II RNCE IAS abril 2015 copy.key

II Reunião REDE NACIONAL DE CIÊNCIA PARA
EDUCAÇÃO 27 de abril de 2015 Instituto Ayrton
Senna, SP
Competências Cognitivas no Século XXI - Medidas
de Processos de Raciocínio, Conhecimento e
Criatividade em sistemas de avaliação em larga
escala
Dr. Ricardo Primi,
Universidade São Francisco, Itatiba, Brasil
Instituto Ayrton Senna, São Paulo, Brasil
EduLab21
Centro de Conhecimento
do IAS
Março 2015
Objetivos
• Apresentar uma breve revisão sobre os modelos sobre
inteligência (“estado da arte”)
• Relacionar as competências do século XXI com os modelos
mais recentes de inteligência (CHC)
• Refletir e advogar a importância desse modelo para mapear o
que avaliamos atualmente nos sistemas em larga escala e
propor futuros aprimoramentos
Competências do Século XXI
www.p21.org
Aprendizagem e inovação
Criatividade
Pensamento crítico e solução de
problemas
Comunicação e colaboração
Habilidades para carreira e para vida
Flexibilidade
Adaptação à mudança
Gerenciar objetivos e tempo
Trabalhar independentemente
Aprendizagem com auto-direção
Interagir efetivamente com os outros
Trabalhar efetivamente com a
diversidade
Liderança
Responsabilidade para com os outros
Competências do Século XXI
Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century
EDUCATION FOR
LIFE AND WORK
Developing Transferable Knowledge
and Skills in the 21st Century
Committee on Defining Deeper Learning and 21st Century Skills
James W. Pellegrino and Margaret L. Hilton, Editors
Copyright © National Academy of Sciences. All rights reserved.
Board on Testing and Assessment
and
Board on Science Education
Cluster
Division of Behavioral and Social Sciences and Education
Cognitive Processes
and Strategies
Copyright © National Academy of Sciences. All rights reserved.
COGNITIVE
COMPETENCIES
Knowledge
Creativity
Terms Used for 21st
Century Skills
Critical thinking, problem
solving, analysis,
reasoning/argumentation,
interpretation, decision making,
adaptive learning, executive
function
32
TABLETABLE
2-2 Clusters
of 21st
Century
Competencies
2–2 Clusters
of 21st
Century
Competencies
O*NET Skills
System skills,
process skills,
complex problemsolving skills
Main Ability/
Personality Factor
Main ability factor:
fluid intelligence (Gf)
Information literacy (research
Content skills
using evidence and recognizing
bias in sources); information and
communications technology
literacy; oral and written
communication; active listening
Main ability factor:
crystallized intelligence
(Gc)
Creativity, innovation
Main ability factor:
general retrieval ability
(Gr)
Complex problemsolving skills
(idea generation)
Cluster
Intellectual
Openness
INTRAPERSONAL
COMPETENCIES
Work Ethic/
Conscientiousness
Positive
Core SelfEvaluation
Terms Used for 21st
Century Skills
O*NET Skills
Main Ability/
Personality Factor
Flexibility, adaptability, artistic [none]
and cultural appreciation,
personal and social
responsibility (including cultural
awareness and competence),
appreciation for diversity,
adaptability, continuous
learning, intellectual interest and
curiosity
Main personality factor:
openness
Initiative, self-direction,
responsibility, perseverance,
productivity, grit, Type 1 selfregulation (metacognitive skills,
including forethought,
performance, and selfreflection), professionalism/
ethics, integrity, citizenship,
career orientation
[none]
Main personality factor:
conscientiousness
Type 2 self-regulation (self[none]
monitoring, self-evaluation, selfreinforcement), physical and
psychological health
Main personality factor:
emotional stability
(opposite end of the
continuum from
neuroticism)
33
continued
Cluster
INTERPERSONAL
COMPETENCIES
SOURCE: Created by committee.
34
TABLE 2-2 Continued
Terms Used for 21st
Century Skills
O*NET Skills
Main Ability/
Personality Factor
Competências do Século XXI
•
"do século XXI" ou "mais valorizadas em nosso tempo”?
•
capacidades já conhecidas
•
Qual o “estado da arte" do campo de estudo da inteligência? Qual o conjunto
de evidências que baseiam os sistemas de avaliação em larga escala ?
•
Quais medidas compõem os sistemas de avaliação em larga escala ? Todos
os fatores cognitivos relevantes são cobertos ?
•
Abordagens da Inteligência
•
Educação foco no conhecimento
•
Psicologia foco nos processos mentais
Breve histórico da evolução das
teorias sobre a inteligência
Fator g de Charles Spearman
•
1904 General intelligence objectively determined and
measured
•
"positive main fold”. Invenção da análise fatorial.
Modelo bifatorial. “energia mental”. neogenese.
•
(a) edução de relações capacidade maior ou menor
de estabelecer relações entre duas ou mais idéias;
•
(b) edução de correlatos capacidade maior ou menor
que as pessoas demonstram de criar novas idéias a
partir de uma idéia e uma relação.
•
(c) apreensão das experiências capacidade à
rapidez e à acuidade com que as pessoas percebem
os estímulos, e bem como aos processos de;
Quase 100 anos depois:
Duncan, J., Seitz, R. J., Kolodny, J., Bor,
D., Herzog, H., Ahmed, A., ... & Emslie, H.
(2000). A neural basis for general
intelligence. Science, 289(5478), 457-460.
Teoria do Investimento de
R. Cattell
•
Inteligência Fluida Gf: capacidade de
raciocínio “uso de operações mentais
controladas deliberadamente para
resolver problemas novos... que não
podem ser resolvidos
automaticamente” .. “ operações
mentais como: fazer inferências, formar
conceitos, classificar, gerar e testar
hipóteses, identificar relações,
compreender implicações, resolver
problemas, extrapolar e transformar
informações” (MacGrew & Evans,
2004; Kane & Gray, 2005)
•
Inteligência Cristalizada Gc Riqueza
(profundidade e amplitude) do estoque
dos conhecimentos adquiridos
Variáveis Cognitivas e Não Cognitivas na
Escola: Modelo de J. B. Carroll (1963)
Quantidade de tempo
engajado na tarefa
Aprendizagem
efetiva
=
Quantidade de tempo
necessário
Quantidade de
tempo engajado
na tarefa
=
Oportunidade
+
Motivação/Per
sistência
Quantidade de
tempo necessário
1
=
Capacidade
prévia
+
Qualidade do
ensino
2014
*AMAR25DOM19*
MATEMÁTICA
E SUASimportados
TECNOLOGIAS
Uma loja vende produtos
e
QUESTÃO 137
nacionais entre
vestidos
camisas e casacos.
