Effects of a mindfulness-based intervention on study and learning processes in a sample of university students

Abstract

This study analysed the effects of a mindfulness-based intervention (MBI) on motivation, learning strategies and approaches, procrastination, and mindfulness in university students. Forty-two students (22 in the experimental group and 20 in the control group) participated in an experimental design with pre-test, post-test, and four-month follow-up measures. The results showed that the intervention was associated with a significant increase in deep motivation, deep learning strategies and approaches, and mindfulness levels, as well as an observed decrease in superficial motivation and strategies, and academic procrastination. The effects were maintained at the follow-up measurement, evidencing the stability of the changes. These findings indicate that mindfulness training appears to favour a more autonomous and self-regulated motivational profile, may promote deep learning, and tends to contribute to the reduction of maladaptive behaviours associated with studying, such as procrastination.

Resumen

El presente estudio analiza los efectos de un programa de intervención basado en mindfulness sobre la motivación, las estrategias y enfoques de aprendizaje, la procrastinación y la atención plena en estudiantes universitarios. Han participado 42 estudiantes (22 en el grupo experimental y 20 en el grupo control) en un diseño experimental con medidas pretest, post-test y seguimiento a los cuatro meses. Los resultados muestran que tras la intervención se observa un incremento significativo en la motivación profunda, las estrategias y enfoques de aprendizaje profundos y los niveles de mindfulness, así como una disminución observada en la motivación y estrategias superficiales y en la procrastinación académica. Los efectos se mantienen en la medición de seguimiento, evidenciando la estabilidad de los cambios. Estos hallazgos indican que el entrenamiento en mindfulness parece favorecer un perfil motivacional más autónomo y autorregulado, puede promover el aprendizaje profundo y asociarse a la reducción de conductas desadaptativas asociadas al estudio, como la procrastinación.

Introduction

Academic motivation is regarded as a fundamental driver in school life, as it determines the direction, intensity, and persistence of learning behaviour (Ryan & Deci, 2000, 2020). For their part, Study processes comprise the habits, routines, and self-regulatory behaviours that mediate between motivation and academic performance, including planning, monitoring, time management, concentration, and self-assessment, which are central components of self-regulated learning models (Panadero, 2017). Similarly, learning strategies refer to the cognitive and metacognitive procedures that students use to organise, process and retrieve information, playing a crucial role in the regulation and control of learning processes (Dignath & Veenman, 2021). These three interrelated constructs have been consistently identified as strong predictors of academic performance at different educational stages (Fong et al., 2021).

Academic motivation, according to Self-Determination Theory (Deci & Ryan, 2000), is organised on a continuum ranging from amotivation to intrinsic motivation, passing through different degrees of extrinsic motivation. This theory maintains that the satisfaction of basic needs for autonomy, competence and relatedness is essential to sustaining high-quality motivation. In the school context, students who experience intrinsic motivation tend to be more deeply involved in their studies, use elaborative strategies, and show greater psychological well-being (Ryan & Deci, 2020).

In this sense, we can consider self-regulated learning as the process by which students plan, monitor, control, and evaluate their learning, using metacognitive, cognitive, and motivational strategies (Theobald, 2021). This type of learning includes components such as time management, goal planning, self-reflection, and effort control, which are linked to an increase in academic performance and a reduction in dropouts and failure rates among university students (Simón-Grábalos et al., 2025). On the other hand, academic procrastination is a particularly prevalent problem among university students and is conceptualised as a failure of self-regulation influenced by motivational, emotional and attentional factors (Franco et al., 2025; Sirois, 2023). Academic procrastination, understood as the voluntary and irrational delay of tasks despite anticipating negative consequences, is one of the main challenges of student self-regulation and is associated with high levels of stress, low self-efficacy, and poorer academic marks (Sirois, 2023; Yue et al., 2024).

In recent years, interest in mindfulness as an educational intervention strategy has increased significantly. In the context of higher education, this interest is particularly linked to the search for approaches that strengthen self-regulation, attentional control and motivational quality in students, which are key variables in teaching and learning processes. The scientific literature indicates that the practice of mindfulness is associated with improvements in cognitive and motivational variables directly related to learning (Chen & Qi, 2025; Phan et al., 2022).

