All a-board: sharing educational data science research with school districts

• URL: http://arxiv.org/abs/2304.08967v2
• Date: Wed, 5 Jul 2023 15:45:30 GMT
• Title: All a-board: sharing educational data science research with school districts
• Authors: Nabeel Gillani and Doug Beeferman and Cassandra Overney and Christine Vega-Pourheydarian and Deb Roy
• Abstract summary: We conduct randomized email outreach experiments and surveys to explore how local school districts respond to boundary changes. Approximately 4,320 elected school board members across over 800 school districts informed of potential boundary changes. Media coverage of the research drives more dashboard engagement, especially in more segregated districts.
• Score: 20.799163734027466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Educational data scientists often conduct research with the hopes of translating findings into lasting change through policy, civil society, or other channels. However, the bridge from research to practice can be fraught with sociopolitical frictions that impede, or altogether block, such translations -- especially when they are contentious or otherwise difficult to achieve. Focusing on one entrenched educational equity issue in US public schools -- racial and ethnic segregation -- we conduct randomized email outreach experiments and surveys to explore how local school districts respond to algorithmically-generated school catchment areas ("attendance boundaries") designed to foster more diverse and integrated schools. Cold email outreach to approximately 4,320 elected school board members across over 800 school districts informing them of potential boundary changes reveals a large average open rate of nearly 40%, but a relatively small click-through rate of 2.5% to an interactive dashboard depicting such changes. Board members, however, appear responsive to different messaging techniques -- particularly those that dovetail issues of racial and ethnic diversity with other top-of-mind issues (like school capacity planning). On the other hand, media coverage of the research drives more dashboard engagement, especially in more segregated districts. A small but rich set of survey responses from school board and community members across several districts identify data and operational bottlenecks to implementing boundary changes to foster more diverse schools, but also share affirmative comments on the potential viability of such changes. Together, our findings may support educational data scientists in more effectively disseminating research that aims to bridge educational inequalities through systems-level change.

Related papers

• BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies [19.063382873258675]
  We describe a collaboration with a US public school district serving nearly 150,000 students to design "BoundarEase" BoundarEase is a web platform that allows community members to explore and offer feedback on potential boundaries. A user study with 12 community members reveals that BoundarEase prompts reflection among community members on how policies might impact families beyond their own.
  arXiv  Detail & Related papers  (2025-03-11T15:30:53Z)
• Merging public elementary schools to reduce racial/ethnic segregation [0.6437284704257459]
  "School mergers" involve merging the school attendance boundaries, or catchment areas, of schools. We find that pairing or tripling schools in this way could reduce racial/ethnic segregation by a median relative 20%. We make our results available through a public dashboard for policymakers and community members to explore further.
  arXiv  Detail & Related papers  (2025-02-14T14:36:28Z)
• Contextual Stochastic Optimization for School Desegregation Policymaking [13.670408636443831]
  This paper develops a joint redistricting and choice modeling framework, called redistricting with choices (RWC) The main methodological contribution of the RWC is a contextual optimization model that minimizes district-wide dissimilarity. The results also reveal that predicting school choice is a challenging machine learning problem.
  arXiv  Detail & Related papers  (2024-08-22T17:40:06Z)
• Auditing for Racial Discrimination in the Delivery of Education Ads [50.37313459134418]
  We propose a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. We find evidence of racial discrimination in Meta's algorithmic delivery of ads for education opportunities, posing legal and ethical concerns.
  arXiv  Detail & Related papers  (2024-06-02T02:00:55Z)
• Getting aligned on representational alignment [93.08284685325674]
  We study the study of representational alignment in cognitive science, neuroscience, and machine learning. Despite their overlapping interests, there is limited knowledge transfer between these fields. We propose a unifying framework that can serve as a common language for research on representational alignment.
  arXiv  Detail & Related papers  (2023-10-18T17:47:58Z)
• Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets [56.67097829383139]
  We focus on the nuanced distinctions in framing of social media and traditional media outlets concerning a series of U.S. Supreme Court rulings. We observe significant polarization in the news media's treatment of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus.
  arXiv  Detail & Related papers  (2023-09-18T06:40:21Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
  Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection [55.2629939137135]
  This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection. We focus on addressing two real-world challenges that affect fair trial sites selection: the data modalities are often not complete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity. We evaluate FRAMM using 4,392 real-world clinical trials ranging from 2016 to 2021 and show that FRAMM outperforms the leading baseline in enrollment-only settings.
  arXiv  Detail & Related papers  (2023-05-30T20:44:14Z)
• Impacts of Differential Privacy on Fostering more Racially and Ethnically Diverse Elementary Schools [18.35063779220618]
  The U.S. Census Bureau has adopted differential privacy, the de facto standard of privacy protection for the 2020 Census release. This change has the potential to impact policy decisions like political redistricting and other high-stakes practices. One under-explored yet important application of such data is the redrawing of school attendance boundaries to foster less demographically segregated schools.
  arXiv  Detail & Related papers  (2023-05-12T21:06:15Z)
• Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools [31.737460075609103]
  Most U.S. school districts draw "attendance boundaries" to assign students to schools near their homes. We simulate alternative boundaries for 98 US school districts serving over 3 million elementary-aged students. Our results show the possibility of greater integration without significant disruptions for families.
  arXiv  Detail & Related papers  (2023-03-14T02:50:40Z)
• Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits [60.4933541247257]
  This paper shows a real-world adaptive experiment on how students engage with instructors' weekly email reminders to build their time management habits. Using Multi-Armed Bandits (MAB) algorithms in adaptive experiments can increase students' chances of obtaining better outcomes. We highlight problems with these adaptive algorithms - such as possible exploitation of an arm when there is no significant difference.
  arXiv  Detail & Related papers  (2022-08-10T00:30:52Z)
• How diverse is the ACII community? Analysing gender, geographical and business diversity of Affective Computing research [0.0]
  ACII is the premier international forum for presenting the latest research on affective computing. We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers. Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences.
  arXiv  Detail & Related papers  (2021-09-12T18:30:36Z)
• Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions [7.50215102665518]
  We show that there is a shift in the distribution of scores obtained by students that the DOE classifies as "disadvantaged" We show that centrally planned interventions can significantly reduce the impact of bias through scholarships or training.
  arXiv  Detail & Related papers  (2020-04-22T20:50:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.