Showing posts with label learning. Show all posts
Showing posts with label learning. Show all posts

Wednesday, October 05, 2011

Research byte: Discovery-based learning not that good? A meta-analysis

I believe that meta-analyses, which quantitatively organizes large numbers of studies re: a topic, is research that needs to be listened to. Here is another, this one raising questions regarding the efficacy of discovery-based learning. I looks like more explicit/structured learning is better.

Double click on images to enlarge.







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Wednesday, August 17, 2011

Beyond IQ series: The "big picture" model of educational productivity context for the Model of Academic Competence and Motivation (MACM)




Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This second installment in the Beyond IQ series provides the the over-arching empirical and theoretical backdrop that led to the development of the MACM framework. [All installments in this series (and other related posts and research) can be found by clicking here]. Research on models of school learning, and the seminal work represented by Walberg's theory of educational productivity, provided the "big picture" framework for the development of one component of this larger model of educational productivity--the MACM framework.

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Walberg's (1981) theory of educational productivity, which is one of the few empirically tested theories of school learning based on an extensive review and integration of over 3,000 studies (DiPerna, Volpe & Stephen, 2002). “Wang, Haertel, and Walberg (1997) analyzed the content of 179 handbook chapters and reviews and 91 research syntheses and surveyed educational researchers in an effort to achieve some consensus regarding the most significant influences on learning" (Greenberg et al., 2003, p. 470). Using a variety of methods, Wang, et al. (1977) identified 28 categories of learning influence. Of the 11 most influential domains of variables, 8 involved social-emotional influences: classroom management, parental support, student- teacher interactions, social- behavioral attributes, motivational- effective attributes, the peer group, school culture, and classroom climate (Greenberg et al., 2003). Distant background influences (e.g., state, district, or school policies, organizational characteristics, curriculum, and instruction) were less influential. Wang et al. (1997) concluded that "the direct intervention in the psychological determinants of learning promise the most effective avenues for reform" (p. 210). Wang et al.’s research review targeted student learning characteristics (i.e., social, behavioral, motivational, affective, cognitive, and metacognitive) as the set of variables with the most potential for modification that could, in turn, significantly and positively effect student outcomes (DiPerna et al., 2002).

More recently, Zins, Weissberg, Wang and Walberg, (2004) demonstrated the importance of the domains of motivational orientations, self-regulated learning strategies, and social/interpersonal abilities in facilitating academic performance. Zins et al. reported, based on the large-scale implementation of a Social-Emotional Learning (SEL) program, that student’s who became more self-aware and confident regarding their learning abilities, who were more motivated, who set learning goals, and who were organized in their approach to work (self- regulated learning) performed better in school. According to Greenberg, Weissberg, O'Brien, Zins, Fredericks, Resnick, & Elias, (2003), Zins et al. (2004) assert that “research linking social, emotional, and academic factors are sufficiently strong to advance the new term social, emotional, and academic learning (SEAL). A central challenge for researchers, educators, and policymakers is to strengthen this connection through coordinated multiyear programming"(p. 470).

Walberg and associates’ conclusions resonate with findings from other fields. For example, the "resilience" literature (Garmezy, 1993) grew from the observation that despite living in disadvantaged and risky environments, certain children overcame and attained high levels of achievement, motivation, and performance (Gutman, Sameroff & Eccles, 2002). Wach’s (2000) review of biological, social, and psychological factors suggested that no single factor could explain “how” and “why” these resilient children had been inoculated from the deleterious effects of their day- to-day environments. A variety of promotive (direct) and protective (interactive) variables were suggested, which included, aside from cognitive abilities, such conative characteristics as study habits, social abilities, and the absence of behavior problems (Guttman et al., 2003).