Several empirical studies and systematic reviews have documented the benefits of mindfulness practice in educational settings. For example, Ostermann et al. (2022) conducted a meta-analysis that showed that mindfulness-based interventions have a positive effect on the academic performance of university students. Furthermore, Phan et al. (2022) found in their systematic review that school-based mindfulness programmes produce consistent improvements in attention, emotional regulation, and well-being, with positive repercussions on motivation and learning. The practice of mindfulness can influence these variables through various psychological and educational mechanisms. Firstly, mindfulness training strengthens the ability to focus on the present moment, reducing mental distraction and the phenomenon of ‘mind wandering,’ which improves concentration and persistence in study (Mrazek et al., 2013). Secondly, it promotes more adaptive emotional regulation, reducing academic anxiety and rumination, which facilitates autonomous motivation and the effective application of cognitive strategies (Sanger & Dorjee, 2016). Thirdly, it encourages metacognition, as students become more aware of their own thought processes and the effectiveness of their strategies, improving self-regulation of learning and deep motivation (Nguyen & Dorjee, 2022).

Mindfulness can promote deep motivation by reducing anxiety and academic stress, enabling students to perceive greater competence and autonomy in their learning (Brown & Ryan, 2003). In this regard, Shareefa et al. (2025) showed that mindfulness and self-regulation predict academic self-efficacy and well-being, which translates into more autonomous motivation. Furthermore, mindfulness can enhance the use of deep learning strategies by increasing awareness of one's own cognitive and emotional processes. In this sense, recent meta-analysis studies suggest that self-regulation interventions that include mindfulness components have moderate effects on learning outcomes (Guntur & Purnomo, 2024).

Recent empirical evidence supports this hypothesis. In this regard, Rad et al. (2023) showed in a randomised controlled trial that a brief mindfulness programme significantly reduced academic procrastination in university students. Furthermore, Yue et al. (2024) found that mindfulness influences procrastination, and that this relationship is not direct but mediated by learning vigour (the desire, energy and enthusiasm to learn), which in turn reduces procrastination. Meanwhile, Huang et al. (2025) showed that procrastination is linked to deficits in emotional regulation and self-efficacy, processes on which mindfulness has a positive impact. Therefore, these findings reinforce the idea that mindfulness training can be an effective strategy for addressing academic procrastination in higher education, in line with the recent findings of the study of Franco et al. (2025) in a sample of university students.

The present study

Although previous research has found positive effects of mindfulness-based interventions on wellbeing, stress reduction and, to a lesser extent, academic performance, significant gaps remain in the literature. Firstly, relatively few studies have simultaneously examined the impact of mindfulness on motivational quality, learning strategies and approaches, study processes, and academic procrastination within an integrated framework of self-regulated learning. Secondly, many studies have focused primarily on short-term effects, without incorporating follow-up assessments to analyse the temporal stability of the changes observed. Thirdly, the mechanisms linking mindfulness to deep learning approaches and the reduction of dysfunctional academic behaviours remain insufficiently clarified. This integrative approach therefore constitutes a novel contribution in the context of higher education.

In this context, the present study aims to examine the effects of a structured mindfulness-based intervention on academic motivation, learning strategies and approaches, study processes, procrastination, and mindfulness levels in university students, using a pretest, post-test, and four-month follow-up design. Adopting an integrative perspective based on self-regulated learning theory, this study aims to provide empirical evidence on the role of mindfulness as a possible facilitator of adaptive academic performance.

To this end, the following hypotheses are proposed: (H1) students participating in the mindfulness-based intervention would show significant increases in deep motivation, deep learning strategies and focus, and mindfulness levels compared to the control group; (H2) they would also show significant decreases in superficial motivation, superficial strategies and focus, and academic procrastination; and (H3) these effects would be maintained in the 4-month follow-up assessment.

Method

Design

In order to analyse the effects of the mindfulness programme (independent variable) on study and learning processes (dependent variables), a quantitative experimental study was conducted comparing groups (experimental and control) with pretest-post-test-follow-up measures.

Participants

The sample consisted of 42 university students (11 males and 31 females), aged between 18 and 31 years (M = 20.25, SD = 2.83). The control group included 20 participants (6 males and 14 females), while the experimental group consisted of 22 students (5 males and 17 females). Subjects were randomly assigned to each group, controlling for the gender variable to ensure a balanced distribution and minimise possible biases in the results. No significant differences were found between the groups in terms of age or gender (p > .05).

Instruments

Study and Learning Processes Assessment Questionnaire (CEPEA) (Barca, 1999), adapted for Spanish students based on Biggs' SBQ questionnaire (1987), designed to assess learning approaches in Australian students. This questionnaire assesses the strategies used by students in their learning and their motivations for learning and consists of 42 items on study processes on a Likert scale (1–5). Of the 42 items, 21 correspond to motivations and 21 to strategies grouped into three categories (superficial, deep and achievement) that provide scores for six subscales involving study and learning tasks in general: superficial motivation, deep motivation, achievement motivation, superficial strategy, deep strategy, and achievement strategy. At a second level, scores are obtained for three learning approach scales (superficial, deep, and achievement) that integrate motives and strategies; and finally, the two approach compounds (superficial-achievement and deep-achievement). The total alpha coefficient of the Scale is .82 for the sample studied.

