TY - GEN
T1 - Affect-Targeted Interviews for Understanding Student Frustration
AU - Baker, Ryan S.
AU - Nasiar, Nidhi
AU - Ocumpaugh, Jaclyn L.
AU - Hutt, Stephen
AU - Andres, Juliana M.A.L.
AU - Slater, Stefan
AU - Schofield, Matthew
AU - Moore, Allison
AU - Paquette, Luc
AU - Munshi, Anabil
AU - Biswas, Gautam
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Affect detection
KW - Attitudes
KW - Frustration
KW - Mixed methods
KW - Self-regulated learning
UR - http://www.scopus.com/inward/record.url?scp=85126432040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126432040&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78292-4_5
DO - 10.1007/978-3-030-78292-4_5
M3 - Conference contribution
AN - SCOPUS:85126432040
SN - 9783030782917
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 63
BT - Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
A2 - Roll, Ido
A2 - McNamara, Danielle
A2 - Sosnovsky, Sergey
A2 - Luckin, Rose
A2 - Dimitrova, Vania
PB - Springer
T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021
Y2 - 14 June 2021 through 18 June 2021
ER -