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

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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
DOI 10.1007/s40593-014-0023-y
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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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Figures

Fig. 1 The Wayang Outpost Math Tutor interface. An animated companion provides individualized comments and support
Fig. 1 The Wayang Outpost Math Tutor interface. An animated companion provides individualized comments and supportCC-BY-NC-ND · From the PDF
Image 2
CC-BY-NC-ND · From the PDF
Fig. 2 Two math problem items that involve exactly the same mathematical procedure to solve, but have a different difficulty level -the one on the right is estimated to be more difficult than the one on the left
Fig. 2 Two math problem items that involve exactly the same mathematical procedure to solve, but have a different difficulty level -the one on the right is estimated to be more difficult than the one on the leftCC-BY-NC-ND · From the PDF
Fig. 3 One of the teacher reports highlights math skills (soon Common Core standards) that students in the class found challenging ( yellow ). When clicking on each skill (column 1), a new report is launched that highlights which individual problems involving that math skill were more or less challenging for students in this class
Fig. 3 One of the teacher reports highlights math skills (soon Common Core standards) that students in the class found challenging ( yellow ). When clicking on each skill (column 1), a new report is launched that highlights which individual problems involving that math skill were more or less challenging for students in this classCC-BY-NC-ND · From the PDF
Image 5
CC-BY-NC-ND · From the PDF
Fig. 5 Area chart comparison of performance for a 7th grade of students on the Massachusetts Comprehensive Assessment System (MCAS), for students using vs. not using Wayang Outpost. Students represented by the yellow/green polygon used Wayang Outpost and students represented by the blue polygon did not use the tutor. Distribution of students using Wayang Outpost shifts to the right indicating that more students passed the exam and received a grade of ' proficient ' or ' advanced ' when using Wayang Outpost. Groups of students were matched in terms of teacher of seventh grade students
Fig. 5 Area chart comparison of performance for a 7th grade of students on the Massachusetts Comprehensive Assessment System (MCAS), for students using vs. not using Wayang Outpost. Students represented by the yellow/green polygon used Wayang Outpost and students represented by the blue polygon did not use the tutor. Distribution of students using Wayang Outpost shifts to the right indicating that more students passed the exam and received a grade of ' proficient ' or ' advanced ' when using Wayang Outpost. Groups of students were matched in terms of teacher of seventh grade studentsCC-BY-NC-ND · From the PDF
Image 7
CC-BY-NC-ND · From the PDF
Fig. 7 Mean improvement (and standard deviations) on hardest items of the math pre/posttest. The thick line represents students who received both the Wayang Tutor and math facts retrieval training software; all other groups did not really improve on these harder multi-step items
Fig. 7 Mean improvement (and standard deviations) on hardest items of the math pre/posttest. The thick line represents students who received both the Wayang Tutor and math facts retrieval training software; all other groups did not really improve on these harder multi-step itemsCC-BY-NC-ND · From the PDF
Image 9
CC-BY-NC-ND · From the PDF
Image 10
CC-BY-NC-ND · From the PDF
Image 11
CC-BY-NC-ND · From the PDF
Fig. 11 a. Progress Charts in Wayang show students the accuracy of their answers. b . Tips in Wayang encourage good learning habits
Fig. 11 a. Progress Charts in Wayang show students the accuracy of their answers. b . Tips in Wayang encourage good learning habitsCC-BY-NC-ND · From the PDF
Image 13
CC-BY-NC-ND · From the PDF
Image 14
CC-BY-NC-ND · From the PDF
Image 15
CC-BY-NC-ND · From the PDF
Image 16
CC-BY-NC-ND · From the PDF
Fig. 16 Students reported their confidence before, during and after using the tutor. Low achieving students ( squares ) showed larger gains in confidence when the companions were available than did high achieving students ( circles ). High achieving students did not change much
Fig. 16 Students reported their confidence before, during and after using the tutor. Low achieving students ( squares ) showed larger gains in confidence when the companions were available than did high achieving students ( circles ). High achieving students did not change muchCC-BY-NC-ND · From the PDF

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Citations

Cites

Research works printed but not linked: 4

  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. 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). · DOI
  4. Ogan, 2011, Pervasive social effects of agent-based instructional dialog, Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED 2011), 238

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Metadata

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
ORCID
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)
DOI
10.1007/s40593-014-0023-y
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
Update notices
Open access
Licence
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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