short-paper

Towards better affect detectors: effect of missing skills, class features and common wrong answers

Authors:

Yutao Wang,

Neil T. Heffernan,

Cristina HeffernanAuthors Info & Claims

LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

Pages 31 - 35

https://doi.org/10.1145/2723576.2723618

Published: 16 March 2015 Publication History

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Abstract

The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first attempt analyzed the effect of missing skill tags in the dataset to the accuracy of the affect detectors. The results show a small improvement after correctly tagging the missing skill values. The second attempt added four features related to student classes for feature selection. The third attempt added two features that described information about student common wrong answers for feature selection. Result showed that two out of the four detectors were improved by adding the new features.

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Cited By

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Morais FGoldoni DKautzmann TJaques P(2024)Sensor-free Affect Detection in Learning Environments: A Systematic Literature ReviewRevista Brasileira de Informática na Educação10.5753/rbie.2024.436232(679-717)Online publication date: 21-Nov-2024
https://doi.org/10.5753/rbie.2024.4362
Kautzmann TRamos GJaques P(2024)Detecção de Emoções na Aprendizagem de Programação: Os Efeitos de Usar Estimativas de Conhecimento em Modelos Livres de Sensores que Detectam a Confusão do AlunoRevista Brasileira de Informática na Educação10.5753/rbie.2024.343732(642-678)Online publication date: 31-Oct-2024
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Wang ZWu WZeng CLuo HSun J(2024)Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning processFrontiers in Psychology10.3389/fpsyg.2024.135919915Online publication date: 10-May-2024
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Index Terms

Towards better affect detectors: effect of missing skills, class features and common wrong answers
1. Applied computing
  1. Education
    1. Computer-assisted instruction
  2. Enterprise computing

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Published In

LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

March 2015

448 pages

ISBN:9781450334174

DOI:10.1145/2723576

Conference Chairs:
Josh Baron
Marist College
,
Grace Lynch
SoLAR
,
Nicole Maziarz
Marist College
,
Program Chairs:
Paulo Blikstein
Stanford University
,
Agathe Merceron
Beuth University of Applied Sciences Berlin
,
George Siemens
University of Texas at Arlington

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 16 March 2015

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LAK '15

LAK '15: the 5th International Learning Analytics and Knowledge Conference

March 16 - 20, 2015

New York, Poughkeepsie

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LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Morais FGoldoni DKautzmann TJaques P(2024)Sensor-free Affect Detection in Learning Environments: A Systematic Literature ReviewRevista Brasileira de Informática na Educação10.5753/rbie.2024.436232(679-717)Online publication date: 21-Nov-2024
https://doi.org/10.5753/rbie.2024.4362
Kautzmann TRamos GJaques P(2024)Detecção de Emoções na Aprendizagem de Programação: Os Efeitos de Usar Estimativas de Conhecimento em Modelos Livres de Sensores que Detectam a Confusão do AlunoRevista Brasileira de Informática na Educação10.5753/rbie.2024.343732(642-678)Online publication date: 31-Oct-2024
https://doi.org/10.5753/rbie.2024.3437
Wang ZWu WZeng CLuo HSun J(2024)Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning processFrontiers in Psychology10.3389/fpsyg.2024.135919915Online publication date: 10-May-2024
https://doi.org/10.3389/fpsyg.2024.1359199
Shen SLiu QHuang ZZheng YYin MWang MChen E(2024)A Survey of Knowledge Tracing: Models, Variants, and ApplicationsIEEE Transactions on Learning Technologies10.1109/TLT.2024.338332517(1898-1919)Online publication date: 2024
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de Morais FJaques P(2024)Improving Sensor-Free Affect Detection by Considering Students' Personality TraitsIEEE Transactions on Learning Technologies10.1109/TLT.2023.328000817(542-554)Online publication date: 2024
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Samani CMusial K(2023)Cluster Analysis Using Explainable AI for Confused Learners2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487715(1-6)Online publication date: 4-Dec-2023
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Li HLi YZhang Z(2023)GRU-Attention Interpretable Knowledge Tracking Model with Forgetting Law for Intelligent Education SystemArtificial Intelligence Logic and Applications10.1007/978-981-99-7869-4_25(311-324)Online publication date: 15-Nov-2023
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Toward an Affect-Sensitive AutoTutor

Automatic detection of learner's affect from conversational cues

Broadening the Scope of Affect Detection Research

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