ORIGINAL RESEARCH article

Front. Educ., 20 August 2025

Sec. Assessment, Testing and Applied Measurement

Volume 10 - 2025 | https://doi.org/10.3389/feduc.2025.1590215

Unveiling the hidden mechanisms behind cognitive achievement: a structural equation modeling approach to teacher learning, academic persistence, and school climate

  • Department of Chemistry Education, Faculty of Mathematics and Natural Science, Yogyakarta State University, Yogyakarta, Indonesia

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Abstract

Cognitive achievement in education results from the complex interaction between personal, institutional, and contextual factors. This study investigates how teacher learning characteristics such as instructional competence, personal efficacy, and pedagogical strategies along with school climate and students’ academic persistence, influence cognitive outcomes. The purpose of this study is to analyze the influence of teachers’ learning characters on cognitive achievement by considering the mediating role of school climate and students’ academic perseverance. This study uses a quantitative approach with a covariance-based structural equation modeling method (CB-SEM). The research sample consisted of 1,057 high school students in North Maluku Province, Indonesia. Data were collected using a Likert scale questionnaire and analyzed using SPSS 24, JASP 0.19, and SmartPLS 4. The results showed that professional competence, personal efficacy, and instructional models employed by teachers significantly affected cognitive achievement. In addition, the school climate and academic perseverance are mediators that strengthen the relationship between teachers’ learning character and students’ cognitive achievement. These findings confirm that a practical learning approach supported by a conducive school environment and students’ academic perseverance can contribute to the quality of education as it improves learning outcomes. This research provides insight into educators and education policymakers to develop effective learning strategies.

Introduction

In this modern era, education development is the key to forming modern human beings who can master various sectors of life. Education encourages the development of universal human values individually and collectively, improves public life, and encourages active participation in a democratic society (Sarkar, 2023; Spiel et al., 2018). Implementing good education will produce graduates who are ready to compete and qualified (Sunarya et al., 2024). To form graduates who compete in the world of work, various innovations are needed in holistic learning and to create a competitive character for students (Abuelmaatti and Vinokur, 2024; Kocsis and Pusztai, 2025). There are various variables in determining the quality of education, one of which is the achievement of cognitive achievement (Purković and Kovačević, 2020). Achievement of Cognitive Achievement in Education is influenced by the complex interaction between contextual factors, personal and institutional. In other words, the complexity of school management, teachers’ learning character, and students’ personal (Choi and Lee, 2022; Ma et al., 2017). The output of the quality of education that is aspired to will encourage economic growth, reduce inequality, and improve the quality of life of the community, the country, and the nation (Zickafoose et al., 2024).

Although there are many innovations in the character of teacher learning, the formation of a school climate has improved the quality of education by adopting various variables in producing quality learning outcomes. Still, in reality, it is a challenge (Ni and Wang, 2022). Encouraging teachers to be involved in the competence of reflecting and identifying the learning process as a weakness so that it has an impact on the quality of teaching (Andriyani, 2019). Promoting a culture of collaboration between teachers through peer support and education and learning experiences together is also weak because there is competition among each teacher, so there is a weakening of innovation to improve the continuation of learning (Del Gobbo and Galeotti, 2022; Santaolalla et al., 2020). Not all educators and students have adequate skills in following the learning process, so they need optimal encouragement from teachers and school managers in the learning process (Sebastian et al., 2016). Optimal encouragement from teachers and school education staff can encourage active participation, build a strong social climate in the school, and encourage students to improve their learning competencies (Liu et al., 2023).

The learning character of teachers with various dimensions is a factor that can increase achievement (Al Jaberi et al., 2024). Teacher learning character is defined as a multidimensional construct that includes eight main dimensions: professional competence, personal competence, learning planning, learning evaluation and improvement, personal efficacy, social efficacy, learning model, and learning media. This dimension reflects the teacher’s instructional quality and professional confidence in shaping an effective learning process. Characteristics such as competence and efficacy in teachers can motivate students, and students may have difficulty following the learning stages given by the teacher (Danişman et al., 2020). Characteristics such as teacher efficacy and teacher innovation will also form a good relationship between teachers and students (Binks-Cantrell and Joshi, 2015). Teachers innovate and display exemplary performance in learning by making improvements in learning design, and the use of learning media will form good classroom management (Audisio et al., 2024; Chen and Wang, 2024). In addition to the learning character of the teacher, which is a direct factor, there is also an indirect influence on the achievement of conclusive achievements, including the school climate and internal beliefs of students, such as academic persistence (Archambault et al., 2020).