Academic Procrastination Scale (APS) (Busko, 1998). The Spanish version (Álvarez, 2010) was used, consisting of 16 items scored on a five-point Likert scale from 1 = never to 5 = always. High scores indicate higher levels of procrastination. The Cronbach's alpha coefficient is of .86 for the sample studied.

Mindfulness Attention Awareness Scale (MAAS) (Brown & Ryan, 2003). The Spanish version of the scale (Soler et al., 2012) was used, consisting of 15 items scored on a Likert scale from 1 = almost always to 6 = almost never, and is presented as a quantitative assessment of a subject's state of mindfulness during their daily life, such that high scores would indicate a greater state of mindfulness. The overall internal consistency of the scale, assessed using Cronbach's α statistic, is .897 for our sample.

Procedure

The sample was obtained through the course ‘Learning and Practicing Mindfulness,’ aimed at first-year students of the bachelor’s degree in social education at the University of Almería. Fifty-three students enrolled, of whom 42 were ultimately included in the study. Participants who reported previous experience with relaxation techniques, meditation, yoga or tai chi (n = 2), those who attended less than 80% of the sessions (n = 3) and those who reported in their self-recordings that they had practiced less than 50% of the days (n = 6) were excluded.

The CEPEA, MAAS, and Academic Procrastination Scale questionnaires were administered in the pretest phase to the final sample. Subsequently, participants were randomly assigned to the control and experimental groups, balancing the gender variable. Members of the control group were informed that they would participate in the mindfulness course in a second session due to space limitations.

The experimental group participated in an intervention programme based on Franco’s (2009) proposal, called Flow Meditation, aimed at the unconditional acceptance of private events without attempting to control or replace them, and developed over ten weekly 90-minute sessions during the first four months. The programme integrated elements of Kabat-Zinn's Mindfulness-Based Stress Reduction (1990), mindfulness strategies applied in acceptance and commitment therapy (Hayes et al., 1999), as well as stories and metaphors from the Zen tradition (Deshimaru, 2006) and vipassana meditation (Hart, 1994).

The aim of the intervention was to train participants to accept thoughts, emotions and sensations without trying to change or suppress them, observing them as transient events. The practice consisted of redirecting attention to the present moment, particularly to breathing, avoiding judgements and evaluative reactions. All the sessions were conducted by an expert mindfulness instructor, and were structured as it follows: (1) Sharing of experiences related to the weekly practice; (2) Body scan exercise (10 minutes); (3) Presentation of metaphors and specific exercises; and (4) Mindfulness practice in breathing (20 minutes). Participants were asked to practice daily at home (10-minute body scan exercise and 20 minutes of mindfulness in abdominal breathing). Compliance was recorded on a self-report sheet, with an overall mean of 64% for the body scan and 73% for the breathing practice. At the end of the intervention, the instruments used in the pretest phase were administered again to evaluate the changes obtained in the study variables (post-test phase). Likewise, to verify the stability of the results, a follow-up measurement was taken four months later, following the same procedure as in the pretest and post-test phases. Once the study was completed, the members of the control group received the mindfulness course.

All participants signed an informed consent form, and the study was approved by the Bioethics Committee of the University of Almería, Spain (UALBIO2024/007). The data recorded from each of the instruments were coded alphanumerically, ensuring confidentiality and anonymity, to comply with the Personal Data Protection Law established by the Ethics Committee for Research with Human Subjects (CEISH). The international guidelines for studies with human subjects described in the Nuremberg Code and the Declaration of Helsinki were applied.

Data analysis

Previously, the internal consistency indices for each scale at each point in time were analyzed using Cronbach's alpha and the Omega index. In addition, the asymmetry and kurtosis indices were examined to verify the distribution of the variables. According to the criteria proposed by George and Mallery (2010), values between −2 and +2 for both indices were considered acceptable for testing univariate normality in Likert-type scales. Subsequently, the normality and homoscedasticity of the data were verified, confirming the possibility of using parametric tests. The normality of the data was verified using the Shapiro-Wilk test, considered the most appropriate given the size of our sample. The homogeneity of variances between groups was analyzed using Levene's test on pre-test measures. Finally, for repeated measures ANOVA models, the sphericity assumption was evaluated using Mauchly's test. In those cases where the sphericity assumption was not fulfilled (p < .05), the degrees of freedom were adjusted by applying the Greenhouse-Geisser correction.

