Assessment for learning: The missing element for identifying high potential in low income and minority groups

• Published in: Gifted education international Vol. 37; no. 2; pp. 199 - 208
• Main Author: Renzulli, Joseph S
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.2021; Sage Publications Ltd
• Subjects: Academically Gifted; Admissions policies; Artificial intelligence; Disproportionate Representation; Executive Function; Formative Evaluation; Gifted; Gifted Education; Identification; Intelligence; Leadership; Learning; Local Norms; Low income groups; Low Income Students; Minority Group Students; Minority groups; Productivity; Screening Tests; Soft Skills; Student Characteristics; Student Development; Technology; executive function skills; assessment for learning; identification; Underrepresentation
• ISSN: 0261-4294; 2047-9077; 2407-9077
• DOI: 10.1177/0261429421998304

A major controversy facing the field of gifted education is the underrepresentation of low income, minority, and dual language students. Strategies for addressing this challenge have been to use universal screening and local norms; however, these useful recommendations continue to focus on traditional testing procedures that measure what students already know and overlook other important traits that contribute to high levels of creative productivity. Assessment for learning examines traits such as interests, instructional preference styles, preferred modes of expression, and executive function skills. Although sometimes referred as the “soft skills,” they have gained much more attention on the parts of college admission officers and employers, especially for higher level leadership positions. Instruments that assess these traits are often completed by the students themselves; and technology and artificial intelligence now allow us to administer and analyze them with the same ease used for traditional standardized tests.