research-article

The annotation: a track of reader's personality traits on paper

Authors:

Anis Kalboussi,

Ahmed HadjKacemAuthors Info & Claims

ACMSE '14: Proceedings of the 2014 ACM Southeast Conference

Article No.: 10, Pages 1 - 6

https://doi.org/10.1145/2638404.2638469

Published: 28 March 2014 Publication History

Abstract

In this paper we analyze the influence of the personality traits on the annotator annotation practices, by focusing on the study of the correlation between readers personalities and such features of their annotative activities such as the total number of annotation acts, average number of annotation acts, number of textual annotation acts, number of graphical annotation acts, number of referential annotation acts and number of compounding annotation acts. The analysis presented is based on a dataset of 120 volunteers who provided their annotated documents and the results of a psychometric test (The IPIP-NEO inventory. The proposed model uses the bivariate and the multivariate regression fits to show the correlation of the annotation activity to the reader pesonality traits. Our results show significant relationship between personality traits and such features of annotation practice. This significant relationship proves that annotations can be used to automatically and accurately predict such traits of the annotator personality. We give examples of possible application of our findings in different areas to ameliorate the personnalization process over web.

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Cited By

Kalboussi AOmheni NMazhoud OKacem A(2022)How to Organize the Annotation Systems in Human-Computer Environment: Study, Classification and ObservationsHuman-Computer Interaction – INTERACT 201510.1007/978-3-319-22668-2_11(115-133)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-319-22668-2_11
Mazhoud OKalboussi AKacem AKhazanchi DSiy HGrispos GKwaku Setor T(2020)Assistance of Student by Web Services based on AnnotationsProceedings of the 21st Annual Conference on Information Technology Education10.1145/3368308.3415414(260-265)Online publication date: 7-Oct-2020
https://dl.acm.org/doi/10.1145/3368308.3415414
Mazhoud OKalboussi AKacem A(2018)An Approach of Recommending Personalized Web Services through Annotations in Learning EnvironmentProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282423(253-262)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282423
Show More Cited By

Index Terms

The annotation: a track of reader's personality traits on paper
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Other conferences

ACMSE '14: Proceedings of the 2014 ACM Southeast Conference

March 2014

265 pages

ISBN:9781450329231

DOI:10.1145/2638404

Conference Chair:
Ken Hoganson
Kennesaw State University
,
Program Chair:
Selena He
Kennesaw State University

Copyright © 2014 ACM.

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Publication History

Published: 28 March 2014

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ACM SE '14

ACM SE '14: ACM Southeast Regional Conference 2014

March 28 - 29, 2014

Georgia, Kennesaw

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

Kalboussi AOmheni NMazhoud OKacem A(2022)How to Organize the Annotation Systems in Human-Computer Environment: Study, Classification and ObservationsHuman-Computer Interaction – INTERACT 201510.1007/978-3-319-22668-2_11(115-133)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-319-22668-2_11
Mazhoud OKalboussi AKacem AKhazanchi DSiy HGrispos GKwaku Setor T(2020)Assistance of Student by Web Services based on AnnotationsProceedings of the 21st Annual Conference on Information Technology Education10.1145/3368308.3415414(260-265)Online publication date: 7-Oct-2020
https://dl.acm.org/doi/10.1145/3368308.3415414
Mazhoud OKalboussi AKacem A(2018)An Approach of Recommending Personalized Web Services through Annotations in Learning EnvironmentProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282423(253-262)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282423
Raber FKrüger AMitrovic TZhang JChen LChin D(2018)Privacy PerceiverAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225228(227-232)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3213586.3225228
Hlioui FAloui NGargouri F(2017)Automatic Deduction of Learners’ Profiling Rules Based on Behavioral AnalysisComputational Collective Intelligence10.1007/978-3-319-67074-4_23(233-243)Online publication date: 27-Sep-2017
https://dl.acm.org/doi/10.1007/978-3-319-67074-4_23
Omheni NKalboussi AMazhoud OKacem A(2016)Computing of Learner’s Personality Traits Based on Digital AnnotationsInternational Journal of Artificial Intelligence in Education10.1007/s40593-016-0124-x27:2(241-267)Online publication date: 9-Nov-2016
https://doi.org/10.1007/s40593-016-0124-x
Omheni NKacem A(2016)"i-Read"Proceedings of the 13th International Conference on Intelligent Tutoring Systems - Volume 968410.1007/978-3-319-39583-8_21(221-226)Online publication date: 7-Jun-2016
https://dl.acm.org/doi/10.1007/978-3-319-39583-8_21
Kalboussi AOmheni NMazhoud OKacem A(2015)An Interactive Annotation System to Support the Learner with Web Services AssistanceProceedings of the 2015 IEEE 15th International Conference on Advanced Learning Technologies10.1109/ICALT.2015.57(409-410)Online publication date: 6-Jul-2015
https://dl.acm.org/doi/10.1109/ICALT.2015.57

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