A #metaanalysis of #assessment of self-regulated learning (#SRL) - #selfregulatedlearning #learning #motivation #CAMML #EDPSY #schoolpsychologists #schoolpsychology #conative

Self-regulated learning (SRL) strategies are an important component of models of school learning.  Below is a new meta-analysis of SRL assessment methods.  Overall effect sizes are not large.  More R&D is needed to develop applied practical SRL measurement tools.  SRL is a one of the major components of the 2022 Cognitive-Affective-Motivation Model of Learning; CAMML; click here to access article),

Multimethod assessment of self-regulated learning in primary, secondary, and tertiary education – A meta-analysis.  Learning and Individual Differences (open access—click here to access).

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Julia Ruhl, Franziska Perels , Laura Dorrenbacher-Ulrich

Abstract

Self-regulated learning (SRL) can be measured in several ways, which can be broadly classified into online and offline instruments. Although both online and offline measurements have advantages and disadvantages, the over-dependence of SRL research on offline measurements has been criticised considerably. Currently, efforts are being made to use multimethod SRL assessments. We examined 20 articles with 351 effect sizes that assessed SRL with at least two instruments on at least two SRL components. Most effect sizes were not statistically significant but descriptively higher than others. Combinations of two online instruments showed the highest effect size (r = 0.24). Overall correlations between instruments were highest for university students (r = 0.21). Additionally, results for cognition showed the highest effect size measured with behavioural traces (r = 0.28), and for metacognition measured with microanalysis (r = 0.35). The component of motivation was best measured using self-report questionnaires (r = 0.29).

Educational relevance statement

Self-regulated learning is an important predictor of academical success. It is therefore necessary to measure it as precise and comprehensive as possible. Knowing which instruments are best suited for each age group, SRL component, or reliably predict a specific achievement variable can help educators pick the best instrument for their needs.

Let’s hear it for #conative (#noncognitive) variables in understanding learning—#CAMML #aptitude #traitcomplexes #cognitive #affective #motivation #schoolpsychology

 Variation in the intensity and consistency of attention during learning: The role of conative factors

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Abstract

The present study examined whether conative factors (e.g., self-efficacy, self-set goal difficulty, and task-specific motivation) are reliable predictors of learning and memory abilities and whether any observed relationships could be explained by two related, yet distinct aspects of attention. Specifically, the present study examined whether the relationship between conative factors and overall learning performance is explained by attentional intensity (the amount of attention allocated to a task) and attentional consistency (the consistency with which attention is allocated to said task). In two studies (N’s > 160), participants completed a paired associate’s (PA) cued recall task while pupil diameter was simultaneously recorded to provide an index of the intensity of attention. Measures of working memory, general episodic long-term memory, task-specific motivation, and memory self-efficacy were also included. Study 2 adopted a similar procedure but embedded thought probes into the encoding phase of each list to provide an index of the consistency of attention. Study 2 also added measures of self-set goal difficulty and effective strategy use. Results suggested that all conative factors were related to intensity and consistency in challenging learning contexts. Furthermore, intensity, consistency, and the variance shared between self-efficacy and self-set goal difficulty (r = 0.86) each explained substantial unique variance in learning when controlling for the influence of other important predictors. Overall, results suggest conative factors are important for understanding individual differences in learning and memory abilities, and part of the reason why these factors are associated with improved learning outcomes is due to intensity and consistency.

Comment:  I’ve always believed that conative (non-cognitive) individual difference variables should receive just as much attention as cognitive variables in understanding learning.  In fact, in an invited article, I recently proposed the CAMML (cognitive-affective-motivation model of learning) “crossing the rubicon” model of learning that integrates conative (motivation and self-regulated learning), affective (Big 5 personality) and cognitive (CHC) variables in an overarching framework (building on Richard Snow’s concept of aptitude-trait complexes).  Click here to download or read the CAMML article.  Below are the two key figures for understanding the CAMML model.

