment with textbook content and assigned CEFR levels (from basic to advanced). Final selection was conducted by the Exam Committee and the DEL.</p>
      <p id="_paragraph-27">Typically, test items were randomly distributed across four test versions. Overall, 80% of the items were multiple-choice (with four options), while 20% were open-ended, requiring students to write essays based on course-related topics.</p>
      <p id="_paragraph-28">The EPT, by contrast, is a standardized, centrally designed test administered by the NCA. According to the NCA’s policy documents, the EPT is CEFR-aligned and evaluated using the framework’s rubrics. Table 2 outlines the classification criteria used by the evaluation team to align test items with CEFR levels from Basic to Advanced (see Appendix 1 for cut scores).</p>
      <table-wrap id="tbl2">
        <label>Table 2</label>
        <caption>
          <title>Classification criteria used to align tests with CEFR</title>
          <p id="_paragraph-30"/>
        </caption>
        <table id="_table-2">
          <tbody>
            <tr id="table-row-9fdc455ed0321827dfbfa62c9806a2fd">
              <th id="679b9ffa935ce8211e46ade77a33139d">
                <bold id="_bold-7">EPT</bold>
              </th>
              <th id="54c8b63234d347559aedccdfa8d0d64b">
                <bold id="_bold-8">0 – 45</bold>
              </th>
              <th id="8df07f31062276e03aefc0d26f5562a7">
                <bold id="_bold-9">46 – 79</bold>
              </th>
              <th id="c48173788687f4742b89dcb658af3683">
                <bold id="_bold-10">80 - Above</bold>
              </th>
            </tr>
            <tr id="table-row-1c3d2f38e3bec7253c7ac0b57437c87d">
              <th id="a0011512f94a988695e11d1624e368e7"/>
              <th id="6fe340717b4725a1993f7593eac0d1d6">
                <bold id="_bold-11">Beginner</bold>
              </th>
              <th id="789ff2a0679511d4dbda6cc48738cb10">
                <bold id="_bold-12">Intermediate</bold>
              </th>
              <th id="10bd884d9bc5be07145820423992e140">
                <bold id="_bold-13">Advanced</bold>
              </th>
            </tr>
            <tr id="table-row-43133e7bb4c5695a6da0ba726a171857">
              <td id="badf724b43255bf5aa9566eb4f27f13a">CEFR</td>
              <td id="57059b72aa8e23d4ce86ed373eb0188a">A 1</td>
              <td id="7bee2080ab3d2e49ff998415bb2980ab">A2 – B1</td>
              <td id="7674deb00f2dee25b65b0e60eb8290f9">B1+and Above</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-31">Source: Calculated by the author</p>
      <p id="paragraph-f6a41809c710a89a6cc6eab7874c419b">
        <bold id="bold-bb8625d74a8c65ac949a3ec43245240e">Pedagogic Intervention and PYPT Tests</bold>
      </p>
      <p id="_paragraph-32">Over a 32-week academic period, students were enrolled in two English language courses: ENG 101 and ENG 102. ENG 101 focused on general English, while ENG 102 introduced academic English relevant to students' disciplinary specializations. Students who scored 80 or above on the EPT studied English for 12 hours per week, while those who scored below 45 received 20 hours of instruction per week.</p>
      <p id="_paragraph-33">The Final PYPT assessed overall proficiency at the B2 level of CEFR. In contrast, Tests 1, 2, and 3 evaluated students’ level-specific proficiency at earlier stages in the course. These progressive tests deliberately excluded language components beyond the designated proficiency level to maintain assessment accuracy and validity.</p>
    </sec>
    <sec id="sec-4">
      <title>Data Analysis</title>
      <p id="_paragraph-34">As previously mentioned, the operationalization of the PYP test constructs maintained consistent structural and content parameters across all assessments. Test validity was reinforced by ensuring consistency in item types, scoring rubrics, and the number of raters evaluating the answer sheets. This section presents a detailed analysis of the test results and explores the interaction effects across different variables.</p>
      <p id="_paragraph-35">Across all test conditions, results revealed a moderate to strong correlation, which can likely be attributed to the tests' adherence to CEFR-based rubrics. As shown in Table 3, the bivariate correlations between the EPT and the PYP tests were statistically significant at the 0.01 level, with coefficients ranging from 0.601 to 0.807—indicating moderate to strong positive relationships. The strongest correlation was observed between the EPT and the Final PYP test, followed by Test 3 and the Final Test, and then by Test 1 and Test 2. The high correlation between the EPT and the Final Test may be due to both assessments targeting summative achievement aligned with the CEFR B2 level, whereas the earlier tests emphasized formative assessment. As a result, only the Final Test scores were included in the correlation and comparison analyses with the EPT; Test 1, Test 2, and Test 3 were excluded from further analysis.</p>
      <table-wrap id="tbl3">
        <label>Table 3</label>
        <caption>
          <title>Correlation between the EPT and PYPTs.</title>
          <p id="_paragraph-37"/>
        </caption>
        <table id="_table-3">
          <tbody>
            <tr id="table-row-5cca26be08b87004e4f60bbc0f1d0270">
              <th id="e2a0925f0b25c883e820764bfe73eb5d"/>
              <th id="7f63d9c7a9d0796870e1e03c11943009">
                <bold id="_bold-15">EPT</bold>
              </th>
              <th id="4d24cf305eb11d55fb314204346b5c3f">
                <bold id="_bold-16">Test-1</bold>
              </th>
              <th id="403eed4115b94fc400d0562d5c1eac6a">
                <bold id="_bold-17">Test-2</bold>
              </th>
              <th id="b6ab1ad97bf8a1256b2c9261595411db">
                <bold id="_bold-18">Test-3</bold>
              </th>
              <th id="446a7bb2c2afb6f8602fcd20526ff30c">
                <bold id="_bold-19">Final Test</bold>
              </th>
            </tr>
            <tr id="table-row-7db30a842d426dd3dc8d4d6975f40ea6">
              <td id="7d6a6a3d52c794d4e8fe2f043f5c726a">EPT</td>
              <td id="e8f95cb1f3d9b8a2ddeb02bb71c78d05">1</td>
              <td id="483161e95f8b8d59db061889ffe9e34f"/>
              <td id="7b41495df6a3617aafa912704c508bf8"/>
              <td id="0f81aaef497efc8267773eeb0bc98f2c"/>
              <td id="ce3e1fd35da2f75fdd7f43e1cf9e74fc"/>
            </tr>
            <tr id="table-row-ce9cd74496d7b95789ec72e4fd59b024">
              <td id="ea645c4f3e701da1fbab96adb88d6c65">Test-1</td>
              <td id="a3aa43c106b802289915f49e3b97e1ca">.683<sup id="_superscript-1">**</sup></td>
              <td id="a0de791891cdc9afa79b944e8bd975a2">1</td>
              <td id="e99cb9eb91dbbbd7a1d9004d0e726bf5"/>
              <td id="652d39976dca236c189adce0f98f5cc1"/>
              <td id="b04cd9d65fcb64460c1d092110de51be"/>
            </tr>
            <tr id="table-row-e9c9b79f98233ad6a1c70da64c64ab29">
              <td id="794969a9e816768f8eefd019a3c8893a">Test-2</td>
              <td id="235dd09a26de768d5e95b675d0353940">.601<sup id="_superscript-2">**</sup></td>
              <td id="3198ffca4555bce3d4100decbe62557d">.791<sup id="_superscript-3">**</sup></td>
              <td id="0f04eb115dc1ec6bdbdab2868e7e0c59">1</td>
              <td id="9a5bbeef4eead3b3b15c3522a4e8806f"/>
              <td id="783654898791782c2bcae8d05156f66c"/>
            </tr>
            <tr id="table-row-333571709e3fcbd09717a6e1276a5218">
              <td id="c111033c23cf95b4df7b4a623f645f0a">Test-3</td>
              <td id="6fc6f0e3f195942b1dd8ab82fb734e3e">.642<sup id="_superscript-4">**</sup></td>
              <td id="c469f3f0a028b0f1c23f917dd2741aa1">.752<sup id="_superscript-5">**</sup></td>
              <td id="33cc192089de089182cf5705d5385bf3">.758<sup id="_superscript-6">**</sup></td>
              <td id="a34946b0ab9345b396982ca7547ec171">1</td>
              <td id="8347e8ef3200ec953caa432d3752c833"/>
            </tr>
            <tr id="table-row-d24ac600d3989be175394b242a7b0f9c">
