Asset-Based Decision Making to Address Inequity in Gifted Education Services

Abstract

The field of gifted education has faced issues of equity for decades. Peters (2021) highlights a variety of systemic reasons traditional gifted identification processes may fail to equitably identify traditionally underrepresented students; however, at the core of Peter’s argument is a defense of the gifted and talented label. Common criticisms of gifted education include that it promotes fixed labels attached to opaque instructional practices or services that provide greater advantages to a privileged few (Grissom et al., 2019). The gifted label also attracts stereotypes and misconceptions relative to student behaviors and services. For instance, the label perpetuates the misconception that most students are gifted across academic domains rather than having specific areas of strengths and weaknesses (Lohman et al., 2008). As a result, gifted education services rarely meet the unique needs of the students being served, but rather provide a onesize-fits-all service—the antithesis of a specialized service. Dixson et al. (2021) proposed an approach to gifted education focused on maximizing learning, which involves short-term, malleable decisions using assessment data to immediately inform instruction rather than long-term labels. Essentially, these are existing strategies, primarily used in special education, to monitor progress with data-based decision making designed to support students academically. Three key services include (a) diagnostic labels only as needed, (b) push-in services from specialists ensuring students get instruction aligned with their needs, and (c) responsive services, such as Response to Intervention (RtI), that allow teachers to identify needs and modify instruction more effectively for a larger pool of (potentially or currently) highachieving students.