Pedagogical determinants of cognitive flexibility among pre-service school counselors in Indonesia: A multisite cross-sectional study

Abstract

BACKGROUND:

School counselors are mental-health service providers for students, working in complex and unpredictable situations that demand rapid adaptive responding. Cognitive flexibility is the core adaptive skill for this work, but how counselor education can cultivate cognitive flexibility in pre-service school counselors is still not well established. This study aims to investigate the association between the levels of cognitive flexibility and the pedagogical quality of counseling courses that have been implemented in counselor education programs in Indonesia.

MATERIALS AND METHODS:

A total of 846 pre-service school counselors who had completed theoretical and practical counseling courses under supervision were recruited in this multisite cross-sectional survey study (M_Age = 21.44, SD = 2.64). The respondents in our study came from 21 universities in Indonesia. An ordinal logistic regression model was used for statistical analysis.

RESULTS:

The study found that (a) the scores of theoretical and practical counseling courses under supervisors, (b) students’ ratings of their practical counseling course skills, (c) learning materials, (d) learning models, and (e) evaluation learning process significantly contribute to the levels of cognitive flexibility. Odds ratio analysis showedthat respondents who rated their counseling skills as “very good,” used “real cases” as learning material, adopted an “active learning model,” and used “self-reflection” for learning evaluation were more likely to demonstrate higher levels of their cognitive flexibility (CI = 95%).

CONCLUSIONS:

This study contributes evidence-based insights for counselor educators and university policy makers to enhance pre-service counselor students’ cognitive flexibility through real cases, active learning, and self-reflective evaluation.

Introduction

Cognitive flexibility is a fundamental element of the mental capacities that determine the adaptability of counselors in this complex modern era.[1] As frontline mental-health service providers for students, school counselors need cognitive flexibility as a main adaptive skill to respond effectively to student situations that are increasingly complex, unpredictable, and rapidly changing.[2,3] They are required to handle a variety of students’ mental health problems, which are currently experiencing an increasingly complex escalation with very diverse types,[4] such as academic problems,[5] anxiety,[6] emotional crises,[7] social pressure,[8] family conflicts,[9] and even self-identity issues.[10] These demands and the reality that counseling situations in schools are often complex and full of emotional content[11] can cause psychological burdens on school counselors, which have the potential to greatly reduce the quality of counseling services.[12]

Counselors should have the ability to quickly adapt, formulate efficient strategies, and maintain relevance in their treatment approach.[13] This is where cognitive flexibility plays a key role.[14] Cognitive flexibility enables counselors to employ adaptable and innovative thinking, modify their strategies to accommodate the specific needs of their clients, and effectively navigate unexpected situations by making appropriate decisions.[15] This competence is fundamental for professions that consistently interact with individuals experiencing mental health issues, including counselors.[16] Counselors who have high cognitive flexibility tend to be more adaptive in adjusting to various situations and demands and have high cultural sensitivity[17] and high empathy,[18] so they can provide more effective counseling services.[19]

The most effective way to enhance cognitive flexibility is by providing a constant stream of stimuli, such as a customized pedagogical process.[20,21] Implementing a diverse and challenging educational program can facilitate the development of cognitive flexibility and simultaneously help prospective counselors develop metacognitive strategies that are important for managing and organizing their thinking processes.[22] In many counselor education and health profession programs internationally, pedagogical approaches such as real-case-based learning, active learning strategies, and structured self-reflection are increasingly recommended to prepare students for complex and unpredictable practice demands.[23,24,25] But sadly, empirical evidence linking these specific pedagogical elements to cognitive flexibility, such as in Indonesia, remains limited. So the contribution of each aspect of learning to professional adaptive readiness is still not clearly mapped.

