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Article

Emotional Intelligence, Creativity, and Subjective Well-Being: Their Implication for Academic Success in Higher Education

by
Presentación Ángeles Caballero García
*,
Sara Sánchez Ruiz
and
Alexander Constante Amores
Faculty of Education, Camilo José Cela University, 28692 Madrid, Spain
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(11), 1562; https://doi.org/10.3390/educsci15111562
Submission received: 22 September 2025 / Revised: 5 November 2025 / Accepted: 17 November 2025 / Published: 19 November 2025
(This article belongs to the Section Higher Education)
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Abstract

Professional skills training and academic success are key challenges for contemporary educational systems, particularly within higher education. The labour market increasingly demands well-prepared graduates with specific competencies that are still insufficiently embedded in university curricula. In this context, acquiring new professional skills becomes a decisive factor for students’ employability and competitiveness. At the same time, academic success remains a crucial indicator of educational quality, and its improvement is an urgent priority for universities. In response to these demands, our study evaluates cognitive-emotional competencies—emotional intelligence, creativity, and subjective well-being—in a sample of 300 university students from the Community of Madrid (Spain), analysing their influence on academic success with the aim of enhancing it. A non-experimental, cross-sectional research design was employed, using standardised self-report measures (TMMS-24, CREA, SHS, OHI, SLS, and OLS), innovative data mining algorithms (Random Forest and decision trees), and binary logistic regression techniques. The results highlight the importance of creativity, life satisfaction, and emotional attention in predicting academic success, with creativity showing the strongest discriminative power among the variables studied. These findings reinforce the need to integrate emotional and creative development into university curricula, promoting competency-based educational models that enhance training quality and students’ academic outcomes.

