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AnalyticalInk: An Interactive Learning Environment for Math Word Problem Solving

Authors:

Arun Kulshreshth,

Joseph J. LaViola, Jr.Authors Info & Claims

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages 419 - 430

https://doi.org/10.1145/2856767.2856789

Published: 07 March 2016 Publication History

Abstract

We present AnalyticalInk, a novel math learning environment prototype that uses a semantic graph as the knowledge representation of algebraic and geometric word problems. The system solves math problems by reasoning upon the semantic graph and automatically generates conceptual and procedural scaffoldings in sequence. We further introduces a step-wise tutoring framework, which can check students' input steps and provide the adaptive scaffolding feedback. Based on the knowledge representation, AnalyticalInk highlights keywords that allow users to further drag them onto the workspace to gather insight into the problem's initial conditions. The system simulates a pen-and-paper environment to let users input both in algebraic and geometric workspaces. We conducted an usability evaluation to measure the effectiveness of AnalyticalInk. We found that keyword highlighting and dragging is useful and effective toward math problem solving. Answer checking in the tutoring component is useful. In general, our prototype shows the promise in helping users to understand geometrical concepts and master algebraic procedures under the problem solving.

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References

[1]

Aleven, V., McLaren, B., Roll, I., and Koedinger, K. Toward tutoring help seeking. In Intelligent Tutoring Systems, J. Lester, R. Vicari, and F. Paraguau, Eds., vol. 3220 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004, 227--239.

[2]

Aleven, V., Mclaren, B. M., Sewall, J., and Koedinger, K. R. A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education 19, 2 (2009), 105--154.

Digital Library

[3]

Anderson, J. R., Corbett, A. T., Koedinger, K. R., and Pelletier, R. Cognitive tutors: Lessons learned. The journal of the learning sciences 4, 2 (1995), 167--207.

[4]

Anthony, L., and Wobbrock, J. O. A lightweight multistroke recognizer for user interface prototypes. In Proceedings of Graphics Interface 2010, GI '10, Canadian Information Processing Society (Toronto, Ont., Canada, Canada, 2010), 245--252.

Digital Library

[5]

Anthony, L., Yang, J., and Koedinger, K. R. A paradigm for handwriting-based intelligent tutors. Int. J. Hum.-Comput. Stud. 70, 11 (Nov. 2012), 866--887.

Digital Library

[6]

Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., and Schneider, N. Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse (August 2013).

[7]

Corbett, A. T., and Anderson, J. R. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4 (1994), 253--278.

[8]

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., and Willingham, D. T. Improving students learning with effective learning techniques promising directions from cognitive and educational psychology. Psychological Science in the Public Interest 14, 1 (2013), 4--58.

[9]

Fillmore, C. J. The need for a frame semantics in linguistics. Scriptor, 1977.

[10]

Forbus, K. D., Ferguson, R. W., and Usher, J. M. Towards a computational model of sketching. In Proceedings of the 6th international conference on Intelligent user interfaces, ACM (2001), 77--83.

Digital Library

[11]

Ford, B. Parsing expression grammars: a recognition-based syntactic foundation. In ACM SIGPLAN Notices, vol. 39, ACM (2004), 111--122.

Digital Library

[12]

González-Brenes, J. P., and Huang, Y. Your model is predictivebut is it useful? theoretical and empirical considerations of a new paradigm for adaptive tutoring evaluation. In Proceedings of the 8th Intl. Conf. on Educational Data Mining (2015).

[13]

Gulwani, S., Korthikanti, V. A., and Tiwari, A. Synthesizing geometry constructions. In ACM SIGPLAN Notices, vol. 46, ACM (2011), 50--61.

Digital Library

[14]

Heffernan, N. T., and Koedinger, K. R. The composition effect in symbolizing: The role of symbol production vs. text comprehension. In Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates Hillsdale, NJ (1997), 307--312.

[15]

Heffernan, N. T., Koedinger, K. R., and Razzaq, L. Expanding the model-tracing architecture: A 3rd generation intelligent tutor for algebra symbolization. International Journal of Artificial Intelligence in Education 18, 2 (2008), 153.

Digital Library

[16]

Ilany, B.-S., Margolin, B., et al. Language and mathematics: Bridging between natural language and mathematical language in solving problems in mathematics. Creative Education 1, 03 (2010), 138.

[17]

Jiang, Y., Tian, F., Wang, H., Zhang, X., Wang, X., and Dai, G. Intelligent understanding of handwritten geometry theorem proving. In Proceedings of the 15th international conference on Intelligent user interfaces, ACM (2010), 119--128.

Digital Library

[18]

Koedinger, K. R., Anderson, J. R., Hadley, W. H., and Mark, M. A. Intelligent tutoring goes to school in the big city.

[19]

Koedinger, K. R., Booth, J. L., and Klahr, D. Instructional complexity and the science to constrain it. Science 342, 6161 (2013), 935--937.

[20]

Koedinger, K. R., Brunskill, E., Baker, R. S., McLaughlin, E. A., and Stamper, J. New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine 34, 3 (2013), 27--41.

Digital Library

[21]

Koedinger, K. R., and Nathan, M. J. The real story behind story problems: Effects of representations on quantitative reasoning. The Journal of the Learning Sciences 13, 2 (2004), 129--164.

[22]

Kolodner, J. Case-based reasoning. Morgan Kaufmann, 2014.

[23]

Kushman, N., Artzi, Y., Zettlemoyer, L., and Barzilay, R. Learning to automatically solve algebra word problems. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics (Baltimore, Maryland, June 2014), 271--281.

