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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Problem-solving time and accuracy have been found to increase because of learning materials matched to student interests.
“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]
Personalizing instruction and teaching content can positively impact the learning outcome of the student.
“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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“EdTech that adapt to the user in the course of learning draw on intelligent tutoring systems or adaptive learning technologies (Walkington, 2013) to automate the interaction between the learner and the digital content (Aleven et al. 2016).”
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Implementation of Adaptive Learning Systems: Current State and Potential
Imhof, C., Bergamin, P. B., & McGarrity, S. (2020) · Cognition and exploratory learning in the digital age · doi:10.1007/978-3-030-48190-2_6 · Cited by 65 · see citing works
Kokka, K. (2018) · Urban Education · doi:10.1177/0042085918806947 · Cited by 62 · see citing works
“In addition, using tasks set in real-world contexts may improve mathematics performance (Bottge & Hasselbring, 1993; Carraher, Carraher, & Schliemann, 1985) and engagement (Bae & Kokka, 2016), especially if the tasks are aligned with students’ interests (Walkington, 2013).”
MethodThe Educational Value of Artificial Intelligence and Social Robots
Λαμπρόπουλος, Γ., & Papadakis, S. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82915-4_1 · Cited by 77 · see citing works
Personalization of Instruction: Design Dimensions and Implications for Cognition
Walkington, C., & Bernacki, M. L. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1380590 · Cited by 60 · see citing works
“Results (Bernacki & Walkington, under review) replicated and extended the findings from Walkington (2013): Students receiving personalization reported higher interest, showed greater learning efficiency and had higher performance on paperbased classroom exams than the control group.”
BackgroundMethodResultInfluentialMotivational strategies to engage learners in desirable difficulties.
Zepeda, C. D., Martin, R. S., & Butler, A. C. (2020) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2020.08.007 · Cited by 58 · see citing works
Supporting children's math learning with feedback-augmented narrative technology
Ruan, S., He, J., Ying, R., Burkle, J., Hakim, D., Wang, A., Yin, Y., Zhou, L., Xu, Q., AbuHashem, A. A., et al. (2020) · International Conference on Interaction Design and Children · doi:10.1145/3392063.3394400 · Cited by 59 · see citing works
“We also personalize the narrative in several ways, given personalized instruction and fantasy play can boost engagement and learning [6, 73].”
Walkington, C., Petrosino, A. J., & Sherman, M. F. (2013) · Mathematical Thinking and Learning · doi:10.1080/10986065.2013.770717 · Cited by 57 · see citing works
Pandiangan, P., Sanjaya, I. G. M., & Jatmiko, B. (2017) · Journal of Baltic Science Education · doi:10.33225/jbse/17.16.651 · Cited by 54 · see citing works
Opportunities and challenges of using generative AI to personalize educational assessment
Arslan, B., Lehman, B., Tenison, C., Sparks, J. R., López, A. A., Gu, L., & Zapata-Rivera, D. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1460651 · Cited by 52 · see citing works
“Context personalization can significantly enhance learner motivation and engagement by allowing learners to bring their cultural identity to the learning environment, leading to better learning outcomes (Walkington, 2013; Walkington and Bernacki, 2018, 2019).”
BackgroundAn Interview Reflection on “Intelligent Tutoring Goes to School in the Big City”
Koedinger, K. R., & Aleven, V. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0082-8 · Cited by 51 · see citing works
Who Benefits from Diagrams and Illustrations in Math Problems? Ability and Attitudes Matter
Cooper, J., Sidney, P. G., & Alibali, M. W. (2017) · Applied Cognitive Psychology · doi:10.1002/acp.3371 · Cited by 56 · see citing works
“It is worth noting that some research has shown greater benefits of contextual information for students with lower math ability on harder problems (Walkington, 2013).”
BackgroundInfluentialLiu, S., Guo, X., Hu, X., & Zhao, X. (2024) · Electronics · doi:10.3390/electronics13244876 · Cited by 58 · see citing works
Walkington, C., Clinton‐Lisell, V., & Shivraj, P. (2017) · American Educational Research Journal · doi:10.3102/0002831217737028 · Cited by 48 · see citing works
Exploring the influence of ChatGPT on student academic success and career readiness
Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13148-2 · Cited by 63 · see citing works
“Students with specific knowledge gaps can use ChatGPT for targeted instruction and practice activities, similar to the effectiveness of AI-powered tutors found by (Corry & Carlson-Bancroft, 2014; Walkington, 2013).”