              <td id="c0b881f560aa17d3b6d8f154961a785f">Final Test</td>
              <td id="90aaedce1fb0e87d6b76a537f1401bf0">.807<sup id="_superscript-7">**</sup></td>
              <td id="5eedb841a332041057b852729513dfe1">.775<sup id="_superscript-8">**</sup></td>
              <td id="ad8c8983f233d57933ed4a13bf13ba53">.769<sup id="_superscript-9">**</sup></td>
              <td id="52684aec68ccb2edd4239cab9a23d219">.793<sup id="_superscript-10">**</sup></td>
              <td id="d41a221b776a2c64e23aea0cf0502c90">1</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-38"><italic id="_italic-5">** Correlation is significant at the 0.01 level (2-tailed)</italic><italic id="_italic-6">. </italic>Source: Calculated by the author</p>
      <p id="_paragraph-39">The bivariate correlations between sub-domains of the EPT and those of the Final Test were also significant at the 0.01 level. Correlation coefficients ranged from .482 to .622, suggesting small to moderate relationships among sub-domains. The listening component of the Final Test showed relatively weaker correlations with the EPT sub-domains, while the structure domain of the EPT demonstrated stronger correlations with the Final Test sub-domains. These findings indicate that listening assesses distinct language skills that are not as strongly captured by the EPT, whereas structural knowledge appears to contribute consistently across different types of language assessments.</p>
      <p id="paragraph-585328858b12378cf55f4af1b5cb6e6c">
        <bold id="bold-6bb838cbe467ae4b5055774f60ac062e">What is the degree of the relationship between the EPT scores and the preparatory program tests?</bold>
      </p>
      <p id="paragraph-ce87a61c417e2ac10fd29bb61b1d877c">
        <bold id="bold-260fd00e5ebf8f5a3e4034c83e236941">EPT scores across genders, proficiency levels, and majors</bold>
      </p>
      <p id="_paragraph-40">Factorial ANOVA analyses were conducted to examine the main and interaction effects of gender, proficiency level, and academic major on EPT scores. Table 4 presents the factorial ANOVA results for variance within the EPT scores.</p>
      <table-wrap id="tbl4">
        <label>Table 4</label>
        <caption>
          <title>ANOVA results for the EPT test scores.</title>
          <p id="_paragraph-42"/>
        </caption>
        <table id="_table-4">
          <tbody>
            <tr id="table-row-87e995a4072aa3f1aac7601ee85583b4">
              <th id="dce0565087f880bc2b797a68cd31e9e9">
                <bold id="_bold-21">Source</bold>
              </th>
              <th id="68eff6a1e1ef0b44209a9e489743d2e8">
                <bold id="_bold-22">Sum of Squares</bold>
              </th>
              <th id="c7154f60501d5ec9066ee10f429d5ac1">
                <bold id="_bold-23">Df</bold>
              </th>
              <th id="39043bc244253fd6cec9b58a9119312d">
                <bold id="_bold-24">Mean Square</bold>
              </th>
              <th id="0db872c9ed41ae44f384117e80258c22">
                <bold id="_bold-25">F</bold>
              </th>
              <th id="1a1c45dc3f1dde3aa2906b1c0456bda0">
                <bold id="_bold-26">Sig.</bold>
              </th>
              <th id="8016bd010100d64e891ce8fbc3c6be27">
                <bold id="_bold-27">Partial Eta Squared</bold>
              </th>
            </tr>
            <tr id="table-row-f7fa558881dc821dccd4fff4f235cf15">
              <td id="42d010cb28c2012f800fae74e9ef80cd">Gender &amp; Proficiency level</td>
              <td id="598a63e908c3563a348eee4107c6e9ce">556.768</td>
              <td id="3cddd2030f2a1f25f175583cddae2b2c">2</td>
              <td id="0afffa2c6ac7f84a1401f52906924d9b">278.384</td>
              <td id="214d69b168b1f24cf21cac196980f974">3.833</td>
              <td id="569549647645112ad6e9ccac431b8ccf">.022</td>
              <td id="2acd69eace4e5d6cb48ac67b13f2f748">.003</td>
            </tr>
            <tr id="table-row-3f6fc20623c17ba8c9e5d06a3ac5d4c6">
              <td id="23298dc9bca1039992304143b412f205">Major &amp; proficiency level</td>
              <td id="ed949c5b77e2fb4e94632c1612827bbf">692.421</td>
              <td id="f15eb08f5e5e9eebcdefd9d0703cbed7">6</td>
              <td id="df6620518a86a1857bfc0d37168eaded">115.404</td>
              <td id="106d97378cfbbd40ff0f0537f324447f">1.589</td>
              <td id="4217c608f091f350ad68384b6b82d077">.146</td>
              <td id="247be3d3ad7ed7571f4848238c49b5a5">.003</td>
            </tr>
            <tr id="table-row-b1c9f265ec5852c67eabdbdc75fba8f6">
              <td id="76fab07424eb3173ae930de3a5d82433">Gender &amp; Major &amp; Proficiency level</td>
              <td id="e5462efcc8ac147cac0db3b06b108a72">980.165</td>
              <td id="1d42e77ddb801d148a6287aaffc5d632">6</td>
              <td id="b5b84c2f663d464613dcbac1ee380230">163.361</td>
              <td id="d33de54080406df5abb776b08e82aba9">2.249</td>
              <td id="55aa3c97af80e9ee506c91e98ed2a2eb">.036</td>
              <td id="c333e4ca54c2fbc943b9c42131c05d7c">.005</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-43">Source: Calculated by the author</p>
      <p id="_paragraph-44">The results indicated that gender had a statistically significant effect on EPT scores (<italic id="_italic-7">F</italic>(1, 2889) = 4.159, <italic id="_italic-8">p</italic> = .042 &lt; .05, η² = .001). The main effect for proficiency level was also significant, with an F-ratio of <italic id="_italic-9">F</italic>(2, 2889) = 2271.279, <italic id="_italic-10">p</italic> &lt; .001, indicating substantial differences among proficiency groups. In contrast, the main effect of major was not statistically significant (<italic id="_italic-11">F</italic>(3, 2889) = 1.022, <italic id="_italic-12">p</italic> = .382 &gt; .05). Given these results, a Bonferroni post-hoc test was conducted to explore the mean differences between the categories of the variables. The post-hoc results, shown in Table 5, provide mean difference comparisons across gender and proficiency levels with respect to EPT scores.</p>
      <p id="paragraph-38c78cf539d6456c38d1368b9e4c97d1">
        <bold id="bold-6f7455187aa6a265e0e91036ff8503ae">Is there a statistically significant difference in the EPT scores across gender, proficiency levels, and major variables?</bold>
      </p>
      <p id="paragraph-ea78708e81e14e8b48152d666c4b5f43">
        <bold id="bold-836e6717377d82c95de1df0c81c5bb93">EPT scores across gender, proficiency levels, and major variables</bold>
      </p>
      <p id="_paragraph-45">The Bonferroni post-hoc comparisons revealed that the mean EPT score for male students was significantly different from that of female students, with female students scoring higher, regardless of their academic major.</p>
      <p id="_paragraph-46">Additionally, mean scores increased progressively from the beginner to advanced proficiency levels. The differences between each pair of proficiency levels were also statistically significant, supporting the validity of the predefined EPT proficiency level cut-scores (see Table 5).</p>
      <p id="_paragraph-47">Figure 1 displays the EPT scores as a function of proficiency level across academic majors. The figure shows that the mean scores for each proficiency level remained relatively consistent across the different majors. This suggests that EPT scores are primarily influenced by proficiency level rather than major.</p>
      <table-wrap id="table-figure-c0f42a1ae7ea2df741bfee1ce9b3bded">
        <label>Table 5</label>
        <caption>
          <title>ANOVA Comparisons of the EPT scores across gender and proficiency levels.</title>
          <p id="paragraph-da1f50733dcb0770c393ead7277558c7"/>
        </caption>
        <table id="table-51d43472c784eaaa0247b47d397f3af3">
          <tbody>
            <tr id="table-row-23666df5a24d31aea20c912d9022dd9d">
              <th id="table-cell-1e2095677125613f2597068e2863963c" colspan="5"/>
              <td id="table-cell-43f5bc7f33e6fdf0e7b32500114bba1f" colspan="3">
                <bold id="bold-1">Bonferroni's Post-hoc test (<italic id="italic-1">p </italic>values)</bold>
              </td>
            </tr>