In Indonesian counselor education programs, most counseling practicum sessions are often much smaller than theoretical coursework. Even so, the professional performance of school counselors is largely determined by the depth and breadth of their supervised counseling practice experience.[26] This imbalance indicates the need to stimulate the students’ cognitive flexibility from the very beginning of their theoretical course through targeted pedagogical activities. Furthermore, national regulations define the pedagogical, personal, social, and professional competencies expected of a school counselor.[27] However, these regulations only focus on service delivery and performance expectations rather than on how university-level education counselor curricula should structure learning models, course materials, and assessment practices to cultivate cognitive flexibility as a core professional skill.[28,29] These gaps raise an urgent question: Which specific pedagogical processes in counselor education stimulate cognitive flexibility in Indonesian counselor students? This study addresses this question by empirically examining counseling courses’ pedagogical components as potential drivers of cognitive flexibility.

Therefore, this study makes several innovative contributions to the literature on cognitive flexibility in the context of counselor education. First, the study focuses specifically on pre-service school counselors as a crucial segment of the mental health workforce in low- and middle-income countries, a population that has received far less empirical attention in the cognitive flexibility literature. Second, in this study, the quality of counseling pedagogy is measured through concrete and modifiable aspects of teaching, such as the type of learning materials, learning approaches, and learning evaluations. To our knowledge, no previous research has examined elements of counseling learning as predictors of cognitive flexibility levels in this population.

Materials and Methods

Study design and setting

This study used a cross-sectional survey to identify the association between the quality of counseling pedagogy and the levels of cognitive flexibility among school counselor students. Data were collected at a single point in time through an online questionnaire that included variables of counseling pedagogical quality and a cognitive flexibility measurement instrument.

Study participants and sampling

We used multistage cluster random sampling to ensure comprehensive representation and practical feasibility in collecting nationwide data.[30] During data collection, field conditions indicated that counselor education programs were not evenly distributed in Indonesia’s provinces. Therefore, the sample size was limited to provinces that offered at least one counselor study program with at least “excellent” accreditation according to the National Accreditation Board for Higher Education of the Republic of Indonesia. Twenty-one universities offering counseling education programs across 15 provinces have agreed to participate in this study. Furthermore, to select participants, we applied only the inclusion criterion: pre-service school counselors who had completed all counseling courses, both theory and practice, under supervision. To determine the sample size, we used the population proportion formula, which considers the multistage cluster sampling design. To obtain optimal statistical power, we used assumptions with a confidence level (Z = 1.96), a margin of error of 5% (d = 0.05), and a conservative prevalence of 50% (P = 0.5).[31] Table 1 shows the baseline characteristics of data participants in this study.

Table 1.

Demographic respondent characteristics baseline

Province		University		n		(%)
South Sumatera		Univ. 1		39		4.61
Lampung		Univ. 2		56		6.62
Yogyakarta		Univ. 3		72		8.51
		Univ. 4		37		4.37
East Java		Univ. 5		38		4.49
		Univ. 6		52		6.15
		Univ. 7		50		5.91
Central Java		Univ. 8		30		3.55
		Univ. 9		21		2.48
Jakarta		Univ. 10		57		6.74
South Kalimantan		Univ. 11		40		4.73
East Kalimantan		Univ. 12		24		3.66
South Sulawesi		Univ. 13		33		3.90
North Sulawesi		Univ. 14		41		4.85
East Nusa Tenggara		Univ. 15		44		5.20
		Univ. 16		42		4.96
Bali		Univ. 17		37		4.37
		Univ. 18		34		4.02
North Maluku		Univ. 19		26		3.31
Maluku		Univ. 20		22		3.66
Papua		Univ. 21		28		3.90
		Total		846		100

Data collection tool and technique

Data collection was conducted online by involving counseling lecturers at each university who had been selected as research collaborators in collecting data. Counseling lecturers were responsible for explaining and assisting respondents during the data collection process. Data collection was carried out using a Google Form.