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Self-testing improves academic performance - self-regulatory cognitive processes

This story has been making the rounds in the blogsphere...so I might as well pass it along. Below I provide a link the the coverage on the Science Blog. These types of stories always grab the attention of the media....but there may be some validity to the notion when one thinks of "self-testing" as a cognitive self-regulatory strategy. At the bottom of this post is some material I recently drafted for a research paper. I tried to provide a brief overview of the self-regulatory cognitive domain, a domain that I view as part of the important non-cognitive domain of conative abilities.

To learn something, testing beats studying (click here)

• "Remember the dreaded pop quiz? Despite their reputation as a cruel tool of teachers intent on striking fear into the hearts of unprepared students, quizzes -- given early and often -- may be a student's best friend when it comes to understanding and retaining information for the long haul, suggests new psychology research from Washington University in St. Louis."

Some yet-to-be published background text on self-regulation from the blogmaster.

• The theoretical and empirical self-regulation research, which includes linkages to literature in such domains as self-efficacy, academic goal setting, academic goal orientation, knowledge (domain-specific, strategy) and causal attribution, has been considerable during the past 2 decades (Puustinen Pulkkinen, 2001). Briefly, literature syntheses have identified 5 primary models of SRL (advanced by Boekaerts, Borkowski, Pintrich, Winne, and Zimmerman) (Puustinen Pulkkinen, 2001) and 7 prominent theoretical perspectives (operant, phenomenological, information processing, social cognitive, volitional, Vygotskian, and cognitive constructivist) (Zimmerman, 2001). Although a number of differing models of self-regulated learning exist, most models define academic self-regulation as “an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition” (Pintrich Zusho, 2002, p. 250).

• Most SRL models share a number of common assumptions. According to Pintrich (2000c), these assumptions are:
• The active, constructive assumption, which views “learners as active constructive participants in the learning process” (p. 452).
• The potential for control assumption which assumes that “learners can potentially monitor, control, and regulate certain aspects of their own cognition, motivation, and behavior as well as some features of their environment” (p. 454).
• The goal, criterion, or standard assumption which assumes that “there is some type of criterion or standard (also called goals or reference value) against which comparisons are made in order to assess whether the process should continue as is or if some type of change is necessary” (p. 452).
• The mediation assumption which states that “self-regulatory activities are mediators between personal and contextual characteristics and actual achievement and performance” (p. 453).

• Self-regulated students possess 3 major characteristics and employ 3 major processes (Eccles Wigfield, 2002; Zimmerman, 2000). Self-regulated students typically use a variety of self-regulated strategies, believe they can perform well (positive self-efficacy), and set multiple and varying personal goals. Furthermore, “self-regulated learners engage in three important processes: self-observation (monitoring of one’s activities); self-judgment (evaluation of how well one’s own performance compares to a standard or to the performance of others); and self-reactions (reactions to performance outcomes)” (Eccles Wigfield, 2002, p. 124). Of particular importance to students who experience repeated failure (e.g., students with disabilities) is the finding that students who receive positive feedback from their self-observations and judgments tend to continue to engage in positive goal-directed learning. Conversely, self-observation and judgment that provides frequent unfavorable evaluations and reactions increases the probability of disengagement from learning.

• According to Pintrich’s (Pintrich, 2000c; Pintrich Zusho, 2002) framework for self-regulated learning, most SRL models include 4 major phases (which do not necessarily occur in an a strict linear sequence): (a) planning and activation; (b) monitoring; (c) control and regulation; and (d) reaction and reflection. These 4 phases are conceptualized to operate in all major domains of human behavior—cognition, motivation and affect, and behavior. As a result, in the most general sense, there are at least 12 major SRL “cells” (4 phases-by-3-behavior domains). Given the resultant complexity of the SRL literature and the necessary decision to refrain from in-depth descriptions of the nuances of different underlying theories in this paper, a pragmatic decision was made to only define and describe, in general terms, the 4 major phases of SRL that operate across cognitive, motivation and affect, and behavior. Examples of specific cognitive, motivation and behavior strategies are included for illustrative purposes. Finally, the relatively small amount of research on classroom-based SRL investigation is surprising given the frequent lament from teachers regarding the importance of a student’s “study habits or skills” (Pintrich Zusho, 2002).

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