            <tr id="table-row-0af9d97d0aeab2c2cf31ac34433c108a">
              <th id="table-cell-b31a4901ab33802d85532fbc781b0f66"> Variable </th>
              <th id="table-cell-44da4ea5ee3ffc1c017aa443a0b7dbf8"> Group </th>
              <th id="table-cell-ce46770ac723584355311b127b9130c9"> n </th>
              <th id="table-cell-0d38df8b1fb0d04daaba7494317e0e29"> Mean </th>
              <th id="table-cell-8cfebff48856a93476ae494621ec4079"> SD </th>
              <th id="table-cell-8d6f5a05b0703a065eb550db6568652c"> Male </th>
              <th id="table-cell-3ecb53e05b9b90ff673f526fd65890fa"> Female </th>
              <th id="table-cell-07b8104c3747afbe76b2bb343629c45a"/>
            </tr>
            <tr id="table-row-7da4777c5b962222bf28a06201fa511a">
              <td id="table-cell-e0e4db20df52f5902fe2f959418dea02" rowspan="2">Gender </td>
              <td id="table-cell-4912b14f9a9f12bdf200ea7f5618eda1"> Male </td>
              <td id="table-cell-db4d5b41cb16d1c4690581ba50388ac4"> 1368 </td>
              <td id="table-cell-33bf8aa547e0211647c9120b7a323e8e"> 59.032 </td>
              <td id="table-cell-f8348bb0bf5a757aa8437dcbeb8953ec"> .431 </td>
              <td id="table-cell-e9e40aefab41ad2da2709607565c3dc1"/>
              <td id="table-cell-88092338992d7a4f388e2ef6b1427c18"/>
              <td id="table-cell-e45797ecad4a9596d663eba19423624b"/>
            </tr>
            <tr id="table-row-87ce088078dc22bdd0fb397dc33f295b">
              <td id="table-cell-6116a9841b7002ee14582d7ac4162b53"> Female </td>
              <td id="table-cell-6394f95c45f6f9249123f3850b25b88e"> 1545 </td>
              <td id="table-cell-b7e6c36759c04810306d5b30b48c9ea5"> 60.843 </td>
              <td id="table-cell-4f7156aed47fe5bd9d81450b2eed7ca7"> .777 </td>
              <td id="table-cell-84562167d6ba03b2289b1bcedcf3db0a"> .001 </td>
              <td id="table-cell-29446bc5ae9104482943c03e98def749"/>
              <td id="table-cell-7613f88f17d506c364f930ae5a36627c"/>
            </tr>
            <tr id="table-row-77d23c42b7b361d860393aa71e635647">
              <td id="table-cell-2cc961f7fa03d07072cc6483ac0b5bbd" rowspan="4">Proficiency Level </td>
              <td id="table-cell-88b857257e4beaf417adb782974b5f03"> Group </td>
              <td id="table-cell-efef6b26fb5f8a7d39f6ee1a9b3eb626"/>
              <td id="table-cell-a9866c316052a82affe6b3f59b733f08"/>
              <td id="table-cell-f9414d2fbc94551866b00cadcd37f398"/>
              <td id="table-cell-1c4d927b709e964383db359c884ff89d"> Beginner </td>
              <td id="table-cell-aa6281f64b38f3c582be169d822d4a85"> Intermediate </td>
              <td id="table-cell-4b31de1c6e72a7cbfe26c882553c436b"> Advanced </td>
            </tr>
            <tr id="table-row-4f87afc46208ebafe272231e577d27cf">
              <td id="table-cell-bc6173c38b4d63784d200fd461472c4e"> Beginner </td>
              <td id="table-cell-f5aa78e0074ccd993ba29442f82dd19b"> 678 </td>
              <td id="table-cell-5f9164bec9e6970b4b1b66810b255ad8"> 33.762 </td>
              <td id="table-cell-7aee605025dfe8dae7ed4dcb5cf54003"> .333 </td>
              <td id="table-cell-07e6acae4afce0394a42e69b782c49f7"> - </td>
              <td id="table-cell-2950c3255e0827798f426ca4ab56fe19"/>
              <td id="table-cell-e35dc35f3ceb3e9ad342fe2697cb930f"/>
            </tr>
            <tr id="table-row-3eb38b3e4cb247ff5098c3ed947022ca">
              <td id="table-cell-6b2630da0445838d41c31fb499bba929"> Intermediate </td>
              <td id="table-cell-d3c1ee11376c0e79784d1cfa61ba2a74"> 1538 </td>
              <td id="table-cell-01217e143fd6cc576c8314eb8103f985"> 60.931 </td>
              <td id="table-cell-248214ed8afb50a7fa3389a0c56b1fa7"> .294 </td>
              <td id="table-cell-f934b54556c20fd2584f26e62fcea2c7"> &lt; .001 </td>
              <td id="table-cell-274aabbef58de7e4fd9c85c8688280a5"> - </td>
              <td id="table-cell-de91fd8dd920c9fe3cd9a484b7b072f3"/>
            </tr>
            <tr id="table-row-fb5add189887f02b95aa0c9ac557741a">
              <td id="table-cell-312167079d7de679672362905d70fcb2"> Advanced </td>
              <td id="table-cell-3582a666cae87fa092b094300a2d0cf0"> 697 </td>
              <td id="table-cell-6a85a9b84d80e2e979bdf21a3291c470"> 85.119 </td>
              <td id="table-cell-fb09c7207f7c5168b628289a6a0e0f5c"> 1.256 </td>
              <td id="table-cell-f27d95ad419f7d2f08c097152fce05b9"> &lt; .001 </td>
              <td id="table-cell-d114d7ea0908ba77046897a939fa708b"> &lt; .001 </td>
              <td id="table-cell-85dfa6c6fc8debf6524a841c3acfd3d3"> - </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-50">Source: Calculated by the author</p>
      <fig id="fig1">
        <label>Figure 1</label>
        <caption>
          <title>The EPT scores as a function of proficiency levels and major.</title>
          <p id="_paragraph-51"/>
        </caption>
        <graphic id="_graphic-1" mimetype="image" mime-subtype="png" xlink:href="image1.png"/>
      </fig>
      <p id="_paragraph-53">Figure 2 illustrates EPT scores based on gender and academic major. Visual inspection of the data suggests that the effect of gender was most pronounced in the Health-2/Nursing major. Across all majors—except for Science—female students consistently outperformed male students. In Science, male students slightly outperformed females, although this effect was relatively small compared to differences observed in other majors.</p>
      <fig id="fig2">
        <label>Figure 2</label>
        <caption>
          <title>The EPT scores as a function of gender and majors.</title>
          <p id="_paragraph-54"/>
        </caption>
        <graphic id="_graphic-2" mimetype="image" mime-subtype="png" xlink:href="image2.png"/>
      </fig>
      <p id="_paragraph-56">As shown in Figure 3, which includes data for all three proficiency levels, the female students’ mean scores were higher than the male students across engineering, health 1, and health 2 majors. Moreover, the interaction effect of gender and major on the EPT score was relatively minor, while it was somewhat more prominent for the health-1 group. Unlike the beginner level, the effect of gender on the EPT scores differed across the majors for both the intermediate and advanced proficiency levels. Moreover, the effect of gender was much larger for the health-2/Nursing group than for other majors at beginner and advanced proficiency levels. In comparison, this effect was much larger for engineering at the intermediate level.</p>
      <p id="paragraph-c3b18e4491de3f10fa545a2367d94982">
        <bold id="bold-9b7249bfcd9aa30c55fdc309708a8bdb">Is there a statistically significant difference in the final test scores of the preparatory program across gender, proficiency levels, and major? </bold>
      </p>
      <p id="paragraph-5ec7b24b73e06c2b4639415b44022dc4">
        <bold id="bold-ab5a487384948fc1cee9690c8c2f4263">PYPT final test scores across genders, proficiency levels, and majors </bold>
      </p>
      <p id="_paragraph-57">The factorial ANOVA analyses compared the main and interaction effects of gender, proficiency levels, and major types on the final test of the preparatory program. Table 6 provides the factorial ANOVA results of the main and interaction effects of the variables on the preparatory program final test scores.</p>
      <fig id="fig3">
        <label>Figure 3</label>
        <caption>
          <title>The EPT scores as a function of gender, majors, and proficiency levels.</title>
          <p id="_paragraph-58"/>
        </caption>
        <graphic id="_graphic-3" mimetype="image" mime-subtype="" xlink:href="image3.png"/>
      </fig>
