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Repayo1.
Manuel O.
Malonisio2.
Clarita R Tambong3 1,2Aklan State University.
Banga.
Philippines 3Laboratory High School-College of Teacher Education.
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Jurnal Ilmiah Peuradeun | Copyright A 2025, is licensed under a CC-BY-SA Jurnal Ilmiah Peuradeun The Indonesian Journal of the Social Sciences doi: 10.
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Pages: 1317-1344
BEYOND ADMISSION SCORES: MAPPING THE STRONGEST
PREDICTORS OF LET PERFORMANCE IN BSED GRADUATES
Benedicta D.
Repayo1.
Manuel O.
Malonisio2.
Clarita R Tambong3 1,2Aklan State University.
Banga.
Philippines 3Laboratory High School-College of Teacher Education.
Philippines 2Correspondence Email: mmalonisio@asu.
Received: February 26, 2024 Accepted: May 13, 2025 Published: May 30, 2025 Article Url: https://journal.
org/index.
php/jipeuradeun/article/view/1364 Abstract This study looked into what factors help predict the performance of Bachelor of Secondary Education (BSE.
graduates in the Licensure Examination for Teachers (LET).
It used a descriptive-correlational and longitudinal research design, applying path analysis to examine how high school grades, university admission test scores, college qualifying exam results, and interview scores affect LET results.
Data from 186 graduates were used.
The findings showed that high school grades and college qualifying exam scores had a strong and positive effect on LET performance.
On the other hand, admission test and interview scores did not have a direct impact.
A revised model based on the results showed a good fit and can help improve how students are selected and supported in teacher education The findings emphasize the importance of aligning admission policies with academic competencies and offer a model that can be adapted to improve teacher education practices and licensure outcomes in both national and international contexts.
Keywords: Admission Test.
College Qualifying Exams.
Interview.
Grade Point Average.
Path Analysis.
Licensure Examination for Teachers.
p-ISSN: 2338-8617 | e-ISSN: 2443-2067 JIP-The Indonesian Journal of the Social Sciences .
7 p-ISSN: 2338-8617 Vol.
No.
May 2025 e-ISSN: 2443-2067 Introduction The Licensure Examination for Teachers (LET) plays a key role in maintaining the quality of the teaching profession in the Philippines.
As a mandatory requirement for teacher certification, the LET is not only a personal milestone for education graduates but also a major performance indicator for teacher education institutions.
Across many countries, performance in national licensure exams is increasingly tied to program accreditation, funding decisions, and institutional reputation (Albite, 2019.
Antiojo, 2017.
Igcasama et al.
, 2.
In the case of Aklan State UniversityAe College of Teacher Education (CTE), while its average LET passing rate of 93% is significantly higher than the national average of 36.
64%, the college recognizes the need for further improvement, especially as it aspires for higher accreditation levels and academic distinction (Ginoy et al.
, 2.
Understanding what predicts LET success has become urgent, especially in light of changing policies in student admission and the impact of the COVID-19 pandemic.
Due to health restrictions, traditional admission requirements such as university entrance exams and structured interviews were waived, with schools relying mostly on high school grades as the main basis for entry.
This shift raised important questions: which among the available indicatorsAihigh school grades, admission test scores, qualifying exams, or interviewsAiare most useful in predicting success in the LET? And how can these be used to shape more reliable and responsive admission and retention policies? Previous studies in the Philippines have explored the influence of academic performance, entrance test scores, and English proficiency on LET outcomes (Ferrer et al.
, 2015.
Hena et al.
, 2014.
Pascua & Navalta.
Sawey-Ognayon & Afalla, 2022.
Soriano, 2009.
Valencia, 2.
However, most of these focused only on direct correlations using regression models, without capturing the more complex and layered relationships among various predictors.
To address this limitation, path analysis offers a powerful tool for examining both direct and indirect effects within a unified predictive framework (Byrne, 2016.
Kline, 2.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
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Internationally, similar approaches have been adopted to investigate teacher licensure outcomes in contexts such as the United States.
Europe, and Asia (Cowan et al.
, 2023.
Bieri Buschor & Schuler Braunschweig, 2018.
Ruegg et al.
, 2024.
Ihlenfeldt & Rios, 2.
These studies emphasize the value of multi-dimensional modeling allowing for more accurate predictive In global literature, predictors of professional licensure outcomes are often grouped into entry-level indicators .
, high school GPA, admission test score.
, process-level indicators .
, qualifying exams taken during colleg.
, and exit-level indicators .
, interviews or final practicum evaluation.
(Barton et al.
, 2014.
Poropat, 2.