Questões
de 136
a 180
Uma empresa que organiza eventos de formatura
confecciona canudos de diplomas a partir de folhas de papel
TXDGUDGDV 3DUD TXH WRGRV RV FDQXGRV ¿TXHP LGrQWLFRV
cada folha é enrolada em torno de um cilindro de madeira
de diâmetro d em centímetros, sem folga, dando-se 5 voltas
FRPSOHWDVHPWRUQRGHWDOFLOLQGUR$R¿QDODPDUUDVHXP
cordão no meio do diploma, bem ajustado, para que não
RFRUUDRGHVHQURODPHQWRFRPRLOXVWUDGRQD¿JXUD
Alguns vestidos e todos os casacos fazem parte
QUESTÃO
136importados. Não há produto
dos produtos
importado
em uma
tamanho
grande.
A Figuradisponível
1 representa
gravura
retangular com
qual dentree os
enunciados não
8 Assinale
m de comprimento
6 mfatos
de altura.
poderia ser verdadeiro:
6 metros
A. Carla experimenta uma camisa nacional.
B. Luciana está comprando um casaco pequeno
C. Alberto pegou um casaco grande
D. Adriana experimenta um vestido pequeno.
Em seguida, retira-se o cilindro de madeira do meio
GR SDSHO HQURODGR ¿QDOL]DQGR D FRQIHFomR GR GLSORPD
Considere que a espessura da folha de papel original seja
desprezível.
Qual é a medida, em centímetros, do lado da folha de
papel usado na confecção do diploma?
8 metros
Figura 1
Deseja-se reproduzi-la numa folha de papel retangular
com 42 cm de comprimento e 30 cm de altura, deixando
livres 3 cm em cada margem, conforme a Figura 2.
3 cm
Folha de papel
3 cm
A Sd
B 2 Sd
C 4 Sd
D 5 Sd
E 10 Sd
QUESTÃO 138
o posso levar
ui de novo cantando
ui de novo xaxando
i de novo mostrando
eve xaxar
E treinar o país em segurança digital.
QUESTÃO 102
orena linda
chita
mais bonita
u lugar
Maria, chama Luzia
Zabé, chama Raque
tou aqui com alegria
ROS, A. Óia eu aqui de novo. Disponível em: www.luizluagonzaga.mus.br.
Acesso em: 5 maio 2013 (fragmento).
a canção de Antônio de Barros manifesta
do repertório linguístico e cultural do Brasil.
ue singulariza uma forma característica do
ar regional é:
um desaforo”.
e eu tou aqui com alegria”.
ostrar pr’esses cabras”.
hama Maria, chama Luzia”.
á morena linda, vestida de chita”.
O 101
a escala de 0 a 10, o Brasil está entre 3 e 4 no
gurança da informação. “Estamos começando
ara o problema. Nessa história de espionagem
, temos muita lição a fazer. Falta consciência
al e um longo aprendizado. A sociedade caiu
u que é uma coisa que nos afeta”, diz S.P.,
em segurança da informação. Para ele, devem
ecidos canais de denúncia para esse tipo de
De acordo com o conselheiro do Comitê Gestor
(CGI), o Brasil tem condições de desenvolver
própria para garantir a segurança dos dados
anto do governo quanto da população. “Há
a de conhecimento dentro das universidades
mpresas inovadoras que podem contribuir
medidas para que possamos mudar isso
WILL. Disponível em: www.willtirando.com.br. Acesso em: 7 nov. 2013.
Opportunity é o nome de um veículo explorador que
aterrissou em Marte com a missão de enviar informações
à Terra. A charge apresenta uma crítica ao(à)
A
B
C
D
E
gasto exagerado com o envio de robôs a outros planetas.
exploração indiscriminada de outros planetas.
circulação digital excessiva de autorretratos.
vulgarização das descobertas espaciais.
mecanização das atividades humanas.
Contents lists available at ScienceDirect
Learning and Individual Differences
journal homepage: www.elsevier.com/locate/lindif
The relationship between intelligence and academic achievement
throughout middle school: The role of students' prior
academic performance
Diana Lopes Soares a,⁎, Gina C. Lemos a, Ricardo Primi b, Leandro S. Almeida a
a
b
Centro de Investigação em Educação, Instituto de Educação, Universidade do Minho, Portugal
Departamento de Psicologia, Universidade de S. Francisco, Brazil
a r t i c l e
i n f o
a b s t r a c t
Article history:
Received 4 April 2014
Received in revised form 27 January 2015
Accepted 18 February 2015
Available online xxxx
Keywords:
Intelligence
Academic achievement
Mediation analysis
Investment Theory
Gf–Gc
The association between intelligence and academic achievement is well established. However, how this
relationship changes throughout schooling remains undefined. In this 3-year longitudinal study, 284
Portuguese middle school students completed three reasoning subtests (abstract, numerical, and verbal)
by the end of 7th grade (intelligence), and their academic grades were collected at the same time (prior
academic achievement, AA7) and by the end of 9th grade (final academic achievement, AA9). The main
findings show that i) when intelligence and AA7 are analyzed as two independent predictors of AA9, AA9
is best predicted by intelligence when considering the mediation effect of AA7, and ii) the inclusion of
AA7 in the pathway between intelligence and AA9 produces a considerable increase in the predictive
validity of intelligence. Implications for cognitive assessment and psychological practice are emphasized
based on this Gf–Gc relationship.
© 2015 Elsevier Inc. All rights reserved.
D.L. Soares et al. / Learning and Individual Differences xxx (2015) xxx–xxx
5
1. Introduction
Primi, Ferrão, & Almeida, 2010; Rohde & Thompson, 2007; Watkins,
Lei, & Canivez, 2007; Weber, Lu, Shi, & Spinath, 2013).
General intelligence, also named fluid intelligence – Gf (Cattell,
An extensive body of research has been developed in order to
1971), is usually measured by administering tests of inductive and
understand the relationship between intelligence and achievement
deductive reasoning, which are assumed to reflect the ability to think,
in different life domains, such as job performance (Gottfredson,
solve problems, make inferences, identify relations, and transform
2002; Schmidt & Hunter, 2004), health outcomes (Der, Batty, &
information in a significant way (Lemos, Almeida, & Colom, 2011;
Deary, 2009), or even wealth (Zagorsky, 2007). Particularly in educaNickerson, 2011). Longitudinal growth modeling attests that Gf
tional settings, intelligence plays a crucial role in learning and
predicts, not only the initial level of math achievement, but also, the
academic performance. Several studies show high correlation
rate of change in learning and achievement (Primi et al., 2010).
indices between them, ranging from .50 to .70 (Lynn & Vanhanen,
In turn, academic achievement is usually measured by administering
2012). For instance, Mackintosh (1998) revealed a .50 correlation
tests to assess knowledge that is formally taught in schools. As a broad
between 11-year-olds' intelligence scores and later educational
concept, achievement could be associated with crystallized intelliachievement at the age of 16. In addition, in a 5-year longitudinal
gence – Gc, which is defined as the “depth and breadth of knowledge
study with 70,000 children, Deary, Strand, Smith, and Fernandes
that are valued by one's culture” (Schneider & McGrew, 2012, p. 122).
