@inproceedings{c6d8e6447ae147b992d8a7b0ae399dc3,
title = "Affect sequences and learning in Betty's brain",
abstract = "Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.",
keywords = "Affect, Affect dynamics, Learning analytics",
author = "Andres, {Juliana Ma Alexandra L.} and Luc Paquette and Jaclyn Ocumpaugh and Yang Jiang and Baker, {Ryan S.} and Shamya Karumbaiah and Stefan Slater and Nigel Bosch and Anabil Munshi and Allison Moore and Gautam Biswas",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 9th International Conference on Learning Analytics and Knowledge, LAK 2019 ; Conference date: 04-03-2019 Through 08-03-2019",
year = "2019",
month = mar,
day = "4",
doi = "10.1145/3303772.3303807",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "383--390",
booktitle = "Proceedings of the 9th International Conference on Learning Analytics and Knowledge",
address = "United States",
}