State Metacognition Inventory: validity and reliability in Arabic Educational Contexts

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This study aimed to validate an Arabic version of the State Metacognitive Inventory (SMI) for assessing metacognitive processes in Arabic educational contexts. Methods The study involved 240 Taif University students who completed electronic versions of the SMI and a sociodemographic questionnaire. The research process included rigorous translation procedures, followed by exploratory and confirmatory analyses to test the four-factor structure, internal consistency, and gender measurement invariance, along with examining correlations with personality traits for construct validation. Results Analysis confirmed the four-factor structure of metacognition, demonstrated strong internal consistency with alpha coefficients ranging from .87 to .95, established measurement invariance across genders, and showed appropriate correlations with personality traits supporting construct validity. Conclusions The Arabic version of the SMI has been validated as a reliable and psychometrically sound instrument for assessing metacognitive processes in Arabic educational settings, providing educators and researchers with a valuable tool for understanding metacognition in Arabic-speaking populations. metacognition psychometric validation Arabic adaptation educational assessment cross-cultural psychology Figures Figure 1 Background Metacognition, as an interdisciplinary concept, refers to the ability to reflect upon and to control one's cognitive processes, including knowledge, learning systems, and memory. This cognitive process is fundamental to daily life, enabling individuals to think, to adjust their mental tactics, and to make decisions. Recent research has established that metacognition reflects the capacity for making informed judgments and decisions [ 1 ]. Metacognition is integral for deliberative thinking, where individuals assess the reliability of their concepts and inferences [ 2 ]. In psychological and educational fields, metacognition reflects the ability to become self-determined learners by making informed judgments about mental states and processes [ 3 ]. Moreover, one study [ 4 ] explains that, when students aim to acquire a second language, metacognition facilitates this task by activating prior knowledge, identifying learning gaps, and setting goals to enhance language skills. According to recent study, metacognitive monitoring and control are essential for deliberative thinking because they help people assess the trustworthiness of their judgments and preserve coherence in their cognitive processes [ 2 ]. Overall, metacognition is an essential component of effective learning and cognitive growth, emphasizing the significance of metacognitive processes in both educational and psychological realms. Measuring metacognition in education involves two distinct approaches: offline and online methods. Offline approaches, such as self-reported questionnaires and interviews, are commonly used due to their ease of administration and cost-effectiveness [ 5 ]. Online approaches provide real-time data that can more accurately capture metacognitive processes during task performance [ 5 ]. According to a research paper [ 6 ], educators can identify students who might benefit from focused interventions to improve metacognitive skills in authentic educational contexts by measuring metacognitive knowledge, monitoring, and control through surveys and performance assessments. Among the multiple measurement tools, the State Metacognitive Inventory (SMI) provides a theoretical framework that is designed to assess metacognitive skills, including planning, monitoring, cognitive strategies, and awareness. Developed by O'Neil and Abedi [ 7 ], the SMI aims to provide a reliable and valid measure of these skills, particularly in educational settings. Their research indicates that the SMI's subscales demonstrate strong reliability and construct validity, making it a useful tool to evaluate students' metacognitive abilities. The research established that the SMI has high alpha reliability estimates (above .70) and is unidimensional, indicating that each subscale effectively measures a single construct of metacognition [ 7 ]. To date, the inventory has been adapted and validated for various cultural contexts, including Mexican and Turkish populations, demonstrating its reliability across different languages and cultures. With the Mexican adaptation [ 8 ], the study established validity through a rigorous process that included consultation with two metacognition experts and initial testing with 60 students before final validation with 908 university students. Using confirmatory factor analysis, they reduced the original inventory of 20 articles to 16 articles, retaining the four-dimensional structure (awareness, cognitive strategy, planning, and self-control) and the four-point Likert scale, and adding specific exam context explanations to articles. The Turkish adaptation [ 9 ] used a comprehensive translation protocol involving bilingual experts and a forward-backward translation method. The process included initial translation by a bilingual translator, review by two subject matter experts with US doctorates, and final review by three biology educators. Unlike the Mexican version, the Turkish adaptation retained all 20 original elements but changed the context from “test” to “project” to better accommodate project-based assessment. The validity of the adaptation was confirmed through interviews with 15 students from different study groups. Both adaptations demonstrate the flexibility of the inventory in cross-cultural applications while retaining its essential psychometric properties. The Mexican version achieved reliability with omega coefficients of 0.880 [ 8 ], while the Turkish version showed Cronbach's alpha values ​​between 0.69 and 0.73 for subscales [ 9 ]. These successful adaptations highlight different but equally valid approaches to cultural change, whether through structural adaptation or contextual adaptation, while maintaining the instrument's fundamental purpose of measuring metacognitive states. The theoretical framework underlying SMI focuses on the constructs of self-knowledge, self-regulation, and monitoring – essential components of metacognition. This framework has been validated through psychometric analyzes such as confirmatory factor analysis, which supports the three-factor structure of the SMI, although reliability varies by dimension, with self-knowledge showing the most robust reliability indices [ 10 ] Regarding the Arabic context, the development and validation of metacognitive assessment tools are particularly scarce. While the current tools are predominantly Western-based, research shows that learning and metacognitive processes have cultural influence. This situation creates a significant gap in Arabic educational settings, where simple translations of Western tools may fail to capture cultural nuances and educational traditions. This study aims to validate and to adapt the State Metacognitive Inventory for Arabic-speaking populations. Through translation procedures and psychometric analysis, the study seeks to develop a culturally appropriate Arabic version (SMI-Ar) while examining the tool's reliability, factor structure, and measurement equivalence across demographic groups at Taif University. Additionally, the construct validity of the SMI-Ar was investigated through relationships with personality traits using the Big-Five inventory-10 (BFI-10). The research investigation is guided by questions examining whether the SMI-Ar maintains its original four-factor structure, demonstrates adequate psychometric properties, shows measurement invariance across gender and academic levels, and correlates meaningfully with the Big Five personality traits. These objectives collectively aim to establish the SMI-Ar as a valid tool to assess metacognitive processes in Arabic educational settings. Methodology Participants For this study, the sample consisted of 240 volunteer students from Taif University:149 (62%) females and 91 (38%) males. The participants' ages ranged from 18 to 25 years (M = 20.3, SD = 1.7). The sample was stratified by academic level, with an approximately equal distribution between the lower level (4th level or below; 51.7%, n = 124) and upper level (5th level and above; 48.3%, n = 116). The inclusion criteria were being native Arabic speakers and being 18 years old with informed consent to participate in this study. The number of participants was determined using the subject-to-items ratio method (20 items, 10:1 ratio, plus 20% for potential missing value). 