The Cognitive Flexibility Inventory (CFI), 20 items consisting of two factors (alternative and control), was employed to measure cognitive flexibility.[32] Each item is rated on a 7-point Likert scale ranging from “strongly disagree” to “strongly agree.” In this study, we first conducted forward-back translation for each item using two professional translators. Next, to verify the two factors, we conducted a confirmatory factor analysis (CFA) that showed five items with very low factor loadings and contributing to poor model fit were removed in the first test. In the second test, analysis of the 15-item model showed acceptable global fit (CFI = 0.931, TLI = 0.954, RMSEA = 0.047, SRMR = 0.044). All standardized factor loadings exceeded 0.50, indicating adequate item–factor relations. Meanwhile, internal consistency is satisfactory for the total scale and subscales (Cronbach’s α = 0.836 – 0.841, McDonald’s ω =0.828 – 0.834). A score <45 had a level of “low,” a score of 46–75 had a level of “middle,” and a score of 76–105 had a level of “high” cognitive flexibility.

To collect data on the pedagogical quality of counseling, we conceptualized it based on the higher education pedagogy literature that treats it as a multidimensional construct encompassing: (a) students’ perceptions, (b) learning materials, design, and evaluation, and (c) learning assessment.[33,34] We define three domains of pedagogical quality for counseling courses. The first domain measures students’ perceptions of the counseling pedagogical process, which uses a Likert-5 point scale (“very bad/dissatisfied” to “very good/satisfied”) and consists of three items: (1) students’ assessment of their mastery of counseling theory, (2) students’ assessment of their counseling practice skills, and (3) students’ satisfaction with the counseling lecture process they underwent. The second domain, learning course design, consists of five items: (1) learning material (“real case,” “non-real case,” and “mix-case”), (2) learning models (“non-associative learning,” “associative learning,” and “active learning”), (3) evaluation of learning process (“self-reflective” and “non self-reflective”), (4) online media learning usage (“yes”/”no”), and (5) type of learning references (“textbook” and “combined textbook and article research”). The third domain, the learning assessment report, consists of two items: (1) theoretical counseling course score and (2) practicum counseling course results score; both were under supervision scoring.

To determine whether the data on the pedagogical quality of the counseling course were worthy of further analysis, we conducted several tests on each domain. The first domain relates to the construct of student perception; an exploratory factor analysis (EFA) using principal axis factoring with oblique rotation suggested a one-factor solution for this domain (all items loaded ≥0.40, which explained 64.12% of the total variance). This domain also showed good internal consistency (Cronbach’s α = 0.881; McDonald’s ω = 0.864). For the second domain, a panel of four experts in counseling education and educational psychology reviewed the categories for relevance, representativeness, and clarity. Experts rated each category on a 4-point scale; all categories achieved satisfactory content validity coefficients (Aiken’s V ranging from 0.88 to 0.96). Meanwhile, to reduce the impact of cross-institutional grading differences and potential self-report inaccuracy in the third domain, we recoded these grades into three ordinal categories (low, medium, high), which were used as approximate indicators of students’ relative course achievement in the analyses. Taken together, the three domains provide psychometrically adequate indicators of pedagogical quality for use in the regression analyses.

Ethics considerations

The research protocol has undergone a series of reviews by the Ethics Review Board of Universitas Negeri Malang under the auspices of the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia, with approval number 20.06.8/UN32.14.2.8/LT/2024. All respondents provided informed consent to participate in the study and agreed to publish the research findings according to the approved ethical guidelines.

Statistical analysis method

We started the statistical analysis procedure by processing the summary statistics (frequencies, percentages, means, and standard deviation). Cross-tabulations were used to formulate the characteristics of respondents in the form of frequencies and percentages. Because the dependent variable, cognitive flexibility level, is coded as an ordered categorical variable (low, medium, high), we used an ordinal logistic regression model (proportional odds model) to examine the relationship between pedagogical indicators and cognitive flexibility.[35] This approach is recommended when the outcome has more than two ordered categories and avoids the loss of information that can arise if the variables are dichotomized.[36] Before fitting the model, we assessed multicollinearity using the Variance Inflation Factor (VIF). No independent variable exceeded a VIF of 10.00, which is commonly taken as indicating serious multicollinearity, suggesting that multicollinearity was not a major concern in our data.[37] The proportional odds assumption was evaluated with the test of parallel lines, where a nonsignificant result (P > 0.05) was interpreted as evidence that the proportional odds assumption was not violated.[38] Overall model fit was evaluated using the pseudo R² indices (McFadden and Nagelkerke).[39] Next, we estimated unadjusted (univariate) models and then entered all significant variables into a multivariable model for each predictor.[40] We report odds ratio (OR), 95% confidence interval (CI), and P values as measures of statistical and practical significance.[41] A predictor was considered significantly associated with cognitive flexibility if the OR was notably different from 1 (OR ≥1.50 or ≤0.67), the CI 95% did not include 1, and the P value was less than 0.05. Statistical procedures were executed using the JASP software 0.95.4 version.[42] Our data analysis and results are publicly available at https://osf.io/9a4gj/ Open Science Framework.

