Validation of the Mizan Meta-Memory and Meta-Concentration Scale for Students (MMSS) among Nigerian health care students

Abstract

Introduction

Meta-memory and meta-concentration play crucial roles in self-regulating cognitive processes, influencing learning and problem-solving abilities. The Mizan Meta-Memory and Meta-Concentration Scale for Students (MMSS) was developed as a concise tool to assess students’ perceptions of their meta-memory and meta-concentration abilities, rather than cognitive performance itself. However, its applicability to Nigerian healthcare students has not been examined. Given the rigorous cognitive demands of medical education, this study aims to validate the MMSS among Nigerian healthcare students, with a focus on its psychometric properties and reliability.

Methods

A cross-sectional study using simple random sampling was conducted among 299 healthcare students (Medicine and Surgery, Dentistry, and Physiotherapy) at the University of Ibadan. Participants completed an online survey containing the MMSS, a nine-item questionnaire divided into two subscales: meta-memory and meta-concentration. Internal consistency was evaluated using Cronbach’s alpha and McDonald’s omega. Exploratory factor analysis (EFA) was performed to assess construct validity, while confirmatory factor analysis (CFA) was used to determine model fit.

Results

The MMSS demonstrated strong internal consistency both for the MMSS global and subscales (Cronbach’s alpha = 0.875, 0.808, 0.857; McDonald’s omega = 0.871, 0.805, 0.859). EFA confirmed a two-factor structure, with the meta-memory subscale explaining 50.26% of the variance and the meta-concentration subscale accounting for 12.78%. CFA results indicated an acceptable model fit (CFI = 0.974, TLI = 0.959, RMSEA = 0.066, SRMR = 0.041, χ²/df < 2.284, PCLOSE = 0.127), supporting the scale’s validity. The MMSS was found to be a reliable measure of cognitive self-regulation among Nigerian healthcare students.

Conclusion

The findings support the use of the MMSS as a valid and reliable measure of self-perceived metacognitive regulation among Nigerian healthcare students. Its application in educational settings may enhance strategies for learning and self-regulation. Given its strong psychometric properties, the MMSS can be applied in educational settings to enhance learning strategies and cognitive self-regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-026-04647-7.

Introduction

Memory and cognition are fundamental to human functioning, shaping learning, decision-making, and overall well-being. These processes enable individuals to acquire, store, retrieve and regulate information while adapting to changing environmental demands [1]. Within this domain, meta-memory and meta-cognition represents higher order regulatory processes concerned not with performance itself, but with awareness and monitoring of cognitive activity. Meta-memory refers to an individual’s awareness and understanding of their memory capabilities, including the capacity to assess, monitor, and regulate memory processes [2]. This awareness informs strategic decisions about encoding, rehearsal and retrieval. Meta-cognition encompasses broader regulatory mechanisms that allow individuals to plan, monitor and evaluate their thinking processes [3, 4]. Meta-memory and meta-concentration represent domain-specific components of metacognition, focusing respectively on awareness and regulation of memory processes and attentional control. These constructs align with broader metacognitive frameworks, including monitoring–control models and self-regulated learning theories, which emphasize individuals’ ability to evaluate and regulate their cognitive processes.

Several instruments have been developed to assess perceptions of meta-memory and metacognition functioning, including the Metamemory in Adulthood Questionnaire (MIAQ) [5], the Multidimensional Metamemory Skills Scale (MDMS) [6], the Eyewitness Metamemory Scale (EMS) [7], the Multifactorial Memory Questionnaire (MMQ) [8], the Metacognition Self-Assessment Scale (MSAS) [9], and the Mizan Meta-Memory and Meta-Concentration Scale for Students (MMSS) [10]. These measures primarily assess self-perceived cognitive monitoring and regulatory tendencies rather than objective memory or academic outcomes.

The Mizan Meta-Memory and Meta-Concentration Scale for Students (MMSS) was developed by Manzar and colleagues in 2018 as a concise instrument designed to assess two interrelated components of metacognitive awareness among university students [10]. Its development drew on items adapted from established instruments, including the metacognition assessment instrument [11], the Questionnaire of Metamemory in Adulthood [12], and the EURO-D scale [13]. Following expert review and pilot testing, the scale was reduced to nine-items comprising five meta-memory and four meta-concentration items. The original validation demonstrated good internal consistency and a stable two-factor structure. Subsequent validation among healthcare professionals in Saudi Arabia further supported its structural robustness across-cultural settings [14]. Conceptually, the MMSS is grounded in monitoring and control models of metacognition, which distinguish awareness of cognitive states from the execution of cognitive tasks. The instrument therefore captures students’ subjective appraisal of their regulatory processes rather than their actual cognitive accuracy or performance. This distinction is important, as perceived cognitive regulation may not necessarily correspond directly to objective academic or clinical outcomes.