2. Measures a. State Metacognitive Inventory (SMI) The original version (English) of the State Metacognitive Inventory (SMI) was designed to measure metacognitive states during a specific learning situation [ 7 ]. The questionnaire consists of four dimensions (awareness, cognitive strategy, planning, and self-checking), with five self-report items for each dimension using a 4-point Likert scale ranging from 1 (not at all typical for me) to 4 (very typically me). A higher score indicates excellent metacognitive awareness and regulation. a. Demographic questionnaire This survey was designed to collect essential information from this study sample. These data included age, gender, and academic level (categorized as lower level: ≤4th level and upper level: ≥5th level). b. The Big Five Inventory − 10 (BFI − 10) The Big Five Inventory-10 (BFI-10), Arabic version [ 11 ], was used to assess predictive validity due to its brevity and efficiency. The survey consists of self-report items and had five major personality dimensions (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness). The participants’ answers were rated on a 5-point Likert scale (1 = Strongly disagree, to 5 = Strongly agree). 2. Translation process The primary version of the SMI-Ar was developed via a back-translation process. Two independent bilingual experts in education and psychology (Arabic and English) translated the original questionnaire from English to classic Arabic. The new version was subjected to back-translation by five other bilingual experts in order to verify content equivalence and cultural adaptation. In the next stage, the pre-final version was tested with a target sample of 30 people (age 18–25). The results revealed that 92% of the participants found the 20 items to be clearly and culturally appropriate. At the same time, 8% of the participants found ambiguity with the Arabic terminology for 3 items (7, 12, and 15). Based on this feedback, these points were revised and minor changes were made. Finally, this tool with its 20 items was refined, and a definitive version was approved. 3. Data analyses: To evaluate the psychometric propriety of the SMI-Ar, a set of statistical analyses SPSS and AMOS (V.26) was used. Descriptive statistics (mean, standard deviation, skeweness and ketosis) were conducted to examine the data's distribution characteristics. For internal ratability, Cronbach’s alpha coefficient was calculated for each dimension, with the acceptable value at 0.70 level. Concerning the factorial validity, the study conducted both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) with oblique target rotation and the maximum likelihood estimator. The study examined the sampling adequacy via Kaiser-olkin (KMO, Acceptable value at 0.70) and Bartlett’s test of sphericity (statistically significant at P < .05). The CFA was performed to confirm the SMI-AR's factor structure with acceptance of model fits based on the following criteria: chi-square/df ratio ( 0.40 and were statistically significant at p ≤ 0.001 were considered for interpretation. For the invariance measurement, the study performed multi-group confirmatory factor analysis (MGCFA) based on gender (males vs females). It tested three levels: configural (same factor structure), metric (equal factor loading), and scalar invariance (equal intercept). Standard indices were used for the model's fit assessment (χ², CFI, TLI, RMSEA, and SRMR), in order to evaluate the invariance for females and males. Regarding discriminant validity, the study investigated the relationship between the SMR-Ar and the BFI-10-Ar dimensions using a correlation analysis. The expectation was a moderate to strong correlation (r < 30) between the different tool’s variables. 4. Data collection procedures: For this study, approval was obtained from Taif University’s Research Ethics Committee (approval number HAPO-02-T-105). The data collection was conducted between September and November 2024, using online administration with Google forms. The survey link was distributed via official university Email and via student group. All participants should provide informal consent in the beginning, before completing the questionnaire. Results a. Descriptive statistic and Ratability Assessment: Table 1 shows the means and standard deviations for each item across its subscale. Preliminary analyses for the 240 participants, with no missing data, demonstrated a normal distribution with an acceptable range for skewness values (-0, 86 to -0, 13) and for kurtosis values (-0.95 to 0.18). Moreover, the mean scores ranged from 2.89 to 3.36, with standard deviations between 0.65 and 0.92, signifying adequate variability. Regarding the internal consistency of the SMI’s Arabic version, the result showed strong psychometric properties. Cronbach’s alpha coefficient was excellent with score of .95 for both the Awareness and cognitive strategy subscales, .89 for Planning, and 87 for Self-checking. Table 1 Descriptive Statistics for the SMI-Ar Items and Cronbach’s Alpha . items Means SD Skewness Kurtosis Cronbach's α Awareness 3,00 2,99 -,355 -,221 ,95 1. I was aware of my own thinking. 2,89 ,841 -,133 -,915 5. I was aware of which thinking technique or strategy to use and when to use it. 2,92 ,844 -,177 -,905 9. I was aware of the need to plan my course of action. 2,92 ,852 -,175 -,951 13. I was aware of my ongoing thinking processes. 2,98 ,901 -,367 -,880 17. I was aware of my trying to understand the test questions before I attempted to solve them. 2,90 ,837 -,154 -,880 Cognitive Strategy 3,07 ,67 -,433 -,498 ,95 3. I attempted to discover the main ideas in the test questions. 3,02 ,894 -,431 -,828 7. I asked myself how the test questions related to what I already knew. 3,17 ,848 -,707 -,332 11. I thought through the meaning of the test questions before I began to answer them. 3,13 ,864 -,559 -,694 15. I used multiple thinking techniques or strategies to solve the test questions. 3,07 ,902 -,562 -,691 19. I selected and organized relevant information to solve the test questions. 3,12 ,803 -,572 -,333 Planning 3,00 3,07 -,388 -,691 ,89 4. I tried to understand the goals of the test questions before I attempted to answer. 3,28 ,654 -,360 -,734 8. I tried to determine what the test required. 3,20 ,824 -,794 -,019 12. I made sure I understood just what had to be done and how to do it 3,32 ,781 -,834 -,194 16. I determined how to solve the test questions. 3,34 ,671 -,520 -,742 20. I tried to understand the test questions before I attempted to solve them. 3,36 ,718 -,860 ,181 Self Checking 3,12 ,62 -,566 ,104 ,87 2. I checked my work while I was doing it. 3,17 ,801 -,557 -,542 6. I corrected my errors. 3,10 ,759 -,343 -,683 10. I almost always knew how much of the test I had left to complete. 3,15 ,848 -,615 -,526 14. I kept track of my progress and, if necessary, I changed my techniques or strategies. 3,08 ,834 -,463 -,679 18. I checked my accuracy as I progressed through the test. 3,15 ,838 -,582 -,579 b. Confimatoty factor analysis (CFA) This section of the statistical analysis examines the structure of the SMI’s Arabic version and its adjustment to the theoretical model of metacognition. For this task, the study considered one unique model: a multidimensional model with four correlated, first-order factors. Both exploratory (EFA) and confirmatory (CFA) factor analyses were conducted. The results showed excellent sampling adequacy (KMO = 0.89), as well as the Bartlett’s test of Sphericity (χ² = 351.220, df = 164, p < .001), which indicated good data factorability. Table 2 Model Fit Indices for Confirmatory Factor Analysis Measure Description The level for a good fit Factors χ² Chi-square -- 351.220 df Degree of freedom -- 164 p Probability value of χ² ⩽.005 < .001 X²/ddl The discrepancy divided by degrees of freedom Less than 5 2.142 CFI Comparative fit index ⩾.90 .960 TLI Tucker-Lewis index ⩾.95 .954 NFI Normed fit index ⩾.95 .928 RMSEA Root mean square error of approximation < .08 .069 SRMR Standardized root-mean-square residual < .08 .024 GFI Goodness of fit index ⩾.90 .917 AGFI Adjusted goodness of fit index ⩾.90 .944 The EFA was performed by using component analysis with Varimax Rotation. The results revealed a four factor Structure explaining 71.73% of the total variance. The first factor was “cognitive strategy” and accounted for 21.63% of the variance. The second factor was “awareness” and explained 21% of the variance. Factor three “self-checking,” contributed 15% of the variance while the fourth factor, “Planning and explain only 13% of the variance. In the second stage, the CFA analysis confirmed the four-factor structure, with satisfactory fit indices as presented in Table 2 : χ²/df = 2.142, CFI = 0.960, RMSEA = 0.069 (90% CI [0.059, 0.079]), GFI = 0.878, and NFI = 0.928. Factor loadings in the CFA model were substantial: Awareness (0.63-1.00), Cognitive Strategy (0.92–1.08), Planning (0.53-1.00), and Self-Checking (0.78-1.00). Inter-factor correlation demonstrated a good relationship among Awareness, cognitive strategy and self-checking (all, r > .34) while “planning” showed relatively