Despite their use in various underexplored in Philippine research using path analysis.
Furthermore, the role of non-cognitive indicators such as communication skills, often measured through interviews, has not been established.
To guide this study, we use a conceptual model that assumes these three types of indicators influence one another and collectively predict LET This conceptual classification reflects broader theoretical models of student progression and professional formation.
From a theoretical standpoint, the study is anchored in Cognitive Load Theory, which suggests that learners with strong foundational knowledge are better able to manage the demands of complex cognitive tasks such as licensure exams (Sweller, 2.
The integration of interview scores also introduces a socio-cognitive dimension, drawing on frameworks that recognize communication skills as integral to teacher effectiveness (MountfordZimdars & Moore, 2020.
Poropat, 2.
The study tests these assumptions using actual student data and a revised path model based on empirical Figure 1 shows the conceptual model.
JIP-The Indonesian Journal of the Social Sciences .
9 p-ISSN: 2338-8617 Vol.
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May 2025 e-ISSN: 2443-2067 Figure 1.
The conceptual model In the conceptual model, it is assumed that High School Grade Point Average (HSGPA) serves as a foundational academic indicator, directly influencing University Admission Test (UAT) scores.
College Qualifying Examination (CQE) scores, interview scores, and ultimately.
LET ratings.
Research indicates that HSGPA is a strong predictor of college academic performance, often surpassing standardized test scores in predictive validity.
This suggests that students with higher HSGPAs are likely to perform better in subsequent academic assessments and evaluations (Burke et al.
, 2.
UAT scores are also assumed to directly influence CQE scores, interview scores, and LET ratings.
These assumptions are based on the premise that strong performance in general academic aptitude tests translates into better performance in college qualifying exams and structured interviews, which in turn impacts licensure examination results (Sawyer, 2.
Similarly.
CQE is assumed to directly influence interview scores and LET ratings.
Performance in college qualifying examinations reflects a studentAos mastery of subject matter and critical thinking skills (Zandvakili et al.
, 2.
, which are essential for success in interviews and professional licensure exams.
While specific studies linking CQE scores to interview performance are limited, the general correlation between academic performance and 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
Repayo et al.
subsequent evaluations supports this hypothesis.
Moreover, interview scores are assumed to directly influence LET ratings.
Interviews assess candidatesAo communication skills, professionalism, and subject knowledge, all of which are pertinent to the teaching course.
Strong interview performance may indicate a higher likelihood of success on the LET, as both require demonstration of comprehensive understanding and application of educational principles (Gimbert & Chesley, 2.
Through this model, the study aims to determine the predictive power of entry-level .
igh school GPA, admission test score.
, processlevel .
ollege qualifying exa.
, and exit-level .
indicators on the LET performance of BSEd graduates using path analysis.
It also seeks to validate a theoretical model showing how these indicators are interrelated in the context of teacher education.
Method This study used a quantitative research design that included both descriptive and correlational methods.
The goal was to build and test a theoretical model that predicts performance in the Licensure Examination for Teachers (LET) based on several indicators.
Descriptive research helped in describing the profile of the BSEd graduates, while correlational research looked at how different variables relate to one another (Creswell, & Plano Clark, 2018.
Fraenkel & Wallen, 2.
Because the study followed graduates across different academic records and performance over time, it also used a longitudinal approach to observe patterns in their LET outcomes (Thomas, 2.
The participants were 186 graduates of the Bachelor of Secondary Education (BSE.
program from the College of Teacher Education (CTE).
To be included in the study, graduates had to meet the following criteria:
they must have completed their degree requirements during the specified academic years, .
they should have complete academic records, and .
they must give consent to participate.
Graduates with missing data JIP-The Indonesian Journal of the Social Sciences .
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May 2025 e-ISSN: 2443-2067 or who did not provide consent were excluded.
Stratified random sampling was used to make sure that both batches of graduates were fairly Table 1 shows the distribution of the sample.
Table 1.
The distribution of bachelor of secondary education graduates Batch Frequency Percentage Total The data used in this study were secondary data collected from school records.
These included High School Grade Point Average (HSGPA).
University Admission Test (UAT) scores.
College Qualifying Examination (CQE) scores, and interview results.
LET ratings were obtained with permission from the Philippine Regulatory Commission (PRC).
Before using the data, proper approval was secured from the school and concerned Participant confidentiality was maintained by assigning unique codes and securely storing data in password-protected files accessible only to the research team.
The University Admission Test.
College Qualifying Examination, and Interview were institutionally developed tools.