This research is necessary because it seeks alternatives to various factors of teachers’ learning character with the mediation of the school climate and internal factors such as academic persistence (Amsalu and Belay, 2024; Hammar Chiriac et al., 2023). Support for improving teacher competence, performance, efficacy, and innovation can trigger the growth of student motivation and increase academic persistence in learning (Gaganao and Odon, 2024).

Another factor that can affect cognitive achievement is the school climate, where physical and academic conditions become other factors influencing indirectly (Maxwell et al., 2017).

This research offers an integrative approach by examining students’ cognitive achievement through the dimensions of teachers’ learning character influenced by the mediating role of the school climate and academic persistence. The character of teacher learning, which consists of competence, performance, efficacy, and innovation, is an important factor in the process of knowledge transfer, character building, and motivation that teachers do directly or indirectly to students. In addition to these direct influences, this study also emphasizes the importance of the role of contextual and psychological mediators. The school climate, as a representation of the school’s institutional and physical conditions, plays a role in shaping a learning environment that supports academic achievement. Meanwhile, academic persistence that reflects students’ commitment to learning and resilience in the face of academic challenges acts as an internal mediator that connects the influence of teachers’ character to student learning success. This study responds to gaps in the previous literature that generally examined teacher factors separately and has not examined in depth the mediation mechanisms that link teacher behavior to student achievement. By combining Bandura’s sociocognitive theoretical approach and Bronfenbrenner’s educational ecology, this study presents a comprehensive theoretical framework in explaining the dynamic interactions between teacher characteristics, school contexts, and students’ internal dispositions in influencing cognitive achievement variations.

The research problems in this study focus on how teachers’ learning characteristics affect students’ cognitive performance, as well as how the mediating role of school climate and academic persistence in these relationships. In this context, this study aims to reveal more deeply the direct and indirect relationship between teachers’ learning characteristics and students’ academic achievement through school environment mediators and student learning diligence. Based on this formulation, this study asks three main questions, namely: (1) How does the teacher’s learning characteristics directly affect students’ cognitive achievement? (2) Does the school climate mediate the influence of teachers’ learning characteristics on students’ cognitive achievement? and (3) Does academic persistence act as a mediator in the relationship between teachers’ learning characteristics and students’ cognitive achievement? The relationship between these variables is visualized in the conceptual model presented in Figures 1, 2.

Figure 1

Figure 2

Research method

Research design

The design of this study uses a quantitative approach with the analytical method of covariant base structural equation modeling (CB-SEM) to test the dimension of teachers’ learning character on cognitive achievement mediated by school climate variables and academic persistence. This approach was chosen because of the objective and systematic measurement of the variables studied.

Population and sample/material

The research sample was collected from 6 districts/cities in North Maluku Province, Indonesia, and the total research sample was 1,057 high school students. The distribution of the research sample is as follows: as many as 534 students from Ternate city, 132 students from South Halmahera district, 126 students from Morotai Island district, 116 students from Halmahera Utasa district, 96 students from Tidore Islands city and 53 students from West Halmahera district. The distribution of samples based on data distribution is seen in the following map image. Stratified random sampling was applied to ensure proportional representation of students across six districts (Figure 3).

Figure 3

Instrument/procedure

The arrangement of the instrument was carried out in three stages. The first instrument was prepared based on theoretical studies through the approach of adaptation, modification, and extraction of scientific sources for the preparation of the framework of variables and dimensions as well as the arrangement of statement items and questions for cognitive achievement variables (Bichi et al., 2019; Stransky et al., 2023). Second stage is validated by experts, at this point through five experts to validate the content of the instrument, which is then analyzed using the Aiken approach (Correro-Bermejo et al., 2024; Martinez-Rincon et al., 2022; Pedraz-Petrozzi et al., 2021). The third stage is an empirical test, which is carried out to test whether the instrument is suitable for use (Kumlien et al., 2017; Larasati et al., 2020; Parmaningsih and Saputro, 2021). By involving 157 high school students who were different from the sample to be studied, after conducting a validity and reliability test using SPSS 24 and the results of the instrument used for the research stage. The questionnaire used to collect the research data uses Google Forms, and the questionnaire book is distributed. The scale used in assessing the questionnaire items is a Likert scale with an interval from 1 (disagree) to 4 (strongly agree).