moderate correlations with compared to the other factors. This pattern suggested that while Awareness, Cognitive Strategy, and Self-Checking represent related metacognitive processes, Planning might operate as a more distinct component. Measurement invariance: Table 3 Measurement Invariance Results Across Gender Model χ² df CFI RMSEA SRMR CFI RMSEA Configural 515.33 328 .943 .062 .048 -- -- Metric 533.45 344 .941 .060 .052 − .002 − .002 Scalar 562.78 364 .937 .061 .054 − .004 .001 Table 3 presents the results relative to the invariance measurement based on gender. The Arabic version of the SMI proved to be remarkably consistent in its discriminant validity. The configural model achieved satisfactory model fit indices, supporting three key points: equivalent factor structure, equal factor loading, and equal item intercepts between the male and female populations (ΔCFI ≤ .01, ΔRMSEA ≤ .015). These results suggested that the SMI-Ar measures the same constructs in the same way across male and female students. Discriminant validity The results of a Pearson correlation between the four dimensions of the SMI-Ar and those of the BFI-10-Ar revealed a significant relationship between all variables for trait personality and meta-cognition, with the exception of “planning” which showed no significant correlation with the other variables. The relationship between metacognitive strategy and personality trait demonstrated acceptable moderate correlation. In contrast, “self”- checking” appeared as the central variable that formed a robust and positive correlation with both cognitive strategy (r-.622**) and personality trait (r = .566**). Table 4 Descriptive Statistics and Bivariate Correlations with BFI. Mean SD 1 2 3 4 5 6 7 8 9 Awareness 2,92 ,78 1 Congnitive_strategy 3,10 ,79 ,518 ** 1 Planing 3,29 ,54 ,322 * -,017 1 Self-cheking 3,12 ,62 ,556 ** ,622 ** ,006 1 Extraversion 3,45 ,79 ,347 ** ,317 ** ,052 ,358 ** 1 Agreeableness 2,61 ,93 -,012 -,063 ,032 -,063 ,147 * 1 Conscientiousness 3,28 ,73 ,237 ** ,301 ** -,002 ,424 ** ,272 ** ,332 ** 1 Neuroticism 3,42 ,78 ,210 ** ,345 ** -,007 ,322 ** ,342 ** ,268 ** ,389 ** 1 Openess_to_exp 3,51 ,88 ,329 ** ,306 ** ,087 ,400 ** ,344 ** ,275 ** ,546 ** ,411 ** 1 Discussion This study set out to assess the psychometric properties of the Arabic version of the State Metacognitive Inventory (SMI-Ar), in order to delete the gap for measuring state metacognition in an Arabic context. To this end, the original English version of the SMI was translated to classic Arabic following the back- translation method [ 12 ]. The Arabic version with 20 items, revealed several key findings. First, the analysis noted robust internal consistency coefficients (.87-.95), which were particularly superior to the ones reported in previous cultural adaptations, including Turkish version (α ≈ .70) [ 9 ] and a Mexican adaptation [ 8 ]. On the other hand, these results aligned with the original version [ 7 ]. Based on the results, the analysis affirmed the excellent reliability of the SMI-Ar and the tool's psychometric integrity across linguistic and cultural validations. Another significant aspect that emerged from this study concerns the SMI’s four-factor structure. Examining the adjustment of the data to this model via EFA and CFA provided empirical support for the SMI’s universality across cultures. Interestingly, the analysis revealed an excellent adjustment of the data to this model, with robust model fit indices. In the factor-structure confirmation with EFA and CFA, the four factors explained 71.73% of the total of invariance, a higher percentage compared to the original version (65%) [ 7 ] and the Mexican version (68%) [ 8 ]. Beyond these initial observations, the confirmatory factor analysis provided statistical evidence for the four-factor structure, with the fit indices notably surpassing or aligning with the original, Turkish and Mexican versions. Specifically, the model achieved a chi-square/df ratio of 2.142 and showed a better fit than the Mexican adaptation (χ²/df = 2.89; [ 8 ]). For the comparative fit index (CFI = .96) exceeds, also, Turkish version (CFI = .92; [ 9 ]) and Mexican version (CFI = .94; [ 8 ]). This observation suggested a more stable factor in the Arabic version. This result is likely due to the rigorous translation process and careful cultural adaptation procedures employed in its development. Moreover, in Arabic educational contexts, metacognitive assessment tools are scarce and predominantly Western-based, failing to capture local cultural nuances in learning processes and institutional structures like gender segregation. Perhaps most notably, the Root Mean Square Error of Approximation (RMSEA = .069; 90% CI [0.059, 0.079]) indicates good model fit, comparing favorably with the Turkish adaptation (RMSEA = .076) and showing slight improvement over the Mexican version (RMSEA = .071). This fit enhancement is particularly meaningful given study’s [ 7 ] original emphasis on the importance of precise error estimation for metacognitive measurement. These comparative results suggest that the SMI-Ar not only maintains, but also potentially enhances the psychometric integrity of the original instrument in the Arabic context. The superior fit indices might be attributed to the rigorous translation process and careful cultural adaptation procedures which were employed for in this study, as emphasized by other study discussion about cross-cultural assessment adaptation [ 5 ]. Another key finding, concern the invariance measurement. A study supported the importance of valid measurement tools across different demographic groups [ 6 ]. For this study, the examination of this point was conducted via a gender variable, in accordance with the original version [ 7 ] and other versions (Turkish: [ 9 ] and Mexican: [ 8 ]). The configural, metric, and scalar invariance indicated that the SMI-Ar functions equivalently for both male and female students, ensuring a fair assessment regardless of gender. While the Mexican version had partial invariance [ 8 ], the findings demonstrate full configural, metric, and scalar invariance (ΔCFI ≤ .01, ΔRMSEA ≤ .015). This result attested to a robust invariance for the SMI-Ar, given the gender-segregated nature of many educational institutions in Arabic-speaking countries. On the other hand, the correlational patterns between metacognitive components and personality traits revealed several interesting relationships. The moderate correlations between self-checking and conscientiousness (r = .424) and between openness to experience and metacognitive awareness (r = .329), aligned with theoretical expectations about the intersection of personality and the metacognitive processes [ 13 ]. However, the weak correlations with agreeableness suggested that the SMI-Ar successfully measures distinct constructs for general personality traits, supporting the tool’s discriminant validity. The strong interconnections observed among self-checking, cognitive strategy use, and awareness (r > .50) mirrored the findings of other study [ 14 ], suggesting that these components form a cohesive metacognitive framework. However, the relative independence for planning processes may indicate that certain metacognitive components operate more autonomously in specific cultural contexts [ 5 ]. From an educational perspective, these findings have significant implications. The strong psychometric properties support using the SMI-Ar to assess students' metacognitive abilities in Arabic educational settings, addressing the need for culturally appropriate assessment tools [ 3 ]. The distinct nature of the planning factor suggests that educators might need to pay special attention to how planning strategies are taught and assessed in Arabic cultural contexts, especially when considering emphasis on cognitive adaptability across different learning environments [ 4 ]. Moreover, the robust correlation patterns between the metacognitive components and the personality traits provide valuable insights for educational interventions. Following study framework on metacognition and abstract thought [ 2 ], these relationships suggest that metacognitive development might be influenced by individual personality characteristics, particularly in terms of conscientiousness and openness to experience. This understanding could help educators modify metacognitive instruction to the students' individual differences. Conclusion The Arabic version of the State Metacognitive Inventory (SMI-Ar) illustrated strong psychometric properties and excellent cultural adaptation for Arabic-speaking populations with established reliability, validity, and measurement invariance across gender. This questionnaire provided a valuable tool to assess metacognition processes for specific learning situations in an Arabic educational context. Future research should focus on exploring the instrument's predictive validity and investigating cultural influences on metacognitive planning processes. Declarations Ethics approval and consent to participate: For this study, approval was obtained from Taif University’s Research Ethics Committee (approval number HAPO-02-T-105). Availability of data and materials: The datasets used and analyzed during the current study are available from the corresponding author at [email protected] upon reasonable request. Competing interests : The authors declare that they have no competing interests. Funding : Deanship of Graduate Studies and Scientific Research, Taif University Authors' contributions: SMA and OHA conducted literature review, wrote introduction, and participated in questionnaire translation from English to Arabic. WB performed statistical analyses, interpreted results, and coordinated the back-translation process from Arabic to English. HZ and OHA wrote the discussion section and supervised the linguistic validation process. All authors participated in questionnaire validation procedures, contributed to references, critically reviewed the final manuscript, and approved the final version. *Corresponding author: WB Acknowledgements : The authors would like to acknowledge Deanship of Graduate Studies and Scientific Research, Taif University for funding this work. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References Metcalfe J. Metacognition. Oxford University Press eBooks. 2024;1485–504. Shea N. Metacognition of Inferential Transitions in advance. J Philos. 2024. Murat Tezer. Cognition and Metacognition in Education. IntechOpen eBooks. 2024. Bukowski L. Cognitive Dependability Engineering. 2023. Veenman MVJ, van Cleef D. 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Educación. 2022;28(2):1–14. Alansari B, The Big Five Inventory (BFI). Reliability and validity of its Arabic translation in non clinical sample. Eur Psychiatry. 2016;33(S1):S209–10. Sperber AD. Translation and validation of study instruments for cross-cultural research. Gastroenterology. 2004;126(1):S124–8. Faustino B, Branco Vasco A, Oliveira J, Lopes P, Fonseca I. Metacognitive self-assessment scale: psychometric properties and clinical implications. Appl Neuropsychology: Adult. 2019;28(5):1–11. Immekus JC, Imbrie PK. Educ Psychol Meas. 2008;68(4):695–709. Dimensionality Assessment Using the Full-Information Item Bifactor Analysis for Graded Response Data. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5767392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398883732,"identity":"26af27a8-86a3-4db3-a9eb-7b284dcc7439","order_by":0,"name":"Sarah M Alajlan","email":"","orcid":"","institution":"Department of Leadership and Educational Policies Taif University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"M","lastName":"Alajlan","suffix":""},{"id":398883733,"identity":"241b4f6b-ee8c-429e-aa5c-bf29f0c9d6bd","order_by":1,"name":"Wissal Boughattas","email":"data:image/png;base64,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","orcid":"","institution":"Ministry of Youth and Sports, Tunis","correspondingAuthor":true,"prefix":"","firstName":"Wissal","middleName":"","lastName":"Boughattas","suffix":""},{"id":398883734,"identity":"ea5c3cd6-6bba-4c1f-88fa-79038fb6fb7f","order_by":2,"name":"Obaidalah H Aljohani","email":"","orcid":"","institution":"Department of Leadership and Educational Policies Taif University","correspondingAuthor":false,"prefix":"","firstName":"Obaidalah","middleName":"H","lastName":"Aljohani","suffix":""},{"id":398883735,"identity":"e15ae330-b944-4272-a58e-e6090558dd0a","order_by":3,"name":"Hela Znazen","email":"","orcid":"","institution":"Manouba University","correspondingAuthor":false,"prefix":"","firstName":"Hela","middleName":"","lastName":"Znazen","suffix":""}],"badges":[],"createdAt":"2025-01-05 10:53:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5767392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5767392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73292531,"identity":"51523496-dbe8-4344-8b65-74896301bcbd","added_by":"auto","created_at":"2025-01-08 14:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConfirmatory Factor Analysis Model of the Arabic Version of State Metacognitive Inventory (SMI-Ar)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5767392/v1/207f90b1661dde44664edddc.png"},{"id":73792273,"identity":"7a095cc1-7d93-455c-b8ce-6c008701e9d2","added_by":"auto","created_at":"2025-01-14 17:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5767392/v1/8f24ef93-f7ce-42b2-8242-daedcb3c49f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"State Metacognition Inventory: validity and reliability in Arabic Educational Contexts","fulltext":[{"header":"Background","content":"\u003cp\u003eMetacognition, as an interdisciplinary concept, refers to the ability to reflect upon and to control one's cognitive processes, including knowledge, learning systems, and memory. This cognitive process is fundamental to daily life, enabling individuals to think, to adjust their mental tactics, and to make decisions. Recent research has established that metacognition reflects the capacity for making informed judgments and decisions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metacognition is integral for deliberative thinking, where individuals assess the reliability of their concepts and inferences [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn psychological and educational fields, metacognition reflects the ability to become self-determined learners by making informed judgments about mental states and processes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, one study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] explains that, when students aim to acquire a second language, metacognition facilitates this task by activating prior knowledge, identifying learning gaps, and setting goals to enhance language skills. According to recent study, metacognitive monitoring and control are essential for deliberative thinking because they help people assess the trustworthiness of their judgments and preserve coherence in their cognitive processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Overall, metacognition is an essential component of effective learning and cognitive growth, emphasizing the significance of metacognitive processes in both educational and psychological realms.\u003c/p\u003e \u003cp\u003eMeasuring metacognition in education involves two distinct approaches: offline and online methods. Offline approaches, such as self-reported questionnaires and interviews, are commonly used due to their ease of administration and cost-effectiveness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Online approaches provide real-time data that can more accurately capture metacognitive processes during task performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to a research paper [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], educators can identify students who might benefit from focused interventions to improve metacognitive skills in authentic educational contexts by measuring metacognitive knowledge, monitoring, and control through surveys and performance assessments.\u003c/p\u003e \u003cp\u003eAmong the multiple measurement tools, the State Metacognitive Inventory (SMI) provides a theoretical framework that is designed to assess metacognitive skills, including planning, monitoring, cognitive strategies, and awareness. Developed by O'Neil and Abedi [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the SMI aims to provide a reliable and valid measure of these skills, particularly in educational settings. Their research indicates that the SMI's subscales demonstrate strong reliability and construct validity, making it a useful tool to evaluate students' metacognitive abilities. The research established that the SMI has high alpha reliability estimates (above .70) and is unidimensional, indicating that each subscale effectively measures a single construct of metacognition [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, the inventory has been adapted and validated for various cultural contexts, including Mexican and Turkish populations, demonstrating its reliability across different languages and cultures. With the Mexican adaptation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the study established validity through a rigorous process that included consultation with two metacognition experts and initial testing with 60 students before final validation with 908 university students. Using confirmatory factor analysis, they reduced the original inventory of 20 articles to 16 articles, retaining the four-dimensional structure (awareness, cognitive strategy, planning, and self-control) and the four-point Likert scale, and adding specific exam context explanations to articles.