(2007) found a .81 correlation between intelligence at the age of 11
Accordingly, Schneider (2013) claims that intelligence is
and educational achievement at the age of 16 in 25 academic
related to potential, and achievement to the execution of potential.
subjects. Other studies also identify intelligence as a relevant predicAlthough considered as separate abilities, both are viewed as two
tor of academic achievement (Colom & Flores-Mendoza, 2007;
aspects of the g factor according to Cattell's investment theory
Karbach, Gottschling, Spengler, Hegewald, & Spinath, 2013; Laidra,
In light
of Cattell'sINTEL
model,=
fluid
intelligence
is one
of the
& Allik, 2007;
Lemos,
Abad, achievement,
Almeida, & Colom,
2013;
Fig. 2. Model of indirect effect Pullmann,
from intelligence
to final
academic
through
prior (1971).
academic
achievement.
intelligence;
AA7
= academic
achievement in 7th grade
main causes
achievement,
since NR
more
to learn
predicts P7 = Portuguese score in 7th
(prior grade); AA9 = academic achievement in 9th grade (final grade); AR = abstract reasoning;
VR =ofverbal
reasoning;
=capacity
numerical
reasoning;
more efficient and rapid learning. This potential is invested in
grade; E7 = English score in 7th grade; Mat7 = mathematics score in 7th grade; SC7 = sciences score in 7th grade; P9 = Portuguese score in 9th grade; E9 = English score in 9th
experiences, and is transformed into knowledge, that is, crystal⁎ Corresponding author at: Centro de Investigação em Educação, Universidade do
grade; Mat9 = mathematics score
in
9th
grade;
SC9
=
sciences
score
in
9th
grade.
lized intelligence. In the process of transformation of potential
Minho, Campus de Gualtar, 4710-057 Braga, Portugal. Tel.: +351 927433844.
(Gf) into fulfilled potential (Gc), other factors play a role, such as
E-mail address: [email protected] (D.L. Soares).
http://dx.doi.org/10.1016/j.lindif.2015.02.005
only prior level of academic achievement is a direct predictor of subsedifferent analytic approaches.
In model 1, intelligence and prior
1041-6080/© 2015 Elsevier Inc. All rights reserved.
quent learning, explaining about 70% of its variance. However, when the
academic achievement were considered together as a direct predictor
Please
cite
this
article
as:
Soares,
D.L.,
et
al.,
The
relationship
between
intelligence
and academic
achievement
throughout middle
school:
The role
indirect
effect
of intelligence
on final
academic
achievement through
of final academic achievement. In model 2, both variables were tested
of students' prior academic..., Learning and Individual Differences (2015), http://dx.doi.org/10.1016/j.lindif.2015.02.005
prior level of academic achievement (model 2) is tested, intelligence
as autonomous predictors of final academic achievement, and direct
only has an indirect effect on final academic grades. When including
and indirect effects were also tested.
b·sicas
47
Estudos deHabilidades
Psicologia 2002,
7(1), 47-55
Habilidades b·sicas e desempenho acadÍmico em universit·rios
ingressantes
Ricardo Primi
Ac·cia A. Angeli dos Santos.
Claudette Medeiros Vendramini
Universidade S„o Francisco
Resumo
Recentes estudos sobre o desenvolvimento cognitivo adulto referem-se ‡ distinÁ„o entre inteligÍncia
fluida como a capacidade geral de relacionar idÈias complexas, formar conceitos abstratos e derivar
implicaÁıes lÛgicas a partir de regras gerais e inteligÍncia cristalizada como a capacidade de derivar
conhecimento a partir de esquemas organizados de informaÁıes sobre disciplinas especÌficas. Para
verificar a possÌvel relaÁ„o entre a habilidade cognitiva requerida e a ·rea de conhecimento, este
estudo foi proposto com o objetivo de investigar as correlaÁıes entre medidas de inteligÍncia fluida e
cristalizada com desempenho acadÍmico em 960 alunos ingressantes dos cursos de Medicina, Odontologia, Engenharia Civil, Matem·tica, Psicologia, Pedagogia, Letras e AdministraÁ„o. As correlaÁıes encontradas indicam que o desempenho acadÍmico est· associado a diferentes perfis de habilidades cognitivas.
0,22
S ó c . G e o g r.
0,75
N a t. M a t.
0,82
Palavras-chave: Habilidades b·sicas, InteligÍncia fluida e cristalizada, AvaliaÁ„o educacional, Teorias de inteligÍncia.
0,88
0,50
Gc
0,43
Abstract
in freshmen students
0 , 0 9 Basic
(t= abilities
2 , 5 5 and
, pscholastic
< 0 , 0achievement
5)
Recent studies of adult cognitive development have distinguished two core abilities: fluid intelligence
as a general capacity to infer complex relationships, abstract concepts, and to deduce logical implications
from general rules; and crystallized intelligence as a capacity to solve problems by using organized
knowledge schemes from specific disciplines. In order to investigate a possible relationship between
cognitive abilities and area of study, this research studied the correlation between fluid and crystallized
intelligence measures and academic achievement among 960 freshmen students of eight areas of
study: Medicine, Dentistry, Psychology, Business, Engineering, Mathematics, Education, and
Literature. The results have indicated that the academic achievement is correlated with different
abilities profile.
L in g . V e rb .
RM G
0,52
0,57
0,77
RLD
RI
CL
0,69
0,65
0,48
RM
0,49
DP
0,45
-­‐ 1 , 0 4
0 , 4 1 (t= 9 , 3 9 , p < 0 , 0 0 1 )
Key words: Basic abilities, Fluid and crystallized intelligence, Educational assessment, Intelligence theories.
