Self-Regulation and Students Well-Being: A Systematic Review 2010–2020

Abstract

In recent years, there has been particular interest in studying the relationship between student self-regulation and variables such as students’ well-being, satisfaction, and school engagement. Although in other fields such as healthcare, self-regulation in different areas seems to influence individuals’ well-being, this is not so well established in the educational arena. We performed a systematic search of research articles published between 2010 and 2020 which explored the relationships between self-regulation and student well-being. The present article presents a report of a systematic review of 14 research articles. The analysis showed that some executive functions and self-regulation strategies employed in the learning process, and some self-regulatory deficits are significantly associated with different dimensions of student well-being.

1. Background

The interaction between self-regulation and various educational and personal variables has been extensively studied in educational psychology for over twenty years. The most important findings have tended to indicate that self-regulation of learning is significantly related to motivation, goal-setting, self-efficacy, persistence, and academic performance [1,2,3,4,5,6,7]. It would not be precipitous to say that self-regulation of learning is based on a framework of bidirectional relationships which mediate between personal and contextual variables.

In this context, over recent years there has been particular interest in the study of the relationship between self-regulation and variables such as student well-being, satisfaction, and school engagement. Despite self-regulation in other fields such as healthcare having been shown to influence individuals’ well-being [8,9,10,11,12,13], this has not been so solidly established in the educational arena. The present study centers on whether self-regulation skills in the educational context are associated with student well-being. Promoting student well-being is part of teaching, and we would expect learners’ self-regulatory skills to be associated with their well-being and academic satisfaction.

The evaluation of well-being is complex due to the multidimensional nature of the construct, as well as the dispersion in the measures and overlap between well-being and the quality-of-life construct. In the literature, there is a clear differentiation between subjective well-being—happiness—and psychological well-being. Whereas subjective well-being includes cognitive measures about satisfaction with life and measures of the experience of positive and negative affects [14,15], psychological well-being is seen as a multidimensional concept that usually distinguishes six factors (self-acceptance, positive relationships with others, autonomy, mastery of the environment, purpose in life, and personal growth [16,17]). Widening the focus from well-being includes the construct of quality of life [18], which describes a person’s physical, psychological, mental, social, and functional well-being in terms of interpersonal relationships, social inclusion, personal development, material well-being, self-determination, and both legal and human rights [19].

Considering the objective of this research, we must not lose sight of the fact that the self-regulation of learning is also understood as a complex multidimensional construct that we approach by considering the use of learning strategies and the skills and abilities needed to monitor and organize one’s own learning, manage time, maintain attention, reduce anxiety, delay gratification, and persist in the pursuit of set goals [20]. In this regard, it is common to find measures of self-regulation in terms of the use of self-regulation strategies, coping strategies and resources, measures of executive processes that underlie the self-regulatory process, and measures in terms of self-regulatory deficits [21,22,23].

Research on these two constructs is extensive and prolific, however, to our knowledge, no meta-analytic or systematic review studies have been conducted to explore the interaction between these two constructs in the educational context; therefore, we believe this topic deserves further investigation.

We performed a systematic review in order to specify how and to what extent self-regulation of learning has been linked to student well-being over the last ten years. Given the empirical evidence of the relationship between well-being and various measures associated with self-regulation such as autonomy or autonomous regulation [24,25], setting goals [26,27,28,29], self-efficacy, academic performance [30,31], and self-control [32,33], it is worth asking how closely self-regulation is linked to—and how much does it affect—student well-being [34,35,36]. With this objective, we reviewed in accordance with the protocol for systematic reviews or meta-analyses from the PRISMA declaration for systematic examination [37].

2. Method

2.1. Eligibility Criteria

The following conditions were applied to select articles found in databases. First, studies included were published between 2010 and February 2020, either in Spanish or English language. Only articles with the full text available from researchers or research groups located in Europe, North America, South America and Oceania were included. In addition to language, country of publication and time, other inclusion/exclusion criteria were taken into account. All research articles included (excluding qualitative, theoretical, and meta-analytical works) focused on an educational context. The samples’ studies had to consist of students and, finally, the measures and analyses had to prove a certain relationship between them (positive, negative or null relationship), excluding exploratory studies that do not provide information on the interaction between variables.

2.2. Search Strategy

The search strategy comprised an initial systematic review of articles published in various databases such as MEDLINE, Academic Search, PsycINFO, SCOPUS, WOS, SCIELO and REDALYC. The aim of the search was to find papers that explored self-regulation in the educational context and related it to academic achievement and student well-being.