Haertel, Walberg, and Weinstein (1983) identified 8 major models of school learning that are either based on psychological learning theory (Glaser, 1976) or time-based models of learning (Bennett, 1978; Bloom, 1976; Carroll, 1963; Cooley & Leinhardt, 1975; Harnischfeger & Wiley, 1976). Despite variations in names of constructs, Haertel et al. (1983) found that most of the 8 theories included variables representing ability, motivation, quality of instruction, and quantity of instruction. Constructs less represented in the models were social environment of the classroom, home environment, peer influence, and mass media (Watson & Keith, 2002). Haertel et al.’s (1983) review of theories, multiple quantitative syntheses of classroom research, and secondary data analyses of large- scale national surveys (Reynolds & Walberg, 1992), generally support Walberg's global model of educational productivity. Walberg’s model specifies that:
Classroom learning is a multiplicative, diminishing-returns function of four essential factors—student ability and motivation, and quality and quantity of instruction—and possibly four supplementary or supportive factors—the social psychological environment of the classroom, education-stimulating conditions in the home and peer group, and exposure to mass media. Each of the essential factors appears to be necessary but insufficient by itself for classroom learning; that is, all four of these factors appear required at least at minimum level. It also appears that the essential factors may substitute, compensate, or trade off for one another in diminishing rates of return: for example, immense quantities of time may be required for a moderate amount of learning to occur if motivation, ability, or quality of instruction is minimal (Haertel et al., 1983, p. 76)
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An important finding of the Walberg et al. large scale causal modeling research was that nine different educational productivity factors were hypothesized to operate vis- à-vis a complex set of interactions to account for school learning. Additionally, some student characteristic variables (motivation, prior achievement, attitudes) had indirect effects (e.g., the influence of the variable “went through” or was mediated via another variable).

The importance of the Walberg et al. group’s findings cannot be overstated. Walberg’s (1981) theory of educational productivity is one of the few empirically tested theories of school learning and is based on the review and integration of over 3,000 studies (DiPerna et al., 2002). Walberg et al. have identified key variables that effect student outcomes: student ability/prior achievement, motivation, age/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). In the current context, the first three variables (ability, motivation, and age) reflect characteristics of the student. The fourth and fifth variables reflect 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 learning equation.

More recently, Wang, Haertel, and Walberg (1993) organized the relevant school learning knowledge base into major construct domains (State & District Governance & Organization, Home & Community Contexts, School Demographics, Culture, Climate, Policies & Practices, Design & Delivery of Curriculum & Instruction, Classroom Practices, Learner Characteristics) and attempted to establish the relative importance of 228 variables in predicting academic domains. Using a variety of methods, the authors concluded that psychological, instructional, and home environment characteristics (“proximal” variables) have a more significant impact on achievement than variables such as state-, district-, or school-level policy and demographics (“distal”variables). More importantly, in the context of the current document, student characteristics (i.e., social, behavioral, motivational, affective, cognitive, metacognitive) were the set of proximal variables with the most significant impact on learner outcomes (DiPerna et al., 2002).

A sampling of the major components of the school learning models summarized by Walberg and associates is presented in the figure below (double on figure to enlarge or click here for another on-line version). The student characteristic domain in the figure is the primary focus of this series and the MACM framework.




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Thursday, April 16, 2009

Monday, March 02, 2009

School recess and learning

Thanks to the ENL blog fir this story tip

http://eideneurolearningblog.blogspot.com/2009/03/recess-essential-for-improving.html


Sent from KMcGrew iPhone (IQMobile). (If message includes an image-
double click on it to make larger-if hard to see)

Friday, January 23, 2009

Reading and dyslexia: Should RAN run and hide?

I just read an excellent article that investigated the relative importance of phonological awareness, naming speed (RAN-rapid automatized naming), orthographic knowledge, and morphological awareness in understanding reading achievement. Although caution is in order given the total sample size (n=93), this study is an excellent example of the type of research we need more of in educational psychology.

First.....one great feature of the article is the description and definition of the four different reading-related constructs that have recently become recognized as important in learning to read. I would recommend reading the introduction just to better one's understanding of phonological awareness, etc.

