The Cognitive-Affective-Motivation Model of Learning (CAMML): Standing on the Shoulders of Giants

Abstract

The Cognitive-Affective-Motivation Model of Learning (CAMML) is a proposed framework for integrating contemporary motivation, affective (Big 5 personality) and cognitive (CHC theory) constructs in the practice of school psychologists (SPs). The central tenet of this article is that SPs need to integrate motivation alongside affective and cognitive constructs vis-à-vis an updated trilogy-of-the-mind (cognitive, conative, affective) model of intellectual functioning. CAMML builds on Richard Snow’s seminal research on academic aptitudes—which are not synonymous with cognitive abilities. Learning aptitude complexes are academic domain-specific cognitive abilities and personal investment mechanisms (motivation and self-regulation) that collectively produce a student’s readiness to learn in a specific domain. CAMML incorporates the “crossing the Rubicon” commitment pathway model of motivated self-regulated learning. It is recommended SPs take a fresh look at motivation theory, constructs, and research, embedded in the CAMML aptitude framework, by going back-to-the-future guided by the wisdom of giants from the field of cognition, intelligence, and educational psychology.

Character strengths and fluid intelligence

“…One possible explanation is that perceived ease in dealing with intellectual content and solving problems (due to higher cognitive ability) promotes a higher preference for engaging in such content, resulting in an increased love of learning. Thus, affective-motivational traits, such as love of learning, might not only drive the investment of (fluid) cognitive abilities into (crystallized) cognitive resources (in line with ideas of investment trait, e.g., von Stumm, 2018; see also McGrew, 2022), but also represent a consequence of cognitive abilities, as has been suggested for the FFM dimension of openness (Ziegler et al, 2012). However, these and other possible explanations cannot be further explored in cross-sectional data and remain open for future study.…”

Carroll’s Three-Stratum (3S) Cognitive Ability Theory at 30 Years: Impact, 3S-CHC Theory Clarification, Structural Replication, and Cognitive–Achievement Psychometric Network Analysis Extension

“…2 More recently, Whilhelm and Kyllonen ( 2021 ) articulated how the strengths and limitations of Carroll’s work can serve as a springboard for new research focusing on such topics as (1) the central importance of working memory for Gf and recognizing Gf content facets, (2) reconceptualizing Gc as more akin to the Cattell-like notion of a general capacity ( g c ) that includes, aside from verbal abilities, other forms of acquired knowledge, achievement, and domain-specific expertise, (3) better understanding the complex nature, measurement, and scaling of speed factors (Gs, Gt, and Gps), (4) the relation between idea production (Gr) and the emerging societal need to better understand and foster creativity, and (5) the need to incorporate social, emotional, and collaborative intelligence constructs in a more comprehensive intelligence framework. Whilhelm and Kyllonen ( 2021 ) proposed five research strategies for moving the study of intelligence forward, including the integration of cognitive and non-cognitive traits (e.g., motivation, interests, and personality) consistent with contemporary notions of Snow’s aptitude trait complexes ( Ackerman 1996 , 2018 ; Corno et al 2002 ; McGrew 2022 ).…”

“…As per Cattell’s investment theory ( Cattell 1971 , 1987 ), the consistent application of fluid reasoning ( g f ) to learning via schooling and experience generates new crystallized acquired knowledge ( g c ) ( Schneider and McGrew 2018 ). The general mechanisms of this investment come from societal (e.g., educational resources), familial (e.g., resources and expectations), and personal (e.g., personality, motivation, and volition) investment mechanisms ( McGrew 2022 ; Schneider and McGrew 2018 ).…”

Application of Network Multimedia Extension Topology Algorithm in Wisdom Cloud Sharing of English Teachers' Scientific Research Platform

“…(2)From the established area on the edge of the sequential took out a nodes in the node list, if the node is in the area on the list of on list as the starting point of the nodes along the area according to clockwise traversal, as every meet next convergence by the best route to select the best path ‚ then jump to this path to proceed clockwise traversal, until back to the starting point for that completed a child area and stores it. In the figure 1, the extraction topology is defined [14][15][16][17][18][19].…”