The objective was to improve the results over time and produce an overview that would help us reach conclusions about various aspects of research on self-regulation over the last ten years. The search terms related to self-regulation used in the initial search of the databases are: Self-Regulation, Regulation, Self-Regulation Learning, Cognition, Metacognition, Self-Regulated Strategies and Executive functions. On the other hand, the well-being related search terms used in the initial search of the databases are Well-Being, Quality of life, Subjective well-being, Wellness, Welfare, Health and Happiness.

2.3. Types of Studies, Participants and Outcome Measures

For this review, we selected studies that examined the relationship between self-regulation of learning and well-being in the educational field, as our aim was to determine whether there was a relationship between these two theoretical constructs. This meant that all of the studies we examined needed to look at student self-regulation (whether directly or indirectly) where the outcome variable was academic well-being (subjective or psychological) or quality of life. Studies which did not have direct or indirect measures of self-regulation and studies using theoretical, qualitative or meta-analytical methodologies were excluded.

In terms of participants, the review included studies covering a range of student samples, from early childhood and primary education to college, university, or equivalent levels. Age, race, gender, socioeconomic status, and other sociodemographic information were not subject to limitation. Studies in which the participants were exclusively adults (not students), parents or teachers were excluded.

There are two types of well-being in the conceptual framework we followed, subjective and psychological. Both have multiple dimensions making it difficult to find a single definition that completely frames the concept. Our review covered studies that understood well-being as having good mental health, feeling highly satisfied with life/the academic context, having a sense of meaning or purpose, the ability to manage stress, having a positive or negative affect, having self-esteem, being optimistic and empathetic, and perceiving social support, among others.

2.4. Selection Process

The process of selecting the articles to include in the study followed various phases (see Figure 1 for a flow diagram outlining the process). The first phase was to identify the papers in the databases noted above using various combinations of search terms and time parameters.

Following that, the pre-selected articles were screened to eliminate duplicates, articles which appeared incomplete, and articles that were not in English or Spanish. After that reduction, we began the eligibility process, in which each paper was reviewed in detail to refine and specify the search.

Papers were eligible for review if they specifically explored the relationship between self-regulation and well-being in educational contexts. They were excluded if self-regulation was not examined, the study was not in an educational context, the sample was not students, the study did not have quantitative results, or the study had not been done in the stipulated locations.

2.5. Meta-Analysis

To perform the meta-analysis, we used the effect size measurements in each study because they indicate the magnitude of the effect produced in the variables. Effect size is a statistical index that measures the magnitude of the experimental effect in each study and is not affected by the sample size, unlike statistical significance tests [38]. There are different families of effect size indices whose proper application depends on the purpose of the study, the design used, and the types of variables measured [39,40,41]. The most frequently used effect size indexes in meta-analyses are as follows [42,43]:

• The “d” family, particularly indicated for studies applying designs with two groups with the result measured continuously. It consists in calculating the difference between the means of the two groups and, optionally, dividing that by the joint standard deviation of both groups.
• The “r” family, specially indicated for correlational studies, is based on obtaining a correlation coefficient.
• The risk index family, indicated for studies with two groups in which the outcome variable is dichotomous (e.g., pass vs. fail, improvement vs. no improvement).

The nature of the selected studies made it impossible to statistically combine the results from each study. We decided to continue the review qualitatively, making a systematic review of the selected articles. Throughout the review, the results are provided in detail in terms of effect size in order to demonstrate the relationships between the variables described.

3. Findings

3.1. Description of Included Papers

The initial search in the databases gave 2252 results. We selected 131 full-text articles to review against the eligibility criteria and once that evaluation was complete, a total of 14 articles were included in this review. At the end of this article in Appendix A, the articles included are listed with information about the aims of the study, the measures used, the results, and the characteristics of each article (n = 14). Appendix B lists the articles which were excluded and the reasons for the exclusion (n = 114). In Appendix C,

Table A3. Publication status of the articles selected for review.

