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Taking a justice-oriented approach to equity in Computer Science (CS) education, this paper questions the dominant discourse in CS education and asks what truly makes CS learning consequential from the perspective of youth. We define CS learning as consequential by focusing on its transformative impact on youth identity, agency, and perceptions of the world within and beyond CS classrooms, regardless of whether or not they pursue CS in the future. Our research-practice partnership used qualitative data, specifically longitudinal interview data with 30 students up to three years after they first experienced a high school CS class in a large public school district on the west coast serving majority Latinx, urban, low-income students. Our findings suggest that in order for CS learning to be meaningful and consequential for youth, learning must involve: 1) freedom for youth to express their interests, passions, and concerns; 2) opportunities for youth to expand their views of CS and self; and 3) teacher care for students, learning community, and subject matter. The findings have significant implications for the broader “CS for All” movement and future efforts to reform policy agendas aiming for a more justice-centered CS education.
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Measuring interest group agendas in regulatory proposals: a method and the case of US education policy
We introduce a new way to measure interest group agendas and demonstrate an approach to extending the CAP topic coding scheme to policy domains at lower levels of analysis. We use public comments on regulatory proposals in US education policy to examine the topics contained in policy arguments. We map the education policy space using a data set of 493 comments and 5315 hand-coded comment paragraphs. A unique measurement model accounts for group and topic diversity and allows us to validate our approach. The findings have implications for measuring topic agendas in lower-level policy domains and understanding group coalitions and competition in education policy. We contribute to text-as-data approaches tracing policy change in the study of public policy. The findings suggest the relationship between issue attention observed by scholars and larger policy reform movements.
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- Award ID(s):
- 1827494
- PAR ID:
- 10291334
- Date Published:
- Journal Name:
- Interest Groups & Advocacy
- ISSN:
- 2047-7414
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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BackgroundLiving kidney donation currently constitutes approximately a quarter of all kidney donations. There exist barriers that preclude prospective donors from donating, such as medical ineligibility and costs associated with donation. A better understanding of perceptions of and barriers to living donation could facilitate the development of effective policies, education opportunities, and outreach strategies and may lead to an increased number of living kidney donations. Prior research focused predominantly on perceptions and barriers among a small subset of individuals who had prior exposure to the donation process. The viewpoints of the general public have rarely been represented in prior research. ObjectiveThe current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. The resultant data set was made available in the public domain to facilitate further investigation of this topic. MethodsWe collected comments using Python-based web-scraping tools from the New York Times, YouTube, Twitter, and Reddit. We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning. ResultsA total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation. ConclusionsThe current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1057/s41309-021-00129-w