Application of ChatGPT for automated problem reframing across academic domains
Einarsson, H., Lund, S. H., & Jónsdóttir, A. H. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100194 · Cited by 46 · see citing works
Sajja, R., Sermet, Y., & Demir, İ. (2025) · IEEE Access · doi:10.1109/access.2025.3554222 · Cited by 69 · see citing works
“For instance, a study involving ninth-grade Algebra I students demonstrated that personalized problems aligned with students’ interests in sports, music, and movies led to faster and more accurate problem-solving, particularly benefiting those struggling within the tutoring environment [44].”
Designing personalised instruction: A research and design framework
Kucirkova, N. I., Gerard, L. F., & Linn, M. C. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13119 · Cited by 44 · see citing works
“Many hope that this challenge will benefit from data-based technologies (eg, Walkington, 2013).”
BackgroundWalkington, C., Clinton‐Lisell, V., & Sparks, A. (2019) · Instructional Science · doi:10.1007/s11251-019-09481-6 · Cited by 45 · see citing works
Adaptive Learning Technology Relationship with Student Learning Outcomes
White, G. L. (2020) · Journal of Information Technology Education Research · doi:10.28945/4526 · Cited by 41 · see citing works
Hyde, J. S., Canning, E. A., Rozek, C. S., Clarke‐Rubright, E. K., Hulleman, C. S., & Harackiewicz, J. M. (2016) · Journal of Research on Adolescence · doi:10.1111/jora.12253 · Cited by 40 · see citing works
“Related research in mathematics education has established the effectiveness of personalization in fostering students’ learning (Cordova & Lepper, 1996; Walkington, 2013; Walkington, Petrosino, & Sherman, 2013).”
BackgroundWalkington, C., Clinton‐Lisell, V., RITTER, S., & Nathan, M. J. (2015) · Journal of Educational Psychology · doi:10.1037/edu0000036 · Cited by 40 · see citing works
“However, the result that familiar contexts or topics are associated with higher performance on algebra word problems, has been recently replicated in the context of online curricula (Walkington, 2013).”
BackgroundResultInfluentialEliciting Mathematics Interest: New Directions for Context Personalization and Example Choice
Høgheim, S., & Reber, R. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2016.1268085 · Cited by 39 · see citing works
“For example, Walkington (2013) proposed that the familiar contexts provided by context personalization can help in grounding abstract mathematical concepts for learners, making content more intelligible (see also Boaler, 1994).”
BackgroundPersonalized learning and ESSA: What we know and where we go
Zhang, L., Yang, S., & Carter, R. A. (2020) · Journal of Research on Technology in Education · doi:10.1080/15391523.2020.1728448 · Cited by 38 · see citing works
“Studies on context personalization are usually grounded in research in cognition, motivation, and emotion (see Cordova & Lepper, 1996; Walkington, 2013).”
BackgroundMeasuring Learner Satisfaction of an Adaptive Learning System
Lim, L., Lim, S. H., & Lim, R. W. Y. (2022) · Behavioral Sciences · doi:10.3390/bs12080264 · Cited by 37 · see citing works
“With adaptive learning systems, learners could take ownership of their learning experience [9] and accelerate future learning [10].”
Background“Math word problem generation with mathematical consistency and problem context constraints,”
Wang, Z., Lan, A., & Baraniuk, R. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.484 · Cited by 39 · see citing works
“…generation method can generate MWPs tailored to each student’s background and interests, providing students with a personalized learning experience (Walkington, 2013) that often leads to better engagement and improved learning outcomes (Connor-Greene, 2000; Karpicke, 2012; Karpicke and Roediger,…”
BackgroundMethodNasrullah, A., Marlina, M., & Dwiyanti, W. (2018) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v13i12.8636 · Cited by 36 · see citing works
Guo, Y. M., Klein, B. D., & Ro, Y. K. (2019) · Studies in Higher Education · doi:10.1080/03075079.2019.1593348 · Cited by 35 · see citing works
“More importantly, course design in which instructional materials are adapted to fit with individual student interests has been found to improve learning outcomes in the acquisition of math skills (Walkington 2013).”