Compared with longer instruments, the MMSS offers a brief and structured approach that integrates both meta-memory and meta-concentration within a single framework. Its brevity enhances feasibility in educational research settings. Although the MMSS has been validated among Ethiopian university students [10] and nurses in Saudi Arabia [14], its applicability within West African healthcare training contexts has not been examined. Psychometric validation across cultural and educational environments is necessary because response patterns, interpretation of Likert items and perceptions of self-regulation may vary across contexts.

At present, no study has examined the MMSS among Nigerian healthcare students. Medical education is cognitively demanding, requiring sustained attention, extensive knowledge acquisition and adaptive learning strategies [15, 16]. Within theoretical models of self-regulated learning, metacognitive awareness is understood as a foundational mechanism that may support strategic learning behaviours [17]. However, the present study does not examine academic performance, clinical competence or predictive outcomes. Rather, it evaluates whether the latent structure and reliability of the MMSS are replicated within this specific educational population.

The demands of lifelong learning in healthcare further underscore the conceptual relevance of metacognitive monitoring processes [18, 19]. Healthcare professionals are expected to continuously update knowledge and adapt to evolving clinical guidelines. In theory, awareness of one’s cognitive regulation may influence engagement with learning strategies. Nevertheless, such associations remain theoretical within the context of this study and were not empirically tested.

Given the cognitive demands of medical training and the limited availability of locally validated metacognitive instruments, it is important to determine whether the MMSS demonstrates adequate psychometric properties within a Nigerian healthcare student sample. The MMSS is particularly relevant for healthcare students, whose training requires sustained attention, accurate recall, and effective self-monitoring during learning and clinical decision-making. The aim of this study was therefore to evaluate the reliability and construct validity of the MMSS in this population.

Methods

Participants and sample size

This study included 299 students enrolled in Medicine and Surgery, Dentistry, and Physiotherapy degree programmes at the University of Ibadan. The sample size was calculated using Slovin’s formula for a finite population: n = N / 1 + (Ne²), where n represents the required sample size, N is the total population, and e is the margin of error. Given a total population of 1,280 students and a margin of error of 5%, the computed sample size was 305 students. A total of 305 students were invited to participate; however, 299 completed the survey, yielding a response rate of 98%. For psychometric analysis, the 299 completed responses were used. Given that the MMSS comprises nine items, this provided an n/p ratio of 33.2 (299/9), where n is the sample size and p is the number of items in the scale. The inclusion criteria required participants to be actively enrolled in one of the listed healthcare programmes and to provide informed consent prior to participation.

Study design, sampling technique and procedure

This study employed a cross-sectional design with simple random sampling. The sampling frame comprised all healthcare students enrolled in the Physiotherapy, Dentistry, and Medicine and Surgery degree programmes at the University of Ibadan. Lists of eligible students were obtained from class representatives of the respective programmes and compiled into a unified sampling frame. A computer-based random number generator was then used to select participants, ensuring that each student had an equal probability of inclusion. Selected students were contacted via email or WhatsApp and invited to participate in the study. The questionnaire, structured into three sections comprising informed consent, sociodemographic characteristics, and the nine-item MMSS, was administered using Google Forms. Since English is the official language of instruction at the University of Ibadan, the questionnaire was provided in English. The informed consent section detailed the study objectives, procedures, and participants’ rights, and participants were encouraged to seek clarification from the researchers before completing the survey.

Survey instrument

The Mizan Meta-Memory and Meta-Concentration Scale (MMSS) was used to assess meta-memory and meta-concentration abilities among students. The MMSS is a psychometrically validated scale designed to measure two core components of metacognitive function: meta-memory and meta-concentration. Two previous studies have validated this scale among a sample of university students and health professionals [10, 14]. The scale consists of nine items divided into two subscales: the Meta-Memory Subscale (BMMS) and the Meta-Concentration Subscale (BMCS). The BMMS consists of five items that assess self-perceived memory awareness and control, while the BMCS consists of four items evaluating awareness of concentration ability. Each item is rated on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher scores indicating better meta-memory and meta-concentration abilities. The total MMSS score ranges from 9 to 45, with subscale scores ranging from 5 to 25 for BMMS and 4 to 20 for BMCS.

