poster

Self-Evolving Adaptive Learning for Personalized Education

Authors:

Kristin L. Wood,

Kwan Hui LimAuthors Info & Claims

CSCW '20 Companion: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing

Pages 317 - 321

https://doi.org/10.1145/3406865.3418326

Published: 17 October 2020 Publication History

Abstract

Primary and secondary education is a crucial stage to build a strong foundation before diving deep into specialised subjects in colleges and universities. To excel in the current education system, students are required to have a deep understanding of knowledge according to standardized curriculums and syllabus, and exam-related problem solving skills. In current school settings, this learning normally occurs in large classes of 30-40 students per class. Such a "one size fits all'' approach may not be effective, as different students proceed on their learning in different ways and pace. To address this problem, we propose the Self-Evolving Adaptive Learning (SEAL) system for personalized education at scale.

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Cited By

Yadav NYadav PGoar V(2024)Digital Learning and Social SustainabilityImpact of Digitalization on Education and Social Sustainability10.4018/979-8-3693-1854-6.ch001(1-38)Online publication date: 28-Jun-2024
https://doi.org/10.4018/979-8-3693-1854-6.ch001
Yıldırım H(2024)Assessment of effective factors on student performance based on machine learning methodsJournal of Intelligent Systems: Theory and Applications10.38016/jista.13839987:2(43-55)Online publication date: 26-Sep-2024
https://doi.org/10.38016/jista.1383998
Kumar HMusabirov IReza MShi JWang XWilliams JKuzminykh ALiut M(2024)Guiding Students in Using LLMs in Supported Learning Environments: Effects on Interaction Dynamics, Learner Performance, Confidence, and TrustProceedings of the ACM on Human-Computer Interaction10.1145/36870388:CSCW2(1-30)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687038
Show More Cited By

Index Terms

Self-Evolving Adaptive Learning for Personalized Education
1. Applied computing
  1. Education
2. Computing methodologies
  1. Artificial intelligence

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Information

Published In

cover image ACM Conferences

CSCW '20 Companion: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing

October 2020

559 pages

ISBN:9781450380591

DOI:10.1145/3406865

General Chairs:
Matthew Bietz
University of California, Irvine
,
Andrea Wiggins

Copyright © 2020 Owner/Author.

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2020

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CSCW '20

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SIGCHI

CSCW '20: Computer Supported Cooperative Work and Social Computing

October 17 - 21, 2020

Virtual Event, USA

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Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

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sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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Citations

Cited By

Yadav NYadav PGoar V(2024)Digital Learning and Social SustainabilityImpact of Digitalization on Education and Social Sustainability10.4018/979-8-3693-1854-6.ch001(1-38)Online publication date: 28-Jun-2024
https://doi.org/10.4018/979-8-3693-1854-6.ch001
Yıldırım H(2024)Assessment of effective factors on student performance based on machine learning methodsJournal of Intelligent Systems: Theory and Applications10.38016/jista.13839987:2(43-55)Online publication date: 26-Sep-2024
https://doi.org/10.38016/jista.1383998
Kumar HMusabirov IReza MShi JWang XWilliams JKuzminykh ALiut M(2024)Guiding Students in Using LLMs in Supported Learning Environments: Effects on Interaction Dynamics, Learner Performance, Confidence, and TrustProceedings of the ACM on Human-Computer Interaction10.1145/36870388:CSCW2(1-30)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687038
Alvarez-Icaza IMolina-Espinosa JSuárez-Brito PAlvarado-Reyes I(2024)Adaptive Evaluation for Barriers Elimination: The OpenEDR4C Platform2024 12th International Conference on Information and Education Technology (ICIET)10.1109/ICIET60671.2024.10542786(429-433)Online publication date: 18-Mar-2024
https://doi.org/10.1109/ICIET60671.2024.10542786
Varina HOsadchyi VShevchenko S(2023)Enhancing adaptive learning: leveraging interactive exercises through the LearningApps serviceCTE Workshop Proceedings10.55056/cte.56210(281-293)Online publication date: 21-Mar-2023
https://doi.org/10.55056/cte.562
Bao YHosseini HDoyle MStephenson BDorn BSoh LBattestilli L(2023)Mind the GapProceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569749(778-784)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569749
Gautam SRosson MAkgun M(2023)Exploring Potential Contributions of Social Learning to Adaptive Learning SystemsExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585758(1-6)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544549.3585758
Hong D(2023)How much is a “feedback” worth? User engagement and interaction for computer-supported adaptive quizzingInteractive Learning Environments10.1080/10494820.2023.2176521(1-16)Online publication date: 1-Mar-2023
https://doi.org/10.1080/10494820.2023.2176521
Sekeroglu BAbiyev RIlhan AArslan MIdoko J(2021)Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible RemediesApplied Sciences10.3390/app11221090711:22(10907)Online publication date: 18-Nov-2021
https://doi.org/10.3390/app112210907
Liu JWood KLim K(2021)Strategic and Crowd-Aware Itinerary RecommendationMachine Learning and Knowledge Discovery in Databases: Applied Data Science Track10.1007/978-3-030-67667-4_5(69-85)Online publication date: 25-Feb-2021
https://doi.org/10.1007/978-3-030-67667-4_5
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