BackgroundLearner differences in theory and practice†
Kalantzis, M., & Cope, B. (2016) · Open Review of Educational Research · doi:10.1080/23265507.2016.1164616 · Cited by 30 · see citing works
“Such is the objective of adaptive, personalized or differentiated instruction which calibrates learning to individuals (Walkington, 2013; Wolf, 2010).”
BackgroundLee, S., Lee, J., & Jeong, Y. (2022) · Journal of Marketing Research · doi:10.1177/00222437221130712 · Cited by 48 · see citing works
“Previous studies show that education technology that encourages a self-directed, interactive, and collaborative learning environment has positive effects on students’ academic outcomes (e.g., Barrow, Markman, and Rouse 2009; Walkington 2013).”
Walkington, C., & Hayata, C. (2017) · ZDM · doi:10.1007/s11858-017-0842-z · Cited by 34 · see citing works
Zhang, L., Carter, R. A., Basham, J. D., & Yang, S. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12725 · Cited by 31 · see citing works
A one stop shop? Perspectives on the value of adaptive learning technologies in K-12 education
Divanji, R. A., Bindman, S. W., Tung, A., Chen, K., Castaneda, L. M., & Scanlon, M. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100157 · Cited by 29 · see citing works
“Although reliable data are not available about the prevalence of ALTs, recent research suggests that these tools are commonly used in K-12 classrooms [1 – 5].”
BackgroundValue Reappraisal as a Conceptual Model for Task-Value Interventions
Acee, T., Weinstein, C., Hoang, T. V., & Flaggs, D. A. (2018) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1381830 · Cited by 23 · see citing works
“Some relevance interventions have focused on modifying course content so that it is more appealing, relevant to students, and applicable to the real world (see Harackiewicz, Smith, & Priniski, 2016; Walkington, 2013; Woolley, Rose, Orthner, Akos, & Jones-Sanpei, 2013).”
BackgroundInstructional Interventions That Support Student Engagement: An International Perspective
James, N. P., Shernoff, D. J., Bressler, D. M., Larson, S. C., & Sinha, S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813413-9.00008-5 · Cited by 27 · see citing works
Ellis, B. J., Abrams, L. S., Masten, A. S., Sternberg, R. J., Tottenham, N., & Frankenhuis, W. E. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009350051 · Cited by 28 · see citing works
Adaptive learning: a cluster-based literature review (2011-2022)
Фадєєва, Л. О. (2023) · Educational Technology Quarterly · doi:10.55056/etq.613 · Cited by 27 · see citing works
“Walkington [196] showed that an interest-based personalization intervention within an intelligent tutoring system for secondary mathematics improved learning outcomes for struggling students.”
BackgroundABC problem in elementary mathematics education: Arithmetic before comprehension
Boote, S. K., & Boote, D. N. (2016) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-016-9350-2 · Cited by 27 · see citing works
“Key word strategies focus on surface features and ignore the deep structure of relationships within the question text (Sowder 1988; Walkington 2013; Parmar 1992) and are antagonistic to building the mathematical proficiency of conceptual understanding (National Research Council 2001).”
BackgroundLin, L., Lin, X., Zhang, X., & Ginns, P. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09933-7 · Cited by 39 · see citing works
“Moreover, due to a limited number of studies that reported information on learners’ prior knowledge, we were only able to obtain the information from four studies (i.e., Awo-fala et al., 2011; Cordova & Lepper, 1996; Ku & Sullivan, 2000; Walkington, 2013).”
Using AI for Adaptive Learning and Adaptive Assessment
Khine, M. S. (2024) · Springer eBooks · doi:10.1007/978-981-97-9350-1_3 · Cited by 33 · see citing works
Kim, J. S., Asher, C. A., Burkhauser, M., Mesite, L. M., & Leyva, D. (2019) · AERA Open · doi:10.1177/2332858419872701 · Cited by 26 · see citing works
“More recently, a style of personalized learning called context personalization emphasizes the role of both cognitive and non-cognitive factors in fostering deeper learning (Walkington, 2013; Walkington & Bernacki, 2018).”
