Skip to main navigation Skip to search Skip to main content

The Influence of Different Measurement Approaches on Student Affect Transitions Using Ordered Networks

  • Nidhi Nasiar
  • , Andres Felipe Zambrano
  • , Jaclyn Ocumpaugh
  • , Alex Goslen
  • , Jonathan Rowe
  • , Jessica Vandenberg
  • , Jordan Esiason
  • , Stephen Hutt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Affect detection is integral to creating affect-sensitive learning systems, but the impact of measurement methods needs further research. This paper uses ordered network analysis (ONA) to compare the affect dynamics of two suites of affect detectors trained on complementary data (i.e., labels from an in-the-moment self-reporting (SR) tool vs labels from field observations) in game-based learning. We then use ONA difference models to assess how divergence in learning and motivational measures impact affect dynamics.

Original languageEnglish (US)
Title of host publicationAdvances in Quantitative Ethnography - 6th International Conference, ICQE 2024, Proceedings
EditorsYoon Jeon Kim, Zachari Swiecki
PublisherSpringer Science and Business Media Deutschland GmbH
Pages195-203
Number of pages9
ISBN (Print)9783031763311
DOIs
StatePublished - 2024
Externally publishedYes
Event6th International Conference on Quantitative Ethnography, ICQE 2024 - Philadelphia, United States
Duration: Nov 3 2024Nov 7 2024

Publication series

NameCommunications in Computer and Information Science
Volume2279 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference6th International Conference on Quantitative Ethnography, ICQE 2024
Country/TerritoryUnited States
CityPhiladelphia
Period11/3/2411/7/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Affect Dynamics
  • Epistemic Network Analysis
  • Transition Analysis

Fingerprint

Dive into the research topics of 'The Influence of Different Measurement Approaches on Student Affect Transitions Using Ordered Networks'. Together they form a unique fingerprint.

Cite this