First, descriptive statistics were obtained to explore the mean scores and standard deviations of the participants in each of the variables. Subsequently, the main analyses of the research were carried out using a 3 × 2 mixed repeated measures ANOVA. This analysis was applied due to the longitudinal nature of the design, which requires evaluating the same participants at three different points in time: pretest, post-test, and follow-up at four months. This method is the most appropriate because it allows for control of inter-subject variability, as each participant acts as their own control, which increases the statistical power to detect significant changes and allows for accurate analysis of the interaction between the group factor (experimental vs. control) and the temporal evolution of the studied variables. The relevant F statistics were extracted, in accordance with the sphericity assumption calculated using Mauchly's test. Bonferroni post hoc tests were also performed to determine the levels of the variables that were significant. On the other hand, to obtain intragroup data, a one-way ANOVA of repeated measures was carried out in both the experimental and control groups, including time (pre-post-follow-up) as a factor. Due to the small sample size in the intragroup comparisons, the analyses were completed with the effect size using Hedges' g. Hedges' g is a measure of effect size that adjusts Cohen's d for small samples, making it particularly useful in post hoc tests (such as Bonferroni) where the sample size per pair may be limited. Unlike Bonferroni's value, which indicates statistical significance, Hedges' g quantifies the magnitude of the practical difference between two specific groups (Rodríguez-Sabiote et al., 2025). Data analysis was performed using the statistical software IBM SPSS Statistics 25.0.

Results

Reliability and distribution indices

The reliability of the scales was assessed by calculating the internal consistency for each group (experimental and control) at the three measurement points (pretest, post-test, and 4-month follow-up). Following current recommendations for reporting reliability (Viladrich et al., 2017), both Cronbach's alpha (α) and McDonald's omega coefficient (Ω) are presented, the latter being preferable as it is a less biased estimate of reliability. As shown in Table 1, the coefficients obtained showed robust internal consistency. Most Omega (Ω) values exceeded the threshold of .80, the criterion recommended by Viladrich et al. (2017, p. 756) for studies where the comparison of means between groups is central. In specific cases where the coefficient was slightly below .80 (range .77 – .79), the values were considered acceptable given the sample size and the nature of the scales, always remaining above the traditional limit of .70.

Table 1.

Reliability coefficients of the different scales in the experimental and control groups

Variables		Experimental			Control
		M	α	Ω	M	α	Ω
Superficial motivation	Pretest	23.00	.79	.81	22.65	.78	.79
	Postest	20.09	.81	.82	22.75	.80	.83
	4 months	20.45	.82	.83	22.40	.79	.81
Deep motivation	Pretest	21.41	.83	.84	20.20	.81	.81
	Postest	23.00	.85	.88	19.90	.82	.85
	4 months	23.36	.84	.85	20.40	.80	.83
Achievement motivation	Pretest	23.50	.86	.87	22.50	.85	.87
	Postest	22.45	.85	.85	23.50	.84	.86
	4 months	22.18	.87	.89	23.25	.85	.88
Superficial strategy	Pretest	22.18	.77	.78	23.45	.78	.82
	Postest	20.95	.79	.80	23.20	.76	.79
	4 months	20.82	.80	.83	23.05	.77	.78
Deep strategy	Pretest	20.91	.81	.82	22.45	.80	.83
	Postest	23.41	.84	.85	21.50	.81	.81
	4 months	24.05	.83	.86	21.40	.79	.82
Achievement strategy	Pretest	23.14	.82	.84	22.60	.83	.84
	Postest	23.45	.84	.85	22.20	.82	.86
	4 months	23.64	.85	.86	23.25	.84	.85
Superficial focus	Pretest	22.59	.80	.81	23.05	.79	.82
	Postest	20.52	.81	.81	22.97	.80	.83
	4 months	20.63	.82	.84	22.72	.78	.80
Deep focus	Pretest	21.15	.84	.85	21.32	.82	.82
	Postest	23.20	.86	.89	20.70	.83	.85
	4 months	23.70	.85	.86	20.90	.81	.84
Achievement focus	Pretest	23.31	.83	.84	22.55	.82	.85
	Postest	22.95	.84	.87	22.85	.83	.84
	4 months	22.90	.85	.86	23.25	.84	.85
Deep compound/ Achievement	Pretest	44.47	.88	.88	43.87	.87	.90
	Postest	46.15	.89	.92	43.55	.88	.89
	4 months	46.61	.90	.91	44.15	.87	.90
Superficial compound/ Achievement	Pretest	45.90	.86	.87	45.60	.85	.88
	Postest	43.47	.87	.88	45.82	.84	.85
	4 months	43.54	.88	.91	45.97	.86	.87
MAAS	Pretest	39.36	.89	.90	41.50	.88	.88
	Postest	45.00	.90	.93	37.95	.89	.90
	4 months	42.41	.91	.92	39.50	.88	.91
Procrastination	Pretest	42.59	.87	.88	38.00	.86	.89
	Postest	37.73	.88	.89	39.60	.87	.88
	4 months	39.59	.89	.92	39.30	.86	.87

