Showing posts with label MACM. Show all posts
Showing posts with label MACM. Show all posts

Wednesday, May 06, 2026

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

I am currently working to expand my skill set by incorporating AI tools. Although adapting to new technologies can be challenging, leveraging these resources offers significant benefits for professional growth.


This AI Brief was produced by requesting Google NotebookLM—recommended by Dr. Adam Lockwood—to generate a narrative summary of my 2022 PDF article describing the Cognitive-Affective-Motivation-Model of Learning (CAMML). While I found the first results promising, I made more edits to enhance its accuracy and informativeness. My next goal is to use AI to summarize multiple articles, find similarities and differences, and potentially create comparative tables (again following guidance graciously provided by Dr. Lockwood).


These incremental steps mark my transition toward utilizing AI to support one of my primary professional interests: producing informative blog and social media posts aimed at professionals such as school psychologists and special education teachers working with students who often are marginalized in educational contexts. The goal is to help bridge the gap between theory, technology, research, and practical application.

 

Feedback is encouraged and may be directed to iqmcgrew@gmail.com or via the social media platform (LinkedIn, Twitter/X, BlueSky) comment feature where this blog post was discovered. I’m hoping to add AI Briefs as a regular feature of IQs Corner Blog and associated social media platforms.

 

 

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

 

Dr. Kevin McGrew with assist from Google NotebookLM

 

The Cognitive-Affective-Motivation Model of Learning (CAMML; McGrew, 2002)[1] is a proposed theoretical framework designed to integrate contemporary motivational, affective, and cognitive constructs into a unified model for the practice of school psychology. The central thesis of the framework is that school psychologists must move beyond a narrow focus on intelligence (general intelligence or psychometric g in particular) to embrace an updated "trilogy-of-the-mind" model, which views intellectual functioning as the inseparable interaction of cognition, conation (motivation/volition), and affect.


Theoretical Foundations and the Rebirth of Conation

 

The CAMML framework is heavily rooted in the seminal work of Richard Snow, specifically his research on aptitude trait complexes. McGrew argues that the field of school psychology has historically neglected Snow’s broader definition of aptitude—which includes personality and motivational differences alongside cognitive abilities—and instead, has favored a restricted view of aptitude as synonymous with IQ or psychometric g.

 

CAMML seeks to resurrect conation (the proactive part of motivation connecting cognition and affect to behavior) as a core pillar of intellectual functioning. By "standing on the shoulders of giants" like Snow, Spearman, and Wechsler, the model asserts that cognitive processes cannot be understood in isolation from the "nonintellectual" (conative) factors that drive and direct them. For example, David Wechsler defined intelligence as "the aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment." While this is the core of Wechsler’s definition, he also strongly emphasized this capacity is influenced by non-intellective (conative) variables such as drive, persistence, interest, emotional states, and personality traits.

 

Structural Components of the CAMML Framework

 

The model organizes individual differences characteristics into three functional categories, which could be called the 3-D model.

 

      Affective “Dispositions”: These are distal-to-learning traits, primarily represented by the Big 5 personality traits (specifically Openness and Conscientiousness) and their associated social-emotional facets (e.g, curiosity, creativity, persistence, focus, determination). These personality traits act as dispositions that indirectly influence learning through more proximal mechanisms.

 

      Motivational "Drivers": Motivation is conceptualized as the initiation of behavior, formed by achievement orientations (e.g., goals, interests) and self-beliefs (e.g., self-efficacy, self-concept). These constructs are typically domain-specific (e.g., math) and work in synergistic "complexes" to energize a student's readiness to act.[2]

 

      Volitional "Directors": Volition, or self-regulated learning (SRL), represents the post-decisional phase of action. These are the mechanisms that direct, control, and regulate behavior toward goals once a commitment to learn has been made.

 

A more detailed list of the 3-D CAMML domain constructs and definitions is available here. See figure below for a visual representation of the major affective and conative MACM constructs (click on image to enlarge for easy viewing and reading).