These instruments were initially validated by a panel of experts for content validity before they were While the tools have been widely used in the college for many years and are considered acceptable based on internal benchmarks, a limitation is that no official records of their reliability and psychometric properties .
ike internal consistency or test-retest reliabilit.
have been This raises concerns about the strength of their measurement properties, even though their continued use is institutionally supported and historically accepted.
Data analysis was done using SPSS and AMOS software.
Descriptive statistics such as mean and standard deviation were used to describe the To examine relationships between variables, the study used 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
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PearsonAos r.
Stepwise Multiple Regression, and Structural Equation Modeling (SEM) through path analysis.
SEM was applied to test the fit of the proposed theoretical model and to measure both direct and indirect effects between SEM is useful for testing models that include both observed and hidden .
variables and for examining complex cause-and-effect relationships (Hair Jr et al.
, 2.
As noted by Mertler and Reinhart .
, path analysis also accounts for error terms in its calculations, which reflect influences not captured by the included variables.
To determine how well the model fits the data, the study used several indicators such as the Chi-square value.
RMSEA (Root Mean Square Error of Approximatio.
SRMR (Standardized Root Mean Square Residua.
CFI (Comparative Fit Inde.
, and AIC (AkaikeAos Information Criterio.
, as recommended by Hooper et al.
These fit indices helped in deciding whether the proposed model should be retained, revised, or rejected.
Results and Discussion Before delving into the detailed statistical results and their interpretations, this section presents the findings of the study based on the data gathered from 186 BSEd graduates.
Each subsection corresponds to a specific variable identified in the conceptual model, including High School GPA.
University Admission Test scores.
College Qualifying Exam results, interview ratings, and LET performance.
The results are presented sequentially to show descriptive statistics, relationships among variables, and the outcomes of the regression and path analyses.
These findings aim to test the initial assumptions of the model and provide empirical evidence to guide admissions and retention strategies in teacher education.
Results High School Grade Point Average (HSGPA) of the BSEd Graduates Table 2 presents the High School Grade Point Average (HSGPA) of the BSEd graduates.
The overall mean HSGPA across both batches was JIP-The Indonesian Journal of the Social Sciences .
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Specifically.
Batch A had a mean of 90 (SD = 2.
while Batch B had a slightly higher mean of 89.
41 (SD =
The close range of mean scores suggests that both batches were admitted with relatively similar academic qualifications.
Table 2.
The High School Grade Point Average of the BSEd Graduates Batch Total Mean Interpretation Very Satisfactory Very Satisfactory Very Satisfactory The close similarity in HSGPA between the two batches indicates a This consistency reduces the possibility of baseline academic disparity influencing the studyAos outcomes.
Moreover, the very satisfactory rating suggests that the institution maintains selective admission criteria, which may positively affect subsequent academic success and licensure These findings reinforce the role of prior academic performance as a foundational indicator in teacher education pathways.
University Admission Test (UAT) Scores of the BSEd Graduates Table 3 shows the University Admission Test (UAT) scores.
The combined mean score for all participants was 76.
35, interpreted as Fairly Satisfactory.
Batch A had a higher mean of 78.
43 (SD = 6.
, compared to Batch B with a mean of 74.
08 (SD = 5.
This result emphasized a noticeable difference in UAT scores between batches, which may reflect the differences in the academic preparation of the examinees.
Table 3.
The University Admission Test Scores of the BSEd Graduates Batch Total Mean Interpretation Fairly Satisfactory Did Not Meet Expectations Fairly Satisfactory 1.
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The noticeable gap in UAT scores between Batch A and Batch B suggests heterogeneity in test preparedness, which may be attributed to variations in senior high school curriculum quality, access to review resources or differences in the administration of entrance testing.
While the combined interpretation still falls within the Aufairly satisfactoryAy category, the lower mean of Batch B may have implications for their readiness to engage with higher-level academic content during the teacher education program.
This reinforces the importance of considering the validity and consistency of university admission testing as part of student selection processes.
College Qualifying Examination (CQE) Scores of the BSEd Graduates Table 4 reports the scores on the College Qualifying Examination (CQE).
The average score across both batches was 76.
62 (SD = 4.
, which also falls under the Fairly Satisfactory category.
Batch A scored a slightly higher mean of 77.
35, while Batch B obtained a mean of 75.
The data indicate that while students generally performed within acceptable limits, the slightly lower mean of Batch B could suggest gaps in content mastery or critical thinking skills.
Table 4.
The College Qualifying Examination Scores of the BSEd Graduates Batch Total Mean Interpretation Fairly Satisfactory Fairly Satisfactory Fairly Satisfactory Although both batches performed within the fairly satisfactory range, the slightly higher CQE scores for Batch A may indicate stronger content retention or test-taking ability.