1. Introduction

In 1996, the Delors Report, prepared by UNESCO as a global proposal for rethinking education in the 21st century, already emphasised the importance of addressing not only cognitive aspects but also emotional ones in the teaching–learning process. This humanistic and holistic vision of education influenced numerous international initiatives and was particularly emphasised in Europe through the Bologna Declaration of 1999 and the Tuning Educational Structures in Europe project (González & Wagenaar, 2003). Together with the White Papers of the National Agency for Quality Assessment and Accreditation, produced between 2004 and 2007, these initiatives laid the foundations for the current undergraduate and postgraduate curricula in Spanish universities.
Similarly, since 2015, the Organisation for Economic Cooperation and Development (OECD) has been advocating for emotional education for children and young people and promoting policies and guidelines for its implementation in schools and among teachers. However, recent data show that emotional education is a priority and “an urgent but absent need in educational centres in Spain” (Instituto de Inteligencia Emocional y Neurociencia aplicada, 2021, p. 23). These emotional aspects, along with others associated with subjective well-being (SWB), remain a latent need in education, in general, and particularly in higher education, especially due to their impact not only on personal growth and social progress but also on academic and professional success (UNESCO, 2022).
In this regard, various meta-analyses and systematic literature reviews have demonstrated significant relationships between variables such as emotional intelligence (EI), happiness, life satisfaction, and academic achievement (AA). For instance, MacCann et al. (2020) showed that EI consistently predicts AA across different educational levels, as well as creativity (Alzoubi et al., 2021). Quílez-Robres et al. (2023) confirmed this relationship in the university context, highlighting its role as a protective factor. Ferrari et al. (2022) found that digital interventions aimed at psychological well-being have positive effects on university students, while Hobbs et al. (2022) demonstrated that university courses in positive psychology significantly improve students’ emotional well-being. Similarly, Maharaj and Ramsaroop (2024) emphasise the importance of strengthening EI as a means of promoting well-being in contexts of adversity, in line with the Sustainable Development Goals (SDGs), particularly SDG 3 on health and well-being.
Alongside these, creativity also plays a transversal role in sustainable development committed to the 2030 Agenda. To face new challenges and provoke new changes, creative individuals capable of seeing what others do not, whether at a macro or micro level, are needed (Panicello, 2022). However, when evaluated, creativity has been found to be unequal across regions, has decreased in the last decade (UNESCO, 2022), and is barely present in university degrees (Pérez Ordóñez et al., 2021) or in teaching methodologies (Caballero-García et al., 2019). As we enter higher education, we lose this educational competence to find a more common orientation towards productivity (Limiñana Gras et al., 2010).
In this context, recent systematic literature reviews and meta-analyses have evidenced significant relationships between creativity and key variables such as AA, creative thinking, problem-solving, educational environment support, and well-being (Acar et al., 2021; Alzoubi et al., 2021; Chang et al., 2025; Guo et al., 2025). Moreover, emerging technologies such as virtual reality and artificial intelligence are increasingly seen as effective tools for enhancing creativity among university students (Yu & Wang, 2025). These findings support the integration of creativity into university curricula as an essential 21st-century competence, necessary for comprehensive, innovative, and contextually responsive education.
The labour market demands that the educational system provide training in professional competencies, which remains a challenge for improvement. EI, creativity, and SWB have long been researched in the academic world; however, few guidelines have been provided to national agencies based on these results (Panicello, 2022). We lack a comprehensive governmental approach to cultural governance, as well as data and indicators that serve as a basis for the development and monitoring of policies that improve student outcomes and make them more competitive socially, academically, and professionally (UNESCO, 2022).
In this context, enhancing excellence in AA among students remains both a goal and a challenge, as well as a persistent concern within the educational field (Lamas, 2015), given that it continues to be an unresolved need in many contexts. Identifying the variables that determine academic success or failure not only contributes to improving individual outcomes but also serves as a valuable tool for assessing the quality and equity of the university system (Fernández Mellizo & Constante Amores, 2020). In line with this perspective, our study focuses on the analysis of psychoeducational variables that have shown a significant impact on AA in the university context, such as EI, creativity, and the emotional (happiness) and cognitive (life satisfaction) components of SWB. The selection of these variables is supported by growing scientific evidence based on systematic reviews and meta-analyses, which underline their relevance in contemporary university settings (Bücker et al., 2018; Gajda et al., 2017; MacCann et al., 2020; Quílez-Robres et al., 2023).
EI skills affect students both personally and academically (Fernández Berrocal et al., 2008), but the results of studies examining this relationship are ambiguous and even contradictory (Jiménez Morales & López Zafra, 2013). Generally, significant relationships have been found between EI and AA, with different directions: negative, with adaptability and stress management (Deniz et al., 2009), but also positive (Pérez Pérez & Castejón, 2006), with clarity (Sánchez Ruiz, 2020), attention and emotional regulation (Jiménez Morales & López Zafra, 2013), and of moderate intensity (Pérez Pérez & Castejón, 2006), confirmed by some meta-analyses (Carrillo et al., 2020; Idrogo Zamora & Asenjo Alarcón, 2021; Quílez-Robres et al., 2023; MacCann et al., 2020), or low (Sánchez Ruiz, 2020). However, other authors have not been able to confirm this relationship (Orejarena Silva, 2020; Romero Caballero et al., 2022); hence, some authors argue the non-existent or low predictive capacity of general EI on AA (Belmonte, 2013; Deniz et al., 2009). The statistical significance of this prediction increases in higher education students (Ahmed et al., 2019). The data even vary depending on the type of measure used. EI measured as a personality trait predicts between 6.2% (Parker et al., 2004) and 8–10% (Deniz et al., 2009), respectively, of AA. Using an ability measure, Sánchez Ruiz (2020) demonstrated that both attention and emotional clarity, separately and together, significantly correlated with AA. Attention predicted 2%, clarity 1.2%, and together 2%. In the study by Fernández Lasarte et al. (2019), the joint prediction was 3.2%. Emotional regulation does not seem to have the strength to predict AA (Fernández Lasarte et al., 2019); hence, authors like Supervía and Bordás (2019) have stated that EI moderates, but does not predict, AA.
Research on the relationship between creativity and AA in university populations also tends to find significant positive correlations (Sospedra Baeza et al., 2022), but there is no agreement on their intensity (Chiecher et al., 2018). Recent studies (Sánchez Ruiz, 2020) and meta-analyses (Gajda et al., 2017) confirm this positive and low correlation (r = 0.22), even slightly lower in higher education. Other meta-analyses have reported a medium-to-large effect size (g ≈ 0.62) (Akpur, 2023). Similarly, predictive studies have found that creativity only predicts a small part of AA: between 1.4% (Naderi et al., 2010), 1.6% (Sánchez Ruiz, 2020), or even an average of 5% (Gajda et al., 2017). The relationship between the two variables seems to depend more on the evaluation measure used, the area evaluated, and the evaluation methodology employed (Belmonte, 2013). The effects are low but stronger when creativity is measured with psychometric tests and AA is evaluated through standardised tests (Gajda et al., 2017).
The relationship between SWB and AA has also been a subject of debate. Most studies have found positive associations (Carranza Esteban et al., 2017) of medium-to-low character (Arhuis Inca, 2019) between the two variables. In higher education, the results confirm this positive relationship between the emotional component of SWB (happiness) and AA, varying in intensity depending on the study: between moderate and high (Carranza Esteban et al., 2017), low (Osornio et al., 2011; Ramírez & Fuentes, 2013), or very low after a positive emotional intervention (Puertas Molero et al., 2020; Salazar Almeida, 2021; Sánchez Ruiz, 2020), as confirmed by meta-analyses such as that of Bücker et al. (2018). Some studies have even shown that happiness is a predictor of AA; in the case of Osornio et al. (2011), 6.4% of its variability; in Ramírez and Fuentes (2013), 1.8%, and in Sánchez Ruiz (2020), 1.2%. Studies that have analysed the relationship between the cognitive component of SWB (life satisfaction) and AA in university students also obtain significant positive correlations of low intensity (Gómez Álvarez & García Valenzuela, 2022; Sánchez Ruiz, 2020). Life satisfaction is the component of SWB that best predicts AA (Akin & Akin, 2016; Balkis, 2013). In the study by Sánchez Ruiz (2020), it explained between 1.6 and 1.8% of the evaluated performance.
Overall, these findings, sometimes contradictory and other times inconclusive, have led us to the need for further research in this area. The purpose of our research was to analyse the impact and predictive nature of cognitive-emotional variables such as EI, creativity, happiness, and life satisfaction on the academic success of our students. Additionally, we aimed to promote a competency-based university education grounded in this empirical data to meet the confirmed national and international demand in the educational and labour sectors, making our students more academically and professionally competitive.
Our specific objectives were as follows:
  • To identify which cognitive-emotional variables among those studied are most strongly associated with academic success.
  • To determine the influence of these variables on the likelihood of academic success.
  • To describe the profile of the student who achieves academic success.
We expected that all the cognitive-emotional variables assessed—EI, creativity, happiness, and life satisfaction—would contribute significantly to students’ academic success, and that EI would have the greatest explanatory power in predicting academic success (H1). We also expected that the typical profile of academically successful students would be defined by all of these cognitive-emotional variables (H2).