      <p id="_paragraph-60">The factorial ANOVA results in Table 6 indicate that the main effect of gender on the Final Test scores was significant (<italic id="_italic-16">F(1, </italic>2889<italic id="_italic-17">)= </italic>83.339<italic id="_italic-18">, p=</italic>.00<italic id="_italic-19">&lt; .05,, η2 = </italic>.028), indicating a significant difference between male and female students' scores in the Final Test. Likewise, the main effect for proficiency level yielded an F ratio of <italic id="_italic-20">F</italic><italic id="_italic-21">(2, </italic>2889<italic id="_italic-22">)= </italic>664.093<italic id="_italic-23">, p=0.00), </italic>and the main effect for major variables yielded an F ratio of <italic id="_italic-24">F</italic><italic id="_italic-25">(3, </italic>2889<italic id="_italic-26">)= </italic>25.076<italic id="_italic-27">, p=0.00</italic><italic id="_italic-28">, </italic>indicating a significant difference between proficiency levels and major types. Moreover, proficiency level had the largest effect size compared to other independent variables. Unlike EPT scores, the interaction effect of major types on the Final Test was statistically significant, along with the main effect of gender and proficiency levels. The Bonferroni post-hoc test was conducted to investigate the significant mean difference between the categories of the independent variables.</p>
      <table-wrap id="tbl6">
        <label>Table 6</label>
        <caption>
          <title>ANOVA results for the preparatory program Final Test scores.</title>
          <p id="_paragraph-62"/>
        </caption>
        <table id="_table-6">
          <tbody>
            <tr id="table-row-1843422c068170a96018737cd29869d2">
              <th id="b7fc4fdf9396ac98ff121a131d0b8f8f">
                <bold id="_bold-37">Source</bold>
              </th>
              <th id="a52870b90b7ce80ff69ce2c8b66a7a97">
                <bold id="_bold-38">Sum of Squares</bold>
              </th>
              <th id="f31b6ac00458028da9ccc6d8afbe69fa">
                <bold id="_bold-39">Df</bold>
              </th>
              <th id="dd3c73f6e79d217f5de1cff4c7a42244">
                <bold id="_bold-40">Mean Square</bold>
              </th>
              <th id="ab9c024cccbcf3a9c161dbd7a69eff97">
                <bold id="_bold-41">F</bold>
              </th>
              <th id="0bfed297b9cd64bc54b6f3570b8e2d9f">
                <bold id="_bold-42">Sig.</bold>
              </th>
              <th id="99271bf84eaf05f1ad173eeee6fcc012">
                <bold id="_bold-43">Partial Eta Squared</bold>
              </th>
            </tr>
            <tr id="table-row-708a24d16d7420cc1d7ba7cb13e9d39a">
              <td id="8ae654dcd5501d084f24668a2082dfeb">Gender &amp; Proficiency levels</td>
              <td id="e109dfe0bb633a5950370ea88789890a">511.188</td>
              <td id="87a0ef08c0179c261c1ad0bc30382a0d">2</td>
              <td id="5f721642237717f63b23ecee56e56bc4">255.594</td>
              <td id="b27c55ee4877f3dd489976606249e434">8.421</td>
              <td id="3c09263e4c560e375cd851e1cd1f5cd5">.000</td>
              <td id="fe9bb011828c438e809f9b607dadc98f">.006</td>
            </tr>
            <tr id="table-row-e4fa25576308b95c9a3a0cbf6bdc200d">
              <td id="1d9d8202dffcb9c773adde76aeda72f7">Gender &amp; Major</td>
              <td id="def40b95b11839e91f57a2ca5b6a3a3d">353.510</td>
              <td id="a99da6b573528e2e53927ce6624e8e58">3</td>
              <td id="27cf070dc33e021819e3830a63ea21e6">117.837</td>
              <td id="341b6d571c1d093ab459ad1f11b6c557">3.882</td>
              <td id="56cace90f3181c3ec1f1a6601a8cbbbe">.009</td>
              <td id="e09f2cdc174968f7a321a23d9a183cfc">.004</td>
            </tr>
            <tr id="table-row-b8bb9a91420438daa6b34dcf8ed0671f">
              <td id="a0ab5e8297fe6be30be2631d180746c2">Proficiency levels &amp; Major</td>
              <td id="a07bd8d474d23dd88d501f3c63d687d8">632.687</td>
              <td id="8c0d135ccc7ad35bc53d1a48bdec42ff">6</td>
              <td id="7bcb578693ed57fd3b256fe0a4c370f2">105.448</td>
              <td id="cc18fb419f8f1d0bd79fbbe1e3f68cf6">3.474</td>
              <td id="64d5cb813722c141e75fb9b73b5df4f1">.002</td>
              <td id="1df9243b42c2217ae593ff0b57369f9d">.007</td>
            </tr>
            <tr id="table-row-369ace545fb559a1240b90bfdd46cf28">
              <td id="f8917601952c8ece22c4f57f366437c0">Gender &amp; Proficiency levels &amp; Major</td>
              <td id="354f8379e5e3ea020300038992ea9e15">413.740</td>
              <td id="bade30c9495d92fc69b82a57063ca809">6</td>
              <td id="d99297636c0a8fccda372c31a937080b">68.957</td>
              <td id="f160aaa9166c5b6a58b658641225f5d2">2.272</td>
              <td id="f9cca733f0de7462427899e2200ab9a7">.034</td>
              <td id="94f1e4f50f4b1854d33822844fd0f8a3">.005</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-63">Source: Calculated by the author</p>
      <p id="paragraph-48ce008cab5d5d58594310f94b38943e">
        <bold id="bold-5f22ff19f3d50ed966aab51744901c8a">Is there a statistically significant difference in the preparatory program final test sub-domain scores across gender, proficiency levels, and major variables?</bold>
      </p>
      <p id="paragraph-5e13e2ddb70d3756d48fa999d871489e">
        <bold id="bold-dcf8136dfe8114fcfd7f5bcfd57655d2">Difference in the preparatory program final test sub-domain scores across gender, proficiency levels, and major variables</bold>
      </p>
      <p id="_paragraph-64">Factorial ANOVA analyses were conducted to examine the main and interaction effects of gender, proficiency level, and academic major on the Final Test scores of the Preparatory Year Program (PYP). Table 6 presents the factorial ANOVA results for these variables.</p>
      <p id="_paragraph-65">As shown in Table 6, the main effect of gender on the Final Test scores was statistically significant (<italic id="_italic-29">F</italic>(1, 2889) = 83.339, <italic id="_italic-30">p</italic> &lt; .001, η² = .028), indicating a meaningful difference between male and female students. Similarly, the main effect for proficiency level yielded an F-ratio of <italic id="_italic-31">F</italic>(2, 2889) = 664.093, <italic id="_italic-32">p</italic> &lt; .001, and the main effect for academic major yielded <italic id="_italic-33">F</italic>(3, 2889) = 25.076, <italic id="_italic-34">p</italic> &lt; .001, indicating statistically significant differences across both variables. Among the independent variables, proficiency level had the largest effect size.</p>
      <p id="_paragraph-66">Unlike the EPT scores, the interaction effect of major was statistically significant in the Final Test scores, along with the main effects of gender and proficiency level. A Bonferroni post-hoc test was conducted to further investigate the significant mean differences among the categories of the independent variables.</p>
      <table-wrap id="tbl7">
        <label>Table 7</label>
        <caption>
          <title>ANOVA Comparisons of the Final Test scores across categories of gender, proficiency levels, and major.</title>
          <p id="_paragraph-68"/>
        </caption>
        <table id="_table-7">
          <tbody>
            <tr id="table-row-e64ad3cc391d9cd149d358aad9985a70">
              <th id="d1dbd3c0fa43ee9a74bf3f083307297a" colspan="5"/>
              <th id="49226c9ecafd79de22de76486a78b504" colspan="3">
                <bold id="_bold-45">Bonferroni's post-hoc test</bold>
                <bold id="_bold-46">(p values)</bold>
              </th>
            </tr>
            <tr id="table-row-7ac7386e9f10fd80b0d4b277cc50a918">
              <th id="aa29da0bbc9ce6c65584950c7d319aef">
                <bold id="_bold-47">Variable</bold>
              </th>
              <th id="ab56efd39988f35b0ba5fd6a2f172122">
                <bold id="_bold-48">Group</bold>
              </th>
              <th id="dc13bbcaa7e75b245780c71c002638f7">
                <bold id="_bold-49">
                  <italic id="_italic-35">N</italic>
                </bold>
              </th>
              <th id="3e5c350f293bd118cc93ecf35da5f3e8">
                <bold id="_bold-50">Mean</bold>
              </th>
              <th id="a02b1418e1a1bd7bfef44cb6b10cbaa0">
                <bold id="_bold-51">
                  <italic id="_italic-36">SD</italic>
                </bold>
              </th>
              <th id="5d933bdea5cbf3fd3cf6a4b50f061cde">
                <bold id="_bold-52">Male</bold>
              </th>
              <th id="1713534ca576f51f3783cc90720bcf51">