Gf
C
om as transformaÁıes pelas quais a sociedade tem
passado, cada vez mais tem sido apontado que ser·
necess·rio ao profissional das prÛximas geraÁıes,
n„o apenas o domÌnio de conhecimentos especÌficos, mas a
capacidade de se adaptar rapidamente e assimilar novas informaÁıes de um mundo em constante transformaÁ„o. Isso
vem se tornando um prÈ-requisito b·sico para o perfil do
profissional do novo milÍnio. Portanto, a universidade dever·, cada vez mais, produzir estratÈgias que privilegiem, n„o
sÛ a aprendizagem de conte˙dos, mas tambÈm a aprendizagem de estratÈgias de adaptaÁ„o a situaÁıes novas.
Essa capacidade, definida pela psicologia como ìinteligÍncia geralî, foi alvo de v·rias pesquisas desde o inÌcio
deste sÈculo (Almeida, 1988; Sternberg, 1981). Os estudos
psicomÈtricos, que investigaram como as habilidades humanas se estruturam, chamaram-na de fator g, enquanto em
estudos mais recentes desenvolvidos pela psicologia
cognitiva, v·rios dos processos que poderiam ser classifica-
Meta análise de John B.
Carroll
•
Carroll (1993): marco histórico nas teorias
psicométricas
•
Revisou os principais estudos psicométricos dos
últimos 60 anos (1500 referências)
•
Refez a análise fatorial de 461 matrizes de
correlação destes estudos
•
Apresentou um modelo integrado de Três
Camadas
•
Modelo foi integrado na Teoria Cattell-Horn-Carroll
(CHC) organizando a inteligência em Camadas 1.
Fator g, II 10 Fatores Amplos e III 70 fatores
específicos
Modelo Cattell-Horn-Carroll
Contemporary psychometric research has converged on
the Cattell-Horn-Carroll (CHC) theory of cognitive abilities as the
consensus working taxonomy of human intelligence
McGrew, K. (2009). Editorial: CHC theory and the human cognitive abilities project: Standing on the shoulders
of the giants of psychometric intelligence research, Intelligence, 37, 1-10.
McGrew (2009), McGrew & Schneider (2012, 2013)
Stratum III
(general)
g
Gf
Carroll and Cattell-Horn Broad Ability Correspondence
(vertically-aligned ovals represent similar broad domains)
A. Carroll Three-Stratum Model
Gc
Gy
Gv
Stratum II (broad)
Gu
Gr
Gs
Gt
80+ Stratum I (narrow) abilities have been
identified under the Stratum II broad abilities. They
are not listed here due to space limitations
(see Table 1)
B. Cattell-Horn Extended Gf-Gc Model
Gf
Gc
SAR
Gsm
Gv
Ga
TSR
Glm
Gs
CDS
Grw
Gq
C. Cattell-Horn-Carroll (CHC) Integrated Model
D. Tentatively identified Stratum II (broad)
domains
g
Gf
Gc
Gsm
Gv
Ga
Glr
Gs
Gt
Grw
Gq
Gkn
Gh
(Missing g-to-broad ability arrows acknowledges that Carroll and Cattell-Horn disagreed on the validity of the general factor)
CHC Broad (Stratum II) Ability Domains
Gf
Gc
Gsm
Gv
Ga
Glr
Gs
Gt
Grw
Gq
Fluid reasoning
Comprehension-knowledge
Short-term memory
Visual processing
Auditory processing
Long-term storage and retrieval
Cognitive processing speed
Decision and reaction speed
Reading and writing
Quantitative knowledge
Gkn
Gh
Gk
Go
Gp
Gps
General (domain-specific) knowledge
Tactile abilities
Kinesthetic abilities
Olfactory abilities
Psychomotor abilities
Psychomotor speed
(see Table 1 for definitions)
Gk
Go
Gp
Gps
Grw Gq
Gc Gkn
Acquired Knowledge
Go
Ga
Gk
Gh
Gv
Gp
Sensory
Motor
Sensory-Motor Domain-Specific Abilities
(From Schneider & McGrew, 2012)
Gf
Gps
Gsm Glr
Memory
Conceptual Grouping
Functional Grouping
Gs
Gt
General Speed
Parameters of Cognitive Efficiency
Domain-Independent General Capacities
Cattell-Horn-Carroll
Theory of Cognitive Abilities
Controlled
Motor
Perception
Attention
Knowledge
Fluid
Gp
Psychomotor
Gk
Gv
Reasoning
Gf
Ga
Gsm
Gh Go
Short-Term
Abilities
Gq
Grw
Gc
Gkn
Memory
Gps
Gt
Gs
Glr
Psychomotor
Speed
Attentional
Learning Efficiency
Speed
of Perception
Fluency
& Retrieval Fluency
Speed
Cattell-Horn-Carroll
Theory of Cognitive Abilities
Controlled
Motor
Perception
Attention
Knowledge
Fluid
Gp
Psychomotor
Gk
Gv
Reasoning
Gf
Ga
Gsm
Gh Go
Short-Term
Abilities
Gq
Grw
Gc
Gkn
Memory
Gps
Gt
Gs
Glr
Psychomotor
Speed
Attentional
Learning Efficiency
Speed
of Perception
Fluency
& Retrieval Fluency
Speed
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Glr: Fluência de Produção
Psychology of Aesthetics, Creativity, and the Arts
2014, Vol. 8, No. 4, 000
© 2014 American Psychological Association
1931-3896/14/$12.00 http://dx.doi.org/10.1037/a0038055
Divergent Productions of Metaphors: Combining Many-Facet Rasch
Measurement and Cognitive Psychology in the Assessment of Creativity
Ricardo Primi
University of São Francisco
This article presents a new method for the assessment of creativity in tasks such as “The camel is
________ of the desert.” More specifically, the study uses Tourangeau and Sternberg’s (1981) domain
interaction model to produce an objective system for scoring metaphors produced by raters and the
many-facet Rasch measurement to model the rating scale structure of the scoring points, item difficulty,
and rater severity analysis, thus making it possible to have equated latent scores for subjects, regardless
of rater severity. This study also investigates 4 aspects of the method: reliability, correlation between
quality and quantity, criterion validity, and correlation with fluid intelligence. The database analyzed in
this study consists of 12,418 responses to 9 items that were given by 975 persons. Two to 10 raters scored
the quality and flexibility of each metaphor on a 4-point scale. Raters were counterbalanced in a
judge-linking network to permit the equating of different “test forms” implied in combinations of raters.
The reliability of subjects’ latent quality scores was .88, and the correlation between quality and quantity
was low (r ! ".14), thus showing the desired separation between the 2 parameters established for the
task scores. The latent score on the test was significantly associated with the profession that requires idea
production (r ! .19), and the latent scores for the correlation between creativity and fluid intelligence
were high, # ! .51, even after controlling for crystalized intelligence (r ! .47). Mechanisms of fluid
intelligence, executive function, and creativity are discussed.