2. Materials and Methods

The research methodology was quantitative, cross-sectional, and non-experimental or ex post facto in design. Its development followed a dual strategy: descriptive and exploratory, applying the Random Forest algorithm and decision trees; and predictive, using the binary logistic regression technique.

2.1. Participants

The final sample consisted of 300 university students, 23% (n = 68) men and the remaining 77% (n = 232) women, aged between 18 and 47 years (M = 21.72, SD = 4.427), who voluntarily participated in the study and gave their informed consent. They were selected from a population of 230,563 students enrolled in public and private universities in the Community of Madrid (Spain). To ensure the representativeness of the sample and the accuracy of our estimates or hypothesis tests, we calculated the minimum necessary sample size (254 students) and the maximum sampling error assumed with this selection (López-Roldán & Fachelli, 2015). The final sample exceeded the estimated size by 8.11% and proved to be adequate (Kleeberg-Hidalgo & Ramos-Ramírez, 2009), with an acceptable margin of error (5.65%) and a confidence level of 95%.

2.2. Instruments

The Trait Meta-Mood Scale (TMMS-24) by Salovey and Mayer (1990), Spanish version by Fernández-Berrocal et al. (2004), was used to assess overall EI and three of its dimensions: Attention, Clarity, and Emotional Repair. The scale comprises 24 Likert-type items with five response options. The original version demonstrated adequate psychometric properties, with reliability coefficients ranging from 0.82 to 0.85 across the three dimensions. In our study, Cronbach’s alpha values (Attention, α = 0.82; Clarity, α = 0.79; Repair, α = 0.80) confirmed the good internal consistency of the instrument (Kaplan & Saccuzzo, 2009).
The Creative Intelligence Test (CREA) by Corbalán Berná et al. (2003) was employed to measure creative ability through the generation of questions in response to visual stimuli (A, B, and C) within a short time frame (4 min). The original test showed adequate psychometric properties (reliability = 0.875; concurrent validity = 0.792). In our sample, Cronbach’s alpha values (Crea-A, 0.80; Crea-B, 0.77; Crea-C, 0.81) confirmed the good internal consistency of the test (Kaplan & Saccuzzo, 2009).
The Subjective Happiness Scale (SHS) by Lyubomirsky and Lepper (1999) and the Oxford Happiness Inventory (OHI) by Hills and Argyle (2002) were used as test–retest measures of subjective happiness. The SHS is a general measure of happiness that assesses a molar category of well-being as a global emotional psychological phenomenon. It consists of four Likert-type items with seven response options and meets psychometric criteria for reliability and validity (reliability between 0.79 and 0.80; concurrent validity between 0.52 and 0.72). In our study, Cronbach’s alpha was 0.70, indicating good internal consistency (Kaplan & Saccuzzo, 2009). The Oxford Inventory is also a measure of overall happiness. It comprises 29 Likert-type items with six response options, assessing motivational-behavioural, affective, cognitive, and physiological aspects of the construct. Its psychometric properties demonstrate adequate validity and reliability (reliability = 0.90; test–retest correlation = 0.78). In our research, we obtained a Cronbach’s alpha of 0.79, indicating good internal consistency (Kaplan & Saccuzzo, 2009), and a test–retest correlation of 0.68 (moderate).
The Satisfaction with Life Scale (SLS) by Diener et al. (1985) and the Overall Life Satisfaction (OLS) scale by Campbell et al. (1976) were used as test–retest measures of life satisfaction. The former assesses the cognitive aspects of well-being, specifically the degree of satisfaction with life as a whole. It consists of five Likert-type items with seven response options and demonstrates satisfactory validity and reliability (α = 0.84). In our study, Cronbach’s alpha was adequate (0.81). The OLS is a single-item measure using a 10-point Likert scale, which has shown adequate convergent validity with other satisfaction measures such as the SLSS, BMSLSS, and PWI-SC (correlations ranging from 0.50 to 0.62).
AA data were extracted from students’ official grade records. To control for structural variability across programmes and degrees, performance was assessed as the average grade for course competencies and learning outcomes. For the purposes of this study, this was transformed into a dichotomous variable dividing the sample into academic success/failure. Drawing on previous proposals such as that of Fernández Mellizo and Constante Amores (2020), we created a relative performance variable using the following equation: Relative performance = average course grade − average grade for the degree programme. We then dichotomised the criterion variable (0 = failure, 1 = success), considering a student to have achieved academic success if their relative performance was equal to or greater than zero, or if their grade was equal to or above the average for their degree programme. Conversely, students whose relative performance was below zero were classified as having experienced academic failure. This methodological strategy enabled a fairer comparison between students within homogeneous educational contexts. The resulting percentages are shown in Table 1.