                <bold id="_bold-53">Female</bold>
              </th>
              <th id="41cc0286bfdaddd66cc26acc37b93a23"/>
            </tr>
            <tr id="table-row-2279cbaa0bfc86ead2b4ec28468dec47">
              <td id="f7ebd59b3560109a7739f82a5d2639ea" rowspan="2">Gender</td>
              <td id="46fbcd38efb0e1227b3a2d7dc62715ca">Male </td>
              <td id="c4b99751b2246b2e6955b39d0d1acde4">1411</td>
              <td id="3b8790496eea6cfaf830e3e31696b4c8">31.748</td>
              <td id="2c4af674852e906b6b168c6e5d08c90c">.200</td>
              <td id="126cbb2b5b20338a18371234cef87f2e"/>
              <td id="0137ea46905c361787d6ff3d5d0cdd79"/>
              <td id="1b4a78ef9d121f640a7359244e7ce0a9"/>
            </tr>
            <tr id="table-row-49d79b4f878859549e4664443db9aefa">
              <td id="f71ef7777fd03a0d6be4f9cea1d05a64">Female </td>
              <td id="1b52a2ea6f50a4b88de20489df0ca839">1625</td>
              <td id="245ec3ac4a24ec2f18daf344fa077586">34.837</td>
              <td id="52c37f368f236840acd6ae639a1cc5c2">.273</td>
              <td id="4289e29cfc1d69c44fc04a58f5a098a3">.001</td>
              <td id="df2295da6fc3a418ccd414c47ac6f829"/>
              <td id="82fd9b89b859b3df426a995003647cbc"/>
            </tr>
            <tr id="table-row-e821b7ce17c7989f3684b95dfd9e49d5">
              <td id="4d1566fc794812d662853c2922c3a85a" rowspan="4">Proficiency Level</td>
              <td id="d21787f325e6689fd229d72bc0636c3d">Group</td>
              <td id="71b109c956ddea92e00f0062fc3c593b"/>
              <td id="0efd31a9e157bdd3091b6957913fece9"/>
              <td id="0242945304d47fe8e5c74ec986e71e25"/>
              <td id="5d9b5fc1efa8011a0608d62425fa94d6">Level-1</td>
              <td id="cb1339446122e6230e54f6002c9a26de">Level-2</td>
              <td id="a801e52f521042e40e63257f32bec20e">Level-3</td>
            </tr>
            <tr id="table-row-95f305e9a15ce9caea9c1cec54b80e43">
              <td id="c56e6f9f13ef9739dff6b975890b83fd">Beginner</td>
              <td id="1c71daa9b339ed7e446fd70abcf8cd4f">678</td>
              <td id="1c26cdeff713ecf53479cea9ab9aa99c">24.436</td>
              <td id="ce073cd3b2d6b105f1a097bc89a964f9">.370</td>
              <td id="c12c39c2fd2fc87e3938d51b031d8d39">-</td>
              <td id="d660c9b08c7aad8b0586c991f6d1ca37"/>
              <td id="272ac810110bd72dc9896103c2b1233f"/>
            </tr>
            <tr id="table-row-1a3b96c84a1e345c2ab19dbac43f9656">
              <td id="3df80c682a0ec7cf8dd509324a9f4c2d">Intermediate</td>
              <td id="0e244a4b6d25cf63d8873a0c805e1c70">1538</td>
              <td id="aadb18aacdb00623d9fd2780938b318c">33.836</td>
              <td id="b210046ea5039c1ed4292f2bb669e3b7">.183</td>
              <td id="fc084f8bc2d1c3c7260d3632fc1b6b80">&lt; .001</td>
              <td id="b28d08ba8572678c4ef3d80c1bbbcb76">-</td>
              <td id="45d66a084647a4632e6d3c0d96ec08d6"/>
            </tr>
            <tr id="table-row-fde287a47cafab85e3d267f951101683">
              <td id="5e5ef7a21a6cb640df8d58cad60b70c9">Advanced</td>
              <td id="570d6d346b039531951b0b666a025ac5">697</td>
              <td id="249c4eac39ddfd909ce8af2143f69cd8">41.606</td>
              <td id="d04bb42b3a3ef224536d04473007f1c7">.296</td>
              <td id="7c019e611041f778966238084a9aa2e0">&lt; .001</td>
              <td id="1e21beba2d5df206225d925f37ac0492">&lt; .001</td>
              <td id="ea638c85cbca05fc1f444d3656a52a3d">-</td>
            </tr>
            <tr id="table-row-a37788eac32b9e61b5bcf2fbc02e79a1">
              <td id="b3034757f9dad536e9ffdc7e94f99ea9" rowspan="5">Major</td>
              <td id="879e39627e1e2e27f0e0f1a9bad9eb65">Group</td>
              <td id="a6e957bc12118cc6cca62560e82ef133"/>
              <td id="84b60ed1eb0d2112e3549a241956ea12"/>
              <td id="3e30349ca53e16cc5b6783900cb7aca9"/>
              <td id="c63b0d03e2715f6f3ae35e0244278699">Engineering</td>
              <td id="11493f3fd9c710797d3ad0b129e6df4e">Science</td>
              <td id="000d2fb1eb5ea65e3005f590cff4634d">Health-1</td>
            </tr>
            <tr id="table-row-a04a5cd9e161da55eead4e0d4ecacd6d">
              <td id="2661f28399eae0c6a80694d03934f15c">Engineering</td>
              <td id="0e629dcc4c285fbb05625afe0b2ea8f7">513</td>
              <td id="3fd545aa420bb38e79d4ea361fa7ffc4">34.890</td>
              <td id="0b4d0cfb0cd0016bff2ff52c201c1757">.370</td>
              <td id="ea547091282110544d309c8ddb802329">-</td>
              <td id="03a6befbfe663cb96102eff40a99589a"/>
              <td id="acc322ef80db42d3896edbd5bdaaaf4e"/>
            </tr>
            <tr id="table-row-cd666283dcc059cf4b6903f4037d4433">
              <td id="b25186a5125f736c8f08ac6d17c0b8be">Science</td>
              <td id="6c8070c2b243e3628165785c4be33548">1344</td>
              <td id="32279eb715a127326abf1ae92409cf6f">33.927</td>
              <td id="e1bcedc306147621a23f9b4a4fe8989d">.183</td>
              <td id="4bf1633f028962e3b91f8e9dfb783355">.153</td>
              <td id="d2bb1eeb07b50ac9e4ee4363ee6efbaa">-</td>
              <td id="e06158ac24e5cdfd1f5eb38157344004"/>
            </tr>
            <tr id="table-row-3408a6c46ee8ac24adb81c430ceff0d0">
              <td id="43d4fe8c2cc41f4bb06d679995647426">Health 1</td>
              <td id="95f0d7abbf2c88fa47cd2ed3a9af1adf">776</td>
              <td id="d56e5250f914390615cc893d652d9f5f">34.044</td>
              <td id="afd4675d04c703663c6fe97b9dad9caa">.321</td>
              <td id="41ff0e92d56369fa13f6c9f7f016ba98">&lt; .001</td>
              <td id="465a8c48c6d5d0029c6dba0b0181870c">&lt; .001</td>
              <td id="ac0518b338ee1947c8d3216e55fc7ccd">-</td>
            </tr>
            <tr id="table-row-2feccf3aaaa639e2577fda69c18c70bf">
              <td id="81ac4ffdd86669413abd9cc128a1279c">Health 2</td>
              <td id="4c7c231bf855a6bf7134f7da31bca474">280</td>
              <td id="c7a5a7fccbc7bfcc57d66dc78b5737a9">30.309</td>
              <td id="dfc561e67c355f72fc3c02d61cfb9098">.430</td>
              <td id="8e93c454b97c92b054f6920d593c91b6">&lt; .001</td>
              <td id="ca55c1e2518d7f58ba660bae00ec56de">&lt; .001</td>
              <td id="9cc37d445c352be7a4d0f8f39da10f51">&lt; .001</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-69">Source: Calculated by the author</p>
      <p id="_paragraph-70">Figure 4 shows that the effect of gender was notably larger for Engineering than for Health-1. Consistent with the EPT results, female students consistently outperformed male students across all majors, with a relatively smaller effect size for Health-1.</p>
      <fig id="fig4">
        <label>Figure 4</label>
        <caption>
          <title><bold id="_bold-54"/>The final test scores as a function of gender and major.</title>
          <p id="_paragraph-71"/>
        </caption>
        <graphic id="_graphic-4" mimetype="image" mime-subtype="png" xlink:href="image4.png"/>
      </fig>
      <p id="_paragraph-73">Figure 5 presents the Final Test scores by proficiency level across majors. It shows that mean scores for each proficiency level (beginner, intermediate, and advanced) remained fairly consistent across majors. However, Engineering students consistently achieved the highest scores, while the Health-2/Nursing group had the lowest scores across all proficiency levels.</p>
      <fig id="fig5">
        <label>Figure 5</label>
        <caption>
          <title>The final test scores as a function of proficiency levels and majors.</title>
          <p id="_paragraph-74"/>
        </caption>
        <graphic id="_graphic-5" mimetype="image" mime-subtype="png" xlink:href="image5.png"/>
      </fig>