Article	Journal	Year	Impact Factor
Aadland et al. [56]	Frontiers in Psychology	2018	2.129 (Q2)
Bruce et al. [43]	Psychology in the schools	2019	1.134 (Q4)
Cahua et al. [44]	Panamerican Journal of Neuropsychology	2018	-
Elliot et al. [52]	Journal of Personality	2011	2.440 (Q1)
Formina et al. [53]	Behavioral Sciences	2020	0.502 (Q2)
Garcia et al. [45]	PeerJ	2015	2.183 (Q1)
Gaspar et al. [46]	International Journal of Disability, Development and Education	2016	0.576 (Q4)
Guilmette et al. [47]	Learning and Individual Differences	2019	1.916 (Q2)
Howard et al. [54]	Journal of Developmental & Behavioral Pedriatrics	2018	2.256 (Q3)
Kornienko et al. [48]	Psychology in Russia: State of the art	2018	0.216 (Q2)
Lazaro- Vista et al. [49]	Frontiers in psychology	2019	2.067 (Q2)
Litalien et al. [50]	Motivation and Emotion	2013	1.844
Perry et al. [55]	Youth & Society	2018	2.523 (Q1)
Woodward et al. [51]	The Clinical Neuropsychologist	2016	1.564 (Q3)

contains the impact factors to assess the quality of the selected publications.

3.2. Methodology of Included Papers

The majority of the studies were cross-sectional [43,44,45,46,47,48,49,50,51], followed by prospective [52,53,54,55] and experimental/intervention [56].

The measures used to evaluate self-regulation were essentially self-reported [43,44,49,50,52,55] and/or reported by others [48,54,56]. Only the study by Woodkward et al. [51] used measures of self-regulating behavior through direct observation and information from others.

3.3. Evaluation of Self-Regulation

Self-regulation is a complex construct with a broad history of research which, while enriching educational practice, can make it hard to give the construct a clear identity and differentiate it from other associated constructs. In fact, faced with the complexity of combining the evaluation criteria of the construct of self-regulation in educational contexts, we decided to perform a qualitative review and dispense with meta-analytical processes. In order to give a sufficiently analytical picture, below we aim to differentiate between studies which assessed the use of strategies in different phases or areas of self-regulation, studies focused on the assessment of self-regulation deficits, and studies which evaluated executive processes.

Approaching self-regulation based on strategy use allows us to see an association with phases and areas of the process [57]. Bryce et al. [43] used the Intentional Self-Regulation Questionnaire [58] to evaluate students’ behavioral hope, collecting information related to planning in terms of goal selection and the optimization of resources. They also collected executive information about flexibility in using alternative means to stay engaged with the task at hand (compensation).

Litalien et al. [50] used a combination of measures taken from Emmons [59], Little [60] and Sheldon and Elliot [61] and, using the SDT framework [62], evaluated personal goals and the relative autonomy index for each personal goal. These measures fall within the initial phase of the self-regulation process as they refer to the selection and identification of personal goals.

Fomina et al. [53] used Morosanova’s Self-Regulation Profile Questionnaire [63] to evaluate a series of aspects related to planning, such as goal setting, modeling significant conditions, and programing activity, they also addressed supervision of the process, evaluating results, flexibility, independence, and responsibility. Similarly, García et al. [54] used the Regulatory Mode Questionnaire [64] to assess self-regulation, differentiating between aspects related to learner planning and availability—assessment—and aspects related to the implementation of learning processes—locomotion. Locomotion is understood as all of those characteristics and actions that allow one to advance towards goals, whereas assessment is an initial phase of the process which allows the learner to reflect, compare, and assess the potential results of implementation. Guilmette et al. [47] used a selection of items adapted from Wrosch et al. [65] to study the capacity to manage goals, measuring variables such as persistence, assertiveness, and consistency in doing tasks, and the tendency to make positive reappraisals.

Coping during the execution phase was also assessed by Cahua et al. [44] using the Adolescent Coping Scale [66], which differentiates between three coping types: productive coping, aimed at resolving problems, staying optimistic, and having a socially connected attitude; a relationship-oriented style focused on resolving problems through the support of others—seeking social support, seeking spiritual support, seeking professional help, social action; and an unproductive coping style, which combines the use of avoidant strategies—worry, investing in close friends, wishful thinking, ignoring the problem, seeking to belong, reducing stress, not coping, keeping it to oneself, and self-blame. On similar lines, Elliot et al. [52] used the Coping Responses Inventory [67] to evaluate Avoidance coping in two subscales, one of cognitive avoidance—avoiding thinking realistically about problems—and one of behavioral avoidance, which refers to strategies such as emotional discharge, aimed at reducing tension by expressing negative feelings rather than addressing the problem.

Within this framework of more emotional management, Gaspar et al. [46] created their own scale based on the framework of the International Resilience Research Project [68]. They used a measure of resilience to assess executive skills for managing resources for a task or in adversity depending on the source: provided external support, developed inner strengths, and acquired interpersonal and problem-solving skills.

