Automated Author Profile

Gehlbach, Hunter

Johns Hopkins University

Current S-Index

3.6

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.8

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

73.1%

Average FAIR Score per dataset

Total Citations

0

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Page Gehlbach AERA Open 2017 (Version: V0)

Deep reinforcement learning using convolutional neural networks is the technology behind autonomous vehicles. Could this same technology facilitate the road to college? During the summer between high school and college, college-related tasks that students must navigate can hinder successful matriculation. We employ conversational artificial intelligence (AI) to efficiently support thousands of would-be college freshmen by providing personalized, text message–based outreach and guidance for each task where they needed support. We implemented and tested this system through a field experiment with Georgia State University (GSU). GSU-committed students assigned to treatment exhibited greater success with pre-enrollment requirements and were 3.3 percentage points more likely to enroll on time. Enrollment impacts are comparable to those in prior interventions but with substantially reduced burden on university staff. Given the capacity for AI to learn over time, this intervention has promise for scaling personalized college transition guidance.

Authors

  • Page, Lindsay C. ;
  • Gehlbach, Hunter
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.3886/e129643January 2020

Page Gehlbach AERA Open 2017 (Version: 1)

Deep reinforcement learning using convolutional neural networks is the technology behind autonomous vehicles. Could this same technology facilitate the road to college? During the summer between high school and college, college-related tasks that students must navigate can hinder successful matriculation. We employ conversational artificial intelligence (AI) to efficiently support thousands of would-be college freshmen by providing personalized, text message–based outreach and guidance for each task where they needed support. We implemented and tested this system through a field experiment with Georgia State University (GSU). GSU-committed students assigned to treatment exhibited greater success with pre-enrollment requirements and were 3.3 percentage points more likely to enroll on time. Enrollment impacts are comparable to those in prior interventions but with substantially reduced burden on university staff. Given the capacity for AI to learn over time, this intervention has promise for scaling personalized college transition guidance.

Authors

  • Page, Lindsay C. ;
  • Gehlbach, Hunter
0 Citations0 Mentions73% FAIR1.8 Dataset Index
10.3886/e129643v1January 2020