Beyond IQ: Mining the “no-mans-land” between Intelligence and IQ: Journal of Intelligence special issue

I am pleased to see the Journal of Intelligence addressing the integration of non-cognitive variables (personality; self-beliefs; motivational constructs; often called the “no-mans land” between intelligence and personality— I believe this catchy phrase was first used by Stankov) with intellectual constructs to better understanding human performance. I have had a long-standing interest in such comprehensive models as reflected by my articulation of the Model of Academic Competence and Motivation (MACM) and repeated posting of “beyond IQ” information at my blogs.

Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.

Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.

In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).

Click on images to enlarge.







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The importance of differential psychology for school learning: 90% of school achievement variance is due to student characteristics

This is why the study of individual differences/differential psychology is so important. If you don’t want to read the article you can watch a video of Dr. Detterman where he summarizes his thinking and this paper.

Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools. Article link.

Douglas K. Detterman

Case Western Reserve University (USA)

Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers' skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed


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