\u003c/p\u003e \u003cp\u003eThe Turkish adaptation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] used a comprehensive translation protocol involving bilingual experts and a forward-backward translation method. The process included initial translation by a bilingual translator, review by two subject matter experts with US doctorates, and final review by three biology educators. Unlike the Mexican version, the Turkish adaptation retained all 20 original elements but changed the context from \u0026ldquo;test\u0026rdquo; to \u0026ldquo;project\u0026rdquo; to better accommodate project-based assessment. The validity of the adaptation was confirmed through interviews with 15 students from different study groups.\u003c/p\u003e \u003cp\u003eBoth adaptations demonstrate the flexibility of the inventory in cross-cultural applications while retaining its essential psychometric properties. The Mexican version achieved reliability with omega coefficients of 0.880 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while the Turkish version showed Cronbach's alpha values ​​between 0.69 and 0.73 for subscales [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These successful adaptations highlight different but equally valid approaches to cultural change, whether through structural adaptation or contextual adaptation, while maintaining the instrument's fundamental purpose of measuring metacognitive states. The theoretical framework underlying SMI focuses on the constructs of self-knowledge, self-regulation, and monitoring \u0026ndash; essential components of metacognition. This framework has been validated through psychometric analyzes such as confirmatory factor analysis, which supports the three-factor structure of the SMI, although reliability varies by dimension, with self-knowledge showing the most robust reliability indices [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eRegarding the Arabic context, the development and validation of metacognitive assessment tools are particularly scarce. While the current tools are predominantly Western-based, research shows that learning and metacognitive processes have cultural influence. This situation creates a significant gap in Arabic educational settings, where simple translations of Western tools may fail to capture cultural nuances and educational traditions.\u003c/p\u003e \u003cp\u003eThis study aims to validate and to adapt the State Metacognitive Inventory for Arabic-speaking populations. Through translation procedures and psychometric analysis, the study seeks to develop a culturally appropriate Arabic version (SMI-Ar) while examining the tool's reliability, factor structure, and measurement equivalence across demographic groups at Taif University. Additionally, the construct validity of the SMI-Ar was investigated through relationships with personality traits using the Big-Five inventory-10 (BFI-10). The research investigation is guided by questions examining whether the SMI-Ar maintains its original four-factor structure, demonstrates adequate psychometric properties, shows measurement invariance across gender and academic levels, and correlates meaningfully with the Big Five personality traits. These objectives collectively aim to establish the SMI-Ar as a valid tool to assess metacognitive processes in Arabic educational settings.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eFor this study, the sample consisted of 240 volunteer students from Taif University:149 (62%) females and 91 (38%) males. The participants' ages ranged from 18 to 25 years (M\u0026thinsp;=\u0026thinsp;20.3, SD\u0026thinsp;=\u0026thinsp;1.7). The sample was stratified by academic level, with an approximately equal distribution between the lower level (4th level or below; 51.7%, n\u0026thinsp;=\u0026thinsp;124) and upper level (5th level and above; 48.3%, n\u0026thinsp;=\u0026thinsp;116). The inclusion criteria were being native Arabic speakers and being 18 years old with informed consent to participate in this study. The number of participants was determined using the subject-to-items ratio method (20 items, 10:1 ratio, plus 20% for potential missing value).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Measures\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ea. State Metacognitive Inventory (SMI)\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe original version (English) of the State Metacognitive Inventory (SMI) was designed to measure metacognitive states during a specific learning situation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The questionnaire consists of four dimensions (awareness, cognitive strategy, planning, and self-checking), with five self-report items for each dimension using a 4-point Likert scale ranging from 1 (not at all typical for me) to 4 (very typically me). A higher score indicates excellent metacognitive awareness and regulation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ea. Demographic questionnaire\u003c/h3\u003e\n\u003cp\u003eThis survey was designed to collect essential information from this study sample. These data included age, gender, and academic level (categorized as lower level: \u0026le;4th level and upper level: \u0026ge;5th level).\u003c/p\u003e\n\u003ch3\u003eb. The Big Five Inventory − 10 (BFI − 10)\u003c/h3\u003e\n\u003cp\u003eThe Big Five Inventory-10 (BFI-10), Arabic version [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], was used to assess predictive validity due to its brevity and efficiency. The survey consists of self-report items and had five major personality dimensions (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness). The participants\u0026rsquo; answers were rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly disagree, to 5\u0026thinsp;=\u0026thinsp;Strongly agree).\u003c/p\u003e\n\u003ch3\u003e2. Translation process\u003c/h3\u003e\n\u003cp\u003eThe primary version of the SMI-Ar was developed via a back-translation process. Two independent bilingual experts in education and psychology (Arabic and English) translated the original questionnaire from English to classic Arabic. The new version was subjected to back-translation by five other bilingual experts in order to verify content equivalence and cultural adaptation. In the next stage, the pre-final version was tested with a target sample of 30 people (age 18\u0026ndash;25). The results revealed that 92% of the participants found the 20 items to be clearly and culturally appropriate. At the same time, 8% of the participants found ambiguity with the Arabic terminology for 3 items (7, 12, and 15). Based on this feedback, these points were revised and minor changes were made. Finally, this tool with its 20 items was refined, and a definitive version was approved.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3. Data analyses:\u003c/h2\u003e \u003cp\u003eTo evaluate the psychometric propriety of the SMI-Ar, a set of statistical analyses SPSS and AMOS (V.26) was used. Descriptive statistics (mean, standard deviation, skeweness and ketosis) were conducted to examine the data's distribution characteristics. For internal ratability, Cronbach\u0026rsquo;s alpha coefficient was calculated for each dimension, with the acceptable value at 0.70 level.\u003c/p\u003e \u003cp\u003eConcerning the factorial validity, the study conducted both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) with oblique target rotation and the maximum likelihood estimator. The study examined the sampling adequacy via Kaiser-olkin (KMO, Acceptable value at 0.70) and Bartlett\u0026rsquo;s test of sphericity (statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;.05). The CFA was performed to confirm the SMI-AR's factor structure with acceptance of model fits based on the following criteria: chi-square/df ratio (\u0026lt;\u0026thinsp;3), Comparative Fit Index (CFI; 0.90), Tucker-Lewis\u0026rsquo;s index (TLI; \u0026ge;0.90), Root Mean Residual (SRMR; \u0026le;0.08), Root Means Square Error of Approximation (RMSEA) (\u0026le;\u0026thinsp;.06). Standardized loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.40 and were statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 were considered for interpretation.\u003c/p\u003e \u003cp\u003eFor the invariance measurement, the study performed multi-group confirmatory factor analysis (MGCFA) based on gender (males vs females). It tested three levels: configural (same factor structure), metric (equal factor loading), and scalar invariance (equal intercept). Standard indices were used for the model's fit assessment (χ\u0026sup2;, CFI, TLI, RMSEA, and SRMR), in order to evaluate the invariance for females and males.