On the other hand, asymmetry and kurtosis indices were examined to verify the distribution of scores across all experimental and control variables. The results indicated that the distribution of data remained within expected parameters. The asymmetry values ranged from −1.35 to .48, showing a moderate tendency toward negative asymmetry typical in self-reported measures of performance and motivation. Meanwhile, kurtosis indices ranged from −.92 to 1.65, without exceeding the critical threshold of 2.0 in any case. These indicators confirm that deviations from normality were not severe enough to invalidate the use of parametric tests to compare means.

Preliminary analyses and assumption checks

Preliminary analyses indicated that the data fulfilled the assumptions for parametric tests in the majority of conditions. The Shapiro-Wilk test showed that the distributions of the variables at baseline did not deviate significantly from normality in either group (p > .05); for example, in deep motivation (experimental: W = .963, p =  .543; control: W = .949, p = .345). Likewise, Levene's test confirmed the assumption of homogeneity of variances for all baseline scores between the experimental and control groups, obtaining non-significant values for all variables evaluated [all F values (1, 40) < 2.75, all p > .05].

In relation to the intra-subject analyses, Mauchly's sphericity test determined that the assumption of sphericity was fulfilled in several variables, including mindfulness or MAAS χ²(2) = 4.700, p = .095, deep strategy χ²(2) = 2.006, p = .367, and deep focus χ²(2) = 5.096, p = .078. However, the sphericity assumption was not strictly met for certain measures, such as procrastination χ²(2) = 8.224, p = .016 and superficial focus χ²(2) = 9.758, p = .008. As a result, the degrees of freedom for the within-subjects effects tests for these specific variables were rigorously adjusted using the Greenhouse-Geisser sphericity estimate (ɛ = .840 and ɛ = .819, respectively). There were no statistically significant differences in gender distribution between the experimental group and the control group (p = .603), age (p = .501), or any of the initial measures of motivation, learning strategy, learning focus, or mindfulness (MAAS) (Table 2).

Table 2.

Comparison of intervention effects between the experimental and control groups on motivation towards learning, learning strategies, procrastination and mindfulness

Variables		Experimental	Control	P^a time*group^a	Eta cuad part^a	Bonferroni intergrupo^b	Bonferroni intragrupo^c
Superficial motivation	Pretest	23.00 ± 4.27	22.65 ± 3.87	<.001	.520	.783	E1-E2*** / E1-E3*** /
	Postest	20.09 ± 3.43	22.75 ± 4.32			.032
	4 months	20.45 ± 3.32	22.40 ± 3.81			.085
Deep motivation	Pretest	21.41 ± 3.43	20.20 ± 2.74	.009	.231	.218	E1-E2*** / E1-E3** /
	Postest	23.00 ± 2.52	19.90 ± 3.04			<.001
	4 months	23.36 ± 2.66	20.40 ± 3.47			.003
Achievement motivation	Pretest	23.50 ± 3.93	22.50 ± 3.59	.388	.047	.397	–
	Postest	22.45 ± 3.12	23.50 ± 4.02			.350
	4 months	22.18 ± 3.24	23.25 ± 3.29			.296
Superficial strategy	Pretest	22.18 ± 4.05	23.45 ± 3.56	.224	.074	.290	E1-E2** / E1-E3* /
	Postest	20.95 ± 2.96	23.20 ± 3.05			.020
	4 months	20.82 ± 3.03	23.05 ± 2.89			.019
Deep strategy	Pretest	20.91 ± 3.19	22.45 ± 2.76	<.001	.403	.104	E1-E2*** / E1-E3*** /
	Postest	23.41 ± 2.53	21.50 ± 2.92			.029
	4 months	24.05 ± 2.03	21.40 ± 3.28			.003
Achievement strategy	Pretest	23.14 ± 4.24	22.60 ± 2.89	.837	.009	.638	–
	Postest	23.45 ± 3.40	22.20 ± 3.31			.234
	4 months	23.64 ± 3.49	23.25 ± 3.55			.730
Superficial focus	Pretest	22.59 ± 2.71	23.05 ± 2.80	<.001	.411	.593	E1-E2*** / E1-E3*** /
	Postest	20.52 ± 1.93	22.97 ± 2.87			.002
	4 months	20.63 ± 1.84	22.72 ± 2.17			.002
Deep focus	Pretest	21.15 ± 2.33	21.32 ± 2.34	<.001	.463	.820	E1-E2*** / E1-E3*** /
	Postest	23.20 ± 1.72	20.70 ± 2.43			<.001
	4 months	23.70 ± 1.86	20.90 ± 2.37			<.001
Achievement focus	Pretest	23.31 ± 2.45	22.55 ± 1.93	.576	.028	.271	–
	Postest	22.95 ± 2.74	22.85 ± 2.52			.899
	4 months	22.90 ± 2.15	23.25 ± 2.27			.620
Deep compound/ Achievement	Pretest	44.47 ± 3.39	43.87 ± 3.63	.243	.070	.582	–
	Postest	46.15 ± 3.54	43.55 ± 3.08			.015
	4 months	46.61 ± 2.47	44.15 ± 3.79			.016
Superficial compound/ Achievement	Pretest	45.90 ± 3.57	45.60 ± 2.68	.070	.127	.755	E1-E2* / E1-E3* /
	Postest	43.47 ± 3.12	45.82 ± 3.83			.035
	4 months	43.54 ± 2.47	45.97 ± 2.78			.005
MAAS	Pretest	39.36 ± 9.84	41.50 ± 10.08	<.001	.621	.491	E1-E2*** / E1-E3** / C1-C2***
	Postest	45.00 ± 10.83	37.95 ± 9.39			.031
	4 months	42.41 ± 10.56	39.50 ± 9.50			.356
Procrastination	Pretest	42.59 ± 9.73	38.00 ± 9.77	<.001	.378	.136	E1-E2*** / E1-E3*** / E2-E3*
	Postest	37.73 ± 9.04	39.60 ± 10.07			.529
	4 months	39.59 ± 9.56	39.30 ± 9.15			.920