      <p id="_paragraph-76">Figure 6 shows that, overall, female students scored higher than male students across all majors and proficiency levels in the Final PYPT. However, the mean scores of students in the Health-2 (Nursing) group were the lowest among all majors for both genders. The interaction effect of gender and major on Final Test scores was relatively smaller in this group. Notably, female students at the intermediate proficiency level scored higher than their male counterparts across all majors, with the gender effect again being most pronounced in Engineering. At the advanced proficiency level, female students also outperformed male students across all majors, with the gender effect being strongest in Engineering and least pronounced in Health-1. Overall, it is evident that female students outperformed male students across all proficiency levels and majors, with particularly strong effects observed in Engineering..</p>
      <fig id="fig6">
        <label>Figure 6</label>
        <caption>
          <title>The final test scores as a function of gender, proficiency levels, and majors</title>
          <p id="_paragraph-77"/>
        </caption>
        <graphic id="_graphic-6" mimetype="image" mime-subtype="" xlink:href="image6.png"/>
      </fig>
      <p id="paragraph-b9eca7c81317900430549b18b4a31532">
        <bold id="bold-8dcc7b52e6d2cc7ced98b3319137a3c7">Performance on sub-domains across gender, proficiency levels, and major </bold>
      </p>
      <p id="_paragraph-79">As previously mentioned, the final PYPT test assessed four sub-domains: reading, vocabulary, grammar, and listening. Writing was primarily evaluated through portfolio assessments. This section examines the main and interaction effects of gender, proficiency level, and academic major on each of the sub-domains of the Final Test using factorial ANOVA methods (see Table 8). </p>
      <table-wrap id="tbl8">
        <label>Table 8</label>
        <caption>
          <title>ANOVA results for subdomains in the final test.</title>
          <p id="_paragraph-81"/>
        </caption>
        <table id="_table-8">
          <tbody>
            <tr id="table-row-91f5180e720c90c2b878a5c19656f71b">
              <th id="e8de18e7ef35da2feb4e563ebd4377c7">
                <bold id="_bold-58">Language Aspect</bold>
              </th>
              <th id="6f4a15672ebe0843d550dcd90c38ec17">
                <bold id="_bold-59">Source</bold>
              </th>
              <th id="d49de5f181574a0b69f8d5c74d17d78f">
                <bold id="_bold-60">Sum of Squares</bold>
              </th>
              <th id="2a9529b04c638bcc0a3e12bd3b9e3586">
                <bold id="_bold-61">df</bold>
              </th>
              <th id="01ae02d1dbdbc23fda88c4736d903f96">
                <bold id="_bold-62">Mean Square</bold>
              </th>
              <th id="80fbb8a57279584511334466b36fe057">
                <bold id="_bold-63">F</bold>
              </th>
              <th id="a3057a50719c976f673b0e1dc00b90b9">
                <bold id="_bold-64">Sig.</bold>
              </th>
              <th id="5a6572a485ac642436594a7e4fd8b868">
                <bold id="_bold-65">Partial Eta Squared</bold>
              </th>
            </tr>
            <tr id="table-row-6e0938e08be8604861051dc4eae173af">
              <td id="56acfb9f9565b1b55234b3040ea46fb9" rowspan="4">Reading</td>
              <td id="c7f747ca4cd6ef99fec67046c44c2e4e">Gender * Proficiency levels</td>
              <td id="bac712b0f2ad26bb5e9fb36a4543739e">48.641</td>
              <td id="5b360347d3d3b48d67764dad430d4e4a">2</td>
              <td id="67ae0d003b502299b7b6f6a964803450">24.321</td>
              <td id="551a1b5d9b1ddee1e89a8b9cf9cb6ba6">7.371</td>
              <td id="208cff1662241d06643a9c99e096edad">.001</td>
              <td id="5725a1c660d999daf32fa74b729224d8">.005</td>
            </tr>
            <tr id="table-row-63516d2b2a725c19f13ca6e32a710a87">
              <td id="0ed1fa4590271094e55e4844d3fb7622">Gender * Major</td>
              <td id="d54c8066a64b9a59b2dc8de8c3f530da">58.895</td>
              <td id="d9771d0566fdef1a645255d8c28e16e3">3</td>
              <td id="59b26e1d0bd8fb78138bcd08d92f31c2">19.632</td>
              <td id="30abc9f37cdd932a0e3d80c926421793">5.950</td>
              <td id="7037cafa31a2f94bf4ebc8a6ddcfd416">.000</td>
              <td id="67b805b8c9a14a4c8f09acf73b782f04">.006</td>
            </tr>
            <tr id="table-row-5458b97ffa54ff7875f521dccac734b6">
              <td id="57179e7fcb41407e7f032beb0aa416d2">Proficiency levels * Major</td>
              <td id="35b66a4c4ed455d377cdbf82a4c2f1ee">101.912</td>
              <td id="f4a14212aa8ab82c80f0fbdafd7acd4e">6</td>
              <td id="6841f1e256dd66c5eec8e24f5c5536ca">16.985</td>
              <td id="0c7647207b90ee785c0c5ca3a7a7576c">5.148</td>
              <td id="c08f3b3c79ab411d13c1cfa22c71dd89">.000</td>
              <td id="2422c4a8c5efb4a2c7ac22bd9227f39b">.011</td>
            </tr>
            <tr id="table-row-4c23229d0ffdacb0963756a5e1f0bcf7">
              <td id="9075deb7c7e2e91d88d5aa62e03cb1b2">Gender * Proficiency levels * Major</td>
              <td id="9e26d1ef99422638647d342e738d4742">41.346</td>
              <td id="0306bbc176ac725d46f83a2432ad2d9a">6</td>
              <td id="13092bcb976f4d1f0942367ad295b874">6.891</td>
              <td id="38c413fd1c92d8617d86e62ac07ee040">2.088</td>
              <td id="b6fa865f67b3e758b485e711ddc11cdb">.<bold id="_bold-66">051</bold></td>
              <td id="9604b7bebc1fc95ba4664da5be743375">.004</td>
            </tr>
            <tr id="table-row-d0a4026183f6d1f5399293984498afa3">
              <td id="d35f0ec532f8f26d3d54abc1bedf2731" rowspan="4">Vocabulary</td>
              <td id="b98fb147b00548209662517ff58673da">Gender * Proficiency levels</td>
              <td id="d4b0c17e1a4463866494c80e10332df6">111.782</td>
              <td id="e2a2c912bddda3b3ac5cc58ffb5a865a">2</td>
              <td id="a8351c91de04a35d427dd48886425daf">55.891</td>
              <td id="1aad744149bf19d9e03476fc39bb9389">10.550</td>
              <td id="db3ad0af8ac69acbfd84fc2147d6063b">.000</td>
              <td id="f27ad94a7c8e72309362c048dcaa5b17">.007</td>
            </tr>
            <tr id="table-row-76d887069abe9ce87a5983d11e6246be">
              <td id="497b030b290ef16e0a36ce93cd72f8ea">Gender * Major</td>
              <td id="9cd6a7d2e083256135f30de0bced17f6">23.919</td>
              <td id="65bfddb616aef916d4fc044b880bee59">3</td>
              <td id="d195e1c365bdf5c14f87ff451f4550b4">7.973</td>
              <td id="c81d89b96c17307cb72d9fb4c4909695">1.505</td>
              <td id="1e424722c7f478925377605cfe51fd6e">.<bold id="_bold-67">211</bold></td>
              <td id="8b2d290897ef25479fcc3144a9e993b8">.002</td>
            </tr>
            <tr id="table-row-1cdfee97cde042e2eb6d4237cd4ef897">
              <td id="b767a5d44554940b10bb5260dfc8f090">Proficiency levels * Major</td>
              <td id="59947c1b112294859dc2bd31db0a24f3">113.328</td>
              <td id="3cf142c7bc23a8f8dd7f430c526801e3">6</td>
              <td id="f8a4b01cf22522b18b87a04a11f90473">18.888</td>
              <td id="528f1491db840f680851f2c9349a4f6c">3.565</td>
              <td id="d87a3805984091ddd20f8ba6ed4f3ec5">.002</td>
              <td id="a663268ccbf50862da2373446ee5b78e">.007</td>
            </tr>
            <tr id="table-row-44d2ca4428c0d6839c2bccc8d65dcdd2">
              <td id="3bf8423e550ab4f41d5d371ba91f88a2">Gender * Proficiency levels * Major</td>
              <td id="f16d16bd3719c5169d85d31c0d806cf3">38.964</td>
              <td id="3a0b989d81306084a8c056a9452a26cf">6</td>
              <td id="7d7d3fee4e72794e291b3ebbbff6abba">6.494</td>
              <td id="e7d629740055c60a70afb50633d3b3ac">1.226</td>
              <td id="9a6fba137a600fcdee450627f3192309">.<bold id="_bold-68">290</bold></td>
              <td id="05b9fef154dcb8e8aaf3992aebc8cffc">.003</td>
            </tr>
            <tr id="table-row-0c5a3722cb1378187aa5f10074562b11">
              <td id="8c4d04543a146d691868063801b459be" rowspan="4">Grammar</td>
              <td id="bcb410274398f972fece905004070c79">Gender * Proficiency levels</td>
              <td id="4e33860de343e1e8e46137c9f2b10d81">30.873</td>
              <td id="0bc91a20f8b74b80eec3df3c9f838925">2</td>
              <td id="cce455f4ca63cb718a4bfaa1ee6949f1">15.436</td>
              <td id="b4e15138662d3309642b475e87a942fa">3.394</td>
              <td id="f20c528739669da65654ff45001cb891">.034</td>