The instrument underwent a linguistic adaptation process, including translation and back-translation to ensure cultural appropriateness. A pilot study was also conducted on a separate sample of 157 students to assess clarity and reliability prior to full deployment (Table 1).

Table 1

VariableDimensionNumber of items
Teacher’s learning characterProfessional competence5
Personal competence5
Learning planning5
Evaluation and improvement5
Personal efficacy5
Social efficacy5
Learning model5
Learning media5
MediationSchool climate10
Academic persistence10

List of measurement items on the research instrument.

Analysis data

The data obtained will be analyzed statistically to identify the relationship between the character dimension of school management and cognitive achievement influenced by the mediation of school climate and academic persistence (Tables 2, 3). The data were analyzed univariate and bivariate using SPSS 24, then reliabilities analysis and Confirmatory factor analysis (CFA) were carried out using the JASP 0.19 application, and at the model evaluation analysis stage, SMARTPLS 4 was used (Figure 4).

Table 2

AspectsDescriptionReferences
Cronbach alpha reliability
  • Less reliable: 0–0.2

  • Somewhat dense: >0.2–0.4

  • Moderately dense: >0.4–0.6

  • Reliable: >0.6–0.8

  • Very dense: >0.8

Hair et al. (2019)
Explanatory factor analysis (EFA)Prerequisite:
  • Bartlett’s Sphericity Test: <0.05

  • KMO: >0.5

  • Item loading factor: 0.3

Hair et al. (2019)

Model measurement estimation criteria.

Table 3

Goodness-of-fit indicatorCriteria
ChiSqr/df<2 (Good), <5 (Acceptable)
RMSEA<0.06 (good), <0.08 (Acceptable)
GFI>0.90 (good)
AGFI>0.90 (good)
PGFI>0.5 (Good)
SRMR<0.08 (Good)
NFI>0.95 (good)
TLI>0.9 (good)
CFI>0.90 (good)

Model measurement evaluation criteria.

Figure 4

Results

Although the Cronbach’s alpha value for the professional competence dimension was 0.673, which is slightly below the conventional threshold of 0.70, it remains acceptable for exploratory research (Shmueli et al., 2019). Moreover, all factor loadings within this dimension exceeded 0.6, and composite reliability values met the standard, supporting the internal consistency of the construct (Table 4).

Table 4

VariableDimensionItemOuter loadingsCronbach’s alphaAVE
Teacher’s learning characterProfessional CompetenceKG10.8020.6730.418
KG20.757
KG50.749
Personal CompetenceKG30.8740.7580.420
KG40.733
KG70.499
KG80.778
Learning planningKiG10.8320.7700.486
KiG20.736
KiG50.869
Evaluation and ImprovementKiG100.7170.8780.563
KiG90.786
Personal efficacyEG10.7370.6570.551
EG100.756
EG20.713
EG60.860
Social efficacyEG30.9330.7560.510
EG40.740
EG70.728
Learning ModelIG10.7670.7550.459
IG100.715
IG20.801
IG60.812
Learning MediaIG30.7990.6870.507
IG40.867
IG70.714
IG80.744
MediationSchool ClimateIS10.7130.6850.583
IS100.766
IS50.809
IS90.769
Academic PersistenceKA100.7340.6880.507
KA30.778
KA60.736
KA70.873

Model measurement estimates.

Regarding the AVE values, while a few dimensions such as professional competence and learning model fell just below the 0.50 threshold, this was offset by strong convergent validity through item loadings and satisfactory HTMT values (Tables 57). According to Afthanorhan et al. (2021), AVE values slightly below 0.5 can still be acceptable when supported by other reliability metrics (Figure 5).

Table 5

Academic persistenceCognitive performanceEvaluation and improvementLearning mediaLearning modelPersonal efficacyPersonality competenciesPlanning learningProfessional competenceSchool climateSocial efficacy
Cognitive performance0.042
Evaluation and improvement0.4840.010
Learning media0.6270.0670.862
Learning model0.0600.0530.6660.421
Personal efficacy0.6280.0320.8650.9860.458
Personality competencies0.5870.0430.9640.8320.5950.858
Planning learning0.5490.0160.9980.8520.6040.8450.980
Professional competence0.1980.0720.5960.6210.6790.6330.6000.586
School climate0.4820.0310.9490.8210.6810.8510.9400.8970.580
Social efficacy0.1290.0250.6680.4550.9370.5000.5990.5970.7060.669

HTMT values between research variables.