\u003c/p\u003e \u003cp\u003eRegarding discriminant validity, the study investigated the relationship between the SMR-Ar and the BFI-10-Ar dimensions using a correlation analysis. The expectation was a moderate to strong correlation (r\u0026thinsp;\u0026lt;\u0026thinsp;30) between the different tool\u0026rsquo;s variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Data collection procedures:\u003c/h3\u003e\n\u003cp\u003e For this study, approval was obtained from Taif University\u0026rsquo;s Research Ethics Committee (approval number HAPO-02-T-105). The data collection was conducted between September and November 2024, using online administration with Google forms. The survey link was distributed via official university Email and via student group. All participants should provide informal consent in the beginning, before completing the questionnaire.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ea. Descriptive statistic and Ratability Assessment:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the means and standard deviations for each item across its subscale. Preliminary analyses for the 240 participants, with no missing data, demonstrated a normal distribution with an acceptable range for skewness values (-0, 86 to -0, 13) and for kurtosis values (-0.95 to 0.18). Moreover, the mean scores ranged from 2.89 to 3.36, with standard deviations between 0.65 and 0.92, signifying adequate variability.\u003c/p\u003e \u003cp\u003eRegarding the internal consistency of the SMI\u0026rsquo;s Arabic version, the result showed strong psychometric properties. Cronbach\u0026rsquo;s alpha coefficient was excellent with score of .95 for both the Awareness and cognitive strategy subscales, .89 for Planning, and 87 for Self-checking.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics for the SMI-Ar Items and Cronbach\u0026rsquo;s Alpha\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eitems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeans\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e,95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. I was aware of my own thinking.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. I was aware of which thinking technique or strategy to use and when to use it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. I was aware of the need to plan my course of action.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. I was aware of my ongoing thinking processes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. I was aware of my trying to understand the test questions before I attempted to solve them.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e,95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. I attempted to discover the main ideas in the test questions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. I asked myself how the test questions related to what I already knew.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. I thought through the meaning of the test questions before I began to answer them.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. I used multiple thinking techniques or strategies to solve the test questions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19. I selected and organized relevant information to solve the test questions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e,89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. I tried to understand the goals of the test questions before I attempted to answer.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. I tried to determine what the test required.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. I made sure I understood just what had to be done and how to do it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. I determined how to solve the test questions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20. I tried to understand the test questions before I attempted to solve them.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf Checking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. I checked my work while I was doing it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. I corrected my errors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. I almost always knew how much of the test I had left to complete.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. I kept track of my progress and, if necessary, I changed my techniques or strategies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18. I checked my accuracy as I progressed through the test.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eb. Confimatoty factor analysis (CFA)\u003c/h2\u003e \u003cp\u003eThis section of the statistical analysis examines the structure of the SMI\u0026rsquo;s Arabic version and its adjustment to the theoretical model of metacognition. For this task, the study considered one unique model: a multidimensional model with four correlated, first-order factors. Both exploratory (EFA) and confirmatory (CFA) factor analyses were conducted. The results showed excellent sampling adequacy (KMO\u0026thinsp;=\u0026thinsp;0.89), as well as the Bartlett\u0026rsquo;s test of Sphericity (χ\u0026sup2; = 351.220, df\u0026thinsp;=\u0026thinsp;164, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), which indicated good data factorability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel Fit Indices for Confirmatory Factor Analysis\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe level for a good fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree of freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbability value of χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩽.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u0026sup2;/ddl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe discrepancy divided by degrees of freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess than 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparative fit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩾.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTucker-Lewis index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩾.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormed fit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩾.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoot mean square error of approximation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized root-mean-square residual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoodness of fit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩾.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted goodness of fit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⩾.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe EFA was performed by using component analysis with Varimax Rotation. The results revealed a four factor Structure explaining 71.73% of the total variance. The first factor was \u0026ldquo;cognitive strategy\u0026rdquo; and accounted for 21.63% of the variance. The second factor was \u0026ldquo;awareness\u0026rdquo; and explained 21% of the variance. Factor three \u0026ldquo;self-checking,\u0026rdquo; contributed 15% of the variance while the fourth factor, \u0026ldquo;Planning and explain only 13% of the variance.\u003c/p\u003e \u003cp\u003eIn the second stage, the CFA analysis confirmed the four-factor structure, with satisfactory fit indices as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.142, CFI\u0026thinsp;=\u0026thinsp;0.960, RMSEA\u0026thinsp;=\u0026thinsp;0.069 (90% CI [0.059, 0.079]), GFI\u0026thinsp;=\u0026thinsp;0.878, and NFI\u0026thinsp;=\u0026thinsp;0.928. Factor loadings in the CFA model were substantial: Awareness (0.63-1.00), Cognitive Strategy (0.92\u0026ndash;1.08), Planning (0.53-1.00), and Self-Checking (0.78-1.00). Inter-factor correlation demonstrated a good relationship among Awareness, cognitive strategy and self-checking (all, r\u0026thinsp;\u0026gt;\u0026thinsp;.34) while \u0026ldquo;planning\u0026rdquo; showed relatively moderate correlations with compared to the other factors. This pattern suggested that while Awareness, Cognitive Strategy, and Self-Checking represent related metacognitive processes, Planning might operate as a more distinct component.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement invariance:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMeasurement Invariance Results Across Gender\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e515.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e533.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e562.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results relative to the invariance measurement based on gender. The Arabic version of the SMI proved to be remarkably consistent in its discriminant validity. The configural model achieved satisfactory model fit indices, supporting three key points: equivalent factor structure, equal factor loading, and equal item intercepts between the male and female populations (ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;.01, ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;.015). These results suggested that the SMI-Ar measures the same constructs in the same way across male and female students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eDiscriminant validity\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe results of a Pearson correlation between the four dimensions of the SMI-Ar and those of the BFI-10-Ar revealed a significant relationship between all variables for trait personality and meta-cognition, with the exception of \u0026ldquo;planning\u0026rdquo; which showed no significant correlation with the other variables.