Affect-Targeted Interviews for Understanding Student Frustration

Ryan S. Baker, Nidhi Nasiar, Jaclyn L. Ocumpaugh, Stephen Hutt, Juliana M.A.L. Andres, Stefan Slater, Matthew Schofield, Allison Moore, Luc Paquette, Anabil Munshi, Gautam Biswas

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


Frustration is a natural part of learning in AIED systems but remains relatively poorly understood. In particular, it remains unclear how students’ perceptions about the learning activity drive their experience of frustration and their subsequent choices during learning. In this paper, we adopt a mixed-methods approach, using automated detectors of affect to signal classroom researchers to interview a specific student at a specific time. We hand-code the interviews using grounded theory, then distill particularly common associations between interview codes and affective patterns. We find common patterns involving student perceptions of difficulty, system helpfulness, and strategic behavior, and study them in greater depth. We find, for instance, that the experience of difficulty produces shifts from engaged concentration to frustration that lead students to adopt a variety of problem-solving strategies. We conclude with thoughts on both how this can influence the future design of AIED systems, and the broader potential uses of data mining-driven interviews in AIED research and development.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
EditorsIdo Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, Vania Dimitrova
Number of pages12
ISBN (Print)9783030782917
StatePublished - 2021
Event22nd International Conference on Artificial Intelligence in Education, AIED 2021 - Virtual, Online
Duration: Jun 14 2021Jun 18 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12748 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Artificial Intelligence in Education, AIED 2021
CityVirtual, Online


  • Affect detection
  • Attitudes
  • Frustration
  • Mixed methods
  • Self-regulated learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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