Experimental, mindfulness training; Control, waiting list; Pre-test: Baseline, measurements prior to intervention; Post-test: measurement at the end of the programme after 2 months. Follow-up 4 months after the end of the intervention. Variables are presented as mean ± SD. N = 22 for the experimental group and N = 20 for the control group.

a

Analysis was performed using a general linear model for repeated measures.

b

Intergroup comparison by time.

c

*p = .05, **p ≤ .01, ***p ≤ .001. Post hoc ANOVA with Bonferroni correction was used. E1 = Experimental group pre-test; E2 = Experimental group post-test; E3 = Experimental group follow-up 4 months after the intervention; C1 = Control group pre-test; C2 = Control group post-test; C3 = Control group follow-up 4 months after the intervention.

Effects of mindfulness-based intervention on study variables (H1 and H2)

First, we explored whether a mindfulness intervention could improve their learning strategies compared to the control group using a general linear model of repeated measures with pre- and post-intervention measurements and a 4-month follow-up after the intervention. We found a statistically significant time × group interaction in superficial and deep motivation, deep strategy, superficial and deep focus, and finally in mindfulness and procrastination, as shown in Table 2. Therefore, compared to the control group, the 10-week mindfulness intervention in the experimental group was associated with a statistically significant reduction in superficial motivation towards learning, superficial learning focus, and procrastination levels. Conversely, a statistically significant increase was observed in deep motivation towards learning, deep learning strategy and deep focus, as well as mindfulness levels. Meanwhile, the control group did not change the levels of any of the variables studied in the different measurements taken. The effect size through partial eta squared ranged from .009 to .621, indicating very different effect sizes depending on the different variables. However, considering the variables previously identified as significant, it ranged from .231 to .621, indicating large effects according to Cohen's reference points for defining small (ŋp² = .01), medium (ŋp² = .06) and large (ŋp² = .14) effects (Cohen, 1988).

Stability of effects at the four-month follow-up (H3)

Having found group differences in superficial and deep motivation, deep learning strategy, superficial focus and deep focus, mindfulness, and procrastination, we examined changes over time in the experimental group and control group separately to see how long the effects would persist in the experimental group. We observed statistically significant changes over time in the experimental group (superficial motivation: F(2, 39) = 42.434, p < .001, ŋp² = .685; deep motivation: F(2, 39) = 7.544, p =  002, ŋp² = .279; deep strategy: F(2, 39) = 15.085, p < .001, ŋp² = .436; superficial focus: F(2, 39) = 30.204, p < .001, ŋp² = .608; deep focus: F(2, 39) = 22.499, p < .001, ŋp² = .536; mindfulness (MAAS): F(2, 39) = 25.419, p < .001, ŋp² = .566; procrastination: F(2, 39) = 13.918, p < .001, ŋp² = .416, but not in the control group, in which only statistically significant changes in mindfulness were observed, but in the opposite way.