[24]

LaViola, Jr., J. J., and Zeleznik, R. C. Mathpad2: A system for the creation and exploration of mathematical sketches. In ACM SIGGRAPH 2004 Papers, SIGGRAPH '04, ACM (New York, NY, USA, 2004), 432--440.

Digital Library

[25]

Oviatt, S. Interfaces for thinkers: Computer input capabilities that support inferential reasoning. In Proceedings of the 15th ACM on International Conference on Multimodal Interaction, ICMI '13, ACM (New York, NY, USA, 2013), 221--228.

Digital Library

[26]

Paquette, L., Lebeau, J.-F., Beaulieu, G., and Mayers, A. Designing a knowledge representation approach for the generation of pedagogical interventions by mtts. International Journal of Artificial Intelligence in Education 25, 1 (2015), 118--156.

[27]

Paulson, B., and Hammond, T. Paleosketch: accurate primitive sketch recognition and beautification. In Proceedings of the 13th international conference on Intelligent user interfaces, ACM (2008), 1--10.

Digital Library

[28]

Rittle-Johnson, B., Siegler, R. S., and Alibali, M. W. Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of educational psychology 93, 2 (2001), 346.

[29]

Russell, S., and Norvig, P. Artificial intelligence: a modern approach.

Digital Library

[30]

Schoenfeld, A. H. Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. Handbook of research on mathematics teaching and learning (1992), 334--370.

[31]

Seo, M. J., Hajishirzi, H., Farhadi, A., Etzioni, O., and Malcolm, C. Solving geometry problems: Combining text and diagram interpretation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17--21, 2015 (2015), 1466--1476.

[32]

Vanlehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., and Wintersgill, M. The andes physics tutoring system: Lessons learned. Int. J. Artif. Intell. Ed. 15, 3 (Aug. 2005), 147--204.

Digital Library

[33]

Zeleznik, R., Bragdon, A., Adeputra, F., and Ko, H.-S. Hands-on math: A page-based multi-touch and pen desktop for technical work and problem solving. In Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology, UIST '10, ACM (New York, NY, USA, 2010), 17--26.

Digital Library

[34]

Zeleznik, R., Miller, T., Li, C., and Laviola Jr, J. J. Mathpaper: Mathematical sketching with fluid support for interactive computation. In Smart Graphics, Springer (2008), 20--32.

Digital Library

[35]

Zhou, L., Dai, S., and Chen, L. Learn to solve algebra word problems using quadratic programming. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (Lisbon, Portugal, September 2015), 817--822.

Cited By

Shi CHu YWang SMa SZheng CMa XLuo Q(2023)RetroLens: A Human-AI Collaborative System for Multi-step Retrosynthetic Route PlanningProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581469(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581469
Rodrigues LPereira FMarinho MMacario VBittencourt IIsotani SDermeval DMello R(2023)Mathematics intelligent tutoring systems with handwritten input: a scoping reviewEducation and Information Technologies10.1007/s10639-023-12245-y29:9(11183-11209)Online publication date: 18-Oct-2023
https://doi.org/10.1007/s10639-023-12245-y
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Index Terms

AnalyticalInk: An Interactive Learning Environment for Math Word Problem Solving
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

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Published In

cover image ACM Conferences

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

March 2016

446 pages

ISBN:9781450341370

DOI:10.1145/2856767

General Chairs:
Jeffrey Nichols
Google Inc, USA
,
Jalal Mahmud
IBM Research, USA
,
John O'Donovan
UC Santa Barbara, USA
,
Program Chairs:
Cristina Conati
University of British Columbia, Canada
,
Massimo Zancanaro
Bruno Kessler Foundation, Italy

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 March 2016

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IUI'16

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IUI'16: 21st International Conference on Intelligent User Interfaces

March 7 - 10, 2016

California, Sonoma, USA

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IUI '16 Paper Acceptance Rate 49 of 194 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Cited By

Shi CHu YWang SMa SZheng CMa XLuo Q(2023)RetroLens: A Human-AI Collaborative System for Multi-step Retrosynthetic Route PlanningProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581469(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581469
Rodrigues LPereira FMarinho MMacario VBittencourt IIsotani SDermeval DMello R(2023)Mathematics intelligent tutoring systems with handwritten input: a scoping reviewEducation and Information Technologies10.1007/s10639-023-12245-y29:9(11183-11209)Online publication date: 18-Oct-2023
https://doi.org/10.1007/s10639-023-12245-y
Liu QJia XYang WTu FWu L(2021)Research on Entity Relation Extraction Based on BiLSTM-CRF Classical Probability Word ProblemsProceedings of the 13th International Conference on Education Technology and Computers10.1145/3498765.3498775(62-68)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3498765.3498775
Huang JLiu QDing YZheng YWu LHuang L(2020)Integrating Mental Models into Intelligent Tutoring Systems for Solving Random Sampling Word Problems2020 Ninth International Conference of Educational Innovation through Technology (EITT)10.1109/EITT50754.2020.00036(163-169)Online publication date: Dec-2020
https://doi.org/10.1109/EITT50754.2020.00036
Morais FBittencourt IIsotani SJaques P(2017)The Use of Handwriting Input in Math Tutoring Systems: An Use Case with PAT2Math2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2017.142(44-46)Online publication date: Jul-2017
https://doi.org/10.1109/ICALT.2017.142
Kang BLaViola JWisniewski P(2017)Examining Interaction Modality Effects Toward Engagement in an Interactive Learning EnvironmentData Driven Approaches in Digital Education10.1007/978-3-319-66610-5_8(97-110)Online publication date: 5-Sep-2017
https://doi.org/10.1007/978-3-319-66610-5_8

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