Self-Efficacy in Learning

Self-Efficacy in Learning

Mirela Ramacciotti

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If you had to pick from options below for what is entailed in learning, which would you choose?

    1.  Development of skills
    2. Appropriation of knowledge
    3. Attitude and values
    4. All of the above
Photo by Anne Davis 773 on Foter.com / CC BY-NC

You probably chose “all of the above,” but even these do not suffice to embrace the complexity of learning, mainly because another, often overlooked, element of the learning equation is left out: relationships. The breadth and depth that supportive relationships can bring to one’s learning trajectory should not be underestimated. Thereby, learning becomes a more substantial and complex issue to understand. However, we do need to understand it. So, let’s go down this road, one stop at a time!

Photo by edtechworkshop on Foter.com / CC BY-NC-SA

First stop: Beliefs

Part of the complexity embedded in relationships lies in understanding how supportive relationships are formed–the forces that lie within. These forces come from the many individuals who embody educational institutions, such as teachers, students, and collaborators. Without a firm grasp on such forces, we may fail to understand the basic elements or the necessary adjustments that pave the way for successful learning trajectories.

A lot of those forces comes from the beliefs we hold about our capacity for learning and that which others attribute to us. In learning settings such as schools and universities, beliefs that students and teachers hold about a learner’s capabilities are central.

Research into teachers’ beliefs and attitudes leaves little doubt that beliefs have a powerful influence on their practices (Ashton & Webb, 1986; Gibson & Dembo, 1984). Likewise, we know that teachers have the greatest influence on student learning (Bransford, Darling-Hammond, & Lepage, 2005; Nye, Konstantanopoulos & Hedges, 2004; Scheerens, Vermeulen, & Pelgrum, 1989).

"Teachers are THE heavyweight when it comes to student learning."
Mirela Ramacciotti
TT Author

Thus far, research has provided plenty of evidence that teachers’ beliefs influence their practices and that teachers’ practices and behavior have, in turn, a great influence on students’ performance (Baker, 1999; Hamre & Pianta, 2001). Since teachers are THE heavyweight when it comes to student learning, what teachers believe their students are capable of doing, and to what extent they believe that their practice can influence a student’s performance, matters–a lot. This set of beliefs can even be considered a predictor of students’ success (Allinder, 1995; Midgley, Feldlaurfer, & Eccles, 1989).

To better understand all the reciprocal influences in these relationships, take a look at the diagram below, where each arrow represents an influence.

202002_14

This illustration shows that the beliefs teachers hold need to be better examined. Let’s then continue on our journey down this road that examines the relationships between teachers and students. Our next stop will look closely at how beliefs shape influence by way of a central concept: self-efficacy.

Second stop: Self-efficacy

In Cognitive Social Theory, developed by Albert Bandura during the second half of the 20th century (Bandura, 1977), social learning was widely examined through the prism of behavior. But unlike Pavlov and Skinner, who examined learning conditions in stimulus and response models, Bandura examined the cognitive process in the social context (Bandura, 1977). He then concluded that there was a reciprocal determinism of influences that operated as follows: the choices that individuals and institutions make are influenced by the strength of their beliefs of efficacy, i.e., how strongly they believe they achieved the goals they originally set. This means that the choices influence the objectives achieved, and vice-versa.

The cogs and wheels in this reciprocal mechanism work like this: beliefs teachers (and also students) hold are based on their attributions about what they are capable of accomplishing. This became known as self-efficacy. Self-efficacy is fueled by one’s own mental representations of the expectation of successfully reaching goals (Goddard, Hoy, & Hoy, 2004; Johnbull, Hardiman, & Rinne, 2013).

