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).

Author links open overlay panel

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.

Leaving no child behind—Beyond cognitive and achievement abilities - #CAMML source “fugitive/grey” working paper now available. Enjoy - #NCLB #learning #EDSPY #motivation #affective #cognitive #intelligence #conative #noncognitive #schoolpsychology #schoolpsychologists

I’ve recently made several posts regarding the importance of conative (i.e., motivation; self-regulated learning strategies; etc.) learner characteristics and how they should be integrated with cognitive abilities (as per the CHC theory of cognitive abilities) to better understand the interplay between learner characteristics and school learning.  These posts have mentioned (and I provided a link) to my recent 2022 article where I articulate a Cognitive-Affective-Motivation Model of Learning; CAMML; click here to access).

In the article I mention that the 2022 CAMML model had its roots in early work I completed as one of the first set of Principal Investigators during the first five years of the University of Minnesota’s National Center on Educational Outcomes (NCEO).  As a result of those posts I’ve had several requests for the original working paper which is best characterized as being “fugitive” or “grey” literature.

The brief back story is that the original 2004 document was a “working paper” (6-15-04; Increasing the Chance of No Child Being Left Behind: Beyond Cognitive and Achievement Abilities, by Kevin McGrew, David Johnson, Anna Casio, Jeffrey Evans) that was written with the aid of discretionary funds from the then Department of Education’s Office of Special Education (OSEP) during the influence of NCLB.  The working draft was submitted but curiously never saw the light of day.

With this post I’m now making the complete 2004 “working paper” (with writing, spelling, and grammar blemish’s in their full glory) available as a PDF.  Click here to access.  Although dated 20 years, IMHO the lengthy paper provides a good accounting of the relevant literature up to 2004, much of which is still relevant.  Below are images of the TOC pages which should give you an hint of the treasure trove of information and literature reviewed.  Enjoy.  Hopefully this MIA paper may help others pursue research and theoretical study in this important area.

Click on images to enlarge for easy reading

The Model of Achievement Competence Motivation (MACM) Part B: An overview of the MACM model

The Model of Achievement Competence Motivation (MACM) is a series of slide modules.  By clicking on the link you can view the slides at SlideShare.  This is the second (Part B) in the series--An overview of the model.  There will be a total of five modules.  The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)

Click here for first of the series (Part A:  Introduction and Background)

Click here for prior "beyond IQ" labeled posts at this blog.

The importance of differential psychology for school learning: 90% of school achievement variance is due to student characteristics

This is why the study of individual differences/differential psychology is so important. If you don’t want to read the article you can watch a video of Dr. Detterman where he summarizes his thinking and this paper.

Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools. Article link.

Douglas K. Detterman

Case Western Reserve University (USA)

Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers' skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed


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Beyond Cognitive Abilities: An Integrative Model of Learning-Related Personal Competencies and Aptitude Trait Complexes

For centuries educational psychologists have highlighted the importance of "non-cognitive" variables in school learning.  Below readers will find a PPT presentation that presents a "big picture" overview of how cognitive abilities and non-cognitive factors can be integrated into an over-arching conceptual framework.  The presentation also illustrates how the big picture framework can be used to conceptualize a number of contemporary "buzz word" initiatives related to building 21st century educationally important skills (social-emotional learning, critical thinking, creativity, complex problem solving, etc.)

The two preliminary images can be enlarged by click on them.

Prior related "Beyond IQ" blog posts can be found here.

Beyond cognitive abilities: An integrative model of learning-related personal competencies and aptitude trait complexes from Kevin McGrew