Background of SRL
In the past 20 years , significant research has been published on self-regulatory learning (SRL) in adult education but little or no on how this learning process is formed by the cognitive and emotional process.
Self-regulatory learning has been published by academic researchers on a worldwide scale, across many cultures and social settings.
Published literature cover strategies to raised understand and implement effective self-regulatory learning and assessment (Springer International Publishing Switzerland, 2014).
With the onset of COVID-19, online teaching and learning became the new norm in education , placing greater demand on learners to require a primary role in self-directing, self-motivating, self-pacing, and self-assessment within the learning process.
Traditional student to teacher relationship has always been a cornerstone of learning.
However, SRL has upended this traditional relationship and has triggered the necessity to redefine the role of instructor within the SRL environment, especially from a cognitive and affective perspective.
This paper discusses the critical role instructors play in adult SRL learning and assessment environment, especially from an affective, neuro-teaching and neuro-learning perspective.
Dyadic Relationship and Neurobiology of SRL
Self-regulated learning has become a cornerstone in course for a few time now, driven more so with the onset of COVID-19 and stay-at-home regulations.
What makes SRL compelling is that the notion that the adult leaner is self-motivated, knows what her/his educational needs are, and has voluntarily decided to initiate the trouble to find out something new.
Self-regulated learning fits the requirements of the working adult learner because she/he has greater control over workplace hours and household time management.
SRL allows the adult learner to regulate the navigation, pacing, and cognitive learning process (Broekaerts & Cascallar, 2006). However, anxiety and uncertainty are built into the SRL process.
Fear of failure or underperformance may be a constant worry that’s either impeded or facilitated by the connection between the trainer and student. the trainer role in overcoming emotional barriers is crucial within the student-teacher relationship.
we all know that the adult learner makes emotional choices that pivots on what’s the perceived value of the course, content difficulty, and course expectations, factors that are defined by the trainer . Unfortunately, despite a plethora of teaching theories, teachers aren’t taught to use affective approaches in ways in which are strategic and purposeful. Affective teaching as a tool within the classroom or online teaching isn’t taught in educational psychology or in teaching curricula. Yet, neuroscience informs us that learning is an emotional process to which the brain absorbs, processes, and retains knowledge supported the engagement of the visceral brain , especially the amygdala and hippocampus.
Challenges with SRL Self-Assessment
Competency during a knowledge base is ultimately the training outcome in education .
However, measuring competency in SRL models remains ambiguous. Such measuring tools as thinking aloud protocols, classroom observations, microanalysis, sequential and temporal analysis and self-reporting all remain incomplete. Student self-assessment, however, has emerged as a big a part of SRL, the idea being that student can best identify the start line within the learning process, within the various intervening formative benchmarks, and culminating summative self-assessment. the matter with self-assessment or self-reporting in SRL, however, is that it’s difficult to properly evaluate academic performance, given institutional standards, local site expectations, course expectations, range of teacher idiosyncrasies required from the learner, and therefore the learner’s own criteria of what constitutes knowledge retention.
Student self-assessment tends to be deeply personal and difficult to disclose in an objective manner (Andrade, 2010).
Therefore, evaluation of student self-learning outcomes remains inaccurate, which could lead on to undermining student’s self-esteem (Schunk, 1996).
Studies show that students are aware that teacher is that the expert on the subject and thus harbor ambivalence toward self-assessing themselves when required to try to to so (Gao, 2009; Panadero, Brown, & Courtney, 2014; Peterson & Irving, 2008). to the present end, teachers ultimately play a big role in assessing the performance of SRL learners. Teacher’s use of rubric models, for instance , are often wont to help guide SRL students in self-assessment without losing critical presence because the “expert”. during this guidance role, teachers implant validation of performance and deepens the affective learning process.
Catalyst Role of Teacher in SRL Deep Learning
In education , Carnegie time units are wont to measure learning. Yet, research shows little correlation between instructional time and cognitive learning (Chen, 2017). However, robust brain studies show emotional impact experienced within the learning process contributes to enhanced cognition, LTM , creativity, and deep reflective learning (Immordino-Yang, 2016, Taylor & Marienau, 2016, Whitman & Kelleher, 2016).
Decades of neuroscience research on how the brain learns have demonstrated that emotions play a central role within the learning process with stress identified because the single most vital obstacle to creativity, cognition, and LTM (Roberson (2014).
Purposeful application of neurotransmitters like dopamine, oxytocin, serotonin, and endorphin in designing curricula activities, facilitating in-class learning and online teaching create deep learning environments. The teacher is central to the creation of a positive affective learning environment.
Because the act of teaching may be a dyadic relationship, whether in-person or online, this relationship is at the guts of learning between the “expert” and therefore the self-regulating student and where the trainer shapes the emotional framework that motives, inspires, and rewards the SRL student to find out in ways in which encourage deep learning and a momentum for life-time learning.