There is only one unequivocal law of human behavior—the law of individual differences. People are more different than they are alike. Probably no environment elicits individual differences sooner in life than formal education.
When asked by teachers or parents to help understand why a particular student is not achieving adequately, school psychologists have traditionally reached for an intelligence battery. Although understanding a student’s general, broad and specific cognitive abilities contributes important information for determining general expectations and the need for special instructional serves, at best, measures of cognitive abilities account for only approximately 40% to 50% of a student’s predicted achievement. Much is still unexplained. Furthermore, attempts at modifying intelligence, or identifying evidence-based cognitive-aptitude-achievement interactions (ATI's) that can be implemented at the level of individual students, have not yet provided the magical link between cognitive ability testing and evidence-based instructional or cognitive modifiability recommendations. It is clear that school psychologists must go “beyond IQ” to help teachers, parents, and students themselves, to maximize student learning.
But…if not IQ…then what? The more appropriate question is “what should be added to cognitive ability assessment information to help school psychologists facilitate the achievement of all learners?” To provide some answers to this question this paper was developed with three primary goals. First, a conceptual framework is presented to help school psychologists better understand the salient non-cognitive individual difference student variables to consider when engaging in learning-related assessments and instructional planning. Second, the primary domains of the model are defined. Finally, how the various domains work within a commitment pathway model to learning (crossing the active learning rubicon) is briefly presented.
Beyond IQ: What Models of School Learning Have Told Us
A number of comprehensive models of school learning have been advanced to describe and explain the school learning process (see McGrew, Johnson, Cosio, & Evans, 2004). Walberg's (1981) theory of educational productivity is one of the few empirically tested theories of school learning. Walberg's model is based on an extensive review and integration of over 3,000 studies (DiPerna, Volpe & Stephen, 2002; Wang, Haertel, and Walberg, 1997). Walberg et al. reported that the following key variables are important for understanding school learning—student ability and prior achievement, motivation, age or developmental level, quantity of instruction, quality of instruction, classroom climate, home environment, peer group, and exposure to mass media outside of school (Walberg, Fraser & Welch, 1986). The first three variables (ability, motivation, and age) reflect student individual difference characteristics. The fourth and fifth variables reflect characteristics of instruction (quantity and quality), and the final four variables (classroom climate, home environment, peer group, and exposure to media) represent aspects of the psychological environment (DiPerna et al., 2002). Clearly student characteristics are important for school learning, but they only comprise a portion of the complete learning equation.
The Walberg research group (see Wang, Haertel, & Walberg, 1993) also concluded that psychological, instructional, and home environment characteristics (proximal variables) had a more significant impact on achievement than variables such as state-, district-, or school-level policy and demographics (distal variables). More important for practicing school psychologists was the conclusion that student characteristics (i.e., social, behavioral, motivational, affective, cognitive, metacognitive) were the set of proximal variables that had the most significant impact on learner outcomes (DiPerna et al., 2002).
Beyond IQ: The Need for a Non-Cognitive Learner Characteristic Taxonomy
If school psychologists are to focus on the most learning-relevant student characteristics (beyond cognitive abilities), what individual difference student characteristic domains should receive priority? Even a partial list of potentially important non-cognitive domains mentioned in the school psychology literature is staggering. Social-emotional learning. Motivation. Self-efficacy. Engagement. Study and homework skills. Resilience. Executive functions. Engaged learning time. Self-regulated learning strategies. Social skills. Social and emotional intelligence. What are the similarities and differences between these different constructs? Does each construct consist of a single dimension or is there a complex model of subdomain characteristics within each construct domain? Where is a school psychologist to start? It my opinion that the answer first lies in outlining a working taxonomy of important non-cognitive learning-related student characteristics.
I am an admitted taxonomist. As stated in the context of human cognitive abilities, Joel Schneider and I stated “A useful classification system shapes how we view complex phenomena by illuminating consequential distinctions and obscuring trivial differences. A misspecified classification system orients us toward the irrelevant and distracts us from taking productive action. Imagine if we had to use astrological classification systems for personnel selection, college admissions, jury selection, or clinical diagnosis. The scale of inefficiency, inaccuracy, and injustice that would ensue boggles the mind. Classification is serious business” (Schneider & McGrew, 2012, p. 99).