2.3. Procedure

Once the necessary permissions were obtained, students were informed about the objectives of the study and the confidential use of their data. Voluntary participation and informed consent were requested prior to the administration of the measurement instruments, in accordance with Organic Law 3/2018 and the General Data Protection Regulation (EU) 2016/679, as well as the ethical standards of the Declaration of Helsinki (WMADH, 2018). Data collection was carried out in person, across four sessions of approximately 45 min each, during regular class hours and respecting the intact classroom system. Although the original study design included both pre- and post-intervention measurements, the present analysis only uses data from the pre-test phase. Measurements were conducted in dedicated sessions for each variable, without longitudinal follow-up, simultaneously, and at different points within the same intervention.

2.4. Data Analysis

Once the data were recorded, they were tabulated and statistically processed. Descriptive statistics (frequencies, percentages, means, and standard deviations) were used to determine the sociodemographic characteristics of the sample.
The external validity of the assessment was ensured by conducting measurements in situations similar to those to which the results were intended to be generalised (ecological validity), and by enhancing the reliability of our data through environmental consistency measures (intact classrooms and control of external variables such as uniformity in the teaching and learning environment). Internal validity of the evaluation was ensured through consistency testing of the measurement instruments employed. used and test–retest repetitions to verify the stability of results over time. Concurrent validity was established via test–retest procedures, using two measurement instruments for variables exhibiting lower temporal stability, such as happiness and life satisfaction. Predictive validity was assessed through the application of data mining techniques.
Before applying the data mining technique, the variance of the predictors was analysed to eliminate, if necessary, independent variables with zero or near-zero variance. At the end of this stage, it was not necessary to eliminate any independent variables, as all showed variability and provided information.
As shown in Table 2, variability was low across all predictors except for those related to creativity, with the highest coefficient of variation observed in stimulus A (CreaA) of the creativity test (69%).
Next, continuous variables were standardised to prevent predictors with larger magnitudes from exerting a disproportionate influence on the model (Raschka & Mirjalili, 2019). To determine which variables most influenced the likelihood of academic success, binary logistic regression was employed due to the qualitative, dichotomous nature of our dependent variable (AA). In interpreting the final logit model, both coefficients and odds ratios were used (i.e., the ratio of the probability of academic success to failure). Regarding effect size, we followed the guidelines of López Martín and Ardura Martínez (2023), where an odds ratio below 1.44 indicates a very small effect; between 1.44 and 2.47, a small effect; between 2.48 and 4.27, a moderate effect; and above 4.28, a large effect. To analyse the percentage of explained variance, the Pseudo R-squared statistic was used, as it is considered the most accurate (Aldas Manzano & Uriel Jiménez, 2017).
From this point and based on the work of Fernández Mellizo and Constante Amores (2020), to identify the variables most closely associated with academic success and determine the order of entry into the predictive model, we used the Random Forest classification technique due to its high accuracy (Raschka & Mirjalili, 2019). Normalised importance was calculated to rank the predictors.
As a preliminary step, we tested the assumption of multicollinearity in two phases. First, we examined correlations among the independent variables. Since the assumption of normality was not met, Spearman’s rank correlation coefficient was used. All correlations were of low intensity (below 0.80) (see Table 3), so no variables needed to be removed.
In the second phase, we ensured that the Variance Inflation Factor (VIF) remained below 10 (Pardo & Ruiz, 2013). All values were close to one and below the commonly accepted threshold of 5, thereby meeting the assumption of multicollinearity. The linearity assumption was tested using the Box–Tidwell procedure. In all cases, the natural logarithm of each predictor was not statistically significant.
Before introducing the independent variables, we examined the presence of outliers. Cook’s distance was below 1 (Aldas Manzano & Uriel Jiménez, 2017), indicating no influential outliers. To assess the calibration of the logistic regression model, the Hosmer–Lemeshow test was used, yielding p-values above 0.05, which confirmed the model’s excellent goodness of fit.
To describe the profile of academically successful students, we used decision trees, which allow for the definition and validation of models to determine which variables (predictors) explain changes in a dependent variable (Castro & Lizasoain, 2012, p. 134), and to create profiles (Breiman, 2001). Regarding growth methods, we employed Classification and Regression Trees (CARTs), which allow for binary partitions and are considered the most accurate and comprehensive procedure (Strobl et al., 2009). In interpreting the decision tree nodes (Figure 2), we took into account both the percentage of the sample and the probability of achieving academic success or high performance.
To evaluate the performance of our predictive model using these techniques (Random Forest, binary logistic regression, and decision trees), we considered the confusion matrix and used as a utility criterion that the model should classify with an accuracy greater than the mode (Raschka & Mirjalili, 2019), which, in our case, was above 51% (see Table 2).
All statistical analyses were conducted using the R programming language, version 4.1.3, employing the rpart and cart packages for decision tree and logistic regression techniques, respectively; the H2O machine learning library for the Random Forest model; and the oddsploty package to calculate 95% confidence intervals for the logit model’s odds ratios. In most cases, we worked with a statistical significance level of α = 0.05 and, in some specific cases, α = 0.01.