 

The "Crossing the Rubicon" Investment Model

 

The functional heart of CAMML is the "Crossing the Rubicon" model, which illustrates the pathway from initial desire to engaged motivated learning. In this model:

 

  1. Pre-decisional Phase: Achievement orientations and self-beliefs drive or prepare the learner to start a wish—>want—> intention sequence, that eventually eventuates in motivated action.
  1. Commitment: When the learner "crosses the Rubicon," they are making a firm commitment to motivated action through cognitive engagement.
  1. Action Phase: Volitional (SRL) strategies steer the cyclical process via action results feedback while the learner invests cognitive abilities (such as those defined by CHC theory) to acquire knowledge.
  1. Outcomes: This personal investment of fluid cognitive processes (Cattell's general gf that subsumes broad Gf, Gv, Ga, Gwm, Gl, Gr, and Gs abilities) during learning results in the development of crystallized knowledge systems (Cattell’s general gc that subsumes broad Gc, Grw, Gq, and Gkn abilities).

 

See figure below for visual representation of “the CAMML crossing the Rubicon model of motivated learning” (click on image to enlarge for easy viewing and reading).




 

Implications for School Psychology Practice

 

CAMML advocates for a paradigm shift in assessment and intervention. It suggests that school psychologists should transition from routine, comprehensive cognitive testing toward more time-efficient selective, referral-focused cognitive assessments combined with the assessment of key conative (non-cognitive) characteristics that contribute to learning aptitude complexes. This approach prioritizes identifying manipulable instructional levers, such as a student's motivational orientation (e.g., intrinsic motivation, interests, goal orientation), self-beliefs (e.g., locus of control, self-efficacy, growth or competence mindset), rather than relying exclusively on cognitive ability scores (especially full-scale IQ or g) that have proven hard to modify.

 

The framework provides a "whole-child" perspective to better address the nuances of individual differences, particularly as students move from the traditional “industrial” model of education (i.e., regularly scheduled, structured, in-class teacher-directed learning) to more of an “information-age” paradigm of education—a paradigm that requires a fuller expression of independent motivated self-regulated learning (SRL).


PS - other CAMML related posts on this blog can be found by clicking here.



[1] All relevant references can be found in McGrew (2022).

[2] The motivation constructs included in the CAMML framework are drawn from earlier efforts to develop the McGrew Model of Achievement Competence Motivation (MACM). A detailed explanation of the evolution and development of the MACM model is available elsewhere (McGrew et al., 2004). A series of recent MACM PowerPoint® modules is available here.


Wednesday, August 06, 2025

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







Friday, January 15, 2021

The McGrew Model of Achievement Competence Model (MACM)--Standing on the shoulders of giants: CJSP article supplementary materials

The Model of Achievement Competence Motivation (MACM) has been  under development since the early 2000's by Dr. Kevin S. McGrew.   The work is (has) been formally presented in an invited article--"The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021), for a forthcoming special issue on motivation in the Canadian Journal of School Psychology). 

Due to the page length constraints of the journal, significant background and explanatory information could not be presented in the article.  Thus, I have "off-loaded" this material for supplementary viewing via on-line PPT slide shows and downloadable PDF files.

Five MACM PPT modules have been posted at SlideShare and can be viewed and downloaded from that site.  For those who would prefer to directly download PDF versions of the PPT modules from one page...here it is.  Below are the titles of the five modules and associated download links.  In addition, the paper includes, in a table footnote, definitions for 16 self-regulatory constructs from a recent article by Sitzman and Ely (2011).  That PDF file is also available from download below.

Enjoy.



The Model of Achievement Competence Motivation (MACM)

The Model of Achievement Competence Motivation (MACM) Part E: Crossing the Rubicon Commitment Pathway Model to Learning

 

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 fifth and final (Part E) in the series.  This one is brief...only 11 slides.  Crossing the Rubicon Commitment Pathway Model to Learning.  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)



You should be able to access the prior modules (A-C) from the link above.

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

Monday, January 11, 2021

The Model of Achievement Competence Motivation (MACM): Part D-Volition and Self-Regulated Learning Domains

 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 fourth (Part D) in the series--Volition and Self-regulated Learning Domains described..  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)



You should be able to access the prior modules (A-C) from the link above.

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

Wednesday, January 06, 2021

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.

Monday, January 04, 2021

The Model of Achievement Competence Motivation (MACM): Part A - Introduction to module series

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 first (Part A) in the series. 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 prior "beyond IQ" labeled posts at this blog.




Wednesday, December 16, 2020

The big picture ecological systems perspective of intelligence (and IQ tests): Is COVID disrupting and rearranging the hierarchy of ecological system influences on children's learning?