Keywords: metaphor production, intelligence, creativity, item response theory, Rasch measurement
duced teste
for prompts
such as “The camel is ________
of the desert”
Nesse
gostaríamos
que você
inventasse metáforas que complete as frases apresentadas. Veja o exemplo abaixo:
(example: boat). This study applies principles of cognitive psychology
The assessment of intelligence has evolved immensely over the last
century (Schneider & McGrew, 2012), and there is now consensus
taxonomy regarding the classification of cognitive factors and methods of assessment that intelligence tests employ. The assessment of
creativity, conversely, has not reached such a level of development.
Recent works discuss methods, propose new ways to assess, and
make a call for the development of different methods to evaluate
creativity (Silvia et al., 2008). This article is a response to this call and
presents a new method for assessing the quality of metaphors pro-
to produce ratings that are potentially more objective and tied to the
underlying associative process that is believed to be the foundation of
idea production. It also illustrates the application of the psychometric
method based on the Rasch model, which is useful when several
judges rate different ideas generated by different people and thereby
provide parameters for simultaneously measuring creativity, item
difficulty, and rater severity.
Ricardo Primi, Graduate Program of Psychology at Univesity of São
Francisco.
This article was financed by the by the Brazilian National Research
Council (CNPq). The author acknowledges the contributions of Débora
Pereira de Barros, Fabiano Koich Miguel, Fernando Pessotto, Gleiber
Couto, Lucas de Francisco Carvalho Maria Beatriz Zanarella Cruz, Marjorie Cristina Rocha da Silva, Marta Petrini, Monalisa Muniz, Priscilla
Rodrigues Santana, Sanyo Drummond Pires, Tatiana de Cássia Nakano,
and Tatiana Freitas da Cunha, who were graduate students working under
the supervision of the author and who contributed in data collection and as
raters of the metaphors.
Correspondence concerning this article should be addressed to Ricardo
Primi, Universidade São Francisco, Laboratório de Avaliação Psicológica
e Educacional [Laboratory of Psychological and Educational Assessment],
Rua Alexandre Rodrigues Barbosa, 45, CEP 13251-900, Itatiba, São Paulo,
Brazil. E-mail: [email protected]
One mode of creativity assessment is based on divergent production tasks that ask people to produce as many ideas as they can
from a prompt. Examples of prompts include the following: “List
words that start with ‘s’” (word fluency), “List opposites of the
word ‘true’” (associational fluency), and “Create titles for a story
plot” (ideational fluency). These tasks are traced back to Guilford’s (1950, 1956, 1957) structure of intellect model, wherein he
defined four factors—fluency (number of ideas), flexibility (shift
in response category or approach to a problem), originality (unconventionality, remote associations, and response cleverness),
and elaboration (amount of details in a response)—that are used in
tests of creativity. These factors are measured using the widely
known Torrance Tests of Creative Thinking (TTCT; Kim, 2006a,
2011; Plucker, 1999).
Some psychometric problems with divergent thinking tests have
been noted (Houtz & Krug, 1995; Plucker & Runco, 1998; Silvia
O camelo é o/a _________________ do deserto
Metáfora
1) Assessment
barco
Creativity
Explicação
No mar o barco é um meio de transporte que anda
balançando como o camelo no deserto
2) moto
Porque a moto é um transporte para uma ou duas pessoas e
anda com pouco combustível como o camelo no deserto que
precisa de pouca água
3) lesma
Porque anda devagar, marcando o chão e rebolando como
camelo
4) Barrichello
Porque quando não está parado está andando devagar
1
Cattell-Horn-Carroll
Theory of Cognitive Abilities
Controlled
Motor
Perception
Attention
Knowledge
Fluid
Gp
Psychomotor
Gk
Gv
Reasoning
Gf
Ga
Gsm
Gh Go
Short-Term
Abilities
Gq
Grw
Gc
Gkn
Memory
Gps
Gt
Gs
Glr
Psychomotor
Speed
Attentional
Learning Efficiency
Speed
of Perception
Fluency
& Retrieval Fluency
Speed
This document is copyrighted by the American Psychological Ass
This article is intended solely for the personal use of the individual u
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Gf: Raciocínio, Memória de Trabalho e
Funções executivas
Psychological Assessment
2014, Vol. 26, No. 2, 000
© 2014 American Psychological Association
1040-3590/14/$12.00 http://dx.doi.org/10.1037/a0036712
Developing a Fluid Intelligence Scale Through a Combination of Rasch
Modeling and Cognitive Psychology
Ricardo Primi
University São Francisco
Ability testing has been criticized because understanding of the construct being assessed is incomplete
and because the testing has not yet been satisfactorily improved in accordance with new knowledge from
cognitive psychology. This article contributes to the solution of this problem through the application of
item response theory and Susan Embretson’s cognitive design system for test development in the
development of a fluid intelligence scale. This study is based on findings from cognitive psychology;
instead of focusing on the development of a test, it focuses on the definition of a variable for the creation
of a criterion-referenced measure for fluid intelligence. A geometric matrix item bank with 26 items was
analyzed with data from 2,797 undergraduate students. The main result was a criterion-referenced scale
that was based on information from item features that were linked to cognitive components, such as
storage capacity, goal management, and abstraction; this information was used to create the descriptions
of selected levels of a fluid intelligence scale. The scale proposed that the levels of fluid intelligence
range from the ability to solve problems containing a limited number of bits of information with obvious
relationships through the ability to solve problems that involve abstract relationships under conditions
that are confounded with an information overload and distraction by mixed noise. This scale can be
employed in future research to provide interpretations for the measurements of the cognitive processes
mastered and the types of difficulty experienced by examinees.
Keywords: inductive reasoning, fluid intelligence, Rasch measurement, matrix reasoning
There has been significant development in cognitive psychology
and the psychometrics of intelligence testing over the last few
decades (Cornoldi, 2006; Deary, 2001; Whitely, 1980; Whitely &
Schneider, 1981). Cognitive task analysis of items that are commonly used in intelligence tests provides a better understanding of
how people represent and process information, which in turn
improves a test’s score for construct validity (Sternberg, 1981).
Psychometric model-based methods, including item response theory (IRT), provide ways to construct scales that show links between test scores and the underlying construct that provide substantial additional interpretations (Embretson, 2006; Wilson,
2005). These methods have recently evolved into cognitive diagnostic assessment models such as that of Tatsuoka (2009).