However, my real excitement for this article is that it directly attempts to deal (at least partially) with the problem of specification error, a type of research design error that occurs when potentially important variables in predictive or explanatory studies are omitted. This type of error can lead to biased estimates of the effects (relative importance) of predictive variables. I've soap-boxed about this before and will not repeat my lengthy diatribe here. Long story short - I believe that much of the "hot" reading/dyslexia research that has recently dominated the educational, special education, and school psychology fields may have too quickly anointed some emperors (phonemic awareness; RAN) and gave them too much credit. Read my prior post at the link above. Unless you've been living under a rock (and you work with kids with reading problems), it seems like there is constant chatter about "RAN this...RAN that...RAN is it....etc." Yes....I am exaggerating to make my point.

Why do I like this current article (or why does it soothe my ranting a tad)? Simple. It did not just study RAN and/or phonemic awareness in isolation as predictors of reading....it allowed them to compete for the explanation of reading together with orthographic knowledge and morphological awareness. And guess what? RAN failed to place in the race! When entered in a simultaneous regression model to predict reading, RAN added nothing to the prediction of reading when phonemic awareness, orthographic knowledge, and morphological awareness where also in the running.

This article suggests that the hype around RAN may have been over-exaggerated, due to specification error in a ton of the hot and sexy reading research that has dominated our professional journals and conferences this past decade.

But don't get me wrong, there is a good body of evidence that suggests that the processes underlying RAN are probably important for early reading. My point, which is buttressed by this article, is that maybe it has been given too much credit....and needs to be knocked down a notch.

I would be remiss if I did not also criticize this current study for also failing to include other potentially important predictors of reading. For example, I would have liked to see the authors also include measures of working memory (Gsm-MW), lexical knowledge (Gc-VL) or vocabulary, perceptual speed (Gs-P), and associative memory (Glr-MA)....based on my reading of the extant reading literature.

I will now get down from my specification error soap box. The take away message is that we need more studies that take off the blinders and include a more comprehensive array of research-based indicators of important constructs related to reading (and all areas of school learning)...so we can ascertain which constructs/abilities are important, and to what degree. Also...I would prefer if these researchers had specified a research- or theoretically-based causal SEM model (with possible direct and indirect causal paths between the constructs)---maybe RAN would be seen as being more important...possibly as a direct or indirect cause (or outcome) of the other predictors.

Below is the article reference, abstract and link for your reading.


Roman, A. A., Kirby, J. R., Parrila, R. K., WadeWoolley, L., & Deacon, S. H. (2009). Toward a comprehensive view of the skills involved in word reading in Grades 4, 6, and 8. Journal of Experimental Child Psychology, 102(1), 96-113. (click here to view/read)

  • Abstract: Research to date has proposed four main variables involved in reading development: phonological awareness, naming speed, orthographic knowledge, and morphological awareness. Although each of these variables has been examined in the context of one or two of the other variables, this study examines all four factors together to assess their unique contribution to reading. A sample of children in Grades 4, 6, and 8 (ages 10, 12, and 14 years) completed a battery of tests that included at least one measure of each of the four variables and two measures of reading accuracy. Phonological awareness, orthographic knowledge, and morphological awareness each contributed uniquely to real word and pseudoword reading beyond the other variables, whereas naming speed did not survive these stringent controls. The results support the sustained importance of these three skills in reading by older readers.
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Thursday, November 20, 2008

Techpsych blog - staying current technology for learning

Check out the Techpsych blog, a sister blog to one I've been reading daily for a good year - Interactive Multimedia Technology.  As written at the site, Techpsych:
  • "is for psychologists, teachers, related professionals, parents, technologists, and others interested in using technology more effectively for learning and communication. This is a place to share resources, links, what works, "how-tos", and lessons learned along the way. Enter a term or phrase in the search box to find what interests you!"
If you want to stay current on emerging technologies, esp. those related to learning and education, these are two "must" blogs.  I'm going to add Techpsych to my blogroll.

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Monday, June 23, 2008

Math and cognitive/intellectual abilities: Special Dev Neuropsych issue

Too much to read...not enough time. The journal Developmental Neuropsychology (rapidly becoming one of my favorite journals) just published a special issue on math and cognitive abilities. The titles make my mouth water....I wish I had time to read them all. The Table of Contents can be viewed by clicking here...as well as the editors introduction.