3. Results

3.1. Cognitive-Emotional Variables and Their Importance in Relation to Academic Success

To rank the evaluated variables according to their importance in relation to academic success, we employed the Random Forest technique. As shown in Figure 1, the most relevant predictors, in descending order, were creativity (CreaC), life satisfaction, and emotional attention (TMMS-24 1). In contrast, the variables least associated with the outcome variable were emotional regulation (TMMS-24 3) and happiness.

3.2. Cognitive-Emotional Variables and Their Impact on Academic Success

To determine which variables had the greatest impact on academic success, we conducted a binary logistic regression model. The resulting model is presented in Table 4.
Of the ten variables included, only two (creativity and life satisfaction) were found to be statistically significant. This set of predictors accounted for 4% of the variability in academic success. Regarding the model coefficients, life satisfaction had the greatest effect. The positive sign of the coefficient indicated that higher life satisfaction was associated with a greater likelihood of academic success. Specifically, for each additional point in this variable, the probability of academic success increased by approximately 21%. Creativity was also positively associated with the outcome variable. For each additional point in this predictor, the likelihood of achieving successful academic performance increased by 1%, although the effect size of these predictors was small (López Martín & Ardura Martínez, 2023). As for model calibration, the null hypothesis was accepted (x^2 (8) = 5.486, p > 0.05), indicating an acceptable model fit.

3.3. Profile of the Academically Successful Student

To describe the profile of academically successful students, we used decision trees. As shown in Figure 2, the highest probability of success (66%) was observed among students with high creativity scores (above 94 points). In contrast, the lowest probability of success was found in two profiles: firstly, students with creativity scores (CreaC) below 94 and life satisfaction scores of 7.5 or lower (32%); secondly, students with creativity scores below 94, life satisfaction scores above 7.5, and emotional attention scores below 22 points (32%).
Table 5 summarises the performance of the predictive models developed. As can be observed, all three algorithms correctly classified outcomes above the reference value of 51% (corresponding to the mode of our dependent variable; see Table 1), suggesting performance superior to chance. Among them, the Random Forest algorithm demonstrated the highest accuracy in estimating the probability of student success or failure. Nevertheless, these results should be interpreted with caution and require further validation with larger samples to enhance their generalisability and robustness.