BackgroundPersonalized Learning in Algebra
Walkington, C., Sherman, M. F., & Howell, E. (2014) · Mathematics Teacher Learning and Teaching PK-12 · doi:10.5951/mathteacher.108.4.0272 · Cited by 25 · see citing works
The Double-Edged Interactions of Prompts and Self-efficacy
Gentner, N. M., & Seufert, T. (2020) · Metacognition and Learning · doi:10.1007/s11409-020-09227-7 · Cited by 25 · see citing works
“In the field of mathematics, tailoring algebra-problems to learners’ situational interest increased learners’ interest and learning of algebra concepts (Bernacki and Walkington 2014; Walkington 2013).”
BackgroundScaffolding middle school mathematics curricula with large language models
Malik, R., Abdi, D., Wang, R., & Demszky, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13571 · Cited by 30 · see citing works
“This may include adding warmup tasks, formative assessments, or culturally relevant adaptations (Meidl & Meidl, 2011; Walkington, 2013).”
Maritime Education and Training in the COVID-19 Era and Beyond
Renganayagalu, S. K., Mallam, S., & Hernes, M. (2022) · TransNav the International Journal on Marine Navigation and Safety of Sea Transportation · doi:10.12716/1001.16.01.06 · Cited by 23 · see citing works
“Adaptive Learning (AL) is considered as the next stage of computer assisted training as AL systems have the ability to provide students with immediate assistance and resources specific to their learning needs, and relevant feedback [46].”
BackgroundKlipfel, K. (2014) · Reference Services Review · doi:10.1108/rsr-08-2013-0043 · Cited by 23 · see citing works
Benefits of adaptive lessons for pre-class preparation in a flipped numerical methods course
Clark, R. M., & Kaw, A. K. (2019) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2019.1617439 · Cited by 23 · see citing works
“Today’s students are used to customization as well as having interaction and control when searching for knowledge and information [1].”
BackgroundVindigni, G. (2023) · European Journal of Theoretical and Applied Sciences · doi:10.59324/ejtas.2023.1(4).66 · Cited by 26 · see citing works
“com EJTAS 2023 | Volume 1 | Number 4 726 allows instruction to be continuously tailored to build upon and respond sensitively to each learner’s existing mental models, misconceptions, and developmental level to scaffold the active assimilation and accommodation processes by which individuals construct new understandin…”
BackgroundAdaptive Learning Systems: Harnessing AI for Customized Educational Experiences
Kolluru, V., Mungara, S., & Chintakunta, A. N. (2018) · International Journal of Computational Science and Information Technology · doi:10.5121/ijcsity.2018.6302 · Cited by 22 · see citing works
“Walkington (2013) illustrated that tailoring instruction to student interests not boosts motivation but also enhances performance emphasizing the importance of relevance in engaging students.”
BackgroundInfluentialInterest-enhancing approaches to mathematics curriculum design: Illustrations and personalization
Clinton‐Lisell, V., & Walkington, C. (2019) · The Journal of Educational Research · doi:10.1080/00220671.2019.1568958 · Cited by 21 · see citing works
“Personalization is an instructional approach that connects mathematics tasks to students’ out-of-school interests in topics such as sports, shopping, and video games (Walkington, 2013).”
BackgroundInfluentialThe Impact of a Personalization Intervention for Mathematics on Learning and Non-Cognitive Factors
Bernacki, M. L., & Walkington, C. (2014) · doi:10.13140/2.1.4342.1445 · Cited by 20 · see citing works
Aslan, S., Alyüz, N., Li, B., Durham, L., Shi, M., Sharma, S., & Nachman, L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100393 · Cited by 28 · see citing works
Ostrow, K., Heffernan, N. T., & Williams, J. J. (2017) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146811711900308 · Cited by 18 · see citing works
“• Adapting instructional materials to students’ personal & peer interests (Walkington, 2013) • Embedding software and dynamics for peer assistance (Walker, Rummel, & Koedinger, 2011) • Examining how confidence affects performance in early algebra (Mazzocco, Murphy, Brown, Rinne, & Herold, 2013)”
BackgroundYoung learners’ motivation, self-regulation and performance in personalized learning
Ackermans, K., Bakker, M., van Loon, A., Kral, M., & Camp, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105208 · Cited by 33 · see citing works
Disembodied AI and the limits to machine understanding of students' embodied interactions
Nathan, M. J. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1148227 · Cited by 17 · see citing works
“…has coincided with growing expectations for computers to deliver data-driven, real-time directives for education, such as personalized learning (Walkington, 2013) and assessment, added pressures from a global pandemic that disrupted standard, in-person learning, and a lack of oversight or…”
BackgroundHidden Gems Breakthrough Dynamic Landscape of Adaptive Learning Technologies for Higher Education
Singh, B., & Kaunert, C. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3641-0.ch010 · Cited by 18 · see citing works
Pozas, M., Löffler, P., Schnotz, W., & Kauertz, A. (2020) · Open Education Studies · doi:10.1515/edu-2020-0118 · Cited by 17 · see citing works
“According to Walkington (2013), contexts activate interest while helping ground abstract ideas and their prior knowledge within concrete real-life experiences.”
