Who is Most Likely to Oppose Federal Tuition-Free College Policies? Investigating Variable Interactions of Sentiments to America’s College Promise

Collier, Daniel; Mishra, Shubhanshu; Houston, Derek A.; Hensley, Brandon O.; Mitchell, Scott A.; Hartlep, Nicholas D.

doi:10.2139/ssrn.3423054

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…2. In Collier et al [2017] and Collier et al [2019], we worked on extracting information from Facebook comments about tuition free college to understand the discourse around the topic as well as alignment between sentiment and political identities.…”

Section: Other Related Workmentioning

confidence: 99%

Information extraction from digital social trace data with applications to social media and scholarly communication data

Mishra¹

2021

SIGWEB Newsl.

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Shubhanshu is a Machine Learning Researcher at Twitter working on the Content Understanding Research team. He finished his Ph.D. at the iSchool, University of Illinois at Urbana-Champaign, where he worked as a research assistant with Dr. Jana Diesner and Dr. Vetle Torvik on projects funded by NIH, NSF, KISTI, and Army Research Lab. Shubhanshu's research work is at the intersection of machine learning, information extraction, social network analysis, and visualizations. His work won then Best Student Paper Award at ASIS&T SIGMET workshop. Shubhanshu has published multiple open source projects as well as open datasets related to his research. More information about Shubhanshu can be found at: https://shubhanshu.com. In his thesis, Shubhanshu worked on unifying the concept of information extraction from social media data and scholarly data under the framework of Digital Social Trace Data (DSTD). This framework allow us to answer questions like conceptual novelty, expertise in scientific communities as well as influential users in social media corpus. He further proposed improved information extraction techniques for social media text by using active, semi-supervised, and multi-task machine learning along with the inclusion of meta-data. Additionally, his work resulted in the development of a visualization framework for DSTD along with multiple open source tools and datasets. For more details visit https://shubhanshu.com/phd_thesis/.

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Section: Other Related Workmentioning

confidence: 99%

Information extraction from digital social trace data with applications to social media and scholarly communication data

Mishra¹

2021

SIGWEB Newsl.

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“…In this section we present a case study and its associated data-set some of which were utilized in our work (Collier et al, 2019b;Collier et al, 2019a) to study the tuition free community college debate. We collected Facebook comments about tuition free community college topic after Barack Obama's America's College Promise (ACP) policy announcement.…”

Section: Case Study: Identifying Tuition Free College Debate Via Face...mentioning

confidence: 99%

“…We have taken care to remove user identities when sharing this data publicly. Please see (Collier et al, 2019a;Collier et al, 2019b) for more details about the labels.…”

Section: Human Annotation Of Sampled Datasetmentioning

confidence: 99%

A Framework for Generating Annotated Social Media Corpora with Demographics, Stance, Civility, and Topicality

Mishra

Collier

2020

SSRN Journal

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“…The resulting strain has led to a reimagining of the social investment into higher education, which in some cases has manifested in Promise policies: place-based tuition-free scholarship policies (Perna & Leigh, 2018). The function of and support for Promises remains hotly debated (Collier, Mishra, et al, 2019), in part due to limited research on student experiences and outcomes associated with Promise (see -Collier & Parnther, 2018;Collier, Parnther, et al, 2019). With most Promise policies in early stages, the opportunities to generate quality research are constrained, with most research fixated on how Promises influence access (Miller-Adams, 2015).…”

Section: Introduction and Purposementioning

confidence: 99%

Bridge of Promise: Comparing the Academic Outcomes of Kalamazoo Promise to Public School Students in a 4-Year Institution

Collier

Fitzpatrick

Houston

et al. 2020

Journal of College Student Retention: Research, Theory & Pr

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This study compares Kalamazoo Promise (KPromise) students to non-Promise, public high school graduating students at a 4-year institution. The final sample for this study was N=9,969; n = 310 (3%) were KPromise students. Descriptive analysis suggests that KPromise students were racially more diverse and less affluent than non-Promise students. Unweighted regressions show that being a KPromise student was correlated with lower college GPA, increased chance of Academic Dismissal, and lowered likelihood of Degree Attainment. Weighting the sample using Inverse Probability Weighing with Regression Adjustment (IPWRA), being a KPromise student was not correlated to any examined outcomes. However, we were unable to generate a suitably similar comparison sample with the variables we could access. Overall, the Kalamazoo Promise allows students to access the university who are so different from the comparison population that it interferes with standard approaches to assessing outcomes. Discussion centers on descriptive differences and highlights the need for better student-level data.

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Who is Most Likely to Oppose Federal Tuition-Free College Policies? Investigating Variable Interactions of Sentiments to America’s College Promise

Cited by 4 publications

References 19 publications

Information extraction from digital social trace data with applications to social media and scholarly communication data

Information extraction from digital social trace data with applications to social media and scholarly communication data

A Framework for Generating Annotated Social Media Corpora with Demographics, Stance, Civility, and Topicality

Bridge of Promise: Comparing the Academic Outcomes of Kalamazoo Promise to Public School Students in a 4-Year Institution

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