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Measuring Arithmetic Word Problem Complexity through Reading Comprehension and Learning Analytics

Author

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  • Maria T. Sanz

    (Department of Didactics of Mathematics, Universitat de València, Av. Tarongers 4, 46022 València, Spain)

  • Emilia López-Iñesta

    (Department of Didactics of Mathematics, Universitat de València, Av. Tarongers 4, 46022 València, Spain)

  • Daniel Garcia-Costa

    (Computer Science Department, Universitat de València, Av. de la Universitat s/n, 46100 Burjassot, Spain)

  • Francisco Grimaldo

    (Computer Science Department, Universitat de València, Av. de la Universitat s/n, 46100 Burjassot, Spain)

Abstract

Numerous studies have addressed the relationship between performance in mathematics problem-solving and reading comprehension in students of all educational levels. This work presents a new proposal to measure the complexity of arithmetic word problems through the student reading comprehension of the problem statement and the use of learning analytics. The procedure to quantify this reading comprehension comprises two phases: (a) the division of the statement into propositions and (b) the computation of the time dedicated to read each proposition through a technological environment that records the interactions of the students while solving the problem. We validated our approach by selecting a collection of problems containing mathematical concepts related to fractions and their different meanings, such as fractional numbers over a natural number, basic mathematical operations with a natural whole or fractional whole and the fraction as an operator. The main results indicate that a student’s reading time is an excellent proxy to determine the complexity of both propositions and the complete statement. Finally, we used this time to build a logistic regression model that predicts the success of students in solving arithmetic word problems.

Suggested Citation

  • Maria T. Sanz & Emilia López-Iñesta & Daniel Garcia-Costa & Francisco Grimaldo, 2020. "Measuring Arithmetic Word Problem Complexity through Reading Comprehension and Learning Analytics," Mathematics, MDPI, vol. 8(9), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1556-:d:411524
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    References listed on IDEAS

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    1. Santiago Alonso-García & Inmaculada Aznar-Díaz & María-Pilar Cáceres-Reche & Juan-Manuel Trujillo-Torres & José-María Romero-Rodríguez, 2019. "Systematic Review of Good Teaching Practices with ICT in Spanish Higher Education. Trends and Challenges for Sustainability," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    2. Esther Garzón Artacho & Tomás Sola Martínez & José Luís Ortega Martín & José Antonio Marín Marín & Gerardo Gómez García, 2020. "Teacher Training in Lifelong Learning—The Importance of Digital Competence in the Encouragement of Teaching Innovation," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
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