I'll make my "copies-in-exchange-for-blog-posts-offer." If anyone would like to read a copy of one of the articles, I'll provide a pdf copy off-line in exchange for a guest blog post re: the article.

I think this issue may be important to us who continue to try to link contemporary research on human cognitive abilities and school learning...this time in math.Technorati Tags: , , , , , , , , , , ,

Thursday, June 12, 2008

IQ Research bytes # 3: Reading fluency and writing dispositions

A couple of interesting articles related to school learning in reading and writing.

First, there has been much recent interest (in the area of LD eligibility) in reading fluency. The following article identifies three different types of fluency....fluency at the word level, at the semantic level of phrases and sentences, and at the level of paragraph text comprehension. All three were found to be related to reading comprehension.

Klauda. S. & Guthrie, J. Relationships of Three Components of Reading Fluency to Reading Comprehension. Journal of Educational Psychology, 100 (2), 310–321 (click to view)
  • Abstract: This study examined the relationships of 3 levels of reading fluency—the individual word, the syntactic unit, and the whole passage—to reading comprehension among 278 5th graders heterogeneous in reading ability. Hierarchical regression analyses revealed that reading fluency at each level related uniquely to performance on a standardized reading comprehension test in a model including inferencing skill and background knowledge. The study supports an automaticity effect for word recognition speed and an automaticity-like effect related to syntactic processing skill. In addition, hierarchical regressions using longitudinal data suggest that fluency and reading comprehension have a bidirectional relationship. The discussion emphasizes the theoretical expansion of reading fluency to 3 levels of cognitive processes and the relations of these processes to reading comprehension.

Second, as I've written before, I have a strong belief in the importance of conative variables in school learning. I've outlined a suggested model (MACM - Model of Academic Competence)of such factors in the "Beyond IQ Project" . The following article touches on some of these variables in the development of a writing dispositions scale. As per the MACM model, I would consider the factors the authors identified as being similar to domains in the MACM model (persistence in writing = academic motivation; confidence in writing = academic self-efficacy and ability conception; passion toward writing = intrinsic motivation and academic interests/attitudes). An important finding from this study is that it is possible to develop empirical measures of MACM constructs.

Piazza, C. & Siebert, C. (2008). Development and Validation of a Writing Dispositions Scale for Elementary and Middle School Students. Journal of Educational Research, 101 (5), 275-285. (click to view)
  • Abstract: The authors report the development and validationof the Writing Dispositions Scale (WDS), a self-report instrument for measuring affective stances toward writing. The authors collected survey data from 854 elementary and middle school students and randomly split the data to facilitate both an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA). The findings of the EFA demonstrated that an 11-item WDS has desirable internal and content reliability and discriminant validity. The CFA supported the item selection of the EFA and demonstrated excellent factorial validity and reliability. The analyses confirmed that writing dispositions are related to 3 affective stances: confidence, persistence, and passion toward writing.

Wednesday, February 20, 2008

Beyond IQ Byte # 4: Achievement goal orientation

Here is Byte # 3 from the Beyond IQ project, a project that outlines a proposed Model of Academic Competence and Motivation (MACM). Today's construct spotlight is on "achievement goal orientation."

Achievement goal orientation

A person’s set of beliefs that reflect the reasons why they approach and engage in academic and learning tasks. A performance goal orientation is exemplified by a concern for personal ability, a normative social comparison with others, preoccupation with the perception of others, a desire for public recognition for performance, and a need to avoid looking incompetent. A learning goal orientation reflects a focus on task completion and understanding, learning, mastery, solving problems, and developing new skills.

Academic goal orientation is based on contemporary “goal-as-motives” theory where it is posited that “all actions are given meaning, direction, and purpose by the goals that individuals seek out, and that the quality and intensity of behavior will change as these goals change” (Covington, 2000, p. 174). Achievement goal theory is particularly important in education as it is believed that by differentially reinforcing some goals (and not others), teachers can influence (change) the reasons why students learn—that is, change their motivation (Covington, 2000).