Since the CQE occurs midprogram, these results likely reflect the effectiveness of curriculum delivery, faculty quality, and student engagement up to that point.
The narrower score range also suggests relative uniformity in the instructional process, which is critical for maintaining academic standards across JIP-The Indonesian Journal of the Social Sciences .
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Interview Scores of the BSEd Graduates Table 5 presents the results of the interview scores.
The overall mean score was 86.
20 (SD = 8.
, interpreted as Very Satisfactory.
Batch A achieved an Outstanding mean score of 90.
20 (SD = 5.
, whereas Batch B averaged 81.
85 (SD = 9.
, indicating a wider variation.
The gap between the batches could suggest disparities in the training or preparation related to communication and interpersonal skills.
Table 5.
The Scores in Interview of the BSEd Graduates Batch Total Mean Interpretation Outstanding Satisfactory Very Satisfactory The substantial difference in interview scores between the two batches raises questions about the consistency and reliability of the interview While Batch A achieved outstanding scores.
Batch B scored significantly lower despite similar academic backgrounds.
This disparity may stem from differences in how interviews were administered, interviewer subjectivity, or training in soft skills.
The variation highlights the importance of standardizing interview procedures and ensuring objective criteria are used, especially when interviews are part of high-stakes evaluation.
Performance in the Licensure Examination for Teachers (LET) of the BSEd Graduates Table 6 summarizes the LET performance of the graduates.
The combined mean LET rating was 79.
99 (SD = 5.
, classified as Fairly Satisfactory.
Batch B outperformed Batch A with a mean score of 82.
00 compared to 78.
This outcome suggests potential differences in instructional quality, support systems, or student motivation across cohorts.
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Table 6.
Performance in the Licensure Examination for Teachers (LET of the BSEd Graduates Batch Total Mean Interpretation Fairly Satisfactory Satisfactory Fairly Satisfactory Interestingly, despite Batch AAos superior performance in earlier indicators like CQE and interviews.
Batch B outperformed in the LET.
This inverse relationship may suggest that non-academic factorsAisuch as examspecific preparation, motivation, support systems, or even stress resilienceAiplayed a role in shaping LET outcomes.
It implies that while academic indicators are important, they may not fully capture the dynamics that influence licensure exam success.
Hence, holistic student support mechanisms before the LET may be just as critical.
The Predictors of the Performance in the Licensure Examinations (LET) of the BSEd Graduates Tables 7 and 8 present the results of the Stepwise Multiple Regression analysis for LET performance.
The overall model significantly predicts the LET performance of the BSEd graduates [R2 = 0.
R2adj = 404.
F .
, .
= 42.
723, p = 0.
Moreover, the model showed that HSGPA ( = 0.
347, p < .
CQE ( = 0.
372, p < .
, and Interview Scores ( = -0.
273, p < .
were significant predictors, while UAT was This indicates that both HSGPA and CQE have strong positive effects on LET performance.
However, the negative coefficient for Interview Scores suggests that higher interview ratings may not translate to better licensure exam outcomes.
Table 7.
ANOVA Results for the Predictors of the Performance in the Licensure Examinations (LET) of the BSEd Graduates Model ANOVAa Sum of Mean Sig.
Squares Square 723* .
Regression 2160.
R2adj JIP-The Indonesian Journal of the Social Sciences .
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Residual Total Note: a.
Dependent Variable: LET Rating, d.
Predictors: (Constan.
HSGPA.
CQE.
Interview, *p<0.
The ANOVA result indicates that the combination of predictorsAi HSGPA.
CQE, and InterviewAisignificantly explains variance in LET With an RA of 0.
413, the model captures a substantial portion of the performance variance, validating the inclusion of these academic and preprofessional indicators in the analytical framework.
This finding provides empirical backing for institutions to strengthen internal evaluation systems aligned with licensure goals.
Table 8.
Model Summary of Multiple Regression Analysis of the Predictors of the Performance in the Licensure Examination for Teachers (LET) of the BSEd Graduates Coefficientsa Model (Constan.
Unstandardized Standardized Coefficients Coefficients Std.
Error
Beta
HSGPA
CQE
Interview Sig.
Note: a.
Dependent Variable: LET Rating The regression coefficients reveal a nuanced understanding of how each predictor functions.
HSGPA and CQE both showed strong positive effects, emphasizing that academic achievement across stages significantly contributes to licensure readiness.
In contrast, the negative coefficient of interview scores contradicts the common assumption that strong interpersonal or communication skills lead to higher success in professional licensure.