Results

Sample description

A total of 23 (2.72%) respondents were excluded from the analysis process because the data provided was incomplete and did not meet the requirements for inclusion. Thus, 823 (97.28%) respondents were eligible to be used in the data analysis process. Most respondents were female (69.14%, n = 569) and mostly came from the age group of 20–25 years old (79.70%, n = 656). Furthermore, based on university type, more than half of the participants came from public universities (60.02%, n = 494) and multireligion universities (64.64%, n = 532).

The level of Indonesian pre-service school counselors’ cognitive flexibility

Based on CFI, of the 823 respondents, 412 (50.06%) reported “middle” level cognitive flexibility. Meanwhile, 186 (22.60%) and 225 (27.33%) reported having “low” and “high” cognitive flexibility, respectively.

Factors associated with the level of cognitive flexibility

Based on the multicollinearity test, none of the independent variables had a Variance Inflation Factor (VIF) greater than 10.00, indicating that none of the independent variables was strongly correlated with the others. We continued the analysis by reviewing the fit of the regression model with the data obtained in this study by calculating the Goodness-of-Fit value, 0.81 >0.05. It means that the logistic regression model fits the research data. After the two initial steps were fulfilled, we performed a univariate logistic regression calculation to identify variables that partially influenced the prevalence of cognitive flexibility. The adequacy of the ordinal logistic regression model showed that the proportional odds assumption was not violated (test of parallel lines: χ²(df = 26) = 28.52, P = 0.28), indicating that the proportional odds model was appropriate for these data. Pseudo-R² indices suggested modest explanatory power (McFadden’s R² = 0.14, and Nagelkerke’s R² =0.36).

Regarding the quality of counseling pedagogical indicators, the univariate ordinal logistic regression analysis revealed that several variables were linked to the level of cognitive flexibility. Three of the variables were associated with very high significance (<0.001), including the average score of the theoretical and practical counseling course, students’ rate of mastery in the theoretical and practical counseling course, learning material, learning model, learning evaluation, online media learning usage, and learning references [Table 2].

Table 2.

Association between the quality of pedagogical counseling of Indonesian pre-service counselor students and cognitive flexibility levels