\u003c/p\u003e \u003cp\u003eThe relationship between metacognitive strategy and personality trait demonstrated acceptable moderate correlation. In contrast, \u0026ldquo;self\u0026rdquo;- checking\u0026rdquo; appeared as the central variable that formed a robust and positive correlation with both cognitive strategy (r-.622**) and personality trait (r\u0026thinsp;=\u0026thinsp;.566**).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics and Bivariate Correlations with BFI.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwareness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongnitive_strategy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,518\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlaning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,322\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-cheking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,556\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,622\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtraversion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,347\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,317\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgreeableness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,147\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConscientiousness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,237\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,301\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,424\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,272\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,332\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuroticism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,210\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,345\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,322\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,342\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,268\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,389\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpeness_to_exp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,329\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,306\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e,400\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e,344\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e,275\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e,546\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e,411\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to assess the psychometric properties of the Arabic version of the State Metacognitive Inventory (SMI-Ar), in order to delete the gap for measuring state metacognition in an Arabic context. To this end, the original English version of the SMI was translated to classic Arabic following the back- translation method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Arabic version with 20 items, revealed several key findings. First, the analysis noted robust internal consistency coefficients (.87-.95), which were particularly superior to the ones reported in previous cultural adaptations, including Turkish version (α\u0026thinsp;\u0026asymp;\u0026thinsp;.70) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and a Mexican adaptation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the other hand, these results aligned with the original version [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Based on the results, the analysis affirmed the excellent reliability of the SMI-Ar and the tool's psychometric integrity across linguistic and cultural validations.\u003c/p\u003e \u003cp\u003eAnother significant aspect that emerged from this study concerns the SMI\u0026rsquo;s four-factor structure. Examining the adjustment of the data to this model via EFA and CFA provided empirical support for the SMI\u0026rsquo;s universality across cultures. Interestingly, the analysis revealed an excellent adjustment of the data to this model, with robust model fit indices. In the factor-structure confirmation with EFA and CFA, the four factors explained 71.73% of the total of invariance, a higher percentage compared to the original version (65%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the Mexican version (68%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Beyond these initial observations, the confirmatory factor analysis provided statistical evidence for the four-factor structure, with the fit indices notably surpassing or aligning with the original, Turkish and Mexican versions. Specifically, the model achieved a chi-square/df ratio of 2.142 and showed a better fit than the Mexican adaptation (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.89; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]). For the comparative fit index (CFI\u0026thinsp;=\u0026thinsp;.96) exceeds, also, Turkish version (CFI\u0026thinsp;=\u0026thinsp;.92; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]) and Mexican version (CFI\u0026thinsp;=\u0026thinsp;.94; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]). This observation suggested a more stable factor in the Arabic version. This result is likely due to the rigorous translation process and careful cultural adaptation procedures employed in its development. Moreover, in Arabic educational contexts, metacognitive assessment tools are scarce and predominantly Western-based, failing to capture local cultural nuances in learning processes and institutional structures like gender segregation.\u003c/p\u003e \u003cp\u003ePerhaps most notably, the Root Mean Square Error of Approximation (RMSEA\u0026thinsp;=\u0026thinsp;.069; 90% CI [0.059, 0.079]) indicates good model fit, comparing favorably with the Turkish adaptation (RMSEA\u0026thinsp;=\u0026thinsp;.076) and showing slight improvement over the Mexican version (RMSEA\u0026thinsp;=\u0026thinsp;.071). This fit enhancement is particularly meaningful given study\u0026rsquo;s [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] original emphasis on the importance of precise error estimation for metacognitive measurement. These comparative results suggest that the SMI-Ar not only maintains, but also potentially enhances the psychometric integrity of the original instrument in the Arabic context. The superior fit indices might be attributed to the rigorous translation process and careful cultural adaptation procedures which were employed for in this study, as emphasized by other study discussion about cross-cultural assessment adaptation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother key finding, concern the invariance measurement. A study supported the importance of valid measurement tools across different demographic groups [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For this study, the examination of this point was conducted via a gender variable, in accordance with the original version [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and other versions (Turkish: [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Mexican: [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]). The configural, metric, and scalar invariance indicated that the SMI-Ar functions equivalently for both male and female students, ensuring a fair assessment regardless of gender. While the Mexican version had partial invariance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the findings demonstrate full configural, metric, and scalar invariance (ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;.01, ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;.015). This result attested to a robust invariance for the SMI-Ar, given the gender-segregated nature of many educational institutions in Arabic-speaking countries.\u003c/p\u003e \u003cp\u003eOn the other hand, the correlational patterns between metacognitive components and personality traits revealed several interesting relationships. The moderate correlations between self-checking and conscientiousness (r\u0026thinsp;=\u0026thinsp;.424) and between openness to experience and metacognitive awareness (r\u0026thinsp;=\u0026thinsp;.329), aligned with theoretical expectations about the intersection of personality and the metacognitive processes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the weak correlations with agreeableness suggested that the SMI-Ar successfully measures distinct constructs for general personality traits, supporting the tool\u0026rsquo;s discriminant validity.