4. Discussion and Conclusions

Our study, conducted within the context of higher education, aimed to explore the relationship between cognitive-emotional variables (EI, creativity, and SWB) and academic success, specifically, to identify their importance and determine their predictive value, as well as to understand the profile of the most successful students, with a view to promoting competence-based university education for improvement. We expected all variables to contribute to academic success, particularly EI (H1).
Our results showed that the variables most strongly associated with academic success, in order of importance, were as follows: creativity, in line with the studies by Chiecher et al. (2018), Sánchez Ruiz (2020), and Sospedra Baeza et al. (2022); life satisfaction, consistent with the findings of Gómez Álvarez and García Valenzuela (2022) as well as Sánchez Ruiz (2020); to a lesser extent, emotional attention, which has been independently supported by other authors (Jiménez Morales & López Zafra, 2013; Sánchez Ruiz, 2020) in higher education populations. Emotional regulation, however, did not appear to predict academic success, as also noted by authors such as Fernández Lasarte et al. (2019).
These findings, which only partially supported our hypothesis, should not be viewed as a limitation but rather as an invitation for theoretical reflection on the role of EI in university contexts. One possible explanation is that, although EI has been associated with AA in previous studies (MacCann et al., 2020; Sánchez Ruiz, 2020), its impact may depend on the type of measurement used (ability vs. trait), the educational context, or its interaction with other variables such as motivation, social support, or perceived stress. From a theoretical perspective, it could be argued that EI—understood as the ability to perceive, understand, and regulate emotions—does not exert a direct influence on AA, but rather modulates intermediate processes such as emotional self-regulation, resilience in the face of frustration, or time management. In this sense, its effect may be more evident in emotionally demanding contexts or in situations of academic adversity, which are not necessarily captured by global measures of academic success such as those used in this study. Therefore, these results suggest the need to revisit explanatory models of academic success, incorporating EI as a mediating or moderating variable rather than a direct predictor. Furthermore, it may be advisable to employ more sensitive or specific instruments to better capture its actual influence in the university context.
Regarding the second objective, determining the influence of cognitive-emotional variables on the probability of academic success, our results reaffirm that both creativity and life satisfaction significantly influence AA, jointly explaining 4% of the variance in academic success. Although this percentage is modest, it is comparable to the 6% explained by university entrance grades in previous studies (Fernández Mellizo & Constante Amores, 2020). Of the two variables, life satisfaction had the strongest predictive effect on academic success, in line with the findings of Balkis (2013). The effect size observed for these two predictors were similar to those typically reported in this type of research (Akin & Akin, 2016; Balkis, 2013; Sánchez Ruiz, 2020, for life satisfaction; Akpur, 2023; Gajda et al., 2017; and Naderi et al., 2010, for creativity), being small yet sufficiently relevant for educational practice. These findings highlight the value of cognitive-behavioural variables, compared to others, when designing educational intervention programmes aimed at promoting academic excellence in higher education, which is both necessary and increasingly demanded.
Finally, we aimed to identify the characteristic profile of students with the highest and lowest levels of academic success. We expected this profile to be defined by all the cognitive-emotional variables assessed (H2). In this regard, the variable that best discriminated higher academic success among those analysed was creativity, consistent with the findings of authors such as Chiecher et al. (2018). Students with high creativity scores (CreaC > 94, representing 23% of our sample) were 66% more likely to achieve academic success. Conversely, the variables that best characterised students with lower academic success were low creativity (CreaC < 94) and medium-to-low life satisfaction (<7.5), this being the least restrictive profile (30%) within the sample.
Our interest in studying academic success in higher education has led to findings that underscore the need to continue promoting changes towards academic excellence, an ongoing concern within the educational community that remains unresolved (Lamas, 2015).
Our research offers four major contributions to the scientific and educational community. First, the originality in selecting these variables (EI, creativity, and SWB) due to their potential relevance to academic success, as opposed to others. These were grouped under the cognitive-emotional category based on their conceptual and measurement characteristics and were studied in relation to academic success rather than AA.
Second, the classification and ranking of these variables by importance revealed that, while life satisfaction has the greatest predictive power for student success, creativity is the most important variable studied and the one that best discriminates the academic success profile.
Third, the innovative use of data mining techniques in the study of cognitive-motivational factors associated with AA. Our literature review identified educational studies that have used data mining in the past five years, but none that applied it to this type of variable or with the aim of predicting academic success.
Fourth, the novel application of the advanced data mining technique Random Forest to determine the most important variables contributing to academic success. Our research further highlights that Random Forest is the most accurate technique, in line with the findings of authors such as Raschka and Mirjalili (2019).
Scientific literature has shown that EI, creativity, happiness, and life satisfaction are positively correlated not only with AA but also with productivity and work quality (Caballero-García & Sánchez Ruiz, 2021). These are considered 21st-century soft skills that must be developed (WEF, 2020).
Recent research, including our own, has demonstrated the impact of emotionally positive traits (Puertas Molero et al., 2020; Salazar Almeida, 2021) and creativity (Sánchez Ruiz, 2020; Caballero-García & Sánchez Ruiz, 2021) on AA, as confirmed by meta-analyses such as those by Brauer et al. (2025) and Bücker et al. (2018).
Given the differential impact that variables such as creativity and life satisfaction have demonstrated on academic success, compared to other cognitive-emotional variables assessed, we consider it essential to move towards educational approaches that recognise and foster these competencies. In line with previous publications (Caballero-García et al., 2019; Caballero-García & Sánchez Ruiz, 2021), we advocate for the need for specialised teacher training in this area, as well as institutional changes and higher education policies aimed at the sustainable development of a curriculum based on cognitive-emotional competencies. These proposals are aligned with the commitments of the 2030 Agenda and call for integrated, specific, regular, and progressively refined intervention programmes, with the aim of strengthening the academic and professional competitiveness of our students and universities.
Nevertheless, the results obtained should be interpreted with caution due to certain methodological limitations. Firstly, the cross-sectional design of the study prevents the establishment of causal relationships between the variables analysed, limiting the conclusions to associations observed at a specific point in time. Future research could adopt longitudinal designs that allow for the observation of changes and sustained effects over time.
Secondly, although the predictive models developed performed better than expected by random assignment, the sample size influenced the choice of validation procedures. For this reason, we decided not to apply cross-validation techniques involving multiple partitions, such as 10-fold cross-validation, as such methods can produce training and testing subsets that are too small in moderate samples, thereby compromising the stability of estimates and the interpretability of the results (Kuhn & Johnson, 2013). This concern has also been raised by Hastie et al. (2009), who warn that in contexts with limited data, cross-validation may induce high variability in performance metrics. Therefore, we acknowledge the need for further validations with larger samples to confirm the robustness and generalisability of the findings. Future studies could address this issue by employing machine learning approaches that incorporate hyperparameter optimisation and more robust cross-validation techniques.
Thirdly, we note that the dichotomisation of AA, defined as success or non-success based on the relative average of grades, while useful for partially controlling heterogeneity across programmes (by avoiding direct comparisons between disciplines with differing demands and assessment criteria) and for the construction of predictive models, may also oversimplify performance variability and obscure important nuances in students’ academic trajectories. A single metric may not be comparable across disciplines, given that curricular structures and evaluation standards vary significantly between fields (as highlighted by studies on intra- and interdisciplinary metric inequalities, such as those by Light et al., 2025). Although the use of relative measures is methodologically sound in interdisciplinary contexts, it may fail to capture other dimensions of academic success, such as progression, retention, or the development of transversal competencies. Recent considerations emphasise that AA is a multifaceted construct encompassing progression, satisfaction, and skills development (Vugteveen et al., 2025). In this regard, the incorporation of continuous or multidimensional measures could enrich the analysis in future studies. It would also be advisable to explore continuous or multilevel models that more accurately represent the variability of AA and the hierarchical structure of the data, thereby avoiding simplifications arising from dichotomisation and enhancing the explanatory power of the analyses.
Fourthly, we acknowledge that the use of self-report instruments to measure variables such as happiness, life satisfaction, or EI, although common in educational research, is subject to social desirability bias and subjective perception. To mitigate these effects, validation strategies were applied, including the use of multiple instruments and test–retest procedures. However, we recognise that the inclusion of complementary measures, such as external assessments or methodological triangulation, could strengthen the reliability and enrich the validity of the findings.
It would also be pertinent to broaden the range of variables considered in this study by incorporating other competencies or soft skills that are highly valued in the labour market, such as teamwork (Canto et al., 2021), resilience (Caballero García et al., 2024; Cieza & Palomino, 2019; Day & Gu, 2015; Felix-Mena et al., 2021), critical thinking (Caballero-García & Sánchez Ruiz, 2022; Canto et al., 2021), leadership (Hsieh et al., 2023), and entrepreneurship (Manso & Thoillez, 2015; Peña et al., 2018), among others, and analyse their impact on students’ academic success (Caballero-García et al., 2019).
The inclusion of motivational and contextual factors (such as family or institutional environment), as well as specific cognitive determinants of performance, such as sustained attention, problem-solving ability, logical reasoning, and cognitive flexibility, among others not included in the present study, could also enhance the predictive power of the model and provide a more comprehensive understanding of AA. In this regard, future research should consider incorporating a broader set of variables, as well as adopting longitudinal designs and more sophisticated statistical models (such as multilevel or continuous models), to enable a deeper and more accurate understanding of AA and its determinants.
An additional limitation of the study is that the constructs were analysed independently. Future research could explore mediating or interactive relationships between them, such as the role of well-being in the relationship between EI and AA, in order to deepen our understanding of these connections.
Finally, the scope of the findings could be enriched through experimental research that evaluates the impact of specific intervention programmes or employs structural equation modelling to explore causal relationships in greater depth.
These considerations help to better contextualise the study’s findings and open up more robust avenues for future research, aimed at enhancing the understanding of academic success from a multidimensional and longitudinal perspective.

