Integrating technology into evaluation

Chair(s)

• 

  John Baek, PhD (he/him/his)

  Senior Education Evaluator
  NOAA Office of Education
  Burlington, Vermont, United States

Presenter(s)

• ZL

  Zhi Li, n/a

  PhD Candidate at Educational Research, Measurement, and Evaluation
  University of North Carolina at Charlotte, United States
• CW

  Carl Westine, n/a

  Associate Professor of Educational Research
  University of North Carolina at Charlotte, United States

Location: Grand Ballroom 2

This multi-paper session will present two papers about projects that explore the use of highly algorithmic approaches to data analysis. The first paper presents the use of machine learning algorithms to predict student dropout outcomes. The second paper presents the use of a tool called Linguistic Inquiry and Word Count (LIWC-22) to quantitatively analyze interview data collected from underrepresented mathematics doctoral students.

Presentations:

• 2:30 PM – 3:30 PM ET

  Machine Learning algorithms for predicting university student dropout
  

  Presenter: Elvira Celardi (she/her/hers) – University of Catania

  Author: Vincenzo Miracula, n/a – University of Catania

  Author: Antonio Picone, PhD (he/him/his) – Physics and Astronomy Department, University of Catania

  Presenter: Antonio Picone
• 2:30 PM – 3:30 PM ET

  Project Monitoring and Evaluation: Application of Data Analytics for Interview Response Grading
  

  Presenter: Zhi Li, n/a – University of North Carolina at Charlotte

  Presenter: Carl Westine, n/a – University of North Carolina at Charlotte