Source: SEM Output using SmartPLS 4; statistical method: CB-SEM.

Table 6

Academic persistenceCognitive performanceEvaluation and improvementLearning mediaLearning modelPersonal efficacyPersonality competenciesPlanning learningProfessional competenceSchool climateSocial efficacy
Academic persistence0.766
Cognitive performance−0.0431.000
Evaluation and improvement0.480−0.0180.723
Learning Media0.622−0.0510.8630.766
Learning Model0.0130.0470.6440.3980.719
Personal Efficacy0.636−0.0480.8560.9790.4420.711
Personality competencies0.620−0.0371.0110.8640.6030.9000.679
Planning learning0.543−0.0261.0170.8240.5930.8471.0280.642
Professional competence0.200−0.0560.5940.6270.6550.6430.6440.5880.676
School climate0.504−0.0180.9720.8270.6650.8611.0120.9260.5940.742
Social
Efficacy
0.1090.0290.6570.4490.9060.4930.6100.5890.6920.6690.768

Evaluation of the validity using the Fornell–Larcker criterion.

Source: SEM Output using SmartPLS 4; statistical method: CB-SEM.

Table 7

GoFEstimated modelInternpertasi
ChiSqr/df3.817Model fit
RMSEA0.053Model fit
GFI0.991Model fit
AGFI0.862Tidak fit
PGFI0.705Model fit
SRMR0.040Model fit
NFI0.901Model fit
TLI0.910Model fit
CFI0.924Model fit

CB-SEM measurement model fit index.

Figure 5

Discussion

This study analyzes the model of teacher learning character (personal competence, professional competence, learning design, evaluation of learning improvement, personal efficacy, social efficacy, learning model, learning media) on cognitive achievement influenced by school climate mediation and academic persistence. Teacher learning characteristics such as efficacy, competence, learning design, and learning evaluation positively affect cognitive achievement (Archambault et al., 2012; Bhai and Horoi, 2019; Flint et al., 2024; You et al., 2021). The relationship between social efficacy and personal efficacy from teachers also impacts improving student learning outcomes (Fan and Williams, 2018). In addition to direct relationships, the mediating influence of a positive school climate can also enhance the relationship between teachers and students, which supports a collaborative environment that will impact student achievement (Dickhäuser et al., 2021). The mediating role of students’ academic persistence, such as commitment and challenges, is a factor that supports the teacher’s learning character in improving student learning achievement (Kikas and Mägi, 2017). This study answers the variable gap that various dimensions of teacher learning character can directly affect teacher achievement and by mediating school climate and academic persistence.

Evaluation of the measurement of the teacher’s learning character model on cognitive achievement influenced by the mediation of school climate and academic persistence by confirming the compatibility of the model where the Goodness of fit (GoF) value indicates that this research model has a match with empirical data (Pho, 2024). The majority of indicators show that the model has met the criteria recommended for analysis in SEM. The main indicators of model compatibility are ChiSqr/df = 3.817, RMSEA = 0.053, GFI = 0.991, PGFI = 0.705, SRMR = 0.040, NFI = 0.901, TLI = 0.910, CFI = 0.924 indicating a fit value or meeting the required GoF value standard (Maia and Lima, 2021). Meanwhile, one indoctrination does not meet the value standard, namely AGFI = 0.862. Although the AGFI value is slightly below the ideal threshold ≥ 0.90, the overall model is still considered acceptable based on a combination of other strong indicators such as RMSEA, CFI, TLI, and SRMR. The slightly low AGFI value can be due to the complexity of the model that includes many constructs and mediation pathways, which can statistically affect the adjustment score. Some previous studies have also shown that AGFIs tend to be sensitive to sample size and the number of indicators in the model (Hair et al., 2019). Taking into account all the other GoF parameters that are in the good category, the validity of the model can still be maintained empirically and theoretically. Overall, this model meets the prerequisite GoF standards (Cho et al., 2020). This is in line with research conducted by Dević (2019). In its findings, it was stated that SEM’s analysis confirmed that teachers’ learning proficiency and learning characteristics significantly improved cognitive achievement and supported model conformity. The model hypothesized in various studies with data shows that the relationship between teacher behavior, school climate, and student learning outcomes is strong (Dević, 2019; Fan and Williams, 2018).