BackgroundTeachers’ understanding of the concept of ‘embedding’ digital technology in education
Feerick, E., Clerkin, A., & Cosgrove, J. (2022) · Irish Educational Studies · doi:10.1080/03323315.2021.2022521 · Cited by 17 · see citing works
“A study by Walkington (2013) used a software programme that asked students about their interests (e.g. sport, music), and then incorporated these interests into algebra instructional material.”
MethodExtending Cognitive Load Theory: The CLAM Framework for Biometric, Adaptive, and Ethical Learning
Vasilaki, E., & Mavrogianni, A. (2025) · Psychology International · doi:10.3390/psycholint7020040 · Cited by 23 · see citing works
Roure, C., & Pasco, D. (2022) · Journal of Teaching in Physical Education · doi:10.1123/jtpe.2021-0283 · Cited by 16 · see citing works
Will AI reshape or deform pharmacy education?
Ali, M. (2024) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2024.102274 · Cited by 15 · see citing works
Spaced E-learning for Sustainable Education
Kapenieks, J., & Kapenieks, J. (2020) · Journal of Teacher Education for Sustainability · doi:10.2478/jtes-2020-0016 · Cited by 15 · see citing works
Leave Her out of It: Person‐Presentation of Strategies is Harmful for Transfer
Riggs, A. E., Alibali, M. W., & Kalish, C. W. (2015) · Cognitive Science · doi:10.1111/cogs.12224 · Cited by 15 · see citing works
“Research illustrating the benefits of using real-world contexts in textbooks has found that this practice increases student engagement (Alexander & Jetton, 1996), highlights the personal relevance of concepts to students (Walkington, 2013), and decreases mathematics anxiety (Martinez, 1987).”
BackgroundEffects of Educational Technology on Mathematics Achievement for K-12 Students in Utah
Brasiel, S., Jeong, S., Ames, C., Lawanto, K., Yuan, M., & Martin, T. (2016) · Journal of Online Learning Research · doi:10.70725/494567cmmgyl · Cited by 15 · see citing works
“…developers can also consider options for development that keep a student engaged, such as allowing the student to select a different avatar or setting, since there is some prior research evidence to support the impact of allowing students to personalize their learning experience (Walkington, 2013).”
BackgroundThe Digital Divide in Language and Literacy Education
Tate, T. P., & Warschauer, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-02237-6_5 · Cited by 15 · see citing works
The Multidimensional Structure of Interest
Draijer, J., Bakker, A., Slot, E. M., & Akkerman, S. F. (2020) · Frontline Learning Research · doi:10.14786/flr.v8i4.577 · Cited by 14 · see citing works
“…appreciated particularly because it generates a learning process that appears natural and intrinsic to the person, and therefore fits with new models of education that depart from person-centered, life-wide and connected learning approaches (e.g., Barron, 2006; Ito et al., 2018; Walkington, 2013).”
BackgroundThe role of students’ interests during computer-assisted learning: A field experiment
Iterbeke, K., Schelfhout, W., & Witte, K. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107168 · Cited by 14 · see citing works
Hao, C., Xu, W., Halim, H. B. A., & Hao, M. (2025) · Language Teaching Research · doi:10.1177/13621688251352595 · Cited by 20 · see citing works
Pelánek, R., Effenberger, T., & Jarušek, P. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09396-z · Cited by 19 · see citing works
“In addition to personalization based on student knowledge state, it is useful to take into account also student interests and to present tasks that are contextualized with respect to these interests (Cordova and Lepper 1996; Walkington 2013).”
- 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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