Different groups of researchers have converged on strikingly similar findings regarding the importance of academic goal orientation for academic success (Snow et al., 1996). The resultant achievement goal theory has received considerable attention during the past decade (Eccles & Wigfield, 2002; Linnenbrink & Pintrich, 2002b). Goal theory focuses on the role that “purpose” plays in motivation attitudes and behavior (Anderman & Maehr, 1994; Eccles &Wigfield, 2002; Maehr, 1999; Snow et al., 1996; Urdan & Maehr, 1995). Goal orientation focuses on the student’s reasons for taking a course or wanting a specific grade (Anderman et al., 2002). In this document, academic goal orientation is defined as an individual’s set of beliefs that reflect the reasons why they approach and engage in academic tasks (Eccles & Wigfield, 2002; Linnenbrink & Pintrich, 2002a; Pintrich, 2000b; Skaalvik & Skaalvik, 2002; Wentzel, 1999).

Although the specific terminology may differ amongst researchers, goal theory typically proposes two general goal orientations (Covington, 2000; Linnenbrink & Pintrich, 2002a). Nicholls and colleagues (e.g., Nicholls, Cobb, Yackel, & Wood, 1990) classify goals as either ego- or task- involved (Eccles & Wigfield, 2002). Dweck and colleagues (see Dweck, 1999) distinguish between performance (such as ego-involved goals) and learning goals (such as task-involved goals). Ames (1992) refers to performance and mastery goals. A performance goal orientation is characterized by self-questions such as “Will I look smart?” and/or “Can I out- perform others?” which reflect a concern for personal ability, a normative social comparison with others, preoccupation with the perception of others, a desire for public recognition for performance, a need to avoid looking incompetent, and “outperforming others as a means to aggrandize one’s ability status at the expense of peers”(Covington, 2000, p. 174). In contrast, a student with a learning goal orientation would more likely ask the questions “How can I do this task?” and “What will I learn?” The learning goal orientation reflects a focus on task completion and understanding, learning, mastery, solving problems, developing new skills, and an appreciation for what one learns (Covington, 2000; Eccles & Wigfield, 2002; Linnenbrink & Pintrich, 2002b; Skaalvik & Skaalvik, 2002).

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Wednesday, January 16, 2008

IQ Bytes # 5: Cattell's "investment" theory hypothesis

Another IQ Byte from the same source as IQ Byte # 4. This tiny morsel explains Raymond Cattell's "investment theory" hypothesis. As stated by Kvist and Gustafsson:

The Investment theory postulates that in the development of the individual there is initially a single, general, relation-perceiving ability which is connected with the maturation of the brain. This ability, which was labeled Gf by Cattell, is thus primarily associated with genetic factors and neurological functioning. It can be applied to any sensory, motor or memory area, and Cattell argued that a child's rate of learning of different tasks (e.g., spatial, numerical, conceptual) depends on this general ability. In particular the child's:
  • … rate of learning in fields demanding insights into complex relations – and these fields include especially the problems of reading, arithmetic, and abstract reasoning with which he struggles at school – will depend appreciably on his level of fluid intelligence (though motivation, goodness of teaching, etc., will still play their part, as with the acquisitions of low relational complexity). (Cattell, 1987, p. 139).
Thus, through practice and experience children develop knowledge and skills and according to the Investment theory these developed abilities (i. e., Gc) are influenced by Gf and by effort, motivation and interest, and also by previous levels of Gc.

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Wednesday, January 02, 2008

CHC cognitive abilities and reading research

In preparation for a NASP 2008 workshop I'm doing with Barb Wendling and Barb Read, I've been completing a select review of key CHC organized and non-CHC organized research dealing with the relations between cognitive abilities (using the CHC theory lens) and reading and math achievement.

I just posted a copy of the reading summary...which is a "work in progress." It currently is "up" on the web in the form of a relatively large visual-graphic mind map that will require extra navigation via your mouse. But, if you start in the upper right hand corner (with the "Overview-Read First" branch) you should understand the gist of this resource.

This will be updated as the workshop approaches. Below is a summary of the workshop from the NASP web page.

