A meta-analysis of the effectiveness of ALEKS on learning
Abstract
Assessment and Learning in Knowledge Spaces (ALEKS) is one of the widely used online intelligent tutoring systems (ITS) in the USA, but it has rarely been included in meta-analyses of ITS efficacy to help students learn. We conducted a meta-analysis to assess the effectiveness of ALEKS on learning. A total of 15 empirical studies were conducted between 2005 and 2015, containing 24 independent samples, which met the inclusion criteria. Our results revealed that ALEKS was as good, but not better than, traditional classroom teaching. Moderator analyses revealed the effect sizes were not significantly different, when ALEKS was used by students at different schooling levels (i.e. secondary or postsecondary), when it was implemented differently (i.e. used as principal or supportive instruction), or when the learning outcome was measured differently (i.e. measured with standardized tests or instructor-designed tests). Surprisingly, effect sizes were greater, when ALEKS was used for shorter periods of time rather than longer periods. Our paper ends with a discussion on the implications of these findings, and how these results can better inform educators using the ALEKS learning system to better promote student learning.
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Open Learner Models have been widely used in AIEd to facilitate learners, teachers and parents in understanding what learners know, how they learn and how AI is being used to enhance learning.
“Open Learner Models [12, 18] have been widely used in AIEd to facilitate learners, teachers and parents in understanding what learners know, how they learn and how AI is being used to enhance learning.”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [18]
Despite being limited in domain, ITS have proven to be effective in providing relevant content to students, interacting with students, and improving students' academic performance.
“Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students [6] and improving students’ academic performance [18, 41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [18]
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“For example, other ITSs such as Cognitive Tutorleveragecorrectnessofresponsestoadjusttaskdifficulty,whileplatformssuchas ALEKSandCarnegieLearning’sMATHiausetimespentonastepandattemptcounts topersonalizestudentlearningpaths(Andersonetal.1995;Fangetal.2019;Ritter andFancsali2016).”
Impact of Intelligent Tutoring System (ITS) on Mathematics Achievement Using ALEKS
Khazanchi, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78270-2_83 · Cited by 3 · see citing works
Waldner, S., Seeber, I., Waizenegger, L., & Maier, R. K. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.049 · Cited by 3 · see citing works
“Yet, past research also acknowledges the high heterogeneity of the effects of pedagogical CAs, suggesting additional (moderator) variables may play a significant role [8].”
BackgroundMethodResultInfluentialLeveraging the Human Classroom with AI-Powered Resources
Adib, N., Sommer, N. M., & Waksmanski-Krynski, N. (2024) · IEEE Digital Education and MOOCS Conference (DEMOcon) · doi:10.1109/democon63027.2024.10747892 · Cited by 3 · see citing works
Evolving and Emerging Perspectives on Transfer of Learning
Hickey, D. T., & Lam, D. P. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199841332.013.21 · Cited by 2 · see citing works
Artificial Intelligence and Assessment Generators in Education: A Comprehensive Review
Boutyour, Y., Idrissi, A., & Uden, L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-65038-3_21 · Cited by 3 · see citing works
Hertel, J. T. (2022) · The Mathematics Enthusiast · doi:10.54870/1551-3440.1599 · Cited by 2 · see citing works
Petergova, A. V., Sokolova, N. E., & Agopyan, N. E. (2025) · Studies in big data · doi:10.1007/978-3-031-78686-0_42 · Cited by 1 · see citing works
Transformative Pedagogy: ChatGPT as a Catalyst for Educational Innovation
Singh, H., Natrajan, N. S., Sanjeev, R., & Singh, A. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-852-120251008 · Cited by 1 · see citing works
Krumsvik, R. J. (2025) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.20.2.5 · Cited by 1 · see citing works
Lee, Y. E., & Doo, M. Y. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-025-09485-x · Cited by 1 · see citing works
MATH 101: An Adaptive Remedial Course
Alvarado, A., Portillo, J. R., & Román, B. H. L. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--40581 · Cited by 1 · see citing works
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“With the deep integration of artificial intelligence technology and education, and the continuous development of mathematics literacy, the assessment of students' literacy proposes new requests more effectively through technical means [1].”
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“The ALEKS Software and the Application in Ecuador The ALEKS (Assessment and Learning in Knowledge Spaces) software is one of the most popular DPL software for mathematics instruction globally (Fang et al., 2019).”
MethodFramework para sistema tutor adaptativo ao raciocínio crítico em contabilidade - STARCC
de Souza, M. C. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.12.2019.tde-22082019-160042
Mamatova Conceptual model of the educational process based on smart technologies
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“Some studies suggest favorable student learning outcomes in these environments [11].”
BackgroundPROCESSES OF DEVELOPMENT OF INTELLECTUAL SYSTEMS IN EDUCATION
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Self-Regulated Learning Theory and Its Teaching Practice
黎, 泳. (2024) · Advances in Psychology · doi:10.12677/ap.2024.148611
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A Conversational Geography Tutor Utilizing Google Maps and LLMs
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How Human-Centered Is AI-Aided Learning in Education?
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Wie belastbar ist die KI-Bildungsforschung?
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Wie belastbar ist die KI-Bildungsforschung?
Friederichs, H. (2026) · doi:10.59350/p0n7d-1qe84
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- Authors
- Fang, Y., Ren, Z., Hu, X., & Graesser, A. C.
- Affiliations
- Central China Normal University (CN)
- University of Memphis (US)
- Corresponding author
- Ren, Zhihong
- Venue
- Educational Psychology
- Publisher
- Informa UK Limited
- Published
- 2018-11-23
- Volume
- 39
- Issue
- 10
- Pages
- 1278-1292
- ISSN
- 0144-3410 (ISSN-L)
- 1469-5820
- OpenAlex
- W2901279185
- Cited by
- 79
- References
- 37
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 21 · 2023: 11 · 2022: 9 · 2021: 14 · 2020: 3 · 2019: 2
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Psychology (publishedVersion)
- 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 › Artificial Intelligence in Education
- Keywords
- ALEKS, intelligent tutoring systems, online learning, mathematics learning, effect size, standardized testing
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