Lázaro-Visa et al. [49] assessed both cognitive strategies and the management of negative emotions in their study. They used Social Attitudes and Cognitive Strategies [69] to incorporate social attitudes and problem-solving strategies—observation and retention of relevant social information, search for alternative solutions for solving conflicts, anticipation, reflection about consequences, and selection of means. They also used the Trait Meta Mood Scale [70] to assess the ability to repair negative emotional states. Woodward et al. [51] also used a measure of emotional self-regulation, using the Emotion Regulation Checklist [71]. Perry et al. [55] used the Motivated Strategies for Learning Questionnaire [72] to assess self-regulation. This subscale assesses metacognitive learning strategies and effort management. In the same study, they used the Africultural Coping System Inventory–Youth Version [73] to evaluate different types of coping skills (emotional debriefing, spiritually centered coping, maintaining harmony and communalism).

Howard et al. [54], Kornienko et al. [48], and Woodward et al. [51] chose to assess self-regulation considering the difficulties students face in regulating their learning. Howard et al. [54] analyzed information provided by parents, teachers, and students in interviews using factors developed by Moffitt et al. [32]. They assessed difficulties in controlling and sustaining attention, emotions, and controlling behavior—such as being restless or overactive.

Kornienko et al. [48] used the Strengths and Difficulties Questionnaire [74] to identify emotional difficulties and peer problems (internalizing problems), and behavioral problems and hyperactivity (externalizing problems).

Finally, Aadland et al. [56], Kornienko et al. [48] and Woodward et al. [51] assessed self-regulation looking at basic cognitive processes or executive functions. In addition to using a measure of behavioral self-regulation in a classroom setting reported by teachers [75], Aadland et al. [56] assessed inhibition, working memory, and cognitive flexibility. Kornienko et al. [48] used The Effortful Control Scale of the Temperament in Middle Childhood Questionnaire [76,77] to assess activation control, attention focusing, inhibitory control, low intensity pleasure, and perceptual sensitivity. Woodward et al. [51] evaluated cognitive functioning through direct observation of the subject, performance of activities, and using the Bayley Scales of Infant Development—Second Edition (BSID-II) [78].

3.4. Evaluation of Well-Being

The measures of well-being in the studies included in our review shared certain characteristics that, as was the case with self-regulation, led us to drop the meta-analytical analysis and obliged us to choose a more qualitative analytical approach. The evaluation of student well-being was addressed considering subjective well-being, but also in terms of psychological well-being and within a wider construct of Quality of Life. Some authors used other measures to report student well-being.

There was a clear predominance of studies measuring solely subjective well-being [50,52] compared to those that measured solely psychological well-being [53]. Two studies included measures of both types of well-being [44,45]. More specifically, Garcia et al. [45] evaluated the two dimensions making up subjective well-being (positive/negative affect and satisfaction with life) and the six dimensions making up psychological well-being (autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance). They used the Positive Affect/Negative Affect Schedule (PANAS) [79] to measure positive/negative affect. This instrument assesses the affective component of subjective well-being by asking the student the extent to which they have experienced positive emotions (interest, satisfaction, happiness, vitality…) or negative emotions (fear, nervousness, guilt, anxiety…) in recent weeks. Satisfaction with life was assessed using the Temporal Satisfaction with Life Scale [80] which assesses the cognitive component of subjective well-being measuring satisfaction with life in the past, present, and future. Psychological well-being was assessed using Ryff’s short test [81], a single scale that measures the six dimensions making up this type of well-being.

Cahua et al. [44] specifically measured satisfaction with the life dimension of subjective well-being and the purpose in the life dimension of psychological well-being. They used the Purpose in Life Test [82]. Through a series of cases, they explored people’s perceptions of the meaning of life, motives and reasons to live, their evaluations of their lives, their perceptions of their own day-to-day lives, objectives linked to specific life actions, and their perceived personal responsibility for their own lives. In terms of satisfaction with life, the study used The Satisfaction with Life Scale [83] which measures people’s perceptions about whether life is close to their ideal, whether they would change anything if they could live their life over, if they feel that the conditions of their life are excellent, whether they have everything they feel to be important in their life, and whether they are satisfied with their life generally.