Therefore, self-efficacy influences both what we do and the results we get. It shapes both approach behaviors, such as the choice of a career or a subject to study, and persistence behaviors, the effort applied and perseverance in achieving a goal. For example, let’s imagine you are thinking of taking a college Advanced English class. Self-efficacy will shape that decision. Previous failures might make you disheartened because of your predictions of the amount of tough reading you will have to face or depressing feedback you might get on an “unsatisfactory” essay. How you deal with these negative expectations are ultimately linked to your own attribution of self-efficacy (Schwartz, Tsang, & Blair, 2016). You could choose to persevere and take the class, or you could give up taking the class entirely, based on your imagined odds of failure.

In other words, what do you attribute success and failure to: external factors (the teacher assigns too many readings), internal factors (I’m terrible at writing), or self-efficacy (I’m just not good enough to take this course)? Can your assumptions be changed? This leads us to our next, and final, stop: examining how attributions are made (and changed) and how they influence learning.

Third stop: Attributions

Learning equips us to emulate behaviors that help us deal with the demands of ever-changing environments. We learn from the stimuli, whether external (those present in the environments we live in) or internal (present in the thoughts we generate). We also learn from the behaviors we produce so that survival and improvement objectives can be successfully attained (Domyan, 2014).

Neural networks are central in this perspective for they provide the biological substrate for learning and the cognitive functions that support it, like memory, attention, reasoning, and decision-making. But what we remember, pay attention to, reason about, and decide upon are ultimately linked to emotions. As such, learning is not separate from the emotions that engender it.

In fact, it is previous emotional experience that guides how–and to what extent–we embrace new learning. Thus, emotions become the beams that hold the fort of cognition together (Heilman, Crişan, Houser, Miclea, & Miu, 2010). Emotions also function as a rudder for judgment and action since neurobiological systems that underpin emotions are the same as those used in decision making (Immordino-Yang & Damasio, 2007). In sum, there is no decision (and we make a lot of them when learning) that is not influenced by emotions.

Every one of us, teacher or student, has an emotional repertoire that fills the moments when we remember situations, imagine reactions, hypothesize scenarios, and reflect on our behaviors. The neural network that enables these constructions is called the Default Mode Network because it goes into full gear precisely when one is otherwise non-occupied, such as when daydreaming (Immordino-Yang, Darling-Hammond, & Krone, 2018).

The default mode network operates not only with the neurons involved in such circuits, but also with certain brain structures responsible for the construction of emotions. These structures are the brain stem, responsible for breathing, heartbeat, and basic processes of consciousness, and the insulas (left and right), that account for the visceral, or gut, sensation and its integration with cognitive processes (Immordino-Yang, Darling-Hammond, & Krone, 2018). It is this combination of circuits and brain structures in a network that allows us to elaborate our beliefs, attributions, and values that manifest themselves through our behavior at the organic–or bodily–level.

From Immordino-Yang, Darling-Hammond, & Krone, 2018

The attributions we make in the construction of our values and beliefs, can in turn, increase the scope (both approach and the persistence behaviors) of our self-efficacy. This involves, as we have seen, biological real estate, that is brain structures and neural connections, and a whole scenario of mental–or mind–states. Mind states are conceptions that we develop about how a given action takes place. They give us a perception of how we perform and shape the constructs of our capabilities, such as intelligence, capacity, and success.

In regard to these states, psychologist Carol Dweck (1986) developed a theory that has caused great repercussions in education. According to her, our mind states and the way that we frame events based on them, define how we face challenges and attribute failures. These frameworks, in turn, define our mental potential for success or failure in a given task; they set our minds towards a pathway and end up shaping our behaviors towards learning.

The concept of mindsets, which can be fixed (I know what I’m good or bad at) or growth (I don’t know if I’m good or bad, but I can always give it a try), allow us, through self-attribution, to invest cognitive and emotional resources to complete–or not–a task.

In dealing with tasks, motivation and personality come to the fore. In relation to motivation, tasks are tackled with either performance or learning objectives in mind.

When holding performance objectives in mind, we do not commit to taking on tasks that require persistence because this would compromise our expectation of success. This is because the previously obtained success has reinforced a fixed idea about our capability and expected performance. In this way, only tasks which do not create novel obstacles or difficulties–and therefore do not compromise our expected performance–are tackled.

