Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning
Open accessAbstract
Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized.
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There is evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015).
“There is also evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015).”
From Adaptive e-learning environment based on learning styles and its impact on development students' engagement (2021) · cites this paper as Murray & Pérez, 2015
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Research works printed but not linked: 12
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El-Sabagh, H. A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00289-4 · Cited by 566 · see citing works
Artificial intelligence-enabled adaptive learning platforms: A review
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100429 · Cited by 207 · see citing works
- Authors
- Coffin Murray, M., & Pérez, J.
- Affiliations
- Kennesaw State University
- Kennesaw State University (US)
- Venue
- Informing Science: The International Journal of an Emerging Transdiscipline
- Publisher
- Informing Science Institute
- Published
- 2015
- Volume
- 18
- Pages
- 111-125
- ISSN
- 1535-0703 (ISSN-L)
- 1547-9684
- 1521-4672
- OpenAlex
- W2130700601
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 11 · 2022: 20 · 2021: 17 · 2020: 28 · 2019: 15 · 2018: 7 · 2017: 7 · 2016: 13 · 2015: 1
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Informing Science and IT Education Conference (publishedVersion)
- Informing Science The International Journal of an Emerging Transdiscipline (publishedVersion, cc-by-nc)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive learning, traditional teaching methods, digital literacy, learning outcomes, personalized learning, learning styles, higher education, educational technology
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