Showing posts with label Richard Snow. Show all posts
Showing posts with label Richard Snow. Show all posts

Tuesday, February 19, 2013

The Motivation and Academic Competence (MACM) Commitment Pathway to Learning Model: Crossing the Rubicon to Learning Action

[2-24-13.  Since this original  blog post appeared, I have received requests for printed copies.  To meet this request I have published this post as the first MindHub (TM) Pub. This publication can be downloaded here.]

 

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.

[click on image to enlarge]








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). [1]   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).
Sounding a similar chord, David Wechsler emphasized the importance of conative abilities, which he referred to as nonintellectual factors (e.g., persistence, curiosity, and motivation) (Zachary, 1990).  In Wechsler’s (1994) own words, “When our scales measure the nonintellectual as well as intellectual factors in intelligence, they will more nearly measure what in actual life corresponds to intelligent behavior” (Wechsler, 1944, p. 103).  More recently Richard Woodcock, first author of the WJ, WJ-R and WJ III, in his Cognitive Performance and Information Processing Models, includes the facilitator-inhibitor domain that includes both internal conative-like characteristics (e.g., health, attention and concentration, cognitive style), along with external variables (e.g., environmental distractions) that can “modify cognitive performance for better or for worse, often overriding the effects of strengths and weaknesses in the previously described cognitive abilities” (Woodcock, 1998, p. 146).

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). 
Further illustrating the impossible task of specifying a single consensus explanatory or causal MACM-achievement model is a recent series of reports from the Center on Education Policy (CEP)--Student Motivation:  School Reform's Missing Ingredient (Usher & Kober, 2012a).  No less than eight different expert or theoretical views of the dimensions of student motivation (which represents only the motivations component of the MACM model in Figure1) were the basis of the CEP’s series of six different policy briefs (Usher & Kober, 2012b).

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.    
[click on slide to enlarge]

In simple terms, a three-stage process is at the heart of understanding the learner’s commitment pathway to learning and achievement.  Learners first address the questions of “can I do this task?” and “do I want to do this task and why?”  These questions reflect the learner contemplating or deliberating over their beliefs regarding what they can do, what they want to do or are being asked to do, and what intentions they form (positive or negative) regarding how to proceed.  Cleary et al. (2010) describe this as the forethought stage, or those processes that occur before the student commits to the learning task.  For example, a student with a strong interest in science and a mastery goal orientation (i.e., wanting to learn for the sake of learning and mastering new skills) would likely decide to deploy strong and sustained engagement and effort on a science project.  Conversely, a learner with a long history of academic failure may not feel they are capable (low self-efficacy) and may want to avoid failure as their primary goal, which would result in a different decision—a different degree of commitment to a science project and possible deployment of self-protection conative style behaviors.  The act of committing to a course of action for a task has metaphorically being called “crossing the Rubicon” (Corno, 1993; Corno et al., 2002).   Once committed to implementing a plan, the success of the student attaining their goals is turned over to their self-regulated learning strategies (volitional controls)—carrying out the plans and intentions.  The student has moved into the domain addressed by the question “what do I need to do to succeed on the task?

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.


Summary Comments

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.

Reference Notes

Most all references cited in this post can be found at McGrew et al. (2004) and McGrew (2007).

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[1] A complete description and discussion of these three primary MACM domains is not possible here. The interested reader should review Corno et al. (2002), and McGrew et al. (2004) and McGrew (2007).  It is important to note that the MACM model is only a partial taxonomy of relevant school-related individual difference characteristics.  The model presented here only lists general categories under the two areas of Social Ability and does not include physical and psychomotor competences, affective or social-emotional characteristics, cognitive abilities, and overarching constructs such as personality, which Corno et al. (2002) include in the more comprehensive big picture taxonomy of aptitude related constructs.  The literature on social intelligence, social cognition, and social skills requires treatment in separate chapters or books.  Social ability is included in the MACM model to reflect an awareness of the importance of social ability and behavior constructs when discussing important non-cognitive characteristics important for school success.