The study results show that the variables in this model have a significant direct influence on cognitive achievement and weak or insignificant relationships. Overall, the study’s results confirm that academic persistence, professional competence, social efficacy, and learning models significantly improve cognitive achievement. Teacher efficacy academically is a significant predictor of perseverance that can motivate students to improve their ability to seek good learning achievement (Lent et al., 2016). Collectively, it is confirmed that the variability of the dimension of teacher learning character has a negative impact on cognitive achievement, as found in Table 8 of the findings of direct influence. These factors are variable, reinforcing cognitive achievement (Andres, 2020).

Table 8

Indirect effectOriginal sample (O)T statistics (|O/STDEV|)p values
Academic persistence → Cognitive performance0.0122.170.030
Evaluation and improvement → Academic persistence0.8570.8420.399
Evaluation and improvement → Cognitive performance−0.1000.1000.317
Evaluation and improvement → School Climate−0.1280.2370.812
Learning Media → Academic persistence−1.5171.9480.041
Learning Media → Cognitive performance0.0151.7780.075
Learning Media → School Climate−0.3020.5490.583
Learning Model → Academic persistence−0.6631.9740.048
Learning Model → Cognitive performance1.45014.8300.000
Learning Model → School Climate0.1840.8890.374
Personal Efficacy → Academic persistence2.0292.3880.017
Personal Efficacy → Cognitive performance−0.0141.4200.155
Personal Efficacy → School Climate0.7531.3440.179
Personality competencies → Academic persistence−0.6360.5550.579
Personality competencies → Cognitive performance0.0980.7300.465
Personality competencies → School Climate−0.6901.1200.263
Planning learning → Cognitive performance0.0770.6570.511
Planning learning → School Climate1.2802.0080.045
Professional competence → Academic persistence0.0080.0320.974
Professional competence → Cognitive performance−1.31516.2120.000
Professional competence → School Climate−0.1231.0190.308
School climate → Academic persistence0.2020.2930.770
School climate → Cognitive performance−0.7006.1600.000
Social Efficacy → Academic persistence0.0920.2720.786
Social Efficacy → Cognitive performance0.2903.0500.002
Social Efficacy → School Climate0.1010.5010.616

A direct influence of teacher learning character dimmest on cognitive achievement.

Source: SEM Output using SmartPLS 4; statistical method: CB-SEM.

The study’s findings on school climate mediation show a high significance level. These findings confirm that a good school climate can shape the variables of teachers’ learning characteristics toward the achievement of teachers. The learning model teachers apply can impact cognitive achievement due to the influence of school climate (Sari and Kismiantini, 2023). According to Maxwell et al. (2017), the school climate plays a significant role in the achievement of students’ academic outcomes. A positive student discipline and behavior climate can improve students’ cognitive achievement (Teng, 2020). In addition, the physical condition and social conditions of the school are the effect of instructional leadership on student achievement (Dutta and Sahney, 2022) (Table 9). This study also found that personal competence and personal efficacy with the mediation of school climate significantly affect cognitive achievement. School climate mediates the relationship between teacher competence and efficacy on students’ cognitive performance (Fan and Williams, 2018; Velásquez and Castellanos, 2024; Zysberg and Schwabsky, 2021). Positive perception of the school climate increases self-efficacy, leading to an increase in contemplative achievement (Fan and Williams, 2018; Zysberg and Schwabsky, 2021). The school climate strongly influences the positive relationship between teacher competence, teacher efficacy, and contingency achievement, as shown in Table 10 of the findings of school climate mediation. These findings show that creating a school climate, both physical and social conditions, is important because it can encourage the improvement of teachers’ learning character, which can ultimately improve students’ cognitive achievement.