But when learning goals are invoked, we undertake to perform new and different tasks, ones which are likely to present challenges. Challenges are welcomed because we truly believe this is what fuels learning–and so there is growth.

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When we are about to tackle a task, it is important to review not only the values that shape our belief of success, but also how our expectations shape our objectives. If our objectives are focused only on performance, we’ll tend to just stick with the procedures we’ve used before, and thus learn little to nothing new.

Meanwhile, if learners opt for removing the limits that their misguided attributions may impose on their self-perceived abilities, the expectation of results achieved will also change. This change will impact their learning processes and transform the patterns of their behaviors-for the better!

References

  • Allinder, R. (1995). An examination of the relationship between teacher efficacy and curriculum-based measurement and student achievement. Remedial and Special Education, 16(4), 247-254.

  • Ashton, P., & Webb, R. (1986). Making a difference: Teachers’ sense of efficacy and student achievement. Harlow: UK: Longman Publishing Group.

  • Baker, J. (1999). Teacher-student interaction in urban at-risk classrooms: Differential behavior, relationship quality, and student satisfaction with school. The Elementary School Journal, 100(1), 57-70.

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.

  • Bransford, D. H., Darling-Hammond, L., & LePage (2005). Preparing teachers for a changing world: What teachers should learn and be able to do. San Francisco, CA: Jossey-Bass.

  • Domjan, M. (2014). The principles of learning and behavior. Scarborough, Canada: Nelson Education.

  • Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040-1048.

  • Gibson, S., & Dembo, M. (1984). Teacher efficacy: A construct validation. Journal of Educational Psychology, 76(4), 569-582.

  • Goddard, R., Hoy, W., & Hoy, A. (2004). Collective efficacy beliefs: Theoretical developments, empirical evidence, and future directions. Educational Researcher, 33(3), 3-13.

  • Hamre, B., & Pianta, R. (2001). Early teacher–child relationships and the trajectory of children’s school outcomes through eighth grade. Child Development, 72(2), 625-638.

  • Heilman, R. M., Crişan, L. G., Houser, D., Miclea, M., & Miu, A. C. (2010). Emotion regulation and decision making under risk and uncertainty. Emotion, 10(2), 257-265.

  • Immordino‐Yang, M., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3-10.

  • Immordino-Yang, M., Darling-Hammond, L., & Krone, C. (2018). The brain basis for integrated social, emotional, and academic development. Washington, DC: National Commission on Social, Emotional, and Academic Development.

  • Johnbull, R., Hardiman, M., Rinne, L. (2013). Professional development effects on teacher efficacy: Exploring how knowledge of neuro-and cognitive sciences changes beliefs and practices. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA.

  • Midgley, C., Feldlaufer, H., & Eccles, J. (1989). Change in teacher efficacy and student self-and task-related beliefs in mathematics during the transition to junior high school. Journal of Educational Psychology, 81(2), 247-258.

  • Nye, B., Konstantopoulos, S., & Hedges, L. (2004). How large are teacher effects? Educational Evaluation and Policy Analysis, 26(3), 237-257.

  • Scheerens, J., Vermeulen, C., & Pelgrum, W. (1989). Generalizability of instructional and school effectiveness indicators across nations. International Journal of Educational Research, 13(7), 789-799.

  • Schwartz, D. L., Tsang, J. M., & Blair, K. P. (2016). The ABCs of how we learn: 26 proven approaches, how they work, and when to use them. New York, NY: W. W. Norton.

Mirela Ramacciotti is a former lawyer, teacher, and translator, turned to education consultancy and materials writing in the area of TESOL and MBE; MSc in Interdisciplinary Studies from Johns Hopkins University (2019) & PhD Candidate in Neuroscience at the University of São Paulo; author of Aprender: Entendendo o Cérebro; runs the site www.neuroeducamente.com.br

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