              <td id="057a825e462c4cd0823dc36025e78cb0">.002</td>
            </tr>
            <tr id="table-row-f3790307e0e8d7498ad445cbd0b2a279">
              <td id="51d69d24c749898334710a2df54796df">Gender * Major</td>
              <td id="b123710d0f38217b23365adc35358600">56.437</td>
              <td id="1cab86eb072bf05cc0e01252f57185a2">3</td>
              <td id="02e9473977d0b6bf7756e11d1095d647">18.812</td>
              <td id="20b11c07f2184be29eba53621dfea9a1">4.136</td>
              <td id="3e7b10c4761c4f2ec9ee657759f051bf">.006</td>
              <td id="0ae0025ac4aec646a774c8783da660be">.004</td>
            </tr>
            <tr id="table-row-cea2f9f207f411ec255f8136ebd9fb6f">
              <td id="b6538a181c37f93045a5127ffcfdf73c">Proficiency levels * Major</td>
              <td id="7b8b75efa6b8f3436b344cb198a40e91">34.370</td>
              <td id="a5bfd79b6b1f0acd8185d205f36b26b4">6</td>
              <td id="043495b3b5e5e51d74a6f34eed2e9034">5.728</td>
              <td id="3440b45926f3ae769eb74e8218c5a249">1.259</td>
              <td id="a7ed2aa2d0e111545eea7fe091858fea">.<bold id="_bold-69">273</bold></td>
              <td id="825f4b45d4a27ce87d3919b7f7109552">.003</td>
            </tr>
            <tr id="table-row-cc4322fea58fd1767d5d8477f916c8af">
              <td id="c9593b666241e0fedaa67705e53c7ad3">Gender * Proficiency levels * Major</td>
              <td id="5909ee556736c935ec91e17979fede85">17.264</td>
              <td id="1b109735fde2273a88ed49ff5e42efae">6</td>
              <td id="1c01d397376f1d5eb9d33bfc4f7dab0d">2.877</td>
              <td id="74eb8d99f0c61cc7ab6c3b89eb277fb4">.633</td>
              <td id="135998f08b7e410d82f650d8c8b82aae">.<bold id="_bold-70">704</bold></td>
              <td id="8c8f7038fbd669349c53243511ad5dc9">.001</td>
            </tr>
            <tr id="table-row-aba06b7aff78a104074251569456c82b">
              <td id="aafea2bb06389eb2f755e6c2c9e6f4dd" rowspan="4">Listening</td>
              <td id="d0768a3a9763f582ed6bb5fe9cbf88b4">Gender * Proficiency levels</td>
              <td id="dec9aaf6ed36a505df7d03873036fe56">4.394</td>
              <td id="410500210356953b5fbd37f9a5c91eb9">2</td>
              <td id="0c3b597113f456dfe0d483d5debc160f">2.197</td>
              <td id="796d0684dc256d4c9d672f677dccf7e7">.907</td>
              <td id="83bd09edbcb2cbe329596a14c1efe07d">
                <bold id="_bold-71">.404</bold>
              </td>
              <td id="ce75a052e747ab5ab728d8814652a51d">.001</td>
            </tr>
            <tr id="table-row-33142e5a4ff6bfe73e9c6df9fadc738b">
              <td id="f8477e2cfa7f50570eb95186b8c50b92">Gender * Major</td>
              <td id="99b03be7ce0ec1d5c84c9943dba78255">5.393</td>
              <td id="5af14b386edc08d4e43a8fb5a7ec1e62">3</td>
              <td id="d51f4cf5f982159d989b8f3d35f1c0a1">1.798</td>
              <td id="372f0fee78436a6e800dfada66e3905f">.742</td>
              <td id="2a5f619e6329467629230aa94f74f817">
                <bold id="_bold-72">.527</bold>
              </td>
              <td id="a978c07fece7ff3b906b35dd78a696ea">.001</td>
            </tr>
            <tr id="table-row-967e9a204575ec60e8ea580589519c46">
              <td id="a486d0957df269097fb45ad2e05f1af1">Proficiency levels * Major</td>
              <td id="364b600c73a785e8800dbfe935e57778">58.957</td>
              <td id="8fd2c9f7421db2cb8bc28c164bf6be26">6</td>
              <td id="4c409480a0dcb46b16bf4630e919cf1d">9.826</td>
              <td id="b328e07500c82e350135ae69b28dd72d">4.057</td>
              <td id="c6f3631cc3672ad4a6fb8c2ee141f5c5">.000</td>
              <td id="a12e5d0b54baf10c888db34894088041">.008</td>
            </tr>
            <tr id="table-row-13052df5cac249aaa8ba1e3a60808a10">
              <td id="83aaf8d2f19e69d8683cf86f92d63141">Gender * Proficiency levels * Major</td>
              <td id="5bc67b9d61ff7303c2cb5f4472a4df5b">30.303</td>
              <td id="018bf984cbdffad2589996718e9ebc3f">6</td>
              <td id="3f9d2fa991d613ffc1346243b7abc1ab">5.050</td>
              <td id="b5b68562fd7de53d23a247330f783d1c">2.085</td>
              <td id="552edaa5c148c2deea69adfb3c7a9e65">.052</td>
              <td id="ce70a9ab14a3913673adbe21b99f8b07">.004</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-82"><italic id="_italic-37">The non-significant results were written in bold</italic><italic id="_italic-38">. </italic>Source: Calculated by the author</p>
      <p id="_paragraph-83">The factorial ANOVA results presented in Table 8 show that the main effect of gender on reading scores was statistically significant. The Bonferroni post-hoc test revealed that female students had higher mean scores than male students, regardless of their academic major or English proficiency level. Additionally, the post-hoc results indicated that the mean differences between majors were statistically significant, except between the Engineering and Science majors.</p>
      <p id="_paragraph-84">Similarly, vocabulary scores varied significantly across gender, proficiency levels, and academic majors. All interaction effects among the independent variables were statistically significant, except for the interaction between gender and major (gender × major). Among all majors, Health-1 students achieved the highest mean vocabulary scores, followed by students in Science and Engineering.</p>
      <p id="_paragraph-85">Regarding grammar, the ANOVA results indicated that the interaction effects between gender and proficiency level, and between gender and major, were statistically significant. Bonferroni post-hoc comparisons confirmed that female students scored significantly higher than male students. With respect to the major, Health-1 students again achieved the highest mean grammar scores, followed by Science and Engineering students.</p>
      <p id="_paragraph-86">In terms of listening performance, statistically significant differences were observed between male and female students. The factorial ANOVA results also revealed significant effects for proficiency level and academic major. However, unlike the other sub-domains, only the interaction effect between major and proficiency level (major × proficiency level) was statistically significant. The Bonferroni post-hoc test showed that female students outperformed male students in listening. Among the academic majors, Science students had the highest mean listening scores, followed by those in Engineering and Health-1.</p>
    </sec>
    <sec id="sec-5">
      <title>Discussion</title>
      <p id="_paragraph-87">This study examined the relationship between gender, academic major, and English language proficiency among Preparatory Year Program (PYP) students by analyzing performance on two assessment types: the English Placement Test (EPT) and the internally developed Preparatory Year Program Tests (PYPTs). The results indicate strong and statistically significant bivariate correlations between the EPT and PYPT scores, with coefficients ranging from .601 to .807. The strongest correlation was observed between the EPT and the Final Test (r = .81), suggesting that both assessments measure similar constructs of language proficiency, particularly at the summative level. These results provide evidence of test reliability and internal alignment with CEFR-based assessment standards.</p>
      <p id="_paragraph-88">The findings confirmed a consistent gender effect across all tests and sub-domains: female students outperformed male students, regardless of their academic major or proficiency level. This result aligns with prior research suggesting that female learners tend to demonstrate higher proficiency in language tests (Green &amp; Oxford, 1995; Goh &amp; Foong, 1997; James, 2010;). Socio-cultural perspectives on education (Hyland, 2007) help explain this trend, emphasizing that gendered social roles and expectations shape learning behaviors and outcomes. In the Saudi context, this is particularly salient. Female students often face strong social and familial pressure to succeed academically, as education is seen as a key pathway to empowerment and opportunity. National initiatives, such as Saudi Vision 2030 (Vision 2030, 2016), which aims to increase female workforce participation, may further reinforce this motivation. Recent data indicate that women’s participation has already surpassed the original 30% target, reaching 35.4%, demonstrating a societal shift that likely impacts academic motivation and performance.</p>
