There is a growing interest in the importance of autonomy in learning, including for adults. This topic seems more relevant than ever, given the increasing importance of elearning, which may require learners to be more independent than in face-to-face courses. One of the most interesting and encompassing concepts on the subject is that of self-regulated learning, which dynamically integrates the fundamental aspects of the act of learning, such as cognition, motivation, metacognition and volition. To help you better understand this concept, here are a few elements taken from the literature review done on the subject by Professor Laurent Cosnefroy.
A definition of self-regulated learning
Formed from the Greek “autos” (self) and the Latin “regula” (rule, law), the term “self-regulation” refers to the capacity of a system to regulate itself, without external intervention, in the event of an internal or external disturbance. It can be used to refer to an organism, a process, a system or a machine. Several researchers have studied self-regulation in learning, and different definitions have emerged from this interest. In his literature review “L’apprentissage autorégulé : perspectives en formation d’adultes,” Laurent Cosnefroy favours Shunk’s (1994) definition of self-regulated learning, which describes it as “a set of processes by which subjects activate and maintain cognitions, affects, and behaviours that are systematically oriented toward the achievement of a goal.” This definition thus presents the learner as an autonomous subject who actively participates on the motivational and metacognitive levels in their learning by making efforts to achieve the set goal(s). This definition assumes that it is not enough to initiate the action of getting to work to maintain it and achieve one’s goals.
The concept of self-regulated learning puts forward the idea that having a wealth of knowledge and learning strategies is not enough to learn; one must also be able to mobilize these resources in an active and sustainable manner using motivational levers. Self-regulated learning is distinct from self-directed learning, although both concepts emphasize autonomy in learning. The latter is broader in scope, focusing on the characteristics of the learning environment that encourage autonomy as well as the psychological characteristics of the learner (Loyens et al., 2008). Research on self-regulated learning and the models that stem from it aims to uncover the psychological mechanisms that articulate autonomy in learning, i.e., the mechanisms that enable learners to get to work and stay focused to achieve their goals, despite obstacles, by taking control of their learning processes and their motivation. Note that research on self-regulated learning is part of the field of motivation and metacognition.
The commonalities of the leading models
From the numerous works on self-regulated learning, five theoretical models (those of Winne, Pintrich, Corno, Zimmerman and Boekaerts) stand out as reference models. Developed between the end of the 1980s and the beginning of the 2000s, these models, which each contribute to enriching this concept, share certain common points. First, they suggest that four conditions are necessary for the learner to govern their learning: having sufficient initial motivation, defining a goal to be achieved, being able to implement self-regulation strategies and being able to self-observe. They then articulate the deployment of self-regulation in three phases: the first phase, which includes cognitive and motivational dimensions, consists of defining plans and goals (cognitive dimension) and weighing up the advantageous but also threatening aspects that could arise from the learning situation (motivational dimension); the second phase – or central phase of self-regulated learning – is when the learner is engaged in the task and, using self-regulation strategies, tries to control their action in order to achieve their goal(s). The third phase is the one in which the learner evaluates their activity with hindsight, which enables them to change their metacognitive beliefs, their perception of the factors involved in success or failure, and their vision of the competency.
In all but one of the models, the progression is non-linear, as the learner can redefine their plans and goals as they progress in learning. Adjustment of goals is an important self-regulatory strategy; as a result, the goal achieved at completion may differ from the one set at the beginning.
Motivation and volition
Engaging in self-regulated learning requires a considerable investment of effort and time. Motivation and volition are two essential and interdependent components in this process. In order to consider getting to work, the learner must have an initial motivation coupled with an intention to perform, which can be summarized as the ability to identify the behaviours most likely to lead to the achievement of one’s goals and the most favourable situations to initiate them (Gollwitzer, 1999; Gollwitzer & Sheeran, 2006). Initial motivation is largely based on the value the learner places on the task and on their belief in their ability to accomplish it, i.e., on their sense of self-efficacy (Bandura, 1986; Zimmerman’s model in Carré & Moisan, 2002). In self-regulated learning, we speak more specifically of “initial” motivation because of the presence of the concept of volition, which in turn refers to the action of getting down to work and staying there. As Corno, whose work on volition is representative of the volitional stream of self-regulated learning, summarizes, “motivation promotes an intention to learn; volition protects it” (2001).
Motivation and volition are part of conation, a broader concept from the field of psychology that refers to the set of psychic processes that lead to action, as opposed to cognition, which refers to the set of psychic processes that lead to knowledge. While motivation has been much more studied than volition, the latter targets a very current problem in this digital age where distractions abound: the difficulty for a majority of learners to get down to work and persevere (Baillet et al., 2016; Poncin et al., 2017).
Multiple goals, conflicting dynamics and the quest for self-esteem
Without goals, no self-regulated behaviour is possible. It is the goals, also called achievement or competence goals, that serve as reference points for the learner to gauge whether they should modify their behaviour during the action, and it is based on the goals that they can evaluate their performance afterwards. It should be noted that, unlike self-directed learning, a goal does not necessarily have to be determined by the learner. To promote engagement in the task, a goal must be specific; and to be more motivating, it must also not be too far away in time – immediate feedback is essential to the learning process and the evaluation of progress. Finally, to the extent that the learner has the knowledge and skills to achieve their goal, they would tend to put more effort into it if it was more challenging than too little.