Wednesday, July 18, 2012

Clarification of Intellectual Ability Construct Terminology


      The terms ability, cognitive ability, achievement, aptitude, aptitude-achievement are tossed around in contemporary psychological and educational assessment circles, often without a clear understanding of the similarities and differences between and among the terms.  For example, what does an “aptitude-achievement” discrepancy, in the context of contemporary models of SLD identification (see Flanagan & Fiorrello, 2010), mean?  Where are the aptitudes in the CHC  model?  It is argued here that it is critical that intelligence assessment professionals and researchers begin to use agreed upon terms to avoid confusion and to enhance collaboration and to facilitate research synthesis.  In this spirit, the figure below illustrates the conceptual distinction between abilities, cognitive abilities, achievement abilities and aptitudes.  These conceptual distinctions are drawn primarily from Carroll (1993)and the work of Snow and colleagues (Corono et al., 2001).    [Click on image to enlarge]

            As reflected in the figure, all constructs in the CHC model are abilities.  As per Carroll (1991), “as used to describe an attribute of individuals, ability refers to the possible variations over individuals in the liminal levels of task difficulty (or in derived measurements based on such luminal levels) at which, on any given occasion in which all conditions appear favorable, individuals perform successfully on a defined class of tasks” (p. 8, italics in original).[1]  In more simple language,“every ability is defined in terms of some kind of performance, or potential for performance (p. 4).”  The overarching domain of abilities includes cognitive and achievement abilities as well as aptitudes (see figure).  Cognitive abilities are abilities on tasks “in which correct or appropriate processing of mental information is critical to successful performance” (p. 10; italics in original).  The key component to the operational definition of cognitive abilities is the processing of mental information (Carroll, 1993).  Achievement abilities “refers to the degree of learning in some procedure intended to produce learning, such as an informal or informal course of instruction, or a period of self study of a topic, or practice of a skill” (p. 17).  As reflected in the above figure, the CHC domains of Grw and Gq are consistent with this definition and Carroll’s indication that these abilities are typically measured with achievement tests.  Most assessment professionals use the terms cognitive and achievement abilities in accordance with these definitions.  However, the term aptitude is often misunderstood.
            Carroll (1993) uses a narrow definition of aptitude—“to refer to a cognitive ability that is possibly predictive of certain kinds of future learning success” (p. 16; emphasis added).  The functional emphasis on prediction is the key to this narrow definition of aptitude and is so indicated by the two horizontal arrows in the figure.  These arrows, which connect the shaded CHC narrow abilities that are combined to predict an achievement ability outcome domain, are the definition of aptitude used in this paper.
 This definition of aptitude is much narrower than the broader notion of aptitude as reflected in the work of Richard Snow.   Snow’s notion of aptitude includes both cognitive and non-cognitive (conative) characteristics of individuals (Corno et al., 2002; Snow et al., 1996).  This broader definition of aptitude focuses on human aptitudes which represent “the characteristics of human beings that make for success or failure in life's important pursuits. Individual differences in aptitudes are displayed every time performance in challenging activities is assessed” (Corno et al., 2002, p. xxiii). Contrary to many current assumptions, aptitude is not the same as ability.  According to Corno et al. (2002), ability is the power to carry out some type of specific task and comes in many forms—reading comprehension, mathematical reasoning, spatial ability, perceptual speed, domain-specific knowledge (e.g., humanities), physical coordination, etc.  This is consistent with Carroll’s definition of ability.  According to Snow and colleagues, aptitude is more aligned with the concepts of readiness, suitability, susceptibility, and proneness, all which suggest a “predisposition to respond in a way that fits, or does not fit, a particular situation or class of situations. The common thread is potentiality—a latent quality that enables the development or production, given specified conditions, of some more advanced performance” (Corno et al., 2002, p. 3; see Scheffler, 1985).  This broader definition includes non-cognitive characteristics such achievement motivation, freedom from anxiety, self-concept, control of impulses, and other (see Beyond IQ project). 
As reflected in the model in the above figure, cognitive and achievement abilities differ primarily in the degree of emphasis on degree of mental information processing (cognitive) and the degree which the ability is an outcome acquired more from informal and formal instruction (achievement).  Here, aptitude is defined as the combination, amalgam or complex of specific cognitive abilities that when combined best predict a specific achievement domain.  Cognitive abilities are always cognitive abilities.  Some cognitive abilities contribute to academic or scholastic aptitudes, which are pragmatic functional measurement entities—not trait-like cognitive abilities.  Different academic or scholastic aptitudes, depending on the achievement domain of interest, likely share certain common cognitive abilities (domain-general) and also include cognitive abilities specific to certain achievement domains (domain-specific).  A simple and useful distinction is that cognitive abilities and achievements are more like unique abilities in a table of human cognitive elements while different aptitudes represent combinations of different cognitive elements to serve a pragmatic predictive function.  For the quantoid readers, the distinction between factor-analysis based latent traits (cognitive abilities) and multiple regression based functional predictors of achievement outcomes (cognitive aptitude) may help clarify the sometimes murky discussion of cognitive and achievement abilities and aptitudes.



