Cited by 263Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsOnline Learning and Analytics

A multimedia Adaptive tutoring system for mathematics that addresses cognition, metacognition and affect

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Arroyo, Ivon, Woolf, Beverly Park, Burelson, Winslow, Muldner, Kasia, Rai, Dovan, Tai, Minghui·2014·International Journal of Artificial Intelligence in Education

Abstract

This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students’ mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students’ metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.

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  • Only one paper from 2014 surveyed other aspects of motivation (e.g., self-efficacy) in a more substantial way.

    “As far as we can tell, only one paper from 2014 surveyed other aspects of motivation (e.g., self-efficacy) in a more substantial way (Arroyo et al., 2014).”

    From Evolution and Revolution in Artificial Intelligence in Education (2016) · cites this paper as Arroyo et al., 2014

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  1. Azevedo, R., Bouchet, F., Harley, J. M., Feyzi-Behnagh, R., Trevors, G., Duffy, M., Taub, M., Pacampara, N., Agnew, L., Griscom, S., Mudrick, N., Stead, V., & Yang, W. (2011). MetaTutor: An Intelligent Multi-Agent Tutoring System Designed to Detect, Track, Model, and Foster Self-Regulated Learning In Proceedings of the Fourth Workshop on Self-Regulated Learning in Educational Technologies. In conjunction with the 15th International Conference onArtificial Intelligence on Education (AIED 2011)
  2. Bull, S., Wasson, B., Johnson, M. D., Petters, D., & Hansen, C. (2012). Helping teachers effectively support group learning. In Proceedings of Workshop on Intelligent Support for Learning in Groups, Intelligent Tutoring Systems
  3. Hartfield, E. (1994). Emotional contagion. United Kingdom: Cambridge University Press.
  4. Heffernan, N., Militello, M., Heffernan, C., & Decoteau, M. (2012) Effective and meaningful use of educational technology: three cases from the classroom. In C. Dede & J. Richards (Eds.), Digital Teaching Platforms (pp. 88-102).
  5. Ogan, A., Aleven, V., Jones, C., & Kim, J. (2011). Pervasive social effects of agent-based instructional dialog. In In Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED 2011) (pp. 238-246).
  6. Roll, I., Aleven, V., McLaren, B., & Koedinger, K. (2007). Designing for metacognition -applying Cognitive Tutor principles to metacognitive tutoring. Metacognition and Learning, 2(2-3), 125-140.
  7. Royer, J. M., & Walles, R. (2007). Influences of gender, motivation and socioeconomic status on mathematics performance. In D. B. Berch & M. Mazzocco (Eds.), Why is math so hard for some children (pp. 349- 368). Baltimore: Paul H. Brookes Publishing Co.
  8. Woolf, B. (2009). Building Intelligent Tutors: Bridging Theory and Practice. San Francisco: Elsevier Inc./

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Authors
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., & Tai, M.
Affiliations
  • Worcester Polytechnic Institute (US)
  • University of Massachusetts Amherst (US)
  • New York University (US)
Corresponding author
Arroyo, Ivon
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2014-12
Volume
24
Issue
4
Pages
387-426
ISSN
1560-4292 (ISSN-L)
OpenAlex
W2094147479
Cited by
263
References
106
Citations by year
2026: 13 · 2025: 36 · 2024: 25 · 2023: 36 · 2022: 31 · 2021: 22 · 2020: 22 · 2019: 18 · 2018: 17 · 2017: 19 · 2016: 11 · 2015: 12 · 2014: 1
FWCI
14.29
Type
journal article
Language
en
Open access
Locations
International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
Funders
  • National Science Foundation (1324385)
  • Office of Naval Research (N0001413C0127)
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 › Computer Science Applications › Online Learning and Analytics
Keywords
adaptive tutoring systems, mathematics education, metacognition, student engagement, student motivation, personalized learning, emotion sensing

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