Showing posts with label Big 5. Show all posts
Showing posts with label Big 5. Show all posts

Wednesday, May 13, 2026

AI Brief: What does Grok say about Richard Snow’s aptitude trait complexes for learning?

 The following is a Grok AI generated summary when prompted (by Dr. Kevin McGrew) to explain Richard Snow’s aptitude complexes on March 7, 2026).

(Grok refers to a generative artificial intelligence developed by xAI, an Elon Musk company, as well as a foundational term in science fiction for deep understanding. Several editorial changes were made to insure accuracy, and select links added, by McGrew—to the Grok summary).

 


Richard E. Snow’s aptitude complexes (introduced in the 1980s–1990s, with key publications in 1987, 1992, and especially the 1996 chapter with Lyn Corno and Douglas Jackson) represent a major shift in how educational psychologists think about aptitudemoving far beyond the traditional narrow view of aptitude as “just cognitive ability” (e.g., IQ or a single test score).

 

Core Definition

Snow redefined aptitude as:

the degree of readiness to learn and to perform well in a particular situation or in a fixed domain.”

It is a predisposition or propensity—a latent quality that enables more advanced performance under specific conditions. Aptitudes are situational and domain-specific: what makes someone ready to succeed in math class may be different from what works in a history seminar or a hands-on lab.  


Aptitude Complexes (the key innovation)

Snow argued that single constructs (like “fluid reasoning” or “achievement motivation”) are insufficient. Instead, success in learning comes from aptitude complexes—dynamic constellations or critical combinations of variables that work together as a coordinated system.

These complexes draw from the classic “trilogy of the mind”:

•  Cognition — abilities and processes for analyzing, interpreting, and solving (e.g., reasoning, knowledge, strategies, cognitive style, CHC abilities).

•  Affect — emotions, anxiety, self-concept, emotion regulation, personality traits.

•  Conation — motivation, volition, goal-setting, effort, persistence, will (the “want to” and “stick with it” aspects).

An aptitude complex is not just a list of traits—it is how these elements assemble and coordinate in real time within a specific task and context. They are amalgams of cognitive, conative, and affective characteristics.

 

The Two Pathways That Build Aptitude Complexes

Snow (and later Corno et al., 2002) described aptitudes developing through two parallel, interacting pathways (sometimes called the commitment pathway and the performance/action pathway):

1.  Commitment Pathway (motivational/affective/volitional)

     •  Assembles motivational resources that energize effort.

     •  Affective and volitional processes modulate how the work proceeds (e.g., regulating anxiety, sustaining intention).

     •  Outcome: Propensity (how likely the person is to engage and persist).

2.  Performance (Action) Pathway (cognitive)

     •  Assembles and deploys cognitive resources (abilities, strategies, knowledge) to do the task.

     •  Outcome: Ability/accuracy in execution.

When the two pathways coordinate effectively in a given “problem space” (the task + the broader situation/context), successful performance occurs. Over repeated experiences, these coordinated processes coalesce into stable aptitude complexes. The learner’s perception of the situation’s constraints and affordances is crucial—aptitudes are dynamic and transactional (person ↔ environment). 

Aptitudes are both input (what the student brings to learning) and product (they develop and change through education).


Snow Academic Aptitude Model (SAAM)

Snow synthesized decades of research into a provisional taxonomy (called the Snow Academic Aptitude Model or SAAM in an unpublished working white 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]) which is now available for download here. It treats academic aptitudes as a “multivariate repertoire” of readiness for general and domain-specific learning. It explicitly includes affect (personality and social-emotional skills) and conative related skills and traits (e.g., achievement motivation, goal orientation, freedom from debilitating anxiety, positive self-concept, self-efficacy, interests) alongside cognitive abilities. The model emphasizes that “there is more to knowing about an individual’s propensity for academic learning than simply knowing their IQ scores!” 

 

Why This Mattered (and Still Matters)

Snow’s work grew out of his famous Aptitude-Treatment Interaction (ATI) research with Lee Cronbach. He showed that simple aptitude scores rarely predict learning outcomes well because real-world performance depends on these richer, situation-specific complexes.

This framework directly inspired later theories (including Kevin McGrew’s 2022 CAMML model, which updates Snow’s complexes with contemporary CHC cognitive theory, Big Five personality, and self-regulated learning mechanisms while retaining the commitment/action pathway structure).

In short, Snow’s aptitude complexes gave us a whole-person, dynamic, domain-specific way to understand why two students with the same IQ can have dramatically different learning outcomes: their aptitude complexes (the coordinated cognitive + affective + conative “package” or “amalgam”) fit the demands of the task and context differently.

Snow’s legacy is summarized in the 2002 book Remaking the Concept of Aptitude (Corno et al.), which remains the definitive extension of his ideas.

 


Sunday, December 22, 2024

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

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



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 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.




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.







- Posted using BlogPress from my iPad

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.






Wednesday, February 08, 2012

Saturday, April 25, 2009

US state persoanlitys as per the Big 5

A bit off task for the focus of this blog, but an interesting post re: a study describing the personality (as per the Big 5 theory) of US states at the

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Monday, October 09, 2006

Cultural variation of the Big 5 personality traits

Gene Expression has been presenting (via multiple posts) an interesting integration of phenotypic variatic of personality traits, as per the Big 5 theory, across different global cultures. Although personality research is a bit off-task for IQs Corner, I've had an ongoing interest in the Big 5 theory given that its empirical birth is similar to that of the CHC Theory of Cognitive Abilities (namely, draws heavily from factor analytic research)


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Monday, May 29, 2006

Big 5 personality traits and CHC intelligence theory: Lets hear it for being old and cantankerous!