This contradiction might be explained by misalignment between interview criteria and exam content, or by the possibility that high interview ratings are awarded subjectively, not based on measurable competencies.
It calls for a critical reevaluation of how interviews are conducted and how much weight they should carry in assessing teacher candidates.
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The Predictors of the University Admission Test Scores of the BSEd Graduates Table 9 shows that the model was statistically significant.
, .
= 716, p < .
001, and accounted for 18.
1% of the variance in UAT scores (RA = Table 10 further reveals that High School GPA (HSGPA) was a significant predictor of UAT performance ( = 0.
426, t = 6.
381, p < .
However, the variance explained remains limited, suggesting that other unmeasured factors may also play a role in determining UAT performance.
Table 9.
ANOVA Results for the Predictors of the University Admission Test Scores of the BSEd Graduates ANOVAa Sum of Df Mean Sig.
R2 R2adj Squares Square Model Regression 796 184 32.
Residual Total Note: a.
Dependent Variable: UAT, d.
Predictors: (Constan.
HSGPA The ANOVA results demonstrate that HSGPA has a statistically significant impact on UAT scores, confirming the continuity between secondary school performance and standardized college entrance assessments.
However, the modest RA value of 0.
181 suggests that HSGPA is not the sole determinant of entrance exam success.
Other factors such as test anxiety, socioeconomic background, or familiarity with the test format may influence student outcomes.
This finding emphasizes the need for a multidimensional approach to interpreting admission test performance.
Table 10.
Model Summary of Multiple Regression Analysis of the Predictors of the University Admission Test Scores of the BSEd Graduates Coefficientsa Model (Constan.
Unstandardized Standardized Coefficients Coefficients Std.
Error Beta Sig.
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HSGPA
e-ISSN: 2443-2067 Note: a.
Dependent Variable: UAT The regression coefficient confirms the significant contribution of HSGPA to UAT performance.
However, the partial predictability underlines that entrance tests may not fully reflect academic potential but rather momentary cognitive performance under pressure.
These results highlight the limitation of over-relying on a single measure of aptitude and support the incorporation of multiple metrics in student selection processes.
The Predictors of the College Qualifying Test Scores of the BSEd Graduates The multiple regression analysis results in Table 11 show that the model was statistically significant.
F .
, .
= 71.
408, p < .
001, explaining 8% of the variance in CQE scores (RA = 0.
Moreover.
Table 12 showed that both UAT ( = 0.
464, p < .
and HSGPA ( = 0.
314, p < .
were found to significantly predict CQE performance.
These findings support the sequential logic of the path model, where performance in earlier academic milestones like HSGPA and UAT leads to stronger outcomes in process-level assessments such as the CQE.
Table 11.
ANOVA Results for the Predictors of the College Qualifying Test Scores of the BSEd Graduates ANOVAa Sum of Mean Sig.
R2 R2adj Squares Square Model Regression 1488.
Residual Total Note: a.
Dependent Variable: CQE, c.
Predictors: (Constan.
UAT.
HSGPA The model predicting CQE performance from HSGPA and UAT demonstrates excellent explanatory power, accounting for nearly 44% of the total variance.
This suggests that both early academic success and entrance aptitude play a critical role in determining performance in program-level 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
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The strength of the model affirms the conceptual sequence of student progression: academic foundation and cognitive ability collectively shape content mastery during teacher education.
Table 12.
Model Summary of Multiple Regression Analysis of the Predictors of the College Qualifying Test Scores of the BSEd Graduates Coefficientsa Unstandardized Standardized Coefficients Coefficients Std.
Error Beta Model (Constan.
UAT
HSGPA
Note: a.
Dependent Variable: CQE Sig.
Notably.
UAT showed a stronger effect on CQE outcomes than HSGPA, indicating that general academic aptitude, as assessed by entrance testing, may have a more direct impact on content-based performance than prior academic averages.
This supports the argument that well-designed, standardized tests can offer valuable predictive insights, provided their validity is well-established.
It also opens space for reevaluating the balance between GPA and aptitude test weighting in admissions.
The Predictors of the Interview Scores of the BSEd Graduates Table 13 shows that the model was significant.
, .
= 11.
193, p = 001, with RA = 0.
057, indicating that it explains only 5.
7% of the variance in interview outcomes.
Likewise, as seen in Table 14.
UAT was the sole significant predictor of Interview Scores ( = 0.
239, t = 3.
346, p = .
Although the effect size is modest, it suggests a positive relationship between general academic ability and performance in structured interviews.
Table 13.
ANOVA Results for the Predictors of the Interview Scores of the BSEd Graduates ANOVAa Sum of Squares Model Regression 793.
Mean Square Sig.
