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Modeling Filipino Academic Affect during Online Examination using Machine Learning

Authors:

Mideth B. Abisado,

Ramon L. Rodriguez,

Antero Rosauro V. Arias, Jr.,

Cheryl Mari M. Isip,

James Darryl D. Bungay,

John Mark V. Cipriano,

Larry A. VeaAuthors Info & Claims

SIGITE '19: Proceedings of the 20th Annual SIG Conference on Information Technology Education

Page 167

https://doi.org/10.1145/3349266.3351387

Published: 26 September 2019 Publication History

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Abstract

A learning environment is comprised of emotional and cognitive activities. The need to incorporate the measurement of student affect during the learning assessment is necessary. This study measured academic affect during assessment and developed an online examination multi-modal academic affect model. Using thematic analysis and expert opinion, the study generated the presence of the following academic affect: relaxed, curious, bored, frustrated, and distracted. The academic affects were annotated with a computed interrater kappa value of 0.62. Annotation by the coders was mapped with the features extracted from a behavior analysis toolkit that produced the Filipino Online Examination Multi-Modal Affect Dataset. The model yielded an accuracy of 92.66%.

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Afzal, S. and Robinson, P. 2010. Modelling Affect in Learning Environments Motivation and Methods. (2010).

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Coppens, L. 2017. Detecting affective states from facial expressions in second language education. January (2017).

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Hammoumi, O. El et al. 2018. Emotion Recognition in E-learning Systems. 2018 6th International Conference on Multimedia Computing and Systems (ICMCS). (2018), 1--6.

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Pekrun, P. et al. 2007. The Control-Value Theory of Achievement Emotions: An Integrative Approach to Emotions in Education. Emotion in Education. 127, 3 (2007), 13--36.

Crossref

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Vea, L. and Rodrigo, M.M. 2017. Modeling negative affect detector of novice programming students using keyboard dynamics and mouse behavior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10004 LNAI, (2017), 127--138.

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Weerapperuma JNawinna DGamage N(2024)Digital Learning Challenges in Tertiary Education in Sri Lanka: A Social Capital PerspectiveIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.330657111:3(3311-3328)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3306571
Mewada DGaikwad SGharat BKamble P(2024)An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)10.1109/ICKECS61492.2024.10616758(1-5)Online publication date: 18-Apr-2024
https://doi.org/10.1109/ICKECS61492.2024.10616758
Sridhar ARajshekhar J(2022)AI-Integrated Proctoring System for Online ExamsJournal of Artificial Intelligence and Capsule Networks10.36548/jaicn.2022.2.0064:2(139-148)Online publication date: 20-Jul-2022
https://doi.org/10.36548/jaicn.2022.2.006
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Index Terms

Modeling Filipino Academic Affect during Online Examination using Machine Learning
1. Applied computing
  1. Education
    1. E-learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Published In

SIGITE '19: Proceedings of the 20th Annual SIG Conference on Information Technology Education

September 2019

206 pages

ISBN:9781450369213

DOI:10.1145/3349266

General Chairs:
Bryan Goda
University of Washington Tacoma, USA
,
Steve Zilora
Rochester Institute of Technology, USA
,
Program Chairs:
George Grispos
University of Nebraska Omaha, USA
,
Daniel Bogaard
Rochester Institute of Technology, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 26 September 2019

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SIGITE '19: The 20th Annual Conference on Information Technology Education

October 3 - 5, 2019

WA, Tacoma, USA

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SIGITE '19 Paper Acceptance Rate 23 of 57 submissions, 40%;

Overall Acceptance Rate 176 of 429 submissions, 41%

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Weerapperuma JNawinna DGamage N(2024)Digital Learning Challenges in Tertiary Education in Sri Lanka: A Social Capital PerspectiveIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.330657111:3(3311-3328)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3306571
Mewada DGaikwad SGharat BKamble P(2024)An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)10.1109/ICKECS61492.2024.10616758(1-5)Online publication date: 18-Apr-2024
https://doi.org/10.1109/ICKECS61492.2024.10616758
Sridhar ARajshekhar J(2022)AI-Integrated Proctoring System for Online ExamsJournal of Artificial Intelligence and Capsule Networks10.36548/jaicn.2022.2.0064:2(139-148)Online publication date: 20-Jul-2022
https://doi.org/10.36548/jaicn.2022.2.006
Roli JPuja GSahil BVichitra S(2022)An AI-Based Online Exam Proctoring Frameworki-manager's Journal on Computer Science10.26634/jcom.10.3.1916110:3(7)Online publication date: 2022
https://doi.org/10.26634/jcom.10.3.19161
Muzaffar ATahir MAnwar MChaudry QMir SRasheed Y(2021)A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global AdoptionIEEE Access10.1109/ACCESS.2021.3060192(1-1)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3060192

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An Academic Affect Dataset: Spontaneous Facial Expressions and Head poses Collected during Online Examination

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An Academic Affect Dataset: Spontaneous Facial Expressions and Head poses Collected during Online Examination

Emotional expression in mathematics e-learning using emojis: A gender-based analysis

How does Students’ Affect in Virtual Learning Relate to Their Outcomes? A Systematic Review Challenging the Positive-Negative Dichotomy

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