Statistical analysis

All statistical analyses were conducted using SPSS version 27.0 and SPSS AMOS version 23.0. Descriptive statistics were computed for continuous variables using means, standard deviations, and ranges, while categorical variables were summarized as frequencies and percentages. Internal consistency reliability was assessed using both Cronbach’s alpha and McDonald’s omega. While Cronbach’s alpha is widely used, it assumes tau-equivalence and may provide biased estimates when this assumption is violated. McDonald’s omega was therefore included as a complementary reliability estimate, given its robustness in the presence of unequal factor loadings. Prior to factor analysis, the suitability of the data was evaluated using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. In addition, distributional properties of the items were examined to ensure that the data were appropriate for factor analytic procedures.

Exploratory Factor Analysis (EFA) was conducted using principal axis factoring. Although varimax rotation was initially explored to facilitate comparison with the original validation study, the final solution is based on Promax rotation. This decision was made due to the presence of moderate inter-factor correlation, supporting the use of an oblique rotation consistent with theoretically related metacognitive constructs. Confirmatory Factor Analysis (CFA) was performed using AMOS to evaluate the factor structure identified in the EFA. Two models were specified and compared: Model A representing the initial two-factor structure, and Model B incorporating theoretically justifiable error covariances between items with similar wording or conceptual overlap. These covariances were introduced cautiously to improve model fit while preserving the theoretical integrity of the constructs.

Model fit was assessed using standard indices, including the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Model comparison was based on improvements in fit indices and parsimony. Inter-item correlations were analysed using Pearson’s correlation matrix to assess relationships among MMSS items.

Ethical Consideration

Ethical approval for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained.

Results

A total of 299 university students participated in the study, with an age range of 16 to 41 years (M = 22.02, SD = 3.137). The sample consisted of 115 females (38.5%) and 184 males (61.5%). Participants were enrolled across three academic programmes: Dentistry (14.0%), Medicine and Surgery (72.9%), and Physiotherapy (13.0%). Students were drawn from levels 100 through 600, with representation ranging from 13.7% to 19.7% at each level (Table 1).

Table 1.

Participants characteristics

Sociodemographic parameter		Frequency	Percent
Age	15–19	65	21.7
	20–24	181	60.5
	25–29	49	16.4
	≥ 30	4	1.3
Sex	Female	115	38.5
	Male	184	61.5
Department	Dentistry	42	14.0
	Medicine and Surgery	218	72.9
	Physiotherapy	39	13.0
Level	100 Level	41	13.7
	200 Level	51	17.1
	300 Level	59	19.7
	400 Level	52	17.4
	500 Level	48	16.1
	600 Level	48	16.1

Preliminary tests confirmed the suitability of the data for factor analysis. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.880, indicating meritorious adequacy, while Bartlett’s Test of Sphericity was significant (χ² = 1161.5, df = 36, p < 0.0001), confirming that the correlation matrix was appropriate for extraction. Communalities ranged from 0.498 to 0.750, suggesting that the extracted factors explained a substantial portion of variance for each item. The determinant of the correlation matrix was 0.019, indicating the absence of multicollinearity.

Exploratory Factor Analysis (EFA) using principal axis factoring (PAF) with Promax rotation yielded a two-factor solution that accounted for 63.04% of the total variance. The first factor, representing Meta-Memory (BMMS), explained 50.26% of the variance, while the second factor, representing Meta-Concentration (BMCS), accounted for 12.78%. Factor loadings ranged from 0.640 to 0.860 for BMMS items and from 0.793 to 0.879 for BMCS items (Table 2). These values indicate that the items demonstrate moderate to strong associations with their respective latent constructs, suggesting that each item contributes meaningfully to the measurement of meta-memory and meta-concentration. The component correlation matrix indicated a moderate correlation between the two subscales (r = 0.579). The oblique rotation allowed for correlated factors, consistent with the observed moderate inter-factor correlation. The scree plot indicated a clear inflection after the second factor, supporting a two-factor solution (Fig. 1). No items were removed during analysis, as all items demonstrated satisfactory factor loadings (≥ 0.60), adequate communalities, and conceptual alignment with their respective factors.

Table 2.

Descriptive statistics of the MMSS

Item	Factor loading (BMMS)	Factor loading (BMCS)	Mean	SD	Cronbach’s Alpha if Item Deleted	Corrected Item-Total Correlation
BMM_1	0.860		3.48	1.082	0.868	0.541
BMM_2	0.824		3.69	1.001	0.861	0.613
BMM_3	0.683		3.81	1.075	0.870	0.518
BMM_4	0.650		3.38	1.021	0.856	0.672
BMM_5	0.640		3.46	1.056	0.865	0.577
BMC_1		0.879	3.22	1.096	0.857	0.665
BMC_2		0.858	3.05	1.032	0.860	0.630
BMC_3		0.806	3.35	1.081	0.854	0.693
BMC_4		0.793	3.03	1.088	0.861	0.622
BMMS			17.81	3.938
BMCS			12.66	3.594

Fig. 1.

