The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes
Abstract
Adaptive learning technologies are emerging in educational settings as a means to customize instruction to learners’ background, experiences, and prior knowledge. Here, a technology-based personalization intervention within an intelligent tutoring system (ITS) for secondary mathematics was used to adapt instruction to students’ personal interests. We conducted a learning experiment where 145 ninth-grade Algebra I students were randomly assigned to 2 conditions in the Cognitive Tutor Algebra ITS. For 1 instructional unit, half of the students received normal algebra story problems, and half received matched problems personalized to their out-of-school interests in areas such as sports, music, and movies. Results showed that students in the personalization condition solved problems faster and more accurately within the modified unit. The impact of personalization was most pronounced for 1 skill in particular—writing symbolic equations from story scenarios—and for 1 group of students in particular—students who were struggling to learn within the tutoring environment. Once the treatment had been removed, students who had received personalization continued to write symbolic equations for normal story problems with increasingly complex structures more accurately and with greater efficiency. Thus, we provide evidence that interest-based interventions can promote robust learning outcomes—such as transfer and accelerated future learning—in secondary mathematics. These interest-based connections may allow for abstract ideas to become perceptually grounded in students’ experiences such that they become easier to grasp. Adaptive learning technologies that utilize interest may be a powerful way to support learners in gaining fluency with abstract representational systems.
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“In fact, problem-solving time and accuracy have been found to increase because of learning materials matched to student interests [200].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [200]
In the citing paper
“As every student is unique, personalizing instruction and teaching content can positively impact the learning outcome of the student (Walkington, 2013)—tutoring systems that incorporate this can be categorized as personalized learning systems or personalized tutoring aids.”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Walkington, 2013
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Cites
Mayer, R. E. (2009) · doi:10.1017/cbo9780511811678 · Cited by 7,800
Mayer, R. (2020) · doi:10.1017/9781316941355 · Cited by 7,777
(1980) · Medical Entomology and Zoology · Cited by 7,534
The four-phase model of interest development
Hidi, S., & Renninger, K. A. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_4 · Cited by 4,702
Clark, R. C., Mayer, R. E., & Thalheimer, W. (2003) · Performance Improvement Journal · doi:10.1002/pfi.4930420510 · Cited by 2,257
Motivating the Academically Unmotivated: A Critical Issue for the 21st Century
Hidi, S., & Harackiewicz, J. M. (2000) · Review of Educational Research · doi:10.3102/00346543070002151 · Cited by 1,689
Schmidt, R. A., & Bjork, R. A. (1992) · Psychological Science · doi:10.1111/j.1467-9280.1992.tb00029.x · Cited by 1,582
Cordova, D. I., & Lepper, M. R. (1996) · Journal of Educational Psychology · doi:10.1037/0022-0663.88.4.715 · Cited by 1,568
Interest, Learning, and Motivation
Schiefele, U. (1991) · Educational Psychologist · doi:10.1080/00461520.1991.9653136 · Cited by 1,453
Interest and Its Contribution as a Mental Resource for Learning
Hidi, S. E. (1990) · Review of Educational Research · doi:10.3102/00346543060004549 · Cited by 1,407
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Interest Matters: The Importance of Promoting Interest in Education
Harackiewicz, J. M., Smith, J. L., & Priniski, S. J. (2016) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/2372732216655542 · Cited by 695
“As Figure 1 summarizes, these interventions target motivational processes expected to influence critical educational outcomes, and take the learner’s phase of interest development into account.”
BackgroundBernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021) · Educational Psychology Review · doi:10.1007/s10648-021-09615-8 · Cited by 618
“…indicate a design choice that benefitted students ’ motivation (Bernacki & Walkington, 2018, date 1) and performance (Bernacki & Walkington, 2018; Walkington, 2013; Walkington & Bernacki, 2019, date 1, 2) such programs will often include multiple components in order to satisfy program…”
BackgroundHwang, G., & Tu, Y. (2021) · Mathematics · doi:10.3390/math9060584 · Cited by 558
“Moreover, the number of individual applications generally increased, and the application domains became more diverse from the first to the third time periods.”
BackgroundDifferentiated Instruction in Secondary Education: A Systematic Review of Research Evidence
Smale-Jacobse, A. E., Meijer, A., Helms‐Lorenz, M., & Maulana, R. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02366 · Cited by 560
“Some recent studies on adaptive or personalized ICT programs, digital pen technologies, and blended learning show that such interventions can support differentiated instruction and have positive effects on student achievement (Walkington, 2013; Chen et al., 2016; Van Halem et al., 2017; Ghysels and Haelermans, 2018),…”
BackgroundAlamri, H., Lowell, V. L., Watson, W. R., & Watson, S. L. (2020) · Journal of Research on Technology in Education · doi:10.1080/15391523.2020.1728449 · Cited by 377
“Learner Needs and Interests – PL course design (Barr & Tagg, 1995; Bernacki & Walkington, 2018; Walkington, 2013; Watson & Watson, 2017) – Inclusion of all possible pathways/tracks within the course objectives and parameters (Barr & Tagg, 1995; Bernacki & Walkington, 2018; Walkington, 2013; Watson & Watson, 2017) – In…”
BackgroundInfluential
Metadata
- Authors
- Walkington, C. A.
- Affiliations
- Southern Methodist University (US)
- Corresponding author
- Walkington, Candace A.
- Venue
- Journal of Educational Psychology
- Published
- 2013-11
- Volume
- 105
- Issue
- 4
- Pages
- 932-945
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2314181201
- Cited by
- 322
- References
- 75
- Citations by year
- 2026: 43 · 2025: 45 · 2024: 57 · 2023: 25 · 2022: 21 · 2021: 18 · 2020: 26 · 2019: 16 · 2018: 11 · 2017: 24 · 2016: 13 · 2015: 12 · 2014: 8 · 2013: 3
- FWCI
- 13.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Psychology (publishedVersion)
- Funders
- National Science Foundation (SBE-0354420)
- US Department of Education (R305B100007)
- 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
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Keywords
- adaptive learning systems, personalized instruction, student interest, intelligent tutoring systems, high school mathematics, algebra word problems, transfer of learning, struggling students
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