Workshop 51: Using CHC Theory to Link Assessment to Interventions

Kevin S. McGrew, PhD, Institute for Applied Psychometrics, St. Joseph, MN; Barbara J. Wendling, BJ Consulting, Dallas, TX; Barbara G. Read, Department of Defense Schools, Okinawa, Japan

  • Sponsored by Riverside Publishing Company and the Woodcock-Muñoz Foundation
  • Saturday, February 9, 2008, 9:00 a.m.–4:00 p.m.
  • This workshop will help school psychologists use the Cattell-Horn-Carroll (CHC) theory of cognitive abilities to link cognitive and achievement test results to a variety of targeted, evidence-based instructional interventions. Although CHC theory can be applied to the interpretation of any broad-based cognitive or achievement battery, workshop examples and case studies primarily use the Woodcock-Johnson III. The CHC broad and narrow abilities test, and underlying cognitive processes that are required for task performance on the WJ III, provide cues to related interventions for improving performance in key areas of academic functioning, or they may be used as the basis for requesting educational accommodations.
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Beyond IQ: A Model of Academic Competence and Motivation

As promised, and ahead of schedule, today I make available the results of over 5 years of work---Beyond IQ: A Model of Academic Competence and Motivation. The gist of the Beyond IQ project is simple - to present a preliminary conceptual framework from which to organize non-cognitive (conative) variables important for school learning...those essential student learning facilitators that are important above and beyond intelligence/cognitive ability.

The material can be accessed one of three ways (and you can switch between each navigation mode via the viewing options in the upper right-hand corner of the web pages).

If you want to navigate via a more traditional web-page expanding outline format, which is available on the left-hand side of the "Home" web page, start here.

If you want a linear "Table of Contents" outline navigation mode, start here.

If you love seeing the "big picture" all at once (using your Gv skills), start with my favorite, the clickable visual-graphic "Overview Map" mind map approach - click here.

Or mix and match navigational methods to meet your temporary whims.

Enjoy. I hope this work stimulates comments, responses, and more importantly, research focused on the development of a validated comprehensive model/framework of conative variables important for school learning (what I often like to call "essential student academic facilitators").

I will maintain a Beyond IQ category label term on the blog page which will allow readers to locate all Beyond IQ related articles with one click.

3-30-08 update. A set of PowerPoint slides that supplement the material at the Beyond IQ EWOK are now available via Slide Share. Click here.

Enjoy

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Sunday, December 30, 2007

Beyond IQ: Update on forthcoming MACM model (Jan 2 instead?)

Blogging on Peer-Reviewed Research
[Double click on image to enlarge]

Last week I announced the forthcoming "Beyond IQ: A Model of Academic Competence and Motivation (MACM) project post. To give a "hint" at the content and scope of this post regarding the MACM proposal, I'd like to draw readers attention to an excellent recent article by Byrnes and Miller (2007) in the journal Contemporary Educational Psychology.

My forthcoming MACM model post is concerned with addressing contemporary calls for a more comprehensive school learning aptitude model/framework. The "opportunity-propensity" model of school achievement of Byrnes and Miller is an excellent example of a similar (and more ambitious) attempt to provide a large heuristic framework (represented in the visual model above) from which to understand school learning. According to Byrnes and Miller, their model is grounded, in part, on the following:

  • "As any comprehensive handbook of educational research illustrates (e.g., Alexander & Winne, 2006), the field of educational psychology is subdivided into distinct research areas such as motivation, instruction, reading achievement, math achievement, and so on. Scholars who specialize in one of these areas tend not to specialize in others. In addition, researchers within each of these areas often focus on specific components of some predictor of achievement (e.g., motivational goals) to the exclusion of other components of that same predictor (e.g., motivational attributions), and also rarely include constructs from other research areas in their studies (e.g., domain-specific skills and aptitudes). Because the problem of student achievement is so complex, it makes sense that various subgroups of researchers would try to make this problem initially more tractable by examining individual or small sets of factors in their studies of achievement. Indeed, much has been learned about these aspects of achievement in the process. However, the continued tendency to focus on a limited number of predictors within each study of achievement has led to two related problems. One is that scientists and policy makers do not have a sense of how all of the various pieces of the achievement puzzle fit together. A second problem is that the relative importance of various predictors is still largely unknown because researchers have not typically included adequate controls in their studies." (p. 599- 600).
I couldn't agree more. The Byrnes and Miller model is an attempt, in the spirit of the "educational productivity" modeling work of Wahlberg and colleagues, to articulate a complete model of school learning.