Despite these developments, test construction procedure has not
yet satisfactorily incorporated these new methods (Embretson,
1994). Thus, the expectation for the future generation of tests is
that cognitive psychology should play an important role in construct representation. By providing a rich theoretical basis for the
creation of purified tasks, it should lead to the development of
instruments with better theoretical grounding. This article presents
an illustration of these methods as they apply to the development
of a fluid reasoning test. First, a review of the psychometric and
cognitive neuroscience definitions of fluid intelligence is presented. A brief review of how fluid intelligence is measured
follows, with emphasis on the justification for new tests based on
modern methodology. Finally, an empirical study of test development is presented.
Nature of Fluid Intelligence
This article was developed as part of a larger project, called Development of a Computerized Componential Fluid Intelligence Test, which was
financed by the Foundation for the Support of Research in the State of São
Paulo (FAPESP, Process 2000/05913-4), by the Brazilian National Research Council (CNPq), and by the University of São Francisco. The author
acknowledges the contributions of Acácia A. Angeli dos Santos, Claudette
Medeiros Vendramini, Fernanda de Oliveira Soares Taxa, Maria de Fátima
Lukjanenko, Franz A. Müller, Isabel Sampaio, Fatima Keiko Kuse, and
Cíntia Heloína Bueno for their valuable comments on drafts of this article
and assistance in collection of the data.
Correspondence concerning this article should be addressed to Ricardo
Primi, Universidade São Francisco, Laboratório de Avaliação Psicológica
e Educacional, Rua Alexandre Rodrigues Barbosa, 45, CEP 13251-900,
According to Schneider and McGrew (2012), fluid reasoning
(Gf) refers to
Figure 1. Examples of fluid intelligence items used in the present study and a summary of sources of
complexity
for
matrix items and their link with fluid intelligence capacities.
the deliberate but flexible
control of attention to
solve geometric
novel “on the
spot” problems that cannot be performed by relying exclusively on
previously learned habits, schemas and scripts. Fluid reasoning is a
multi-dimensional construct, but its parts are unified in their purpose:
solving unfamiliar problems. Fluid reasoning is most evident in abstract reasoning that depends less on prior learning. However, it is also
present in day-to-day problem solving. Fluid reasoning is typically
employed in concert with background knowledge and automatized
responses. (p. 111)
Fluid intelligence is central to understanding the construct of
are not confounded by the degree of mo
results reported here.
Discussion
Psychological Bulletin
2005, Vol. 131, No. 1, 30 – 60
Copyright 2005 by the American Psychological Association
0033-2909/05/$12.00 DOI: 10.1037/0033-2909.131.1.30
Figure 1.
Working Memory and Intelligence: The Same or Different Constructs?
Phillip L. Ackerman, Margaret E. Beier, and Mary O. Boyle
Georgia Institute of Technology
Several investigators have claimed over the past decade that working memory (WM) and general
intelligence (g) are identical, or nearly identical, constructs, from an individual-differences perspective.
Although memory measures are commonly included in intelligence tests, and memory abilities are
included in theories of intelligence, the identity between WM and intelligence has not been evaluated
comprehensively. The authors conducted a meta-analysis of 86 samples that relate WM to intelligence.
The average correlation between true-score estimates of WM and g is substantially less than unity (!ˆ !
.479). The authors also focus on the distinction between short-term memory and WM with respect to
intelligence with a supplemental meta-analysis. The authors discuss how consideration of psychometric
and theoretical perspectives better informs the discussion of WM–intelligence relations.
Since the 1980s, with the major theoretical and empirical developments of the construct of working memory (WM; see, e.g.,
Baddeley, 1986; Richardson, 1996, for reviews) as distinct from
rote or span memory (which is usually referred to as short-term
memory [STM]), several investigators have asserted that WM and
intellectual abilities are highly related or identical constructs.
ThesePsychological
assertions
started with demonstrations that significant corBulletin
2005, Vol. 131, No. 1, 66 –71
relations
were found between some measures of WM and measures of comprehension (Daneman & Carpenter, 1980), and later
between WM and reasoning ability (Kyllonen & Christal, 1990),
and other measures, such as the SAT (e.g., Turner & Engle, 1989).
Recently, several investigators have claimed that WM and general
intelligence (g; or general fluid intelligence, Gf) are essentially the
same constructs. For example:
A hierarchical factor analytic representation of g and first-order
factors, based on a structural equation model of meta-analytically derived
correlations among ability and working memory (WM) measures.
the loading was .40, for Quantitative Reasoning the loading was
.51, for Lexical Knowledge (Verbal) the loading was .37, and for
Memory Span, the loading on g was .36. In the current SEM
analysis, all of these loadings of the first-order factors on g were
As we noted earlier, SEM with meta-a
relations (i.e., where different correlation
measures, different samples, and different
is not optimal from a psychometric persp
results with some degree of skepticism is
less, neither SEM solution points to an in
are isomorphic to one another. We were a
which WM measures and ability measu
single factor in favor of a model with separ
though the estimated correlation between
in this model was .50. The initial model
provide a representation that is more conc
abilities literature, given the presence o
level cognition. The construct is distinguishable from STM and is at
least related to, maybe isomorphic to, general fluid intelligence and
executive attention. (Engle, 2002, pp. 21–22)
No other cognitive factor— knowledge, speed, or learning ability—
correlated with g after the working memory factor was partialed out.
Thus, we have our answer to the question of what g is. It is working
memory capacity. (Kyllonen,
2002,
see also
Kyllonen,
1996)
Copyright
2005p.by433;
the American
Psychological
Association
0033-2909/05/$12.00
DOI: 10.1037/0033-2909.131.1.66
However, the relationship between memory and intelligence
appears to be much more complex than has been asserted by these
investigators. Note that this position is not without its critics. For
example, differential psychologists such as Deary (2000) and
Kline (2000) have expressed substantial skepticism that WM and
general intelligence are even closely linked. In this article, we
evaluate these claims in the context of a meta-analysis of correlaJ. Kane
Davidand
Z. intellectual
Hambrickability measures. In
So central is the role of Michael
WM capacity
in individual differences in
tions between WM measures
Greensboro
Michigan State University
information University
processing of
thatNorth
someCarolina
cognitiveattheorists
equate WM
addition, it is important to note that intelligence theory and the
capacity with g itself. (Jensen, 1998, p. 221)
assessment of intelligence have both involved memory abilities
the past 110 years. Although models of WM represent relaAndrew
A. Conway
Stauffer et al. (1996) found a correlation of " 0.995 between
a factor R. over
tively
developments in the history of the science, an underUniversity
of
Illinois
atrecent
Chicago
representing general intelligence (g) and a factor representing WM.
standing of the construct space for individual differences in intel(Colom, Flores-Mendoza, & Rebollo, 2003, p. 34)1
ligence and WM benefits from a brief review of intelligence and
My colleagues and I used a structural equation modeling analysis to
memory ability research. Thus, we begin with a consideration of
test this and the idea that
theauthors
construct
byAckerman,
WM-capacity
The
agreemeasured
with P. L.
