title: Developing Approaches to Competence: Away from the Metaphor of Competence as a Hidden Object.
abstract: Assessments of linguistic ability amongst inner city African American children in the 1960's and cross-cultural assessments of problem solving skills amongst participants without formal education in several contexts around the world have both demonstrated the need for investigators to distinguish the possible existence of some form of competency from the matter of whether evidence of that competency can be elicited with a particular assessment tool. In both of the previously mentioned cases, the evidence suggests that participants had the competencies in question and failed to demonstrate them (at least partially) as a result of the nature of the assessment context. The current article takes these demonstrations of the need for culturally sensitive assessment as a point of departure, and argues that today, a further development in how competence is understood is necessary. For all its benefits, the idea of culturally sensitive assessment still relies on the problematic characterization of competencies as discrete sets of stable underlying dispositions. Current research from the dynamic systems perspective in psychology suggests that intelligent human action involves the contextual and self-organized emergence of adaptive behavioral solutions, rather than the expression of preplanned, latent competencies. An adequate conceptualization of competence must therefore account for this emergence, rather than positing competencies as hidden or latent behavioral performances.
authors.affiliation: Department of Psychology, Brooklyn College, 2900 Bedford Ave., Brooklyn, NY, 11210, USA. patrickdbyers@gmail.com.
authors.forename: Patrick
authors.initials: P
authors.lastname: Byers
mesh_terms: D001076:Aptitude; D054521:Cultural Competency; D006801:Humans; D011581:Psychological Tests / Q000592:standards*; D011582:Psychological Theory
keywords: Assessment; Competence; Culturally sensitive assessment; Epistemology; Knowledge; Performance