Two studies assessed subjective well-being by simultaneously measuring its two dimensions, positive/negative affect and satisfaction with life [15,83]. Elliot et al. [52] created an index of subjective well-being (SWB) which addressed positive and negative affect measured using the Short Positive Affect/Negative Affect Scale [84], and life satisfaction via The Satisfaction With Life Scale [83]. Litalien et al. [50] measured satisfaction with life using the “satisfaction with present life” subscale from the Temporal Satisfaction with Life Scale [80]. This scale asks questions related to the extent to which a person (talking about the present) would change anything in their life, whether they had everything they wished to have, and whether they were satisfied with their life. The same study also assessed positive and negative affect using the Positive and Negative Affect Schedule (PANAS) [79], which was also used by Garcia et al. [45] in the study in this review.

Two studies focused on evaluating subjective well-being using only the satisfaction with life dimension. Kornienko et al. [48] used the Russian version of the Students’ Life Satisfaction Scale [85]. This scale measures students’ satisfaction with life by their agreement or otherwise with certain statements, such as whether they felt their lives to be better than others’, about the things in their life they would change, and whether they had a good life. Lazaro-Vaisa et al. [49] measured satisfaction with life using the Personal Well-Being Index [86]. This instrument is a general, abstract evaluation of satisfaction with certain aspects of life such as health, standard of living, life achievements, groups subjects are members of, future security, relationships with others, spirituality, and religion.

The most commonly assessed dimension within subjective well-being was positive and negative affect, with four studies. Guilmette et al. [47] measured the full “Positive and negative affect” dimension using the Positive and Negative Affect Schedule (PANAS) [79] described previously, in relation to other studies reviewed [45,50].

We did find other authors who had evaluated the dimension of positive/negative affect by measuring specific negative emotions such as anxiety and stress, although they focused exclusively on the negative affect part of this dimension. For example, Bryce et al. [43] used the Adolescent Stress Questionnaire [87] to measure student stress. Students reported their feelings of stress related to their peers and teachers within a school environment and about their feelings of stress about their future. The same authors used two items from the Generalized Anxiety Disorder scale [88] to measure students’ feelings of anxiety. This instrument aims to gather information related to whether students feel restless, nervous, worried, if they feel muscular tension, or if they have problems sleeping.

Howard et al. [54] chose to carry out face-to-face interviews with students’ parents or guardians. In the interview they asked about students’ mental health concerning ongoing stress or anxiety in the young people’s lives. Finally, Woodward et al. [51] assessed the presence of anxiety in students via the Development and Well-being Assessment (DAWBA) [89] within interviews with parents in order to understand the impact of various disorders, including anxiety disorder.

We found a single study that solely assessed students’ psychological well-being. Fomina et al. [53] used a Russian adaptation of the Well-Being Manifestation Measure Scale [90] to evaluate specific dimensions of psychological well-being. They measured the extent to which participants had experienced certain states related to mastery of the environment (control over events and control over oneself), purpose in life (enjoying each moment in life and finding it exciting), positive relations with others (social participation), self-acceptance (through understanding self-esteem), and personal growth (finding a balance between the different aspects of one’s life).

Addland et al. [56] and Gaspar et al. [46] used the long and short versions, respectively, of the Kidscreen Questionnaire [91] to evaluate the different dimensions of students’ quality of life. Addland et al. [56] reported students’ physical well-being, psychological well-being, relationships with parents, autonomy, social support, and school atmosphere. Gaspar et al. [46] focused on perceived social support and the relationship with parents as indicators of student well-being. On similar lines, Perry et al. [55] assessed student well-being considering the students’ perceptions of social support from parents, teachers, and peers. They used the Teacher Support Scale [92] to measure social support from teachers, which measures the extent to which students feel that their teachers care about them, have a positive regard for them, support them emotionally, and have high expectations for them. Perceptions of parental support were assessed using the Parental Career Behavior Checklist [93], which specifically measures whether the child feels that their parents try to understand their problems or are interested in their academic concerns. The final measure they used was via the Peers’ Academic Aspirations and Support measure [94], assessing perceptions of social support from peers. This scale was created to assess the perceptions of support from peers for specific academic activities and school engagement.

3.5. Executive Functions and Student Well-Being

Although the study by Aadland et al. [56] suggested that there was no relationship between inhibition, working memory, cognitive flexibility and school-related well-being in fifth-grade schoolchildren, Kornienko et al. [48] found that the ability to focus attention had an impact on subjective well-being—satisfaction with life—(β = 0.28, p < 0.001) and would reduce both emotional and/or peer-related problems (β = −0.85, p < 0.001) and behavioral problems (β = −0.58, p < 0.001) in 7–10 year-old children. Inhibitory control also contributed to specifically limiting these latter externalizing problems (β = −0.43, p< 0.001).