Table 9

Indirect effectOriginal sample (O)T statistics (|O/STDEV|)p-values
Social Efficacy → School climate → Academic persistence0.0200.1250.900
Social Efficacy → School climate → Cognitive performance−0.0772.2200.027
Evaluation and improvement → School climate → Academic persistence → Cognitive performance−0.0000.1010.912
Professional competence → School climate → Academic persistence → Cognitive performance−0.0000.3470.733
Social Efficacy → School climate → Academic persistence → Cognitive performance0.0000.2590.802
Personality competencies → School climate → Academic persistence → Cognitive performance−0.0200.5400.579
Learning Model → School climate → Academic persistence → Cognitive performance0.0000.3900.696
Evaluation and improvement → Academic persistence → Cognitive performance0.1091,7020.089
Learning Media → Academic persistence → Cognitive performance−0.1812.9890.002
Learning Model → Academic Persistence → Cognitive Performance−0.0882.0550.040
Personal Efficacy → Academic persistence → Cognitive performance0.2402.8290.004
Personality competencies → Academic persistence → Cognitive performance−0.0861.1400.254
Learning Media → School climate → Academic persistence → Cognitive performance−0.0110.3090.764
Professional competence → Academic persistence → Cognitive performance0.0000.0630.952
School climate → Academic persistence → Cognitive performance0.0220.5910.553
Social Efficacy → Academic persistence → Cognitive performance0.0190.4810.631
Planning learning → School climate → Academic persistence → Cognitive performance0.0320.8200.412
Personal Efficacy → School climate → Academic persistence → Cognitive performance0.0200.5710.558
Evaluation and improvement → School climate → Academic persistence−0.0260.0610.951
Evaluation and improvement → School climate → Cognitive performance0.0951.0820.280
Learning Media → School climate →Academic persistence−0.0610.1500.880
Learning Media → School climate → Cognitive performance0.2102.510.012
Learning Model → School climate → Academic persistence0.0370.2030.839
Learning Model → School climate → Cognitive performance−0.1314.0600.000
Personal Efficacy → School climate →Academic persistence0.1520.2820.778
Personal Efficacy → School climate → Cognitive performance−0.5314.9780.000
Personality competencies → School climate → Academic persistence−0.1390.2850.775
Personality competencies → School climate → Cognitive performance0.4805.2890.000
Planning learning → School climate → Academic persistence0.2580.3910.696
Planning learning → School climate → Cognitive performance−0.8896.8290.000
Professional competence → School climate → Academic persistence−0.0250.1770.860
Professional competence → School climate → Cognitive performance0.093.7710.000

Effects of school climate mediation and academic persistence.

Source: SEM Output using SmartPLS 4; statistical method: CB-SEM.

Table 10

Indirect effectPath coefficientp values
Social Efficacy → School climate → Academic persistence0.0200.625
Learning Model → School climate → Cognitive performance−0.1310.000
Personal Efficacy → School climate → Cognitive performance−0.5310.000
Personality competencies → School climate → Cognitive performance0.3330.000
Planning learning → School climate → Cognitive performance−0.8890.000
Professional competence → School climate → Cognitive performance0.0900.000
Learning Media → School climate → Cognitive performance0.1460.012
Social Efficacy → School climate → Cognitive performance−0.0770.027
Evaluation and improvement → School climate → Cognitive performance0.0950.194

Findings of School Climate Mediation.

This study fills the gap by revealing that school climate is important in mediating the relationship between teacher learning character and student achievement. Confirm that the variety of social efficacy, personality competence, and learning media significantly impact students’ cognitive achievement through the school climate. In addition, it highlights that not all aspects of Education contribute significantly to cognitive achievement, especially in the school climate mediation pathway. This gives a new perspective that the challenges of poor learning planning can have a negative impact on the academic environment and cognitive achievement (Table 11).

Table 11

Indirect effectPath coefficientp values
Learning Media → Academic persistence → Cognitive performance−0.1810.002
Personal Efficacy → Academic persistence → Cognitive performance0.1670.004
Learning Model → Academic persistence → Cognitive performance−0.0880.040
Evaluation and improvement → Academic persistence → Cognitive performance0.0760.089
Personality competencies → Academic persistence → Cognitive performance−0.0860.176
School climate → Academic persistence → Cognitive performance0.0220.384
Social Efficacy → Academic persistence → Cognitive performance0.0190.438
Professional competence → Academic persistence → Cognitive performance0.0000.661

Mediation findings academic persistence.