R2adj JIP-The Indonesian Journal of the Social Sciences .
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Dependent Variable: Interview, b.
Predictors: (Constan.
UAT The ANOVA test confirms that UAT significantly predicts interview performance, though only weakly.
With just 5.
7% of variance explained, this model suggests that the interview process is influenced by other factors not captured by traditional academic indicators.
It may reflect personality traits, social skills, cultural capital, or even language proficiencyAiall of which are not measured by cognitive-based tests.
Table 14.
Model Summary of Multiple Regression Analysis of the Predictors of the Interview Scores of the BSEd Graduates Coefficientsa Unstandardized Standardized Coefficients Coefficients Model Std.
Error Beta (Constan.
UAT Note: a.
Dependent Variable: Interview Sig.
The coefficient result from the regression shows a small but significant positive effect of UAT on interview scores.
While statistically relevant, the practical significance of this relationship is limited.
The weak linkage reinforces the need to treat interviews as a separate construct requiring its validation process.
Relying on academic indicators to predict non-academic performance may result in incomplete or misleading interpretations of student potential.
The Proposed Conceptual Model Figure 2 shows the proposed conceptual model.
In the model.
HSPGA ( = 0.
and CQE ( = 0.
have a large positive influence on LET performance while the interview ( = -0.
has a negative large influence.
On the other hand.
UAT ( = -0.
has a negative small influence on LET 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
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Figure 2.
The Proposed Conceptual Model The proposed conceptual model aimed to integrate entry-level, process-level, and exit-level indicators into a unified structure predicting LET outcomes.
While theoretically justified, the empirical data failed to support several paths in the model.
This disconnects between hypothesis and observation points to the importance of iterative model testing and refinement when dealing with complex human performance systems like teacher licensure.
Furthermore.
Table 15 shows the initial model fit indices.
The proposed model did not meet acceptable fit thresholds with RMSEA = 0.
and SRMR = 0.
000, indicating a poor fit between the hypothesized paths and actual data.
This lack of model fit suggests the need for refinement, particularly in how the relationships among UAT.
CQE.
Interview, and LET performance are conceptualized.
Table 15.
Fit Indices Results and Fit Index Thresholds for the Proposed Model for LET Performance Fit Index Chi-Square Test Statistics p-value RMSEA Acceptable Threshold Low N2 relative to degrees of freedom with an insignificant P value (P>0.
<0.
<0.
03, represent Fit Index Value of the Model X2 = 0.
df = 0 p-value = Cannot be computed JIP-The Indonesian Journal of the Social Sciences .
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Fit Index
SRMR
CFI
AIC
PNFI
Acceptable Threshold excellent fit <0.
>0.
Default model should produce the lowest No threshold levels e-ISSN: 2443-2067 Fit Index Value of the Model Default Model = 30.
Saturated Model = 30.
Independence Model = 266.
The poor model fit statisticsAiparticularly RMSEA and undefined Chi-squareAisuggest that the initial model specification was overly simplistic or misaligned.
Despite including variables with theoretical importance, the structure of their interrelations may not reflect actual causal pathways.
These results emphasize that a strong theoretical grounding must be matched with empirical adequacy to be useful in practical policy design.
The Revised Path Model of LET Performance Based on the revised model in Figure 3.
HSGPA ( = 0.
and CQE ( = 0.
have a large positive influence on LET performance.
On the other hand, interview ( = -0.
has a negative large influence on LET However, the model also shows that UAT has no direct influence on LET performance.
The revised model also shows significant paths on the positive large influence of HSGPA to UAT ( = 0.
and CQE ( = 0.
and on UAT to CQE ( = 0.
Figure 3.
The Revised Path Model of LET Performance 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
Repayo et al.
The revised model corrected the structural limitations of the initial version by eliminating weak or non-significant paths and highlighting stronger relationships supported by data.
It demonstrates a clearer conceptual logic:
academic preparation flows from high school to college-level assessments, which in turn influence licensure outcomes.
The model also de-emphasizes the role of subjective evaluations like interviews, suggesting a realignment of evaluation priorities in teacher education programs.
Table 16 illustrates the improved fit of the revised model.
The revised model produced excellent fit indices: RMSEA = 0.
CFI = 0.
SRMR =
010, and a non-significant Chi-square value (NA = 3.
202, df = 3, p = .
These results demonstrate that the revised model more accurately represents the data, supporting the refined path relationships among the variables.
Table 16.
Fit Indices Results and Fit Index Thresholds for the Revised Path Model for LET Performance Fit Index
Chi-Square Test
Statistics p-value RMSEA
SRMR
CFI
AIC
PNFI
Acceptable Threshold Low N2 relative to degrees of freedom with an insignificant P value (P>0.