3.6. Self-Regulation Strategies and Student Well-Being

In general, self-regulation of learning was positively linked to perceptions of support from parents (r = 0.26, p < 0.01), teachers (r = 0.27, p < 0.01), and even with satisfaction with peers (r = 0.19, p < 0.01) in students aged 13–19 years old (M = 15.71; SD = 1.24) [55].

Although only confidence, perceived self-efficacy, and expectations of success—cognitive hope—were found to be significant buffers for anxiety or future and interpersonal stress in high school (9th to 12th grade), Bryce et al. [43] found that goal selection could reduce future-oriented stress. Litalien et al. [50] reported that students in their final years in upper secondary school (Mage = 19.51; SD = 0.77) who had more autonomous reasons to pursue their personal goals at the end of high school were more likely to report higher enjoyment of their lives and healthier affect two years later. Autonomous goal regulation, as part of the SDT framework (Deci & Ryan, 2000) positively predicted later life satisfaction (β = 0.10, p < 0.01) and positive affect (β = 0.15, p < 0.001), and negatively predicted negative affect (β = −0.11, p < 0.001).

Using a longitudinal design, Fomina et al. [53] found that the ability to create plans in the 4th grade (Mage = 10.31, SD = 0.48) had a positive impact on mental balance (β = 0.60; p < 0.01), sociability (β = 0.62; p < 0.01), self-esteem (β = 0.60; p < 0.01), and the control of self and events (β = 0.48; p < 0.05) in the 5th grade (Mage = 10.62; SD = 0.50). On similar lines, looking at high school pupils (Mage = 17.74; SD = 1.29), Garcia et al. [45] found that activation, proactivity, and forward-striving—striving to achieve a goal—were positively linked to both subjective well-being (r = 0.48; p < 0.01) and psychological well-being (r = 0.58, p < 0.01). Guilmette et al. [47] reported a correlation between determination in goals, consistency, coping with obstacles (persistence), and both positive affect (r = 0.50, p < 0.01) and negative affect (r = −0.25, p < 0.01) in university students (M = 21.11; SD = 1.68).

Although better understanding is needed of how these cognitive-motivational aspects may moderate the relationship between experiences and well-being in achievement contexts, it seems that establishing one’s own goals and commitment to achieving them contribute to students’ well-being.

Fomina et al. [53] looked at students aged 10–12 (M = 10.62; Sd = 0.50) and found a positive impact of the ability to manage their own achievements—evaluating results—on happiness (β = 0.45; p < 0.05) and self-esteem (β = 0.43; p < 0.05). However, with high school pupils (Mage = 17.74; SD = 1.29) a tendency for strategic thinking and assessing different goals and pathways was negatively associated with subjective well-being [45]. From a sample of university students (M = 21.11; SD = 1.68), Guilmette et al. [47] reported a positive correlation between positive reappraisals and positive affect (r = 0.44, p < 0.01) and a negative correlation with negative affect (r = −0.30, p < 0.01). It is worth highlighting here the mediation analysis in this study which suggested that engagement with activities—determination in one’s goals, consistency, and coping with obstacles (persistence)—contributed not only to limiting negative affect, but also reinforced the ability to see the positive side of negative events—positive reappraisal. The role of self-evaluation (rigorously reflecting on and evaluating different possibilities for achieving goals) on student well-being should address, for example, the level of achievement, expectations of success, and the competitiveness of the context, exploring the impact in different age ranges.

Lázaro-Visa et al. [49] reported that the ability to manage negative emotions (β = 0.11, p < 0.01), along with self-esteem, were explanatory self-regulatory factors of student satisfaction in 10- to 18-year-olds (Mage = 12.98; SD = 1.78) when other conditions and contextual variables were included in the regression models. Gaspar et al. [46] looked at a sample of Spanish and Portuguese students aged between 8 and 17 years old (Female: Mage = 9.8; SD = 1.32; Male: Mage = 10; SD = 1.53) and found that, along with self-esteem and optimism, resilience was a personal variable explaining happiness and satisfaction with family, school, and peers in children and adolescents with cognitive and developmental difficulties (β = 0.124, p < 0.01). These results are particularly interesting as children and adolescents with SEN are an at-risk group in terms of subjective well-being (Bullinger, Schmidt, Petersen & The Disabkids-Group, 2002).