I believe that before defining and articulating instructional implications of important non-cognitive student characteristics, the broad domain(s) must first be circumscribed. Furthermore, I believe that any working taxonomy must emerge from the extant empirical and theoretical literature, and not from the advocacy, policy, political arenas or narrow single trait programs of research. Although a variety of models of school learning have been articulated, it is only recently that a model with sufficient breadth and depth, grounded in decades of educational and psychological research, has emerged with the potential to serve as a “bridging” mechanism between educational and psychological theory/research and educational practice.
Based on a large systematic program of educational research and research integration, Richard Snow and colleagues outlined a provisional and promising aptitude for learning taxonomy (Corno et al., 2002; Snow, Corno, & Jackson, 1996). Richard Snow’s work unfortunately has flown under the radar screen of most of school psychology. It is hoped that this brief paper rectifies this oversight by describing a Snow-inspired framework for understanding the most salient non-cognitive student characteristics that influence school learning.
A first attempt at outlining an adapted and updated Snow model, based on a comprehensive review and integration of approximately three decades of contemporary school learning research, was first described by McGrew et al. (2004). This model was next revised as the Model of Academic Competence and Motivation (McGrew, 2007). Figure 1 presents the revision and update of the McGrew 2007 MACM model.
The Model of Academic Competence and Motivation (MACM): A Brief Overview
The MACM model includes the three broad domains of orientations towards self (motivations), volitional controls (cognitive strategies and styles), and orientations towards others (social ability).  As illustrated in Figure 1, the current focus is on the motivational and volitional domains of conation. The term conative, as well as volition, may partially explain why Snow and colleagues work has not been widely infused into education and school psychology. Conative and volition are not commonly used terms in education or psychology and, frankly, would result in puzzled looks from teachers and parents if used to describe characteristics of a student. However, they are important and have been part of a long standing “ancient trilogy of human mental functioning that consists of cognition, affection and conation” (Corno, 1996, p.14, italics added). This current paper seeks to amplify the importance of conative abilities as articulated by many giants in the field of intelligence theory and testing.
PDF files that contain detailed definitions of the MACM characteristics and theoretical foundations can be found here and here.
Conative abilities have long been recognized as the important brides made-trait to cognition when attempting to explain intelligent performance or behavior. The APA Dictionary of Psychology (Vandenbos, 2007) defines conation as “the proactive (as opposed to habitual) part of motivation that connects knowledge, affect, drives, desires, and instincts to behavior. Along with COGNITION and affect, conation is one of the three traditionally identified components of mind” (p. 210; caps in original). Charles Spearman, who all psychologists associate with the birth of the psychometric study of intelligence, recognized the importance of conative abilities. Spearman (1927) stated that “the process of cognition cannot possibly be treated apart from those of conation and affection, seeing that all these are but inseparable aspects in the instincts and behavior of a single individual, who himself, as the very name implies, is essential indivisible” (p. 2). Alfred Binet, who is considered the father of the modern day intelligence test, also recognized the importance of “non-intellectual” factors in cognitive or intellectual performance. According to Corno et al. (2002):
- Binet summed up his investigations in a famous description of intelligence: ‘the tendency to take and maintain a definite direction; the capacity to make adaptations for the purpose of attaining a desired end; and the power of auto-criticism’ (translation by Terman, 1916, p. 45). All three of these phrases refer at least as much to conative processes and attitudes as to reasoning powers. Binet's concept of intelligence was much like Snow's concept of aptitudes (p. 5).
I humbly stand on the shoulders of Spearman, Binet, Wechsler, Woodcock and Snow and recommend that school psychologists organize their thinking regarding essential student learning characteristics within a model of student competence and achievement that recognizes the importance of conative abilities. To remove the terminology barrier to implementing this recommendation, conative abilities have been renamed as motivations (orientations towards self) and cognitive strategies and styles (volitional controls; see Figure 1). Being even more direct and simple, I have modified and extended the key question approach to understanding achievement motivation as presented by Wigfield and Eccles (2002). The major domains represented in the MACM model (see Figure 1) can be reduced to five basic questions (borrowed and revised from Wigfield & Eccles, 2002) school psychologists should ask as they gather and integrate information regarding important non-cognitive school learning-related student information.
- Does the student think they can do the task? This question focuses on understanding the student’s self-beliefs regarding their perceived locus of control, academic self-efficacy, academic self-concept, and ability conception.