\u003c/p\u003e \u003cp\u003eThe strong interconnections observed among self-checking, cognitive strategy use, and awareness (r\u0026thinsp;\u0026gt;\u0026thinsp;.50) mirrored the findings of other study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], suggesting that these components form a cohesive metacognitive framework. However, the relative independence for planning processes may indicate that certain metacognitive components operate more autonomously in specific cultural contexts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom an educational perspective, these findings have significant implications. The strong psychometric properties support using the SMI-Ar to assess students' metacognitive abilities in Arabic educational settings, addressing the need for culturally appropriate assessment tools [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The distinct nature of the planning factor suggests that educators might need to pay special attention to how planning strategies are taught and assessed in Arabic cultural contexts, especially when considering emphasis on cognitive adaptability across different learning environments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the robust correlation patterns between the metacognitive components and the personality traits provide valuable insights for educational interventions. Following study framework on metacognition and abstract thought [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], these relationships suggest that metacognitive development might be influenced by individual personality characteristics, particularly in terms of conscientiousness and openness to experience. This understanding could help educators modify metacognitive instruction to the students' individual differences.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Arabic version of the State Metacognitive Inventory (SMI-Ar) illustrated strong psychometric properties and excellent cultural adaptation for Arabic-speaking populations with established reliability, validity, and measurement invariance across gender. This questionnaire provided a valuable tool to assess metacognition processes for specific learning situations in an Arabic educational context. Future research should focus on exploring the instrument's predictive validity and investigating cultural influences on metacognitive planning processes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eFor this study, approval was obtained from Taif University\u0026rsquo;s Research Ethics Committee (approval number HAPO-02-T-105).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study are available from the corresponding author at\u003cstrong\u003e\u0026nbsp;[email protected]\u0026nbsp;\u003c/strong\u003eupon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests :\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding :\u0026nbsp;\u003c/strong\u003eDeanship of Graduate Studies and Scientific Research, Taif University\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions: SMA\u003c/strong\u003e and \u003cstrong\u003eOHA\u003c/strong\u003e conducted literature review, wrote introduction, and participated in questionnaire translation from English to Arabic. \u003cstrong\u003eWB\u003c/strong\u003e performed statistical analyses, interpreted results, and coordinated the back-translation process from Arabic to English. \u003cstrong\u003eHZ\u003c/strong\u003e and \u003cstrong\u003eOHA\u003c/strong\u003e wrote the discussion section and supervised the linguistic validation process. \u003cstrong\u003eAll authors\u003c/strong\u003e participated in questionnaire validation procedures, contributed to references, critically reviewed the final manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Corresponding author: WB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements :\u0026nbsp;\u003c/strong\u003eThe authors would like to acknowledge Deanship of Graduate Studies and Scientific Research, Taif University for funding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMetcalfe J. Metacognition. Oxford University Press eBooks. 2024;1485\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShea N. Metacognition of Inferential Transitions in advance. J Philos. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurat Tezer. Cognition and Metacognition in Education. IntechOpen eBooks. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBukowski L. Cognitive Dependability Engineering. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeenman MVJ, van Cleef D. Measuring metacognitive skills for mathematics: students\u0026rsquo; self-reports versus on-line assessment methods. ZDM Mathematics Education. 2018;51(4):691\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivers ML, Dunlosky J, Persky AM. Measuring Metacognitive Knowledge, Monitoring, and Control in the Pharmacy Classroom and Experiential Settings. Am J Pharm Educ. 2019;84(5):7730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neil HF, Abedi J. Reliability and Validity of a State Metacognitive Inventory: Potential for Alternative Assessment. J Educational Res. 1996;89(4):234\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinojosa LMM, Cardenas M, Alejandro C. Measurement of Metacognition: Adaptation of Metacognitive State Inventory in Spanish to Mexican University Students. Eur J Educational Res. 2020;9(1):413\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydin H. Adapting State Metacognitive Inventory in Turkish Language and Culture. Biotechnol Biotechnol Equip. 2009;23(sup1):10\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArias WL, Andr\u0026eacute;s C. Renzo Rivera Calcina. An\u0026aacute;lisis psicom\u0026eacute;trico del inventario de estrategias metacognitivas en ni\u0026ntilde;os de 4to y 5to de primaria de Colombia. Educaci\u0026oacute;n. 2022;28(2):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlansari B, The Big Five Inventory (BFI). Reliability and validity of its Arabic translation in non clinical sample. Eur Psychiatry. 2016;33(S1):S209\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperber AD. Translation and validation of study instruments for cross-cultural research. Gastroenterology. 2004;126(1):S124\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaustino B, Branco Vasco A, Oliveira J, Lopes P, Fonseca I. Metacognitive self-assessment scale: psychometric properties and clinical implications. Appl Neuropsychology: Adult. 2019;28(5):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImmekus JC, Imbrie PK. Educ Psychol Meas. 2008;68(4):695\u0026ndash;709. Dimensionality Assessment Using the Full-Information Item Bifactor Analysis for Graded Response Data.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"metacognition, psychometric validation, Arabic adaptation, educational assessment, cross-cultural psychology","lastPublishedDoi":"10.21203/rs.3.rs-5767392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5767392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetacognition is an important phenomenon in psychological and educational fields. This study aimed to validate an Arabic version of the State Metacognitive Inventory (SMI) for assessing metacognitive processes in Arabic educational contexts.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study involved 240 Taif University students who completed electronic versions of the SMI and a sociodemographic questionnaire. The research process included rigorous translation procedures, followed by exploratory and confirmatory analyses to test the four-factor structure, internal consistency, and gender measurement invariance, along with examining correlations with personality traits for construct validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis confirmed the four-factor structure of metacognition, demonstrated strong internal consistency with alpha coefficients ranging from .87 to .95, established measurement invariance across genders, and showed appropriate correlations with personality traits supporting construct validity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe Arabic version of the SMI has been validated as a reliable and psychometrically sound instrument for assessing metacognitive processes in Arabic educational settings, providing educators and researchers with a valuable tool for understanding metacognition in Arabic-speaking populations.\u003c/p\u003e","manuscriptTitle":"State Metacognition Inventory: validity and reliability in Arabic Educational Contexts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 14:34:38","doi":"10.21203/rs.3.rs-5767392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"538190cc-7f65-4de8-a6f1-e93065764acd","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-12T02:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 14:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5767392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5767392","identity":"rs-5767392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}