Cahua et al. [44] found coping styles to be explanatory factors of purposes in life and satisfaction in adolescents aged 14 to 18 (M = 15.47; SD = 0.80). The study by Perry et al. [55], with a sample of adolescents (M = 15.71; SD = 1.24), concluded that satisfaction with others was correlated with the use of coping skills, such as attempting to manage situations through self-expression and creativity—emotional debriefing —(r = 0.19, p < 0.01 and r = 0.19, p < 0.01, for satisfaction with teachers and parents, respectively), through faith or religion—spiritually centered coping (r = 0.21, p < 0.01 and r = 0.20, p < 0.01, for satisfaction with teachers and parents, respectively), through relationships with others (r = 0.26, p < 0.01 and r = 0.25, p < 0.01, for satisfaction with teachers and parents, respectively), or maintaining harmony (r = 0.23, p < 0.01 and r = 0.22, p < 0.01, for satisfaction with teachers and parents, respectively). In contrast, Elliot et al. [52] found that avoidance coping, both cognitive and behavioral, reduced undergraduates’ psychological well-being. Avoiding thinking realistically about problems (β = −0.14; p < 0.05) and reducing tension, expressing negative feelings rather than dealing with the problem (β = −0.19; p < 0.01) reduced subjective well-being in a sample of undergraduates with a mean age of 19.95, even controlling for social desirability, basic traits, and general motivational disposition. One important task for the future will be to explore the differing impact of approach and avoidance regulation. Developing models that specifically incorporate reactivity and proactivity seems to be essential as variables related to individuals’ well-being come into play in the explanations of educational success.

3.7. Self-Regulation Deficits and Student Well-Being

Applying a longitudinal approach, Woodward et al. [51] concluded that poorer early regulatory abilities were a significant predictor of a range of later mental health disorders. More specifically, behavioral and emotional self-regulation in children at 2 years old was an important predictor of ADHD (β = −0.16, p < 0.01), conduct disorder (β = −0.14, p < 0.05), and anxiety disorders (β = −0.14, p < 0.01) at 9 years old. In addition to doubling the risk of self-harming ideation and behavior and suicidal ideation, Howard et al. [54] suggested that problems of self-regulation reported by parents and teachers—the extent to which children can control and sustain attention, control their behavior, and their emotions—in 4–5 year-olds and 6–7 year-olds was associated with a 2-times increase in conditions of depression and/or anxiety in adolescence (OR: 1.73, p < 0.01, CI 95% [1.51–1.94] and OR: 1.70, p < 0.01, CI 95% [1.48–1.91], respectively).

4. Discussion

This systematic review summarized the last ten years of research into the association between self-regulation and well-being in the educational setting, covering a variety of age ranges. A total of 14 articles were considered which explored the issue from self-regulation skills and executive functions in early ages to the use of self-regulation strategies in secondary and pre-university students.

The first general finding the review produced was the recognition of the problem of combining the criteria for evaluating the construct of self-regulation in the educational arena. The multiplicity of approaches and the multidimensional nature of the construct made it impossible to combine specific findings for a meta-analytical exploration. Although the broad empirical evidence that exists enriches educational practice, as we mentioned previously, it also makes it very hard to give self-regulation a single identity or differentiate it from other constructs it is linked to, in our case it also removed the possibility of achieving generalizable, consensus knowledge by not allowing us to statistically combine the research results.

The qualitative, systematic analysis of the articles in this review showed that both some executive functions and some self-regulation strategies employed in the learning process, as well as some self-regulation deficits, are significantly associated with different measures of student well-being.

The relationship between executive functions and student well-being revolves around the ability to focus attention and inhibitory control. The results suggest that students who can focus their attention demonstrate greater satisfaction with life and fewer emotional or behavioral problems, and fewer problems in peer relationships. Along similar lines, those who exhibit greater inhibitory control demonstrate fewer behavioral problems [48,56]. This may be due to the relationship between effort control (focusing attention and inhibitory control) and the self-regulation of learning [95], as well as the relationship with academic performance [96,97]. Based on the evidence, it seems logical that a student with good indices of effort control would better self-regulate their learning and achieve better results, increasing their satisfaction with life. Similarly, self-regulation of the learning process, which also refers to the ability to regulate emotion and behavior during engagement with academic activity, would also result in reduced emotional and behavioral problems as well as resulting in the use of adaptive strategies to express and resolve problems.