The study’s results showed that the confirmation of the factor of academic persistence as a mediator had a significant effect on learning media and cognitive achievement. This unexpected negative mediation effect may be attributed to cognitive overload caused by overuse or misapplication of learning media. Similar findings have been reported in prior studies, such as Law and Stock (2019), indicating that excessive media multitasking may negatively impact students’ focus and persistence. This can happen because there is a reciprocal relationship between student learning perseverance and teacher learning innovation, especially in making learning media (Wu et al., 2024). This strengthens the negative relationship trend, showing other influencing factors besides the learning media variable. Other findings show that personal efficacy mediated by academic persistence significantly affects cognitive achievement. Increased confidence in learning to students is influenced by student commitment and challenges as part of academic persistence to improve student academic achievement (Boudrenghien and Frenay, 2011; You, 2018). The mediation of significant academic persistence on cognitive achievement influences the learning model, but the relationship path is negative. This shows that specific learning models can improve students’ academic persistence, which ultimately leads to students’ Cognitive achievements. A complex and multifaceted relationship provides an adverse effect pathway between academic persistence and cognitive achievement (Law and Stock, 2019; Parry and Le Roux, 2018). However, persistence is generally beneficial and effective in improving cognitive achievement, but it depends on various factors, including the learning model, self-progress, stress management, and a conducive learning environment.

This study makes a new contribution that academic literature highlights the role of academic persistence as a mediator in the relationship between learning factors carried out by teachers or the character of teacher learning and students’ cognitive achievement. These findings show that learning media does not always positively impact cognitive achievement, especially if its use is ineffective and decreases students’ academic persistence. Teachers’ efficacy has a significant role or influence on academic persistence and student achievement. Meanwhile, a model of non-exhaustion can reduce students’ academic persistence and does not impact improving students’ cognitive achievement. These findings confirm that the school environment, such as school climate and academic persistence, play a significant role in determining the accomplishment of Shiva’s cognitive achievement if done well and positively. This study emphasizes that the importance of internal factors of teachers and students, as well as the formation of a climate, greatly determines students’ academic success. Increasing the competence of education providers, including teachers and school stakeholders, is a strategic factor that can affect student achievement.

Conclusion

This study confirms that the learning characteristics of teachers, school climate, and academic persistence are essential in improving cognitive prestige. These findings have significant implications for educators and policymakers and the importance of education in developing effective learning strategies. In particular, this study confirms that professional competence, problem-solving, learning design, and learning models applied by teachers directly and indirectly influence cognitive achievement. Therefore, it is important for teachers to continue to develop capacity through continuous professional training, especially in learning methods, classroom management, and student-centered learning approaches. Schools must also create a school climate that focuses on learning culture because it can encourage innovation and collaboration between teachers to increase the effectiveness of the learning process.

The role of school climate mediation as a mediator in improving student achievement. A conducive academic environment can strengthen the impact of the learning model applied by teachers. Schools need to ensure that the policies and learning practices implemented can create an atmosphere that supports academic engagement and students’ success. Strategic steps need to be taken to promote school leadership, improve supporting facilities, and manage positive social relations in the school environment.

The findings of this study also reveal that academic persistence is a key factor in bridging teachers’ personal efficacy and cognitive achievement. The higher the students’ academic persistence, the more excellent the opportunity for students to achieve optimal learning skills. Therefore, schools and education institutions must integrate learning strategies to increase students’ intrinsic motivation, such as challenge-based learning, self-reflection, and constructive feedback. In addition, the use of learning media must be in accordance with the needs of students because, in learning science, content must be adjusted to the learning media.

Overall, this study provides in-depth insights into the interaction between teachers, the school environment, and students in shaping academic success. The implications of these findings underscore the importance of a multidimensional approach in education that does not only focus on strengthening motivation and resilience. By applying the results of this research to education policies and practices, it is hoped that a learning system that is more adaptive, inclusive, and oriented toward developing student potential can be maximized.

Statements

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

SA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. HS: Supervision, Conceptualization, Validation, Writing – review & editing. ER: Supervision, Conceptualization, Validation, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

We would like to thank the North Maluku Provincial Education Office for giving us permission to conduct research.

Conflict of interest

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Summary

Keywords

teacher learning character, cognitive performance, school climate, academic persistence, CB-SEM model

Citation

Abukasim SM, Sutrisno H and Rohaeti E (2025) Unveiling the hidden mechanisms behind cognitive achievement: a structural equation modeling approach to teacher learning, academic persistence, and school climate. Front. Educ. 10:1590215. doi: 10.3389/feduc.2025.1590215

Received

09 March 2025

Accepted

08 August 2025

Published

20 August 2025

Volume

10 - 2025

Edited by

Syahril Syahril, Bandung State Polytechnic, Indonesia

Reviewed by

Enrique H. Riquelme, Temuco Catholic University, Chile

Anuphum Kumyoung, Loei Rajabhat University, Thailand

Huajie Shen, Fujian University of Technology, China

Updates

Copyright

*Correspondence: Sudarto M. Abukasim,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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