Pairwise comparisons using ANOVA with Bonferroni correction showed that the experimental group exhibited a statistically significant decrease in superficial motivation towards learning in the post-test score (p < .001, Hedges g = 0.75) and at four months post-intervention (p < .001, Hedges g = .66) compared to baseline, showing a medium to large effect size in this case. However, a statistically significant increase was recorded in deep motivation towards learning in the post-test score (p =  .001, Hedges g = .53) and four months after the intervention (p =  .006, Hedges g = .64) compared to baseline, showing in this case a medium effect size. There was also a statistically significant increase in deep learning strategy in the post-test score (p < .001, Hedges g = .87) and four months after the intervention (p < .001, Hedges g = 1.17) compared to baseline, showing a large effect size in this case. In relation to the superficial focus to learning, a statistically significant decrease was found in the post-test score (p < .001, Hedges g = .88) and four months after the intervention (p < .001, Hedges g = .85) compared to baseline, showing in this case a large effect size. In contrast, a statistically significant increase in deep learning focus was observed in the post-test score (p < .001, Hedges g = 1.01) and four months after the intervention (p < .001, Hedges g = 1.20) compared to baseline, showing a large effect size in this case. For mindfulness, the experimental group showed a statistically significant increase in the post-test score (p < .001, Hedges g = .54) and four months (p = .004, Hedges g = .30) after the intervention compared to baseline. Finally, the experimental group showed a statistically significant reduction in procrastination levels after the intervention in the post-test score (p < .001, Hedges g = .52) and four months after completion (p < .001, Hedges g = .31).

Discussion

Effects on deep motivation, deep strategies, deep focus, and mindfulness

Based on the results obtained when comparing the pre-test and post-test scores between the control and experimental groups, hypothesis 1 can be confirmed. Thus, students who participated in the mindfulness-based intervention showed significant increases in deep motivation, deep learning strategies, deep focus, and mindfulness levels compared to the control group.

The increase in deep motivation suggests a shift towards more autonomous ways of academic engagement. From the perspective of Self-Determination Theory (Ryan & Deci, 2020), this result can be interpreted as a strengthening of autonomous regulation, possibly facilitated by greater awareness of one's own internal states and personal values associated with learning. The practice of mindfulness promotes a less reactive relationship with external demands and greater clarity regarding one's own academic goals, which may favour more intrinsic motivation (Shareefa et al., 2025).

In relation to deep strategies and focus, the observed increase can be explained by the effects of mindfulness on attentional regulation and cognitive control. Several current reviews indicate that mindfulness training enhances attentional stability and metacognitive monitoring (Ostermann et al., 2022; Phan et al., 2022). From a psychodidactic perspective, this implies that the student has greater resources to sustain attention, detect internal distractions, and apply elaborative strategies, which facilitates deeper information processing. Likewise, research on mind wandering has shown that higher levels of mindfulness reduce wandering during study, optimising the quality of information processing and the efficiency of using deep strategies such as elaboration and organisation (Baena-Extremera et al., 2021). This change is relevant because mindfulness is a functional basis for self-regulation in learning, as it allows individuals to identify attention lapses, regulate cognitive impulsivity, and maintain engagement in demanding academic tasks (Franco et al., 2016; Nguyen & Dorjee, 2022).

Effects on superficial motivation, superficial strategies, and academic procrastination

Hypothesis 2 is also confirmed when comparing pretest–posttest scores. The intervention produced significant decreases in superficial motivation, superficial strategies and focus, and academic procrastination in the experimental group compared to the control group. The reduction in superficial motivation can be interpreted as a decrease in patterns of involvement based on avoidance of failure or external incentives. Recent literature suggests that mindfulness reduces emotional reactivity to external demands and decreases the automatic activation of defensive responses (Phan et al., 2022). Regarding the decrease in superficial strategies, this result may be linked to reduced mind wandering and improved attentional control. Recent research shows that mindfulness training decreases mind wandering and increases cognitive stability during academic tasks (Bühler et al., 2025). This favours less reliance on memorisation and repetitive strategies, promoting more structured cognitive involvement.

With regard to academic procrastination, the reduction observed after the intervention can be explained by contemporary models that conceptualise procrastination as a failure of self-regulation associated with emotional avoidance (Sirois, 2023). Recent experimental studies have shown that mindfulness reduces procrastination by increasing tolerance to discomfort and improving emotional regulation in the face of tasks perceived as aversive (Franco et al., 2025; Rad et al., 2023). In this sense, the intervention may have modified the student's relationship with academic discomfort, facilitating greater behavioural persistence. In addition, increased mindfulness may have strengthened attentional monitoring and distraction inhibition, facilitating the application of deep strategies and reducing procrastination, as suggested by Nguyen and Dorjee (2022).