M. E. Beier,
and M.abilities
O. Boyle from
(2005)an
thatintelligence
working memory
memory
assessment perspective,
tasks is closely associated
with general
. . . general
WM- fluid intelligence (Gf) or reasoning ability. However, the
capacity
(WMC) fluid
is not intelligence.
isomorphic with
followed
by
a
review
of
research
on
memory
abilities and intellicapacity tasks measure a construct
important to
WMC andfundamentally
Gf/reasoning constructs
arehighermore strongly associated than Ackerman et al. (2005) indicate,
gence theory.
we briefly
review
memory theory and the
particularly when considering the outcomes of latent-variable
studies.Next,
The authors’
reanalysis
of 14 such
underlying
for asserting
thea strong
overlap between WM and
data sets from 10 published studies, representing more
than 3,100 framework
young-adult subjects,
suggests
A meta-analysis
samples
that report correlations
correlation between WMC and Gf/reasoning factorsintelligence.
(median r ! .72),
indicating that of
the86
WMC
and Gf
Phillip L. Ackerman, Margaret E. Beier, and Mary O. Boyle, School of
constructs share approximately 50% of their variance.
This comment
also
the measures
authors’ “execbetween
measures
ofclarifies
WM and
of intellectual abilities is
Psychology, Georgia Institute of Technology.
utive attention” view of WMC, it demonstrates then
that WMC
has greater
discriminant
validity is
than
presented.
A parallel
set of analyses
also provided for STM
Margaret E. Beier is now atAckerman
the Schooletof
Riceand
University.
al.Psychology,
(2005) implied,
it suggests some
future
directions
and
challenges
for
the
scientific
and intelligence for comparison to the WM–intelligence relations.
This research was partially study
supported
by
Air
Force
Office
of
Scientific
of the convergence of WMC, attention control, and intelligence.
We then discuss the implications of the meta-analytic results in the
Research Grant F49620-01-0170 (Phillip L. Ackerman, principal investicontext of both enduring psychometric measurement and theory
gator). We thank the following individuals for providing correlations from
issues.
their empirical research: Scott Chaiken, Sandra Hale, Tim Salthouse, Lee
Swanson,Theorists
Bill Tirre,have
and recently
Werner Wittmann.
We
also
thank
Joni
Lakin
and
WMC is primarily a domain-general construct; (c) WMC is more
speculated that individual differences in
Sarah working
Molouki for
their assistance
obtaining
correlations
and data entry
memory
capacity in
(WMC)
may
explain reasoning
ability,
closely related to Gf and reasoning than is short-term memory
1
and processing.
It is interesting to note that in fact the original Stauffer, Ree, and
general intelligence (Spearman’s g), or both (e.g., Engle, 2002;
(STM). The latter two arguments are now well supported by
Working Memory Capacity and Fluid Intelligence Are Strongly Related
Constructs: Comment on Ackerman, Beier, and Boyle (2005)
Correspondence concerning this article should be addressed to Phillip L.
Caretta (1996) article does not state this conclusion. The higher order factor
Figure 2. A nonhierarchical representation of g and working memory (WM) factors, based on a stru
equation model of meta-analytically derived correlations among ability and WM measures.
Kane, M.J., Conway, A.R.A., Hambrick, D.Z., & Engle, R.W. (2007). Variation in working memory capacity as variation in executive attention
and control. In A.R.A. Conway, C.Jarrold, M.J. Kane, A. Miyake, and J.N. Towse (Eds.), Variation in Working Memory (pp. 21 - 48). NY:
Oxford University Press.
Cattell-Horn-Carroll
Theory of Cognitive Abilities
Controlled
Motor
Perception
Attention
Knowledge
Fluid
Gp
Psychomotor
Gk
Gv
Reasoning
Gf
Ga
Gsm
Gh Go
Short-Term
Abilities
Gq
Grw
Gc
Gkn
Memory
Gps
Gt
Gs
Glr
Psychomotor
Speed
Attentional
Learning Efficiency
Speed
of Perception
Fluency
& Retrieval Fluency
Speed
(Shea, Lubinski, & Benbow, 2001; Wai, Lubinski, & Benbow, 2009;
& Steel, 1979). A number of longitudinal studies based on Project
Webb, Lubinski, & Benbow, 2007). Like Smith (1964) comprehenTALENT’s 11-year follow-up underscore the importance of spatial
sive review ofSpatial
spatial ability,
Super
and
Bachrach
(1957)
document
accomplishmentsand
in STEM
disciplines (Austin & Hanisch,
ability and STEM: A sleeping giant forability
talentforidentification
development
that exceptional general intellectual potential is characteristic of
1990; Gohm et al., 1998; Humphreys & Lubinski, 1996; Humphreys
*
Lubinski
engineers andDavid
physical
scientists
at early ages; however, they went
& Yao, 2002; Humphreys et al., 1993). A recent study comparing
onto stress that
specific
abilities
–
especially
mathematical
reasonthese
data
to TN
modern
longitudinal
findings from the Study of
Department of Psychology and Human Development, Peabody 0552, 230 Appleton Place, Vanderbilt
University,
Nashville,
37203, United
States.
ing and spatial ability – are also salient features of their individuMathematically Precocious Youth (SMPY; Lubinski & Benbow,
ality. Moreover, they also noted the importance of scientific
2006), is especially relevant to understanding the development of
a r t i c l e i n f o
a b s t r a c t
interests, and called for additional longitudinal research, for inSTEM talent (Wai et al., 2009).
Articleyoung
history: adolescents over 10- to Spatial
ability for
is a powerful
sourceet
of individual
differences
that has
neglected in comstance, following
15-years
ascer- systematicWai
al. (2009)
focused
onbeen
participants’
highest degree reReceived 27 January 2010
plex learning and work settings; it has also been neglected in modeling the development of expertise and
taining how these
and
other
personal
attributes,
and
contrasting
ceived
(bachelor’s,
master’s,
or
doctorate),
the
disciplines within
Received in revised form 10 March 2010
creative accomplishments. Nevertheless, over 50 years of longitudinal research documents the important
Accepted
18
March
2010
opportunities and supports, factor into differential
development.
which their
degrees settings
were wherein
earned,
and their
occupations as a funcrole that
spatial ability plays in educational
and occupational
sophisticated
reasoning
Available online 14 April 2010
with
figures, and
patterns,
and shapestion
is essential.