Author Contributions

Conceptualization, P.Á.C.G., S.S.R. and A.C.A.; Methodology, P.Á.C.G.; Software, P.Á.C.G. and A.C.A.; Validation, P.Á.C.G. and A.C.A.; Formal analysis, P.Á.C.G. and A.C.A.; Investigation, P.Á.C.G. and S.S.R.; Resources, P.Á.C.G.; Data curation, P.Á.C.G. and S.S.R.; Writing—original draft, P.Á.C.G., S.S.R. and A.C.A.; Writing—review and editing, P.Á.C.G., S.S.R. and A.C.A.; visualisation, P.Á.C.G. and A.C.A.; Supervision, P.Á.C.G. and S.S.R.; Project administration, P.Á.C.G.; Funding acquisition, P.Á.C.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study is part of the R&D&i Project “Hats & Scamper: Creativos y felices “. Ref. 2014-21, continued in the R&D&i Project “Hacker & Happy: Originales, Audaces e Inteligentes “, Ref. 2015-22, directed by “Presentación Ángeles Caballero García”, and funded by Camilo José Cela University, Madrid, Spain.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and Organic Law 3/2018. The research protocol was approved in the IV Call for Research Grants by the body that granted financial support for the project (Ref. 2015-22), the Vice-Rectorate for Research, Science and Doctoral Studies of Camilo José Cela University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are confidential and cannot be shared publicly due to privacy restrictions and confidentiality agreements. They are available from the corresponding author upon reasonable request.

Acknowledgments

Our thanks to the participating universities, collaborating professors, the students in the sample, and the members of the research group “Aprendizaje Social y Emocional -ASE-” at Camilo José Cela University.

Conflicts of Interest

The authors declare no conflicts of interest.