My forthcoming MACM proposal is less ambitious and deals primarily with the non-cognitive ability portions of these larger models. In the Byrnes and Miller model, and in accordance with the spirit of the late Richard Snow, they refer to these learner characteristics as "propensities". As they stated in their article:

  • Propensity factors, in contrast, are any factors that relate to the ability or willingness to learn content once it has been exposed or presented in particular contexts. Thus, cognitive factors such as intelligence, aptitude, cognitive level, and pre-existing skills would qualify, as would motivational factors such as interest, self-efficacy, values, and competence perceptions (Byrnes, 2003; Corno et al., 2002; Eccles, Wigfield, & Schiefele, 1998; Jones & Byrnes, 2006; Sternberg, Grigorenko, & Bundy, 2001). Self-regulation is a hybrid of cognitive (e.g., beliefs) and motivational (e.g., efficacy) orientations (Pintrich, 2000), so it would also qualify as a propensity factor. We further assume that when the opportunity and propensity conditions are fulfilled in an individual (i.e., they have been exposed to content in an effective manner and were willing and able to take advantage of this learning opportunity), higher achievement will follow directly. As a result, opportunity factors and propensity factors are considered to be proximal causes of achievement.
Stay tuned. I may "move up" the date of the launch of the Beyond IQ" A Model of Academic Competence and Motivation" to January 2nd.


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Wednesday, December 19, 2007

Fine tuning the brain clock results in better school performance? Media report

If you are interested in the concept of an internal brain clock and possible neuro-based interventions to fine-tune the clocks resolution, with the objective to improve academic functioning of school-age children, check out the today's post at the IQ Brain Clock.

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Friday, October 19, 2007

Cognitive efficiency (working memory+Gs) = necessary but not sufficient constructs for learning?

As promised, here are a few thoughts from my Friday afternoon synaptic symphony of musings related to Geary's article on math learning.

On page 482 Geary talks about the core cognitive mechanisms (of working memory and Gf) being mental speed of information processing and attentional control (which I interpret as Engle, Conway et al.'s executive controlled attention). The overlap of these constructs with working memory and Gs (what we, in the land of the WJ III, call cognitive efficiency) is very interesting. In recent presentations I've referred to these core abilities as domain-general constructs...as recent CHC research suggests they are important for learning across almost all domains of human learning, esp. during initial stages of learning. These contrast with domain-specific abilities that appear more specific to learning in specific achievement domains (e.g., Ga and reading; Gf and mathematics).

I like his statement that these mechanisms are "necessary but not sufficient" for the development of secondary abilities (e.g., mathematics; reading). This makes sense. Domain general cognitive efficiency may be a set of necessary, but not sufficient, abilities for learning. They are necessary to learn, but the development of secondary abilities (such as reading and math) may require the addition of other abilities (Glr, Gf, Ga, Gv, etc.)) above and beyond cognitive efficiency.

This also connects with some causal models I've run where working memory, memory span, and Gs are specified as causal mechanism behind other cognitive abilities and achievement.

Just some Friday afternoon musings and thoughts as I "connect some dots" in my quirky store of acquired knowledge.

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Wednesday, August 29, 2007

LD and RTI - guest blog post by Jim Hanson

The following is a guest blog post by Jim Hanson (School Psychologist, M.Ed., Portland Public Schools, Portland, Oregon), a new member of IQs Corner Virtual Community of Scholars project.

Jim recently shared some material (on the CHC listserv) that he and his colleagues had developed in response to new regulations regarding the identification of children with specific learning disabilities (SLD). He received many "me to" requests for copies of the materials he was offering. IQ's Corner invited Jim to share his materials via a guest post and to ask Jim to become a regular guest blogger. He agreed!!!!!