Scree Plot of the MMSS

To validate the factor structure identified in the EFA, a Confirmatory Factor Analysis (CFA) was performed. In the initial model (Model A), fit indices indicated an adequate model fit (χ² (26) = 76.8, p < 0.001; CFI = 0.938; TLI = 0.955; RMSEA = 0.081; SRMR = 0.048; GFI = 0.944; PCLOSE = 0.008). Model A is presented in Fig. 2. A modified model (Model B) was subsequently tested by introducing a limited number of theoretically justifiable error covariances between items within the same subscale that shared similar wording and conceptual overlap (e.g., items assessing closely related aspects of memory monitoring or attentional control). Model B demonstrated improved fit indices (χ² (23) = 52.5, p = 0.001; CFI = 0.974; TLI = 0.959; RMSEA = 0.066; SRMR = 0.041; GFI = 0.965; PCLOSE = 0.127), with a lower χ²/df ratio (2.284) compared to Model A (2.952), indicating a better fit to the data. Model B is illustrated in Fig. 3. The improvement in fit indices between Model A and Model B suggests that allowing for limited correlated measurement error among conceptually related items enhanced model fit without altering the underlying two-factor structure. Factor loadings across both models were moderate to high, indicating that all items contributed meaningfully to their respective latent constructs. This supports the interpretation that the MMSS items adequately represent the underlying dimensions of meta-memory and meta-concentration.

Fig. 2.

CFA path diagram of the MMSS (Model A)

Fig. 3.

CFA path diagram of the covariance adjusted model (Model B)

Item-total correlations were assessed to evaluate individual item performance. Correlations ranged from 0.541 to 0.693, demonstrating moderate to strong relationships between each item and the overall scale (Table 2). Inter-item correlations were all statistically significant (p < 0.001), ranging from 0.239 to 0.692, further supporting the construct validity of the MMSS (Table 3).

Table 3.

Inter-item Correlation matrix

	BMM_1	BMM_2	BMM_3	BMM_4	BMM_5	BMC_1	BMC_2	BMC_3	BMC_4
BMM_1		0.480*	0.460*	0.430*	0.307*	0.388*	0.386*	0.388*	0.290*
BMM_2			0.493*	0.532*	0.515*	0.343*	0.361*	0.404*	0.372*
BMM_3				0.427*	0.358*	0.378*	0.239*	0.350*	0.298*
BMM_4					0.594*	0.406*	0.503*	0.446*	0.467*
BMM_5						0.352*	0.384*	0.398*	0.417*
BMC_1							0.604*	0.692*	0.588*
BMC_2								0.603*	0.495*
BMC_3									0.610*
BMC_4

*p<0.001

In summary, the results of both the EFA and CFA confirmed a two-factor structure for the MMSS, comprising the Meta-Memory and Meta-Concentration subscales. The scale demonstrated strong reliability, adequate validity, and good model fit.

Discussion

This study evaluated the psychometric properties of the Mizan Meta-Memory and Meta-Concentration Scale for Students (MMSS) within a Nigerian healthcare student sample. The findings indicate that MMSS scores demonstrated strong internal consistency and a stable two-factor structure in this context. The present findings are consistent with previous validations of the MMSS conducted in Ethiopian students and Saudi healthcare professionals, which similarly reported a stable two-factor structure and acceptable reliability coefficients [10, 14]. The magnitude of internal consistency observed in this study is comparable to these prior validations, supporting the structural stability of the MMSS across different cultural and educational contexts. The reliability coefficients were comparable to, and in some instances exceeded, those reported for established metacognitive instruments such as the Metacognitive Awareness Inventory (MAI) and the Prospective and Retrospective Memory Questionnaire (PRMQ) [20–22]. These results suggest that the MMSS provides internally consistent measurement of self-perceived meta-memory and meta-concentration, two important skills in medical education [23]. Compared with broader metacognitive instruments such as the Metacognitive Awareness Inventory (MAI), which assess multiple domains of metacognition, the MMSS provides a more focused assessment of meta-memory and meta-concentration within a concise format. This targeted structure may enhance its utility in time-constrained educational settings.

The emergence of two distinct but related factors is consistent with established theoretical models of metacognition. Flavell’s framework diffrentiate metacognitive knowledge and from regulatory processess, while Nelson and Narens described monitoring and control as interdependent components of cognitive regulation [24, 25]. Dual-process perspectives on similarly conceptualise higher order cognitive systems as separable yet interacting systems [26]. The moderate positive correlation observed between the meta-memory and meta-concentration subscales (r = 0.579) aligns with these theoretical expectations, supporting the interpretation that the two domains are related but non-redundant aspects of metacognitive awareness. Comparable structural findings have been reported in prior validations of the MMSS [14], reinforcing the stability of the proposed latent structure across contexts.

