AEA Conference 2021

AEA Conference Tech TIG Sessions 2021

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TIG Business Meeting

  • Thursday November 4, 7:00-8:00pm EST

Register for the virtual discussion to receive the Zoom link here:


How can machines help us to meet the moment? Advances in Natural Language Processing (NLP) and their application to evaluation.

  • Kerry Bruce, Victoria Scott, Gurnimrat Sidhu, Jonathan Scaccia, Jade Lamb, Ivonne Carrillo
  • Friday November 12, 12:30-1:15pm EST

Natural language processing (NLP) and machine learning techniques hold promise for systematizing evaluator’s ability to process large volumes of qualitative data. But where is the state of the field today and how easy or difficult is it for evaluators to make use of these techniques on tight evaluation timelines? Through two use cases, this panel will explore how the field can leverage NLP to meet the needs of the moment and improve evaluation practice. The session will include a discussion of the practical value and key considerations to using machine learning methods for qualitative analysis.

Using Technology to Make Your Data Useful

  • Paula Osborn, Lauren Bloem, Maria Selde
  • Friday November 12, 1:30-2:15pm EST
Database technology can be a useful tool for centralizing performance monitoring data, but also forces users to decide what data to store in the system given the current data quality and which type of data can be housed and visualized in the database. Furthermore, a database can sit idle if the targeted users do not see the value it brings to their day-to-day work. The U.S. Department of State, Bureau of International Narcotics and Law Enforcement (INL) is meeting the moment by harnessing technology to store, analyze, and utilize evidence to become a data-informed learning organization. INL will share its experience rolling out a monitoring and evaluation database to facilitate systemic inquiry and promote Bureau-wide learning. Roundtable discussion will focus on how technology adoption can catalyze reflection on program design and implementation, data quality, and data literacy.