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Education 15 01562 g001
Figure 1. Cognitive-emotional variables associated with academic success, by order of importance. Note: TMMS-24 1, 2, 3 = Emotional attention, clarity, and regulation, respectively; Crea A, B, C = Creativity in response to stimuli A, B, and C; Oxford and Subj. Happ. = Happiness assessed using the Oxford Happiness Inventory and the Lyubomirsky and Lepper scale, respectively; Life Satisfaction and Diener = Life satisfaction assessed using the Diener et al. scale and the general index by Campbell et al., respectively.
Figure 1. Cognitive-emotional variables associated with academic success, by order of importance. Note: TMMS-24 1, 2, 3 = Emotional attention, clarity, and regulation, respectively; Crea A, B, C = Creativity in response to stimuli A, B, and C; Oxford and Subj. Happ. = Happiness assessed using the Oxford Happiness Inventory and the Lyubomirsky and Lepper scale, respectively; Life Satisfaction and Diener = Life satisfaction assessed using the Diener et al. scale and the general index by Campbell et al., respectively.
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Figure 2. Profile of the Academically Successful Student. Note: Creativity (Crea C) = Creativity in response to stimulus C; TMMS-24 1 = Emotional attention; Life Satisfaction = General life satisfaction.
Figure 2. Profile of the Academically Successful Student. Note: Creativity (Crea C) = Creativity in response to stimulus C; TMMS-24 1 = Emotional attention; Life Satisfaction = General life satisfaction.
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Table 1. Number of students with academic success/failure.
Table 1. Number of students with academic success/failure.
n%
Success15351
Failure14749
Table 2. Descriptive statistics of the study’s independent variables.
Table 2. Descriptive statistics of the study’s independent variables.
MeanStandard DeviationCoefficient of Variation
TMMS-24 127.93676.3960.237
TMMS-24 227.1866.5210.240
TMMS-24 328.0866.1420.219
Crea A37.24625.7030.690
Crea B44.50027.2460.612
Crea C68.80327.0800.394
Oxford4.5080.6210.138
Subjective happiness5.0000.9750.195
Life satisfaction 7.6151.5030.197
Diener5.3721.0360.193
Note: TMMS-24 1, 2, 3 = Emotional attention, clarity, and regulation, respectively; Crea A, B, C = Creativity in stimuli A, B, and C; Oxford and Subjective Happiness = Happiness assessed using the Oxford Happiness Inventory and the Lyubomirsky and Lepper scale, respectively; Life Satisfaction and Diener = Life satisfaction assessed using the Diener et al. scale and the general index by Campbell et al., respectively.
Table 3. Spearman Correlation Matrix.
Table 3. Spearman Correlation Matrix.
123456789
1. TMMS-24 1
2. TMMS-24 20.30 **
3. TMMS-24 30.090.44 **
4. Crea A−0.11−0.10−0.06
5. Crea B−0.06−0.07−0.100.66 **
6. Crea C−0.02−0.12 *−0.070.60 **0.70 **
7. Oxford0.090.49 **0.46 **−0.02−0.04−0.03
8. Subjective happiness0.000.43 **0.46 **−0.02−0.04−0.070.66 **
9. Life satisfaction0.020.44 **0.36 **0.000.02−0.050.65 **0.68 **
10. Diener0.050.43 **0.31 **−0.040.02−0.050.61 **0.57 **0.69 **
Note: TMMS-24 1, 2, 3 = Emotional attention, clarity, and regulation, respectively; Crea A, B, C = Creativity in response to stimuli A, B, and C; Oxford and Subjective Happiness = Happiness assessed using the Oxford Happiness Inventory and the Lyubomirsky and Lepper scale, respectively; Life Satisfaction and Diener = Life satisfaction assessed using the Diener et al. scale and the general index by Campbell et al., respectively. * p < 0.05, ** p < 0.01.
Table 4. Explanatory regression model of academic success.
Table 4. Explanatory regression model of academic success.
Coefficient (ES)Odds RatioLower LimitUpper Limit
Creativity0.011 (0.04)1.011 *1.0031.021
Life Satisfaction0.190 (0.082)1.209 *1.0221.408
Intercept−2.2514 (0.734)0.105 **−0.041−0.565
Pseudo R24%
Note: * p < 0.05, ** p < 0.01.
Table 5. Performance Evaluation.
Table 5. Performance Evaluation.
Correctly Classified Cases
Random Forest0.64
Binary Logistic Regression0.59
Decision Tree0.63
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Caballero García, P.Á.; Sánchez Ruiz, S.; Constante Amores, A. Emotional Intelligence, Creativity, and Subjective Well-Being: Their Implication for Academic Success in Higher Education. Educ. Sci. 2025, 15, 1562. https://doi.org/10.3390/educsci15111562

AMA Style

Caballero García PÁ, Sánchez Ruiz S, Constante Amores A. Emotional Intelligence, Creativity, and Subjective Well-Being: Their Implication for Academic Success in Higher Education. Education Sciences. 2025; 15(11):1562. https://doi.org/10.3390/educsci15111562

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Caballero García, Presentación Ángeles, Sara Sánchez Ruiz, and Alexander Constante Amores. 2025. "Emotional Intelligence, Creativity, and Subjective Well-Being: Their Implication for Academic Success in Higher Education" Education Sciences 15, no. 11: 1562. https://doi.org/10.3390/educsci15111562

APA Style

Caballero García, P. Á., Sánchez Ruiz, S., & Constante Amores, A. (2025). Emotional Intelligence, Creativity, and Subjective Well-Being: Their Implication for Academic Success in Higher Education. Education Sciences, 15(11), 1562. https://doi.org/10.3390/educsci15111562

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