The learner’s self-regulating learning behaviour is driven by two priorities, two major goals related to self-representation: acquiring knowledge and skills, and validating the self. This second goal is particularly important for adult learners since their learning project is often inherent to identity issues, to a need for personal fulfillment. This goal implies a constant evaluation of the nature of the learning situation from this perspective: it can be perceived as “threatening” to the learner’s self-esteem if the learner feels that the gap between their knowledge and skills and the task at hand is too great, or it can be seen as “stimulating” if the learner feels equipped to take on the challenge and believes that they can gain more than lose.
In the first case, the need to protect one’s self-esteem by trying to lessen the negative emotions triggered will take precedence over the intention to succeed in the task; in the second case, the learner will be able to engage more readily in learning, while by successfully meeting the challenge their self-esteem can be enhanced and their self actualized positively thanks to a new and advantageous perception of themself. As Garcia and Pintrich (1994) put it, “learners regulate their conduct to bring about a positive self, to maintain a positive current self, or to avoid actualizing a negative self-concept.” However, even when a learning dynamic is set in motion – as opposed to a self-esteem-protecting dynamic – the learner will have to resort to volitional strategies (which protect the intention to learn) to avoid being distracted and succumbing to “competing” activities, which are often more tempting than a learning activity, especially when the latter is imposed.
Multiple and contradictory goals thus guide the learner’s behaviour, conscious or not, which are linked to the representation of the self. The learner must constantly juggle the need to take risks to realize their potential and the need to preserve as much as possible a certain well-being. One of the reasons why the concept of self-regulated learning is so interesting, according to Laurent Cosnefroy, is that it “reintroduces conflict into the heart of learning.” Thus, rather than ignoring this unpleasant but inherent aspect of learning, the theoretical field of self-regulated learning helps uncover its mechanisms to propose ways to manage it better.
Three factors guide the choice of a self-regulatory learning strategy: the goal to be achieved, the characteristics of the task to be accomplished, and the resources of the learner. Self-regulation strategies can be divided into three groups: cognitive and metacognitive strategies, which serve to optimize information processing; volitional strategies, which serve to protect the intention to learn; and so-called “defensive” strategies, which aim to protect self-esteem. As Laurent Cosnefroy points out, while the first group of strategies has been the subject of numerous studies and is familiar to professionals in the teaching field, this is not the case for volitional strategies, which benefit from being better known in this era when learners are struggling to remain engaged in their learning – as mentioned above, researchers (Baillet et al., 2016; Poncin et al., 2017) have indeed noted in the university population studied that volition was problematic for a majority of students.
Based on the studies on volitional strategies that he cites in his literature review, Cosnefroy proposes a taxonomy of volitional strategies (Table I, para 38) to address the lack of consensus on the issue in this field – there are, in fact, five distinct taxonomies, and the difficulty in creating a single one that serves as a reference lies in categorizing the strategies in question. Laurent Cosnefroy drew on Corno (2001), whose research is representative of the volitional stream, to organize his taxonomy according to whether the strategy involves direct or indirect control of internal states. In the first case, it is a question of regulation and motivation strategies that involve attention, motivation and emotion, whereas in the second case, it is a question of intervening in the learning context to impact internal states indirectly.
Strategies for controlling internal states include:
- activation of an approach goal, i.e., to make prominent the reasons for continuing the effort (e.g., self-compensation)
- activation of an avoidance goal, i.e. to highlight the negative consequences of failure (e.g. disappointment of loved ones);
- support for a sense of self-efficacy (e.g., activation of memories of success)
- emotional control (e.g., seeking support from others).
Strategies for controlling the learning environment include:
- structuring the environment, i.e., arranging the workplace to prevent distractions or create a favourable motivational climate (e.g., isolating oneself)
- increasing the resources available, i.e. making the task more manageable by obtaining additional information or renegotiating the prescribed task (e.g., searching for material by going on the Internet or to the library)
- structuring time, i.e. anticipating and programming the actions to be carried out (e.g. defining optimal workloads).
A concept that embraces the complexity of learning
Although it lacks uniqueness and has questions to answer, the field of self-regulated learning offers us an additional angle to better understand the learning process and the autonomy of the adult learner in all their complexity. The researchers who are interested in this field have the merit of taking into account the conflictual dimension of learning, the importance of the concept of the self in the learner’s behaviour and the strategies that they implement not only to maintain or actualize a positive perception of the self but also to protect them from a negative actualization of the self. Finally, the field of self-regulated learning research puts forward the notion of volition, a notion that should more than ever be taken into account as an essential complement to motivation in any learning process.
- Self-training 101
- Self-Training: The Evolution of a Fundamental Concept
- The Learner and The Feeling of Self-Efficacy
- At the very heart of the feeling of personal effectiveness
- [VIDEO] The importance of emotions in learning
- [VIDEO] Metacognition 101
- The 3 Components of Metacognition
- Metacognition in 10 points
- Metacognition in 3 Questions
- Develop Your Metacognitive Skills
- [VIDEO] Neuroscience: Learning in 4 Steps
- Motivation: a driving force for learning engagement
Communication Strategist and Senior Editor @KnowledgeOne. Questioner of questions. Hyperflexible stubborn. Contemplative yogi
Catherine Meilleur has over 15 years of experience in research and writing. Having worked as a journalist and educational designer, she is interested in everything related to learning: from educational psychology to neuroscience, and the latest innovations that can serve learners, such as virtual and augmented reality. She is also passionate about issues related to the future of education at a time when a real revolution is taking place, propelled by digital technology and artificial intelligence.
Leave A Comment