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.
Highlights
Previous work has studied the relationship between performance in mathematics problem-solving and reading comprehension in students of all educational levels [1,2,3]
Reading times were rather dispersed in our group of students, as shown by the high standard deviations in Table 3
We have presented a novel proposal to measure the complexity of an AWP through the student reading comprehension of its statement
Summary
Previous work has studied the relationship between performance in mathematics problem-solving and reading comprehension in students of all educational levels [1,2,3]. Authors such as Pólya [4] and. Our research is framed within the context of arithmetic word problems ( on AWPs or AWP in singular) and focuses on how to measure the complexity of the statements involved To this end, we computed the reading comprehension of students through a technological environment and use learning analytics to predict student performance in solving this sort of problems
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