- Does the student want to do the task and for what reasons? When pondering this question, the goal is to understand the student’s motivational orientations such as degree of academic and intrinsic motivation, type of goal orientation, and the students’ goal setting abilities. Additionally, understanding how a student values school learning and their global and situational academic domain-specific academic interests should be considered.
- What does the student need to do to succeed on the task? High motivation and positive self-beliefs are necessary but not sufficient conditions for succeeding in educational environments. A bridge must link abilities, self-beliefs and motivation with action-oriented behavior. The bridge is the presence of motivational controls or self-regulated learning strategies (e.g., study skills, cognitive and learning strategies, engagement) that allow individuals to manage efforts to accomplish their goal.
- What are the student’s typical ways of responding to the task? This question focuses on determining if a student has characteristic stable styles for approaching learning tasks, success or failure (e.g., self-worth protection; adaptive help-seeking) that either need to be enhanced or modified to insure increased positive achievement outcomes.
- How does the student need to behave towards others to succeed on the task? Traditionally U.S. schools have valued student characteristics such as citizenship, conformity to social rules and norms, cooperation, and positive social behavior. The student who does not know how (or who lacks the appropriate skills) to behave appropriately and responsibly is at increased risk for academic failure and the possibility of not developing a sense of belonging or relatedness.
The MACM Framework and the Commitment Pathway to Learning Model: Crossing the Rubicon to Learning Action
There is no consensus explanatory model outlining how the various constructs included in Figure 1 interact within the MACM model or with other important learner characteristics (e.g., cognitive abilities) to produce positive achievement outcomes. In their introduction to the Handbook of Competence and Motivation, a seminal attempt to corral the major theories and research regarding motivation, self-regulatory processes and competence, Elliot and Dweck (2005) summarize this state of affairs when they stated, with regard to the weaknesses in the achievement motivation literature:
- The literature lacks coherence and a clear set of structural parameters, and the literature is too narrowly focused and limited in scope. In essence, what is commonly referred to as the “achievement motivation literature” represents a rather loose compendium of theoretical and empirical work focused on a colloquial understanding of the term “achievement” (p. 5).
Not only is the number of proposed explanatory models of achievement motivation a barrier to incorporating the MACM learner characteristics into school psychology practice, the complexity of some of the models is not practice friendly. For example, the general expectancy-value model of achievement choices includes 11 separate model components (each with from 1 to 5 subcomponents) and over 12 different unidirectional or bidirectional arrows between components (Eccles, 2005). A model focused just on evaluation anxiety during self-regulation includes 5 model components and 9 unidirectional arrows, while a proposed model of self-handicapping, which is just one self-protection style (i.e., a conative style in the MACM model—see Figure 1) is a figure with 11 different components and 13 different arrows (Rhodewalt & Vohs, 2005). Finally, the MACM model includes two goal-related constructs (academic goal orientation and academic goal setting—see Motivational Orientations component in Figure 1), while a 1996 review of goal constructs in psychology listed 31 theories that have posited goal-like constructs and proposed a 6 domain, 24 subdomain taxonomy of human goals (Austin & Vancouver, 1996).
Against this backdrop, a simplified adaptation of Snow’s dynamic model of conation in the academic domain (Corno, 1993) is presented next. It is assumed that the presentation of a simplified model is a first step towards helping school psychologists see the forrest-from-the-trees and thus, increases the chances of successful integration of the MACM concepts in their assessment and instructional planning repertoire. The MACM-based adaptation and extension of Snow’s model is presented in Figure 2.
Of course, this is on overly simple explanation of an obvious non-linear process where the results of plan implementation and self-regulation may require moving back to the contemplation and planning stage if the initials goals require modification (e.g., a student sets an unrealistic goal to perform perfectly on a math project). Multiple recursive and dynamic iterations occur across the commitment to action pivot point (the Rubicon; see circle of arrows in Figure 1), with motivations modifying cognitive control and regulation strategies, and cognitive strategy feedback requiring goal adjustment and changes in plans, is often required.
It is hoped that this brief description of the MACM model and the MACM Commitment to Learning Pathway Model (Crossing the Rubicon to Learning Action)stimulates thought, research, and further development. Updates to this model will be posted at this blog and will typically be accessible by clicking on the MACM and Beyond IQ blog labels on the blog roll.
Most all references cited in this post can be found at McGrew et al. (2004) and McGrew (2007).