Given the(‘‘g”)
contemporary
push for developing
STEM (sci- spatial, and verDuring the years between Super and Bachrach
(1957)
Smith
of general
and specific
(mathematical,
ence, technology, engineering, and mathematics) talent in the information age, an opportunity is avail(1964), John Keywords:
C. Flanagan et al. (1962) launched
bal)
abilities.
Fig. ability.
1 graphs
andresearch
specific ability profiles
able toProject
highlight Talent,
the psychological
significance
of spatial
Doing the
so is general
likely to inform
Spatial ability
aptitude-by-treatment
and Underwood’s
idea to utilize
individualterminal
differences as
which was expressly
the kind of longitudinal on
study
that Super’sinteractions
of Project
Talent(1975)
participants
earning
degrees in various
STEM
a crucible for theory construction. Incorporating spatial ability in talent identification procedures for
Gifted
NSF team envisioned.
Because of its comprehensiveness
and
size,
disciplines.
Because
highly
congruent
findings
were
observed for
advanced learning opportunities uncovers an under-utilized pool of talent for meeting the complex needs
Talent development
longitudinal findings
from Project TALENT areof an
among
the technological
most
all four
cohorts,
grades
9–12,
the cohorts
were combined. High
ever-growing
world;
furthermore,
selecting
students
for advanced
learning opportuTalent searches
nities in STEM without considering spatial ability might be iatrogenic.
compelling for illustrating the role that spatial
ability plays in
general intelligence and an intellectual orientation dominated by
! 2010 Elsevier Ltd. All rights reserved.
developing expertise in STEM. Project TALENT’s initial data collechigh mathematical and spatial abilities, relative to verbal ability,
In celebrating the distinguished career of Thomas J. Bouchard,
Jr., it seems fitting to recall a vivid memory of what it was like sitting in on Bouchard’s course in individual differences (or ‘‘IDs”),
first as a student and, subsequently, as his graduate teaching assistant for the same course. Twice a week, students would listen to
Bouchard analyze empirical studies and break them apart boneby-bone. Many would appear numb as their deeply held suppositions about human behavior were found to have little empirical
basis. Like Donald G. Paterson’s course in IDs, which prior generations of Minnesota graduate students experienced, students left
Bouchard’s course with new perspectives. Students having the
privilege of experiencing one of these two intellectual giants – over
the 80-year period they successively taught IDs at Minnesota –
could never again look at human behavior through the same lens.
Their newly acquired knowledge base forever changed how they
viewed the human condition and the complex tensions surrounding social order, liberty, and individual differences (Wells, 1937).
One of the more memorable things about Bouchard’s course
was that, occasionally, after presenting a compelling empirical
demonstration to the class, on the power that psychological variables can hold for predicting important socially-valued outcomes
(educational achievements, occupational accomplishments, or life
in general), Bouchard would turn to the class and say: ‘‘See, see,
see what happens when psychologists choose to study real variables.” This point of view was not unrelated to that of Bouchard’s
colleague Paul E. Meehl; Meehl would occasionally wonder out
* Tel.: +1 615 343 1195.
E-mail address: [email protected]
loud whether it would be scientifically prophylactic to require
graduate students in psychology to take a minor in a natural science like biology or genetics. By going so, Meehl speculated, they
might be able to recognize a meaningful scientific contribution, if
they should ever happen to encounter one in psychology! These
two anecdotes set the stage for this contribution, regarding one
of Bouchard’s favorite psychological variables, spatial ability. For
decades, spatial ability has surfaced as a salient characteristic of
young adolescents who go onto develop expertise in science, technology, engineering, and mathematics (STEM), yet applied psychologists and talent development researchers have failed to
fully recognize this important dimension of human individuality.1
Contemporary discourse on the importance of identifying and nurturing STEM talent affords an opportunity to correct for this neglected ‘‘real variable.”
1. Spatial ability and STEM: decades of longitudinal research
For example, during the same year Sputnik was launched, a little known report, Scientific Careers, was published on the psychological characteristics of individuals harboring STEM talent. This
report was based on a NSF committee chaired by Donald Super,
who assembled a distinguished team of psychologists (Harold
1
The referent generality of spatial ability extends well beyond STEM domains and
encompasses among other things the creative arts in particular (Humphreys,
Lubinski, & Yao, 1993). This article, however, will be restricted to STEM domains.
For further reading on the educational and psychological significance of spatial
ability, see Lohman (1988, 1994, 1996).
0191-8869/$ - see front matter ! 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2010.03.022
Fig. 1. Average z-scores of participants on spatial, math, and verbal ability for bachelor’s degrees, master’s degrees, and PhDs are plotted by field. The groups are plotted in
rank order of their normative standing on g (S + M + V) along the x-axis and the line with the arrows from each field pointing to it indicates on the continuous scale where
they are in general mental ability. This figure is standardized in relation to all participants with complete ability data at the time of initial testing. Respective N’s for each
group (Males + Females) were (for bachelor’s, master’s, and doctorates respectively): engineering (1143, 339, and 71), physical science (633, 182, and 202), math/computer
science (877, 266, and 57), biological science (740, 182, and 79), humanities (3226, 695, and 82), social science (2609, 484, and 158), arts (615, M = 171), business (2386,
M + D = 191), and education (3403, M + D = 1505). *For education and business, masters and doctorates were combined because the doctorate samples for these groups were
too small to obtain stability (N < 30). From Wai et al. (2009).
Síntese das abordagens educacional
(conhecimento) e psicológica (processos mentais)
Conclusões …
Conclusões
•
•
Confusões atuais ao se tratar das competências cognitivas:
•
Tratadas com um único rótulo na literatura "QI"
•
Habilidades cognitivas incluídas nos sistemas em larga escala: Gc, Grw e Gq
Comp do sec XXI enfatizam outros fatores amplos (no âmbito cognitivo):
•
Pensamento crítico e solução de problemas: Gf, Gv
•
Criatividade inovação: Glr Gf
•
Variáveis socioemocionais: O, C, A, N-
•
O modelo CHC é importante para mapear quais competências cognitivas estamos
avaliando nos sistemas em larga escala
•
Será que deveríamos monitorar outras capacidades além daquelas já presentes nos
sistemas em larga escala ? Qual seria a utilidade de tal sistema ?
•
Temas para Parte 2: Maleabilidade de g e WM, integração entre modelo CHC e a
neuropsicologia
Obrigado!
[email protected]