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Understanding the factors that lead to teacher success and persistence in high-need school districts is imperative for the success of the students in those districts. Teacher success means many things to different stakeholders in high-needs communities: families, colleagues, and administrators are all positioned to benefit from increased teacher retention, leadership, and/or test scores. However, preparing and supporting teachers in their work towards these successes may be more challenging. In this research study, we worked with six administrators and ten teachers representing four high-need districts in the New York metro area to better illustrate their perspectives on what teachers need to be successful in these contexts. Interpreting qualitative data through feminist, identity, and professional learning continuum framing, we asked: How do administrators and teachers perceive the qualities of teachers who persist in high-need schools? Preliminary findings illustrate that although teachers and administrators are in agreement on the qualities required of teachers, the reality is that teachers embodying these qualities are frequently not those who end up being hired. Thus, there is tension on the school culture and goals for student learning, especially for schools in which teacher attrition is greatest.
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Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-Making
Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making.
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- Award ID(s):
- 2131519
- PAR ID:
- 10437943
- Date Published:
- Journal Name:
- CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3544548.3580996