Characteristic		Category of Cognitive Flexibility						P
		Low n (%)		Middle n (%)		High n (%)
How would you rate your mastery of the counseling theory taught during the course?								0.053
Very Good		8 (5.71)		38 (27.14)		94 (67.14)
Good		28 (11.24)		126 (50.60)		95 (38.15)
Fair		37 (17.13)		154 (71.30)		25 (11.57)
Poor		26(22.22)		82 (70.09)		9 (7.69)
Very Poor		87 (86.14)		12 (11.88)		2 (1.98)
How would you rate your ability to practice counseling taught during the course?								0.005*
Very Good		6 (3.21)		41 (21.93)		140 (74.87)
Good		14 (8.92)		93 (59.24)		50 (31.85)
Fair		31 (14.35)		156 (72.22)		29 (13.43)
Poor		74 (40.88)		101 (55.80)		6 (3.31)
Very Poor		61 (74.39)		21 (25.61)		0
How satisfied were you with the counseling learning process you received?								0.717
Very Satisfied		8 (3.27)		56 (22.86)		181 (73.88)
Satisfied		11 (6.83)		120 (74.53)		30 (18.63)
Fair		64 (25.91)		170 (68.83)		13 (5.26)
Unsatisfied		76 (58.02)		54 (41.22)		1 (0.76)
Very Unsatisfied		27(69.23)		12 (30.77)		0
Theoretical counseling course average study result score under supervision								0.044*
81 – 100 (High)		2 (0.39)		295 (56.84)		222 (42.77)
70 – 80 (Medium)		56 (43.08)		71 (54.62)		3 (2.31)
0 – 69 (Low)		128 (73.56)		46 (26.44)		0
Practicum counseling course average study result score under supervision								0.001**
81 – 100 (High)		11 (2.16)		280 (55.01)		218 (42.83)
70 – 80 (Medium)		64 (33.51)		122 (63.87)		5 (2.62)
0 – 69 (Low)		111 (90.54)		10 (8.13)		2 (1.63)
Learning material analysis process								0.001**
Real Case		10 (2.33)		251 (58.37)		169 (39.30)
Mix Real Case		48 (21.33)		128 (56.89)		49 (21.78)
Non-Real Case		128 (76.19)		33 (19.64)		7 (4.17)
Types of learning models								0.002**
Active Learning		30 (5.52)		308 (56.72)		205 (37.75)
Associative Learning		88 (47.83)		78 (42.39)		18 (9.78)
Non-Associative Learning		68 (70.83)		26 (27.08)		2 (2.08)
Evaluation of the learning process								0.003**
Self-Reflective		81 (13.85)		283 (48.38)		221 (37.78)
Non-Self-Reflective		105 (44.12)		129 (20.33)		4 (1.68)
Online media learning usage to support the counseling course								0.036*
Yes		9 (1.88)		251 (52.29)		220 (45.83)
No		177 (51.60)		161 (46.94)		5 (1.46)
Learning references								0.042*
Only Textbooks		183 (56.83)		134 (41.61)		5 (1.55)
Combine Textbook & Article Research		3 (0.60)		278 (55.49)		220 (43.91)

Note: *P<0.05; **P<0.01

Main factor predicted cognitive flexibility levels

Regarding the pedagogical quality of counseling courses, Table 3 clearly illustrates several key variables significantly and positively predicted the level of cognitive flexibility, consisting of the average of the theoretical counseling course score (OR = 1.116, 95% CI [1.029 – 1.227], P = 0.028), the average of the practicum counseling course score (OR = 1.237, 95% CI [1.045 – 1.465], P = 0.019), students’ self-rating as “very good” in their theoretical mastery (OR = 1.231, 95% CI [1.045 – 1.448], P = 0.017), and students’ self-rating as “very good” in their practical skills (OR = 1.311, 95% CI [1.078 – 1.593], P = 0.008). Indonesian pre-service counselor students who used “real cases” as learning material were 34.4% (OR = 1.344, 95% CI [1.114 – 1.613], P = 0.002) more likely to have a high level of cognitive flexibility compared to others. Furthermore, students whose courses used self-reflection for learning evaluation had about 27.2% higher odds of being in a higher cognitive flexibility category (OR = 1.272, 95% CI [1.091 – 1.478], P = 0.001), and those exposed to active learning models had about 28.4% higher odds (OR = 1.284, 95% CI [1.027 – 1.515], P = 0.002) compared to others. Furthermore, we visualize the results of multivariate ordinal logistic regression in a forest plot [Figure 1].

Table 3.

Multivariate ordinal logistic regression analysis of cognitive flexibility among Indonesian pre-service counselor students, probabilistic prediction

