short-paper

An approach for identifying affective states through behavioral patterns in web-based learning management systems

Authors:

Farman Ali Khan,

Edgar R. Weippl,

A Min TjoaAuthors Info & Claims

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

Pages 431 - 435

https://doi.org/10.1145/1806338.1806418

Published: 14 December 2009 Publication History

Abstract

In a learning environment, the students experience different affective states. Learning environments that takes into account the students' affective state enhance the students' learning, gain and experience. Therefore, it is crucial to provide students with different learning material and activities according to different affective states. To provide learning that considers students' affective states, the primary step is the detection of affective states of a student. In this paper, we present an approach for the detection of affective states from the patterns of students' behavior observed during an online course. By calculating the affective states and then filling that affective state data into the student model of a learning management system a basis for adaptivity is provided.

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Cited By

Kelkar KBakal J(2020)Random Forest Algorithm for Learner’s Confusion Detection Using Behavioral FeaturesInternational Conference on Mobile Computing and Sustainable Informatics10.1007/978-3-030-49795-8_53(551-562)Online publication date: 1-Dec-2020
https://doi.org/10.1007/978-3-030-49795-8_53
Troussas CVirvou MTroussas CVirvou M(2020)Affective Computing and Motivation in Educational Contexts: Data Pre-processing and Ensemble LearningAdvances in Social Networking-based Learning10.1007/978-3-030-39130-0_5(69-98)Online publication date: 21-Jan-2020
https://doi.org/10.1007/978-3-030-39130-0_5
Charanya RKesavan M(2019)Analysis of Factors Influencing the Virtual Learning Environment in a Sri Lankan Higher Studies Institution2019 International Research Conference on Smart Computing and Systems Engineering (SCSE)10.23919/SCSE.2019.8842719(240-244)Online publication date: Mar-2019
https://doi.org/10.23919/SCSE.2019.8842719
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Index Terms

An approach for identifying affective states through behavioral patterns in web-based learning management systems
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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cover image ACM Other conferences

iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

December 2009

763 pages

ISBN:9781605586601

DOI:10.1145/1806338

General Chair:
Gabriele Kotsis
Johannes Kepler University Linz, Austria
,
Program Chairs:
David Taniar
Monash University, Australia
,
Eric Pardede
La Trobe University, Australia

Copyright © 2009 ACM.

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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Publication History

Published: 14 December 2009

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Austrian Science Fund

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iiWAS '09

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iiWAS '09: 11th International Conference on Information Integration and Web-based Applications & Services

December 14 - 16, 2009

Kuala Lumpur, Malaysia

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Cited By

Kelkar KBakal J(2020)Random Forest Algorithm for Learner’s Confusion Detection Using Behavioral FeaturesInternational Conference on Mobile Computing and Sustainable Informatics10.1007/978-3-030-49795-8_53(551-562)Online publication date: 1-Dec-2020
https://doi.org/10.1007/978-3-030-49795-8_53
Troussas CVirvou MTroussas CVirvou M(2020)Affective Computing and Motivation in Educational Contexts: Data Pre-processing and Ensemble LearningAdvances in Social Networking-based Learning10.1007/978-3-030-39130-0_5(69-98)Online publication date: 21-Jan-2020
https://doi.org/10.1007/978-3-030-39130-0_5
Charanya RKesavan M(2019)Analysis of Factors Influencing the Virtual Learning Environment in a Sri Lankan Higher Studies Institution2019 International Research Conference on Smart Computing and Systems Engineering (SCSE)10.23919/SCSE.2019.8842719(240-244)Online publication date: Mar-2019
https://doi.org/10.23919/SCSE.2019.8842719
kelkar KBakal J(2019)Behavioral Feature Analysis For Learner Affect Identification2019 IEEE 16th India Council International Conference (INDICON)10.1109/INDICON47234.2019.9029005(1-4)Online publication date: Dec-2019
https://doi.org/10.1109/INDICON47234.2019.9029005
Iqbal TIqbal SHussain SKhan IKhan HRehman A(2018)Fighting adult illiteracy with the help of the environmental print materialPLOS ONE10.1371/journal.pone.020190213:8(e0201902)Online publication date: 23-Aug-2018
https://doi.org/10.1371/journal.pone.0201902
Laksitowening KHasibuan Z(2015)Personalized e-learning architecture in standard-based education2015 International Conference on Science in Information Technology (ICSITech)10.1109/ICSITech.2015.7407787(110-114)Online publication date: Oct-2015
https://doi.org/10.1109/ICSITech.2015.7407787
Ghergulescu IMuntean C(2012)Measurement and Analysis of Learner’s Motivation in Game-Based E-LearningAssessment in Game-Based Learning10.1007/978-1-4614-3546-4_18(355-378)Online publication date: 25-May-2012
https://doi.org/10.1007/978-1-4614-3546-4_18
Ghergulescu IMuntean C(2011)Supporting Motivation Based Educational Games Through Web 3.0Towards Learning and Instruction in Web 3.010.1007/978-1-4614-1539-8_15(247-264)Online publication date: 15-Nov-2011
https://doi.org/10.1007/978-1-4614-1539-8_15

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