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Intelligent Math Tutor: Problem-Based Approach to Create Cognizance

Published: 12 April 2017 Publication History

Abstract

Mathematical word problems (or story problems) allow students to apply their mathematical problem solving ability to other subjects and real-world situations. Word problems build higher-order thinking, critical problem-solving, and reasoning skills. Generally solving a word problem is associated with mathematical modeling of a real word situation or a concept of another subject which is embedded in the problem. Manually creating word problems require knowledge of other topics a student is learning in parallel. Besides this, modeling mathematics with some other dissociated concept is a time-consuming and labor-intensive task. Due to lack of this integrated knowledge of other topics being taught, the substantive breadth of word problems is often very narrow and is limited to very few concepts. To address this limitation, we built a tool called Intelligent Math Tutor (IMT), which automatically generates mathematical word problems such that teachings from other subjects from a given curriculum can also be incorporated. Our tool thus widens the scope of word problems and uses this problem-solving based approach to indirectly create cognizance in its students. To the best of our knowledge, our tool is the first of its kind tool which explicitly blends knowledge from multiple dissociated subjects and uses it to enhance the cognizance of its learners.

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Joseph M. Furner and David D. Kumar. The Mathematics and Science Integration Argument: A Stand for Teacher Education. Eurasia Journal of Mathematics, Science & Technology Education.
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Frykholm, J., & Glasson, G. (2005). Connecting science and mathematics instruction: Pedagogical context knowledge for teachers. School Science and Mathematics, 105 (3), 127--14
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Koirala, H. P., & Bowman, J. K. (2003). Preparing middle level preservice teachers to integrate mathematics and science: Problems and possibilities. School Science and Mathematics, 145(10), 145--154.
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Andrew McGregor Olney, Arthur C. Graesser, Natalie K. Person: Question Generation from Concept Maps. Dialogs and Discourse, Vol 3(2): 75--99 (2012)
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Heilman, M. and Smith, N. A. (2010b). Good Question! Statistical Ranking for Question Generation. In HLT-NAACL 2010, pages 609--617
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Danqi Chen and Christopher Manning. 2014. A Fast and Accurate Dependency Parser Using Neural Networks. In Proceedings of EMNLP 2014.
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Deane, Paul; Sheehan, Kathleen. Automatic Item Generation via Frame Semantics: Natural Language Generation of Math Word Problems. Annual Meeting of the National Council on Measurement in Education (Chicago, IL, April 22--24, 2003).
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Cited By

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  • (2024)Contextualized and Personalized Math Word Problem Generation in Authentic Contexts Using Generative Pre-trained Transformer and Its Influences on Geometry LearningJournal of Educational Computing Research10.1177/07356331241249225Online publication date: 29-May-2024
  • (2024)Using GPT and authentic contextual recognition to generate math word problems with difficulty levelsEducation and Information Technologies10.1007/s10639-024-12537-x29:13(1-29)Online publication date: 2-Mar-2024
  • (2019)Exploring the Needs and Interests of Fifth Graders for Personalized Math Word Problem GenerationProceedings of the 18th ACM International Conference on Interaction Design and Children10.1145/3311927.3325309(592-597)Online publication date: 12-Jun-2019
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  1. Intelligent Math Tutor: Problem-Based Approach to Create Cognizance

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    cover image ACM Conferences
    L@S '17: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
    April 2017
    352 pages
    ISBN:9781450344500
    DOI:10.1145/3051457
    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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    New York, NY, United States

    Publication History

    Published: 12 April 2017

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    Author Tags

    1. concept mapping
    2. integrated curriculum
    3. intelligent math tutor
    4. mathematical word problems

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    L@S 2017
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    L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale
    April 20 - 21, 2017
    Massachusetts, Cambridge, USA

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    L@S '17 Paper Acceptance Rate 14 of 105 submissions, 13%;
    Overall Acceptance Rate 117 of 440 submissions, 27%

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    Cited By

    View all
    • (2024)Contextualized and Personalized Math Word Problem Generation in Authentic Contexts Using Generative Pre-trained Transformer and Its Influences on Geometry LearningJournal of Educational Computing Research10.1177/07356331241249225Online publication date: 29-May-2024
    • (2024)Using GPT and authentic contextual recognition to generate math word problems with difficulty levelsEducation and Information Technologies10.1007/s10639-024-12537-x29:13(1-29)Online publication date: 2-Mar-2024
    • (2019)Exploring the Needs and Interests of Fifth Graders for Personalized Math Word Problem GenerationProceedings of the 18th ACM International Conference on Interaction Design and Children10.1145/3311927.3325309(592-597)Online publication date: 12-Jun-2019
    • (2019)A Systematic Review of Automatic Question Generation for Educational PurposesInternational Journal of Artificial Intelligence in Education10.1007/s40593-019-00186-yOnline publication date: 21-Nov-2019
    • (2018)Modeling Learners' Interest with a Domain-Independent Ontology-Based FrameworkProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3213598(345-348)Online publication date: 3-Jul-2018
    • (2018)Generating story problems via controlled parameters in a web-based intelligent tutoring systemInternational Journal of Information and Learning Technology10.1108/IJILT-09-2017-008535:3(199-216)Online publication date: 4-Jun-2018

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