<0.
<0.
03, represent excellent fit <0.
>0.
Default model should produce the lowest No threshold levels Fit Index Value of the Model X2 = 3.
df = 3 p-value = 0.
Default Model = 27.
Saturated Model = 30.
Independence Model = 266.
The improved fit indices in Table 16 validate the revised model, with all metrics (RMSEA.
CFI.
SRMR.
Chi-squar.
falling within acceptable This model offers a more accurate representation of how different indicators interact to influence LET performance.
It provides an empirically supported framework that institutions can use to develop evidence-based admission and retention strategies aligned with licensure goals and broader accountability standards.
JIP-The Indonesian Journal of the Social Sciences .
5 p-ISSN: 2338-8617 Vol.
No.
May 2025 e-ISSN: 2443-2067 Discussion The findings of this study confirm that academic indicators, particularly High School Grade Point Average (HSGPA) and College Qualifying Examination (CQE) scores, are the strongest predictors of performance in the Licensure Examination for Teachers (LET).
These two variables consistently demonstrated significant positive effects across regression and path models, highlighting their value in identifying candidates most likely to succeed in licensure.
The results align with Valencia .
and Ferrer et al.
, who noted that prior academic achievements strongly predict licensure success.
In addition, the significant influence of CQE supports the role of intermediate, specialization-based assessments in predicting professional competence (Cahapay & Toquero, 2.
This confirms the assumptions of Cognitive Load Theory (Sweller, 2.
, which asserts that students with stronger academic foundations are better equipped to manage the complex cognitive demands of licensure examinations.
Interestingly, while interview scores were expected to reflect candidatesAo professional readiness and communication skills, the data revealed a significant negative relationship between interview performance and LET outcomes.
This surprising result contradicts conventional wisdom and research suggesting that communication skills are essential for academic and professional success (Dahmani et al.
, 2024.
Parmar et al.
, 2015.
Poropat, 2.
However, the low variance explained by interview scores, combined with anecdotal evidence and institutional feedback, suggests potential flaws in the design or administration of the interview process.
Mountford-Zimdars and Moore .
emphasized, interviews are prone to subjectivity, which can lead to inconsistent ratings depending on the interviewerAos judgment or student background.
These findings call for a thorough review of how communication and interpersonal skills are assessed in teacher education programs.
Another notable finding is the limited predictive power of the University Admission Test (UAT) about LET performance.
While UAT was positively associated with CQE and Interview scores, it had no direct 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
Repayo et al.
significant effect on LET outcomes.
This raises concerns about the testAos content alignment and construct validity.
Malonisio and Malonisio .
and Barton et al.
also highlighted the limitations of using general aptitude tests to forecast complex outcomes like licensure success.
These results reinforce the need to shift from generic admission tools toward more targeted, subject-specific assessments that better align with program The original model, while theoretically sound, failed to meet statistical fit thresholds.
After refinement, the revised model demonstrated excellent fit indices (RMSEA = 0.
CFI = 0.
, validating the updated path structure.
This model emphasizes the sequential and layered relationship among entry-, process-, and exit-level indicators, with CQE and HSGPA emerging as the most critical links to licensure outcomes.
The revised model contributes meaningfully to the literature by offering a practical, evidence-based framework that teacher education institutions can use to refine their admission and retention policies.
These results are particularly useful for programs seeking accreditation or aiming to improve their licensure pass rates.
Beyond the local context, the results have broader relevance to developing countries where teacher education programs are shifting toward outcome-based and competency-driven frameworks.
In countries like Indonesia.
Vietnam, and several African nations, reform efforts increasingly focus on improving teacher quality through more rigorous selection and assessment systems (Bieri Buschor & Schuler Braunschweig.
Cowan et al.
, 2023.
Ruegg et al.
, 2.
The validated model in this study offers a scalable framework that other institutions can adapt to improve licensure outcomes through evidence-based academic metrics.
also addresses the growing need for standardized approaches to evaluating teacher candidates in regions with varying levels of institutional capacity and data availability.
While this study provides a valuable framework for understanding predictors of LET performance, several limitations must be acknowledged.
JIP-The Indonesian Journal of the Social Sciences .
7 p-ISSN: 2338-8617 Vol.
No.
May 2025 e-ISSN: 2443-2067 The study relied on retrospective secondary data, and psychometric information for some institutional tools, like the UAT and interview, was Future research should explore the inclusion of other cognitive and non-cognitive variables such as practicum performance.
English language proficiency, and teaching simulations.