Variable		Odds Ratio [95% CI]		P
The Average of the Theoretical Counseling Course Score		1.116 [1.029 – 1.227]		0.028*
The Average of the Practicum Counseling Course Score		1.237 [1.045 – 1.465]		0.019*
Students’ Rate of Their Theoretical Counseling Course Mastery
Very Good		1.231 [1.038 – 1.448]		0.017*
Good		1.011 [0.012 – 1.024]		0.044*
Poor		1.054 [0.698 – 1.592]		0.799
Very Poor		Ref.
Students’ Rate of Their Practicum Counseling Course Skills
Very Good		1.311 [1.078 – 1.593]		0.008**
Good		1.223 [1.041 – 1.429]		0.022*
Poor		1.061 [1.008 – 1.225]		0.043*
Very Poor		Ref.
Learning material
Real Case		1.344 [1.114 – 1.613]		0.002**
Mix Real Case		1.233 [1.009 – 1.501]		0.041*
Non-Real Case		Ref.
Learning Model
Active Learning		1.284 [1.027 – 1.515]		0.004**
Associative Learning		Ref.		0.053
Non-Associative Learning		1.923 [0.868 – 1.986]		0.031*
Learning Evaluation
Self-Reflection		1.272 [1.091 – 1.478]		0.001**
Non-Self-Reflective		Ref.
Online Learning Media Usage
Yes		1.298 [1.029 – 1.530]		0.004**
No		Ref.
Learning references
Combine Textbook and Article Research		1.116 [1.003 – 1.265]		0.045*
Textbook		Ref.
Cox and Snell, Nagelkerke R2		63.0, 73.4
Classification		78.3

Note: Ref.=Reference category. *P<0.05; **P<0.01

Figure 1.

Forest plot of ordinal logistic regression predicting cognitive flexibility among counseling students

Discussion

The study found that counseling course grades are students’ academic achievement that predicts the level of cognitive flexibility. It is in line with previous studies showing that cognitive flexibility has a direct, positive, and statistically significant effect on academic achievement among university students.[43,44] Through this study, we also found that students’ self-rating of their theoretical mastery and practicum counseling course skills was associated with the prevalence of cognitive flexibility. These self-ratings can be interpreted as indicators of perceived competence or academic self-confidence rather than purely objective performance. Beliefs about one’s ability to master counseling theory and skills are a key component of the self-efficacy framework.[45] In counseling situations, school counselor students who believe they are capable are more likely to approach complex and unpredictable student cases with persistence, evaluate various intervention strategies, and adapt their responses. This behavior reflects creative and adaptive problem-solving.[46,47]

Building on this study, the type of learning material in counseling courses that can positively predict the probability of increasing cognitive flexibility is the use of real cases. Using real cases as instructional materials makes students think about and work through difficult information directly, which helps them become more adaptable and able to think flexibly in real-life situations. This finding is in line with the theoretical framework construct that to stimulate cognitive flexibility in students through the learning process, a problem-based approach is needed in an ill-structured context.[48] Prior studies support that learning through real-life cases encourages cognitive flexibility because students learn to adapt to changing and uncertain situations.[49] Overall, these findings suggest that counseling courses that immerse students in real cases are concretely operationalizing core principles of experiential learning by cycling students through concrete experience, and active experimentation in ways that cultivate cognitive flexibility.

Self-reflective learning evaluation in counseling can be a predictive factor of the prevalence of cognitive flexibility among counseling students. Self-reflective evaluation encourages students to critically assess and reflect on their learning experiences, which increases self-awareness and adaptability.[50] A previous study also demonstrated that engaging in self-reflection enables students to gain insight into their own strengths and shortcomings.[51] When self-reflection is structured within learning evaluation, it enables students to integrate practical experience with theory and to develop metacognitive skills that are important for cognitive flexibility, in line with metacognitive perspectives on learning.[52] Conceptually, supporting adaptive expertise involves encouraging them to shift perspectives, reframe problems, and adapt strategies to new situations.[53] In the context of a counseling course that combines challenging content, skills practice, and reflective evaluation, it can foster adaptive skills in school counselor students. These pedagogical conditions encourage aspiring pre-service counselors beyond routine application of techniques toward a more flexible, principle-based capacity to recognize patterns, reframe client problems, and adapt interventions to the new and complex demands of school counseling.