Temporal stability of effects

Hypothesis 3 is also confirmed, as the improvements observed in post-test scores (both in deep variables, and in the reduction of superficial variables and procrastination) were maintained in the assessment carried out four months after the intervention. The temporal stability of the effects suggests, therefore, that the program did not only generate specific motivational changes, but also possible modifications in the students' self-regulation patterns. This result is consistent with longitudinal research indicating that mindfulness training can produce sustained improvements in attentional and emotional regulation (Chen & Qi, 2025; Nguyen & Dorjee, 2022).

Taken together, these results are aligned with previous literature that has consistently shown that mindfulness-based interventions produce sustained improvements in motivational, attentional, and academic self-regulation variables. Different meta-analyses indicate that mindfulness training has moderate effects on motivation, well-being, and cognitive strategies (Ostermann et al., 2022; Phan et al., 2022), which supports the changes observed in our study in both the post-test and follow-up phases. Similarly, research conducted in university populations (Rad et al., 2023) has shown that increases in mindfulness are associated with improvements in information processing, the use of elaborative strategies, and emotional regulation.

It is important to note that the control group did not show positive changes in any of the variables assessed. In fact, a decrease in mindfulness levels was observed over time, which could be interpreted as a natural deterioration in the absence of intervention. This contrast highlights the effectiveness of the programme implemented and reinforces the hypothesis that the changes observed in the experimental group are due to mindfulness training and not to external factors. Nevertheless, alternative explanations should be considered. One possibility is that part of the improvement observed in the experimental group is due to expectation or placebo effects, especially since this is an intervention with high perceived popularity and an experiential component likely to generate positive expectations.

Implications, limitations, and future lines of research

The findings obtained in this study have important implications for university education. First, the results suggest that incorporating mindfulness training programmes into student education can be an effective tool for enhancing academic self-regulation, improving intrinsic motivation, and reducing maladaptive behaviours such as procrastination. Regular mindfulness practice seems to foster a deeper and more meaningful learning style, which can lead to better academic performance and greater satisfaction with the university experience. Secondly, mindfulness programmes can be integrated into institutional initiatives aimed at student wellbeing. Evidence shows that, in addition to improving learning processes, the practice of mindfulness promotes stress reduction, emotional regulation and the strengthening of resilience (Chen & Qi, 2025). Thirdly, the results suggest that teacher training in mindfulness techniques may be a key factor in ensuring the effectiveness of programmes, in line with the research of Santamaria et al. (2023).

Despite the positive findings, this study presents some limitations that should be considered. Firstly, the waiting list control group design does not allow for the specific effects of mindfulness training to be completely isolated from non-specific intervention factors (i.e., instructor attention, motivation to participate in a novel program, or group cohesion), which may have contributed in part to the improvements found.

Therefore, future studies should incorporate active control groups. Secondly, the sample size was small, which limits the generalizability of the results and the power to detect small effects. Accordingly, future research should be conducted with larger samples. Thirdly, the exclusive use of self-report measures may be influenced by social desirability biases or by changes in metacognitive awareness resulting from the training itself. Fourth, the sample consisted exclusively of students from the same grade and university, so it would be advisable to replicate the study in diverse academic contexts. Finally, although the effects were maintained at the four-month follow-up, it would be desirable to evaluate the stability of these results in the longer term.

Conclusions

In conclusion, the findings of this study provide preliminary evidence that a 10-week mindfulness-based intervention can be associated with positive changes in how university students approach their studies. The observed data indicate a tendency toward more self-regulated and deep learning patterns. However, given the limitations, these results should be interpreted with caution. While the changes were maintained at the four-month follow-up, suggesting some stability, it cannot be definitively concluded that the intervention is the sole cause of these improvements without further replication. Future research could benefit from larger samples and randomized controlled trials to further explore how mindfulness might influence academic variables. Nevertheless, this study suggests that integrating mindfulness into higher education settings represents a promising approach to support students' academic well-being and learning efficiency.

Conflict of Interest

The authors declare that they have no conflict of interest.

CRediT authorship contribution statement

Clemente Franco Justo: Conceptualization, validation, analysis, manuscript writing, writing, revision and editing, supervision; Alberto Amutio Kareaga: Conceptualization, validation, manuscript writing, writing, revision and editing, supervision; Rubén Trigueros Ramos: methodology, analysis; José Manuel Aguilar-Parra: methodology, analysis; Laura C. Sánchez-Sánchez: writing, revision and editing, supervision.