Understanding intelligence testing in the context of Bronfrenbrenner's ecological systems model--is COVID seriously damaging, rearranging, decoupling, etc. the major proximal and distal sources of influence on a child's learning, resulting in a need to look closer at non-cognitive (conative) variables...beyond IQ?

This morning I revisited one of my favorite videos (of those I have posted), first posted in 2015, where I explained how intelligence testing needed to be understood in the context of distal and proximal influences in a child's environment.  I believe that a "big picture" understanding of the wide range of variables that influence school learning requires a "humbling" of the status of intelligence testing, a field where I have spent the majority of my professional career.  After one finishes the video, think about the "big picture" ecological systems model that is described. IMHO, COVID may be seriously impacting that the primary distal and proximal variables that influence (both positively and negatively) school learning (national educational policy; school systems and local community sources of formal and informal support; individual schools; the lack of in class learning; parents working from home or being unemployed), as well as peer interactions in a child's neighborhood (due to social distancing).  Stare at the final big picture figure and reflect on how COVID is disrupting all the primary sets of variables that influence school learning.  The range of disrupted causal influences is staggering. 

The end result, for many children, is learning via distance learning methods, often with the aid of parents who are not educators.  Although intelligence is very important, and may be more important as children must use their abilities to learn more independently, it strikes me that at this point in our countries (global) current crises, it may be the non-cognitive variables that might need better understanding and enhancement.  That is, the conative (aka., noncognitive) "beyond IQ" variables of motivation and self-regulated learning (aka., a part of volition) may be more important today than ever.  To engage in independent, loosely (dis)organized instruction, students who have strong motivation and independent self-regulation learning strategies may have a distinct advantage--those who do not, may be at a serious disadvantage.  Jack Carroll's seminal model of school learning, that spawned decades of research on models of school learning, reminds us, in elegant terms, that aside from key student individual difference variables, the quantity (opportunity for instruction) and quality of instruction are key variables in school learning.  Both of these are being seriously impacted due to COVID.

COVID appears to be a high level all encompassing distal variable (wielding impact at the global, national, community, and school system levels) that is rearranging the the relative importance of  variables in school learning.  Students now, and in the future, may need more assistance in acquiring critical non-cognitive motivational dispositions and independent self-regulated learning strategies in order to maximize what they can from their repertoire of cognitive abilities in order to continue and maintain academic growth.  If may be necessary to revise the degree of influence of distal and proximal school learning influence variables as per Bronfrenbreener's ecological systems model.





Saturday, September 21, 2019

All you need is g? Predicting piano skill acquisition in beginners: The role of general intelligence, music aptitude, and mindset


Abstract
;  This study was designed to investigate sources of individual differences in musical skill acquisition. We had 171 undergraduates with little or no piano-playing experience attempt to learn a piece of piano music with the aid of a video-guide, and then, following practice with the guide, attempt to perform the piece from memory. A panel of musicians evaluated the performances based on their melodic and rhythmic accuracy. Participants also completed tests of working memory capacity, fluid intelligence, crystallized intelligence, processing speed, and two tests of music aptitude (the Swedish Music Discrimination Test and the Advanced Measures of Music Audiation). Measures of general intelligence and music aptitude correlated significantly with skill acquisition, but mindset did not. Structural equation modeling revealed that general intelligence, music aptitude, and mindset together accounted for 22.4% of the variance in skill acquisition. However, only general intelligence contributed significantly to the model (β = 0.44, p < .001). The contributions of music aptitude (β = 0.08, p = .39) and mindset (β = −0.06, p = .50) were non-significant after accounting for general intelligence. We also found that openness to experience did not significantly predict skill acquisition or music aptitude. Overall, the results suggest that after accounting for individual differences in general intelligence, music aptitude and mindset do not predict piano skill acquisition in beginners.




 


Saturday, August 11, 2018

Beyond IQ: Mining the “no-mans-land” between Intelligence and IQ: Journal of Intelligence special issue

I am pleased to see the Journal of Intelligence addressing the integration of non-cognitive variables (personality; self-beliefs; motivational constructs; often called the “no-mans land” between intelligence and personality— I believe this catchy phrase was first used by Stankov) with intellectual constructs to better understanding human performance. I have had a long-standing interest in such comprehensive models as reflected by my articulation of the Model of Academic Competence and Motivation (MACM) and repeated posting of “beyond IQ” information at my blogs.

Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.

Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.

In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).


Click on images to enlarge.







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Saturday, March 17, 2018

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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Monday, November 14, 2016

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.