      <p id="_paragraph-89">The study also found significant main effects of proficiency level and academic major on Final Test performance. Although major was not a significant factor in the EPT, it did influence performance on the PYPT Final Test. Notably, engineering students achieved the highest mean scores, while students in the Health-2 (Nursing) major scored lowest. These results may reflect differences in the cognitive and linguistic demands of various disciplines or variations in instructional practices across departments. While prior studies have broadly categorized disciplines as “hard” or “soft” (Becher &amp; Trowler, 2001; Friedrich et al., 2016), our findings suggest that these distinctions may have nuanced implications for language performance. Future studies could benefit from exploring how disciplinary language practices—such as genre exposure, reading load, and writing requirements—shape learner outcomes.</p>
      <p id="_paragraph-90">Among the sub-domains, listening scores showed the greatest variability and the weakest correlation with EPT sub-domains. This suggests that listening proficiency may rely on factors not fully captured by general language placement tests, such as familiarity with accents, context-specific cues, and real-time processing skills. In contrast, grammar (structure) showed the strongest correlation between tests, indicating that structural language knowledge may be more consistently assessed and developed across test formats. The moderate correlations between other sub-domains (reading, vocabulary, compositional analysis) suggest that while they are aligned with CEFR outcomes, student performance varies across these skill areas, possibly due to differences in instructional emphasis or learning strategies.</p>
      <p id="_paragraph-91">These findings carry practical implications for language educators and curriculum designers. The consistent gender gap, particularly in productive and receptive skills, highlights the need for gender-responsive pedagogy. Male learners may benefit from targeted support, such as differentiated instruction, strategic learning interventions, or mentoring programs aimed at increasing motivation and engagement. Additionally, discipline-specific instruction may need to be more deliberately integrated into English courses to support students in fields with distinct linguistic requirements.</p>
      <p id="_paragraph-92">While the use of large-scale, standardized assessments provides practical and reliable insights into student proficiency, educators should remain cautious of washback effects. When test performance becomes the primary goal of instruction, there is a risk of narrowing curricular focus and diminishing the richness of the language learning experience. Therefore, assessment practices must be complemented by formative feedback mechanisms, peer-review activities, and training in metacognitive strategies that encourage deeper learning.</p>
    </sec>
    <sec id="sec-6">
      <title>Limitations and Future Research</title>
      <p id="_paragraph-93">A notable limitation of this study is the exclusion of potentially influential variables such as rater effects, classroom instruction quality, and students' individual learning histories. While the large sample size enhances the generalizability of findings, future studies should incorporate qualitative approaches, such as interviews, classroom observations, and learner diaries, to provide richer insights into the factors underlying performance differences. Additionally, Future studies could explore the motivation of both genders through personalized support. In particular, schools in Saudi Arabia might provide counseling services that address gender-specific pressures and assist students in navigating their academic goals in relation to their future societal roles. Furthermore, schools could promote diverse learning strategies tailored to the strengths of both genders, such as project-based learning and collaborative activities. It is crucial that future research incorporates qualitative data, as it may offer insights into how male students can enhance their proficiency levels and academic achievement to align with those of their female counterparts.</p>
    </sec>
    <sec id="sec-7">
      <title>Conclusion</title>
      <p id="_paragraph-94">This study aimed to examine the relationship between gender, academic major, and student performance in two English language assessments—the English Placement Test (EPT) and the Preparatory Year Program Tests (PYPTs)—within the context of a Saudi university's Preparatory Year Program. The analysis focused on identifying significant differences in test outcomes across demographic and academic variables. The results provided consistent evidence that gender, particularly female student performance, significantly influenced language proficiency outcomes across all proficiency levels and test formats. While academic major showed some variation in specific domains, it did not exert a significant effect on general language proficiency.</p>
      <p id="_paragraph-95">The study contributes to the growing body of research highlighting gender-based differences in language achievement, particularly in EFL contexts, and reinforces the importance of addressing learner diversity in curriculum and assessment design. Although the study did not directly examine the underlying causes of performance differences, its findings point to the necessity of exploring broader learner-related factors—such as motivation, engagement, and study strategies—to better inform English language teaching practices. While the use of a large, representative cohort enhances the internal validity of the findings, the study acknowledges that variables such as classroom instruction quality, rater reliability, and testing conditions were not included in the analysis. Overall, the study underscores the need for more nuanced, learner-centered approaches to language education in preparatory programs and provides a foundation for further pedagogical reflection and research.</p>
      <p id="_paragraph-96"><bold id="_bold-73">Acknowledgement Statement: </bold>The authors wish to extend their heartfelt gratitude to Dr. Chintalapalli Vijayakumar for his invaluable feedback, insightful critique, and meticulous review of the manuscript, which has greatly enriched its quality and clarity.</p>
      <p id="_paragraph-97"><bold id="_bold-74">Conflicts of interest: </bold>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</p>
      <p id="_paragraph-98"><bold id="_bold-75">Authors'</bold><bold id="_bold-76"> contribution statements:</bold> HA developed the conceptual framework and methodology. ST performed the data analysis. Both authors actively contributed to the discussions, interpretation of results, and the writing of the manuscript..</p>
      <p id="_paragraph-99"><bold id="_bold-77">Funding</bold> <bold id="_bold-78">statements:</bold> As there was no external funding received for this research, the study was conducted without financial support from any funding agency or organization.</p>
      <p id="_paragraph-100"><bold id="_bold-79">Data availability statement: </bold>Data is available at request. Please contact the corresponding author for any additional information on data access or usage.</p>
      <p id="_paragraph-101"><bold id="_bold-80">Disclaimer:</bold> The views and opinions expressed in this article are those of the author(s) and contributor(s) and do not necessarily reflect JICC's or editors' official policy or position. All liability for harm done to individuals or property as a result of any ideas, methods, instructions, or products mentioned in the content is expressly disclaimed.</p>
    </sec>
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