The following is a post by the blogmaster (Kevin McGrew), who is also a member of IQs Corner Virtual Community of Scholars project.

In the mind of most quantoid psychometricians, CHC intelligence theory (aka, Gf-Gc theory) and the Big 5 Personality Theory are the most empirically sound and comprehensive taxonomies of human intelligence and personality. The relations between the Big 5 personality traits and select CHC broain domains (particularly Gf and Gc) have been actively studied during the past decade. However,much of this personality-intelligence relationship research has suffered from model specification error -- the failure to include important constructs in the empirical model being tested. Most personality-intelligence research has suffered from a narrow focus on only a small portion of the complete CHC human cognitive ability taxonomy (namely, Gf and Gc).

Thus, it was a pleasant surprise when I ran across the article below in my weekly search of literature. Baker and Bischsel (in press) investigated the relations between the Big 5 personality traits and the major broad CHC domains (as measured by the --note--WJ III conflict of interest disclosure required...I'm a coauthor of the WJ III). Not only did these investigators link the best cognitive and personality theories, they did so in a relatively large sample of 381 adults that was divided into developmentally-based subgroups of adults.

Not surprisingly, given the greater breadth of cognitive traits investigated and the ability to examine relations across different adult subgroups, this study confirms some prior findings, but more importantly, suggests some new personality-CHC trait relations previously not investigated or, which appear to vary as a function of adult developmetal status. I particularly like the finding that being more disagreeable in old age is associated with higher Gc. Maybe being a bit cantankerous late in life is a good deal !
  • Baker, T. J. & Bichsel, J. (in press). Personality predictors of intelligence: Differences between young and cognitively healthy older adults. Personality and Individual Differences. (click here to view)
Abstract
  • Previous investigations of personality–intelligence relationships have sampled mainly young adults. The present study compared young and older groups in identifying personality predictors of cognitive abilities. A sample of 381 adults was administered the Woodcock–Johnson III Tests of Cognitive Abilities and the Big Five Inventory-44. Participants were separated into three groups: young adults (aged 19–60), older adults that were cognitively comparable to the young, and cognitively superior older adults. Results indicated that Openness and Extraversion predicted cognitive abilities in the young and cognitively comparable old, but the specific abilities predicted were different for the two groups. In the cognitively superior older group, Agreeableness was a negative predictor of Gc (b= -.28) and Conscientiousness and Openness were predictors of short-term memory and visual and auditory processing.

Select excerpts from article

  • Previous studies of intelligence–personality relations have one or more underlying limitations: (a) the sample is restricted to young adults, (b) a limited range of cognitive abilities and/or personality is measured, (c) a small sample size is utilized, and (d) reliability estimates are not reported, so null effects cannot be interpreted. This study seeks to address these limitations by utilizing a large sample of older and younger adults, measuring multiple cognitive abilities and all FFM personality constructs, and reporting reliability estimates for personality and cognition measures.
  • This cross-sectional comparison suggests that personality–intelligence relationships change from younger to older adulthood. The results also suggests that there are diferences in personality–intelligence relationships between those who retain a normal level of overall cognitive ability in old age and those older adults who are cognitively superior. Perhaps most importantly, personality predictors of Gc differed among the three groups studied. Openness and Extraversion were important predictors of Gc in young adults, presumably the time of life when Gc undergoes more development, with those higher in Openness and lower in Extraversion scoring higher on Gc. These factors were not important predictors of Gc in the older groups. Given the robustness of the Openness–Gc relation in prior studies of young adults, the absence of this relation in both of the older groups in the present study suggests that Openness to experience is no longer necessary for the sustenance of crystallized ability in old age. Perhaps Openness is only important for Gc’s development in young adulthood.
  • Instead of Openness, Agreeableness negatively predicted Gc in the cognitively superior old, suggesting that a disagreeable nature goes hand in hand with advanced vocabulary and general knowledge in old age. This result is in accordance with previous research that suggests that those who are highly intelligent are more independent (Harris, Vernon, & Jang, 2005); non-reliance on others means Agreeableness is less necessary.
  • Interestingly, Conscientiousness positively predicted Ga and Gsm, which contradicts previous fndings that Conscientiousness has a negative relationship with intelligence (Moutafi et al., 2004; Moutafi et al., 2005). Moutafi et al. (2004) suggested there is an inverse relationship between Conscientiousness and intelligence because less intelligent people make up for their shortcomings by being more steadfast, and those with higher intellectual abilities do not need to be conscientious. Our results contradict this suggestion as our Conscientiousness–intelligence relationship was found only among the intellectually superior older adults. It may be that in old age Conscientiousness does not necessarily make one ‘‘smarter’’; rather, this trait enables older individuals to perform better on tests of cognition. This explanation makes more sense when considering the abilities that relate to Conscientiousness in this group. The tasks that make up Ga and Gsm appeared to elicit the most frustration in our older subjects, according to anecdotal reports from the research assistants. In addition, both Ga and Gsm, as measured by the WJ-III, tap attentional capacity (Mather & Woodcock, 2001). Previous research also suggests that Conscientiousness, at least in part, re?ects attentiveness (Digman & Inouye, 1986). It makes sense then that high scorers on Conscientiousness were also the best performers in terms of Ga and Gsm.
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