Below are links to the two documents he was distributing. One is in the form of a pdf file (click here to view). The other is a PowerPoint show, which I've made available via Slideshare (click here to view). Below are Jim's comments. His colleagues are listed on the title slide of the PPT show.

  • Federal and most state regulations have changed the critieria for identifying specific learning disabilities from the IQ/achievement discrepancy model to 1) response to intervention (RTI) and/or 2) a pattern of strengths and weaknesses in achievement or performance relative to age, state grade level standards, and intellectual development (PSW). School districts are struggling to interpret what PSW means. Some administrators wish to continue using the IQ/achievement discrepancy model and call it PSW. This ignores voluminous research evidence on the nature and the federal definition of learning disabilities, which define SLD as a weakness in one or more of the basic psychological processes. The reason for some districts' wish to continue with "business as usual" might be that district personnel are not familiar with the neurology of learning disabilities. If they are acquainted with cognitive science, they might still be daunted by the science's diversity of terms among researchers, its technological complexity, and its relation to effectiveness and ease in application across a wide variety of schools and school teams. The proposed reductionist model is based on models by several leading researchers in the field. It is designed as a first step in acquainting administrators with current cognitive science. It may also provide an acceptable research model until personnel can be trained in more expansive and technically adequate methods of identification. Interested persons are welcome to contact Jim Hanson atJaBrHanson@yahoo.com, or the Oregon School Psychologists Association for further questions and comments."

Friday, June 15, 2007

Reading comprehension theory and assessment--recommended reading


Although in education a practical distinction is typically made between intelligence and achievement (reading, math, writing), as per CHC theory, intelligence and achievement are all abilities.....they differ primarily along a continuum (not a dichotomy) based on the degree to which the abilities are learned as a result of indirect or direct formal learning experiences. Thus, theoretical and practical issues surrounding the construct of reading is very germane to this blog.

That being said, I must confess that as a trained school psychologist, my understanding of contemporary theories of reading are not what they should be. I've not kept up with the literature as I should have the past few years. I suspect that most other school/educational psychologists also suffer from a lack of sufficient knowledge of contemporary reading theory and assessment issues.

To rectify this situation I've been doing some reading of select chapters and special reports (recommended to me by others) that address the ultimate goal of reading.....comprehension of text read (reading comprehension). To date I've found two readings particularly informative....as they allowed me to integrate some disparate information I had accumulated into a more coherent reading comprehension knowledge schema. Although the chapter (Assesment of Reading Comprehension--A Review of Past, Present and Future Practices) by Pearson and Hamm (2005) and the special Rand report (2002) are written primarily from the perspective of large-scale group reading assessments, most of the information is relevant to clinical 1-1 reading assessment practices.

I would urge all practicing assessment professionals to take time to skim these two chapters.

Just my 2 cents.

Warning....the files are a bit large and may open/download slow if you are not on a high-speed connection.

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Friday, February 02, 2007

Dr. Raven's 3-20/21-07 education policy seminar

In a prior post I made mention of a forthcoming two-day seminar by Dr. John Raven at the University of Minnesota. Dr. Steve Hughes has now provided a description about this seminar. A summary of the presentation is listed below, along with links to a more detailed ad flyer and registration information.

The Learning Society: How Educators Can Help Our Children Save the Planet (click here for more info)

John Raven, Ph.D.
College of Human Ecology
University of Edinburgh

March 20-21, 9:00-4:30
Room 156, Continuing Education and Conference Center,
1890 Buford Avenue, University of Minnesota, St. Paul Campus
(click here for link to registration)

  • "Join world famous author, researcher, and Competency-Based Education expert John Raven for a special two-day workshop designed for educational policymakers and practitioners. Learn about cutting-edge techniques from organizational psychology that help create a vibrant and innovative educational system — a system where children develop the awareness, confidence, and leadership skills necessary to address meaningful, real-world problems: Problems that will define (or destroy) the future of human life on Earth."
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Tuesday, January 23, 2007