Confirmatory factor analysis further supported the adequacy of the two-factor model. Fit indices fell within acceptable ranges according to established criteria [27], indicating that the hypothesised structure is a reasonable representation of the observed data. While model respecification improved fit, such refinements were limited and theoretically coherent. Nevertheless, replication in larger and more heterogeneous samples would provide additional support for structural stability.

From an applied perspective, the MMSS may offer a brief and feasible means of assessing perceived metacognitive regulation in demanding educational settings. Nigerian medical education is characterised by intensive study schedules and substantial cognitive demands [15, 16]. Within theoretical models of self-regulated learning, awareness of one’s cognitive monitoring processes may influence engagement with learning strategies [23]. However, it is important to emphasise that the present study did not examine academic performance, clinical competence, or behavioural outcomes. The findings therefore relate strictly to measurement properties rather than predictive validity.

The potential relevance of metacognitive awareness to student wellbeing has been discussed in previous literature, including associations with stress and burnout [28]. In this context, the MMSS may serve as a descriptive tool for exploring perceived regulatory processes. Nevertheless, no causal or predictive relationships were tested in this study. Future research incorporating objective academic indicators, behavioural measures or longitudinal designs would be necessary to examine such associations.

The Nigerian context provides an important contribution to the existing evidence base. Many psychometric instruments are developed and validated primarily in Western or Asian populations. Cross-cultural validation is essential to determine whether factor structures and reliability indices remain stable in different sociocultural and educational environments. The present findings provide preliminary support for the applicability of the MMSS within a Nigerian healthcare student population. However, broader multi-institutional studies are required before generalisation beyond similar contexts can be confidently asserted.

The implications of validation extend beyond academic settings. Healthcare professionals operate in cognitively demanding environments that require sustained attention and regulatory awareness. It must be noted that the MMSS assesses self-perceived metacognitive awareness rather than objective cognitive performance or clinical accuracy. The present study does not establish predictive relationships with professional competence or patient outcomes. Future investigations incorporating objective performance measures would be required to explore such possibilities. Conceptually, self-perceived metacognitive awareness may influence how students engage with learning strategies such as planning, monitoring, and self-evaluation, which are central to effective learning in medical education.

While the study provides useful evidence for the MMSS, some shortcomings should be acknowledged. The sample was drawn from a single institution and was predominantly composed of medical students, which may limit generalisability across healthcare disciplines or institutions. Replication across multiple universities and diverse programmes would strengthen external validity. In addition, although internal consistency and structural validity were supported, other aspects of validation, including test–retest reliability and measurement invariance across subgroups were not examined. These represent important directions for future research.

Conclusion

The MMSS demonstrated strong internal consistency and a coherent two-factor structure within this Nigerian healthcare student sample. The findings support its use as a measure of self-perceived meta-memory and meta-concentration in this context. As a concise instrument, the MMSS may be suitable for research and descriptive applications in educational settings. Its validation in Nigeria contributes to the growing body of cross-cultural psychometric research and underscores the importance of evaluating measurement tools within diverse populations. Further multi-institutional and longitudinal studies are warranted to examine broader aspects of construct stability and potential associations with academic or professional outcomes.

Abbreviations

MMSS

Mizan meta-memory and meta-concentration scale for students

BMMS

Brief meta-memory subscale

BMCS

Brief meta-concentration subscale

CFA

Confirmatory factor analysis

EFA

Exploratory factor analysis

SEM

Structural equation modelling

CFI

Comparative fit index

TLI

Tucker-Lewis index

RMSEA

Root mean square error of approximation

SRMR

Standardized root mean square residual

BMM_1 - BMM_5

Brief meta memory items 1 to 5

BMC_1 - BMC_4

Brief meta concentration items 1 to 4

SD

Standard Deviation

Authors’ contributions

AAA conceptualised the study and analysed the data. AAA AOO and DMA wrote the manuscript. All the authors read and approved the manuscript.

Funding

The authors received no external funding for this work.

Data availability

The dataset and instrument used in this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for the study was obtained from the University of Ibadan / University College Hospital Ethics Committee (UI/UCH). Informed consent was obtained from all participants before data collection, and the study adhered to the ethical guidelines of the Declaration of Helsinki. Confidentiality and anonymity of participant data were strictly maintained.

Consent for publication

The participants provided informed written consent.

Competing interests

The authors declare no competing interests.