In addition, longitudinal and cross-institutional studies could validate the generalizability of this model and provide deeper insights into how student preparation translates into professional certification and teaching success.
These findings carry significant implications not only for local teacher education institutions but also for the broader global discourse on teacher quality assurance and certification.
In many developing countriesAiincluding those in Southeast Asia and sub-Saharan AfricaAireforms in teacher education increasingly emphasize the development of reliable, evidencebased models for admission, retention, and licensure.
However, such reforms often lack robust empirical frameworks to validate which indicators genuinely forecast licensure success.
The revised path model proposed in this study offers a scalable and context-sensitive framework that addresses this gap by demonstrating the predictive strength of academic indicators, particularly high school GPA and college qualifying exams, about licensure outcomes.
For countries such as Indonesia and Vietnam, which are transitioning toward competency-based teacher education, these findings offer a basis for aligning admission and internal evaluation practices with licensure targets (Cowan et al.
, 2023.
Bieri Buschor & Schuler Braunschweig, 2.
The finding that interview scores exhibit a negative relationship with LET outcomes calls into question the widespread reliance on subjective or loosely structured interviews across developing education systems, echoing concerns raised by Mountford-Zimdars and Moore .
about the validity and fairness of such tools.
Additionally, in regions where standardized national testing is not uniformly implemented or where institutional resources are limited, prioritizing internal academic assessmentsAilike qualifying examsAi 1.
JIP-The Indonesian Journal of the Social Sciences Beyond Admission Scores: Mapping the Strongest Predictors of Let Performance Benedicta D.
Repayo et al.
can serve as both a practical and predictive mechanism for ensuring teacher quality (Ruegg et al.
, 2024.
Valencia, 2.
Furthermore, this study aligns with international policy frameworks such as UNESCOAos Global Education Monitoring Report and the World BankAos teacher development priorities, which advocate for data-driven mechanisms to improve teacher effectiveness and reduce inequities in teacher preparation systems (Cowan et al.
, 2.
By offering an empirically tested model with excellent fit indices, this study demonstrates the value of structural equation modelling (SEM) as a tool for informing education policy in diverse cultural and institutional contexts (Byrne, 2016.
Kline, 2.
Finally, the contribution of this study extends to the broader discussion on the globalization of teacher standards.
As organizations like SEAMEO and ASEAN intensify regional collaboration on education reforms, the model developed here may serve as a reference for creating shared benchmarks or conducting cross-country validation studies.
Future comparative research could explore the application of this model in multilingual and multiethnic teacher education environments or postpandemic systems facing increased pressure for accountability and adaptability (Dahmani et al.
, 2024.
Ihlenfeldt.
, & Rios, 2.
Conclusion This study established a validated path analysis model that identifies key predictors of LET performance among BSEd graduates, with High School Grade Point Average (HSGPA) and College Qualifying Examination (CQE) emerging as the most influential variables.
These findings highlight the importance of academic preparedness and subjectspecific evaluation in determining licensure success.
In contrast, the weak and negative contribution of interview scores, and the limited role of UAT, emphasize the need to reevaluate the reliability and design of existing nonacademic admission tools.
Theoretically, the study contributes to the ongoing discourse on teacher quality by proposing a multi-layered model rooted in Cognitive Load Theory JIP-The Indonesian Journal of the Social Sciences .
9 p-ISSN: 2338-8617 Vol.
No.
May 2025 e-ISSN: 2443-2067 and supported by international research on academic performance and licensure success.
The model stresses the predictive strength of academic indicators and presents an empirically tested framework that may guide both policy and practice.
Practically, the findings can inform higher education institutions in designing more focused and evidence-based admission policies.
Teacher education programs should consider prioritizing well-validated and contentspecific assessments, such as qualifying examinations, over generic aptitude or subjective interview tools.
Policy-makers and institutional leaders may use the revised model to improve candidate selection, target support for at-risk students, and enhance institutional performance in licensure outcomes.
The model also offers potential for adaptation in other developing countries that are striving to improve teacher education outcomes.
Its application is particularly relevant in systems transitioning to outcome-based education and competency-based certification, where data-informed decision-making is essential.
Institutions in Southeast Asia.
Africa, and similar contexts can draw insights from the model to design better-aligned and transparent teacher evaluation systems.
Future studies should aim to strengthen this model by including more diverse predictors such as practicum evaluations, teaching simulations, and standardized language assessments.
In addition, cross-country validation may be pursued to test the modelAos generalizability and further support its use in global teacher education reform.
Acknowledgement The authors sincerely acknowledge Aklan State UniversityAos College of Teacher Education.
Bibliography