There was also evidence that the use of different learning self-regulation strategies was closely and significantly related to student well-being. We found that goal selection would reduce future-oriented stress, but not only that, if this goal selection was autonomous, it could predict future satisfaction with life, positive affect, and prevent negative affect [43,50]. On similar lines, other authors suggested that being proactive and striving to achieve goals that are set were linked with better subjective and psychological well-being. Determination in seeking set goals, the ability to be consistent in the tasks related to them, and persistence in the pursuit of achieving them have positive effects on positive affect and reduce negative affect [45,47]. In parallel to goal-setting, we found that the ability to create plans impacts student well-being as it improves mental balance, sociability, and control of self and events [53].

Looking at the supervision and assessment phases of the self-regulation of the learning process in this review, it showed that strategies such as evaluating results had positive impacts on happiness and self-esteem at the beginning of primary education, whereas in secondary school, strategies related to the assessment phase such as strategic thinking, and assessing different goals and pathways were negatively linked to student well-being. However, when students reached university level, these same strategies were positively related to positive affect and negatively related to negative affect [45,47,53]. This may be because at the university level there is more determination with goals, as well as greater consistency and persistence, which may contribute to limiting the negative effects, enhancing the ability to see the positive side of negative events.

Another finding from this review revolves around the relationship between managing negative emotions and student well-being and satisfaction [49]. In this regard, resilience may be considered an explanatory personal variable of happiness and satisfaction with family, school, and peers in children and adolescents with cognitive and developmental difficulties [46]. The informative study by Gaspar et al., (2016) showed that when social and personal variables were included, the association between the condition of being a student with SEN and well-being was no longer statistically significant, which may suggest that personal factors such as resilience are protective factors for well-being in these populations.

Coping strategies and styles were also seen to be explanatory factors for purpose in life and satisfaction with life [44]. The types of strategies people used to cope with or manage situations had an impact on one or more of the dimensions of well-being [55]. Students who used strategies of emotional debriefing, spiritually centered coping, or maintaining harmony had higher levels of satisfaction with teachers and parents, bearing in mind the correlation between these two measures. In turn, strategies such as avoidance coping (both cognitive and behavioral) reduced student psychological well-being [52].

Finally, considering the relationship between self-regulation deficits and student well-being, we found that poorer early regulatory abilities were a significant predictor of a range of later mental health disorders, conduct disorders, and anxiety disorders [51]. Similarly, problems with maintaining attention and controlling emotions or behavior in early childhood also had an impact on the likelihood of suffering from anxiety or depression in adolescence [54]. The capacity for attention, as well as the ability to manage one’s behavior and emotions in early childhood may be key in minimizing a broad range of problems linked to health and well-being in adolescence.

5. Conclusions

We believe that some general implications can be drawn from this article. The first of them refers to the need to reach some level of consensus in the scientific community intending to give self-regulation a clear identity based on clearer evaluation criteria in the educational context. This will only be achieved with a continual interaction between and within disciplines that will allow rigorous modification when it comes to defining or conceptualizing the construct. In this regard, there should be a continued exploration of the role of executive functions as they underlie the process of self-regulation of learning in students.

Secondly, both for improving the different dimensions of well-being, as well as to prevent future problems, there needs to be explicit work on setting academic goals, support for goal selection and planning, as well as consistency and persistence in pursuit of those goals. It would also be useful to encourage students to play a greater role in the most evaluative phase of the self-regulatory process, and there should be intervention in the self-evaluation and monitoring of the process and results as that could have a positive impact on the development of adaptive attributional patterns and coping styles.

It is important to highlight the need to strengthen interventions into students’ emotional management or self-regulation, especially in the preservation of self-esteem, focusing specifically on resilience, which is a protective factor in both typical populations and those with special educational needs.

Finally, we must re-emphasize the opportunity to promote self-regulation abilities at early ages, as it is a preventive factor for future well-being and against mental disorders—anxiety and depression—and behavioral disorders—self-harm and suicidal ideation—in adolescence.

6. Limitation

This study has some limitations. Firstly, the reliability of some of the instruments used for evaluating self-regulation was unknown, as most were adaptations or translations from an original. It is also worth noting that the close context of the classroom and teaching styles were not considered, nor were gender differences explored as most of the items were assessed by age. In addition, various articles offered a range of correlation indices without specifically indicating the numerical relationship between the variables, and some articles did not provide the mean or standard deviation for the age of the sample.