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

Journal of #Intelligence special issue: Interplay of intelligence and non #cognitive (#conative) constructs in predicting #achievement - #schoolpsychology #cognitive #EDPSY #education

More info availble re JOI special issue here. Click on image to enlarge for easier reading.

If interested, you might want to check out my pub describing the Cognitive-Affective-Motivation Model of Learning #CAMML)

Research Byte: Specialized Purpose of Each Type of Student #Engagement: A #metaanalysis - #schoolpsychology #EDPSY #learning #motivation #CAMML #CHC

 

This is an open access downloadable article available by clicking here.  Types of student engagement would be interesting constructs to add to the Cognitive-Affective-Motivation Model of Learning (crossing the Rubicon to engaged learning).

Click on images to enlarge for easy reading.

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

Click here to view online

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.

Click on each image to enlarge for viewing