Furthermore, learning models that promote active student engagement in the learning process, such as discussion activities, collaboration, and problem-solving, tend to enhance cognitive flexibility.[54] This finding aligns with previous research and has shown that actively engaging students can enhance their cognitive abilities to approach learning with a receptive mindset, investigate alternate ways, and adapt their thinking in response to different academic requirements.[55] Active learning methods, such as problem-based learning and case studies, promote cognitive flexibility by requiring students to adjust to novel material and evolving situations.[56] These active approaches also integrate varied modes of engagement, accommodating different learning preferences while simultaneously encouraging students to work beyond their dominant learning style.[57] This combination of alignment with and challenge to students’ learning styles may further stimulate flexible thinking in how they process counseling-related information. By prioritizing cognitive flexibility as a core cognitive skill, counselor education can enhance students’ awareness and adaptability in the learning process.[58] These capacities are likely to support their effectiveness and resilience as school counselors after graduation, when they face complex, unpredictable, and rapidly changing demands in real-world practice.

Cross-culturally, our findings align with previous studies in the context of counseling courses that reported a positive relationship between cognitive flexibility and academic outcomes, critical thinking, and self-efficacy among counseling students in several countries.[15,19] At the same time, this study expands the literature that focuses on pre-service counselors in a lower-middle-income, culturally collectivist setting where counselor education programs face large class sizes, heterogeneous institutional resources, and curriculum standards that do not yet explicitly target cognitive flexibility as a core competency. In such a context, the use of structured real-world cases,[59] active learning,[60] and reflective evaluation[61] may be practical options for improving cognitive flexibility among school counselor students. These parallels and contrasts suggest that while the underlying mechanisms may be shared across cultures, their implementation needs to be adapted to local institutional and cultural conditions. These approaches appear to be both theoretically grounded and practically feasible, providing a basis for revising school counseling curricula in Indonesia and potentially in other countries seeking to prepare future counselors for complex, rapidly changing professional demands.

Limitations and recommendations

The limitations of this study include its reliance on self-report measures, which are susceptible to bias, inaccurate respondent recall, emotional influences, situational bias, and social demands. Furthermore, the use of a cross-sectional study design limits the ability to draw conclusions and establish causal relationships between variables.

Recommendations for future research should employ longitudinal and experimental or R and D designs to more rigorously test causal pathways between specific pedagogical practices and the development of cognitive flexibility, including within explicitly designed counseling curricula. Comparing public and private universities, as well as multicultural and monocultural campuses, would also be beneficial to explore how institutional type and campus cultural diversity moderate the effectiveness of real-life case studies, active learning, and self-reflective evaluation in fostering cognitive development among prospective school counselors.

Conclusion

This study has demonstrated that several elements of pedagogical counseling courses are positively and significantly associated with cognitive flexibility levels among Indonesian school counselor students. Furthermore, five key pedagogical counseling course elements are linked to higher cognitive flexibility. These predictive elements are: (1) stronger academic performance in counseling theory and practicum courses, (2) students’ “very-good” self-assessments of their mastery of theoretical and practical counseling course skills, (3) the use of real-life cases as learning materials, (4) the use of active learning models, and (5) the use of reflective learning evaluations.

AI policy statement

The authors used AI-assisted tools only for language editing and proofreading support. The authors reviewed, verified, and approved all final content and take full responsibility for the accuracy and integrity of the manuscript.

Ethical approval

This study was approved by the Ethics Committee of Universitas Negeri Malang, Indonesia, under approval number T/4.2/UN34.9/KP.06.07/2024.

Author’s contributions

LCI: Conceptualization, methodology, investigation, data curation, formal analysis, and writing of the original draft. NH, MR and NE: Validation, methodology, and manuscript review and editing. NBM and WJS: Interpretation of findings and manuscript review and editing.

Data availability statement

The datasets generated and analyzed during the current study are not publicly available due to participant confidentiality and ethical restrictions, but may be made available from the corresponding author upon reasonable request and subject to appropriate approval.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

This research was funded by the Indonesian Education Scholarship (BPI), Center for Higher Education Funding and Assessment (PPAPT), Indonesia Endowment Fund for Education (LPDP), from the Ministry of Higher Education, Science, and Technology of Republic Indonesia (Grand ID: Number: 202209091929).