[1] As noted by Carroll (1993), luminal refers to specifying threshold values used “in order to take advantage o the fact that the most accurate measurements are obtained at those levels” (p. 8).

Tuesday, December 16, 2008

Beyond IQ: Metacognition, self-regulation, self-regulated learning (JER special issue)

Beyond IQ. Readers of this blog should be aware that I firmly believe that in order to understand, explain, and improve educational outcomes for learners, a "bigger picture" approach is necessary. I call it the "Beyond IQ Project." In particular, I've repeatedly sounded the accolades of the late Richard Snow's work on aptitude. I've made many posts related to this notion of aptitude, which includes many constructs such as self-efficacy, motivation, self-regulated learning, etc. I've even proposed a model for integrating these "conative" constructs (Model of Academic Competence and Motivation--MACMM). Most of my posts can be found by clicking on the Beyond IQ tag. I have been encouraged to see serious scholars in the field of intelligence (ISIR members) paying increasing attention to these constructs as they attempt to explain intellectual performance.

Today I ran across yet another special issue of a journal devoted to a major domain of the MACMM model - self regulated learning ("What do I need to do to succeed?"). Below is the table of contents of the current issue of the Educational Psychology Review. It looks EXCELLENT. I can't wait to read the articles. I've made the special issue introduction article available for viewing. If any reader would like to read one (or more) of the articles (I would provide a copy of the pdf file), in exchange for a guest blog post summary to this bog, please contact the blogmaster (iapsych@charter.net)

Why This and Why Now? Introduction to the Special Issue on Metacognition, Self-Regulation, and Self-Regulated Learning - Patricia A. Alexander (click to view).

Metacognition and Self-Regulation in James, Piaget, and Vygotsky - Emily Fox and Michelle Riconscente

Focusing the Conceptual Lens on Metacognition, Self-regulation, and Self-regulated Learning - Daniel L. Dinsmore, Patricia A. Alexander and Sandra M. Loughlin

Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning - Sofie M. M. Loyens, Joshua Magda and Remy M. J. P. Rikers

Self-Regulation of Learning within Computer-based Learning Environments: A Critical Analysis - Fielding I. Winters, Jeffrey A. Greene and Claudine M. Costich

The Role of Teacher Epistemic Cognition, Epistemic Beliefs, and Calibration in Instruction - Liliana Maggioni and Meghan M. Parkinson

Metacognition, Self-Regulation, and Self-Regulated Learning: Research Recommendations - Dale H. Schunk

Metacognition, Self Regulation, and Self-regulated Learning: A Rose by any other Name? - Susanne P. Lajoie

Clarifying Metacognition, Self-Regulation, and Self-Regulated Learning: What’s the Purpose? - Avi Kaplan

An Interview with Dale Schunk - Gonul Sakiz

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