Cited by 70Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsEducational Technology and Assessment

An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory

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Chen, You-Shyang, Wu, Chun-Hui, Chen, Ta-g·2017·EURASIA Journal of Mathematics, Science and Technology Education

Abstract

Adaptive learning for individual learners has recently become popular in education. This study aims to fill the void in the existing literature by building an adaptive e-learning system with self-assessment rubrics based on the dynamic scaffolding theory in response to different student needs. Meanwhile, the purpose of this study is to explore the effectiveness of using adaptive e-learning with dynamic scaffoldings and rubrics in fostering students’ learning outcomes. An experimental design was conducted to evaluate learning effectiveness and learning satisfaction in the Excel (spreadsheet) of the course for using the developed adaptive e-learning system. Sixty undergraduate students from a technology university in central Taiwan participated in this experimental study and executed a pretest and a posttest. Research results revealed that the developed adaptive e-learning system can effectively support students with personalized learning materials and successfully helps students acquired knowledge and develop cognitive abilities. The results recommend that teachers could employ rubrics as a self-assessment tool for supporting students with dynamic scaffoldings to conduct a learner-centered e-learning environment. Additionally, the lack of generalizability is clearly a limitation of the present data due to a few participants. Finally, future research direction of this study was also discussed.

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  • These findings are consistent with other studies (Alshammari & Qtaish, 2019; Chun-Hui et al., 2017), which have revealed the effectiveness of the adaptive e-learning environment.

    “These findings are consistent with other studies, such as (Alshammari & Qtaish, 2019;Chun-Hui et al., 2017), which have revealed the effectiveness of the adaptive e-learning environment.”
  • Chun-Hui et al. (2017) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development.

    “(Chun-Hui et al., 2017) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development.”
  • Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style; this leads to improved learning outcomes (Al-Chalabi & Hussein, 2020; Chun-Hui et al., 2017; Hussein & Al-Chalabi, 2020; Pashler et al., 2008).

    “Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style.This leads to improved learning outcomes (Al-Chalabi & Hussein, 2020;Chun-Hui et al., 2017;Hussein & Al-Chalabi, 2020;Pashler et al., 2008).”

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References 32 · Cited by 70

Authors
Chen, Y.-S., Wu, C.-H., & Chen, T.-G.
Affiliations
  • Hwa Hsia University of Technology (TW)
  • National Formosa University (TW)
Corresponding author
Chen, You-Shyang
Venue
EURASIA Journal of Mathematics, Science and Technology Education
Published
2017-12-07
Volume
14
Issue
3
ISSN
  • 1305-8215 (ISSN-L)
  • 1305-8223
OpenAlex
W2775139539
Cited by
70
References
32
Citations by year
2026: 4 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 12 · 2021: 16 · 2020: 6 · 2019: 2 · 2018: 5
FWCI
2.12
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
Eurasia Journal of Mathematics Science and Technology Education (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
Keywords
adaptive e-learning, self-assessment rubric, personalized learning content, learning outcomes, learning satisfaction, student-centered learning environment, cognitive development

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