An analysis of learner outputs in problem posing as sentence-integration in arithmetic word problems.

Area of Science:

• Educational Technology
• Cognitive Science
• Mathematics Education

Background:

• Learning by problem posing offers significant educational benefits but faces challenges with assessment efficiency.
• Traditional methods require substantial time for evaluating student-generated problems and providing feedback.
• Tablet PC-based software presents a potential solution to streamline this process.

Purpose of the Study:

• To investigate the effectiveness of Monsakun, a tablet PC-based software, in facilitating learning by posing arithmetic word problems.
• To analyze the problem-posing behaviors of elementary school students using Monsakun.
• To determine if the software encourages students to think about the underlying structure of arithmetic word problems.

Main Methods:

• Development of Monsakun software based on the Triplet Structure Model of arithmetic word problems.
• Observational study of elementary school students posing arithmetic word problems using the Monsakun software.
• Analysis of student-posed problems to identify patterns, errors, and structural considerations.

Main Results:

• Students using Monsakun did not pose problems randomly; they demonstrated prior consideration of problem elements.
• Identified frequent errors were meaningful, suggesting students attempted to satisfy multiple constraints.
• Students actively engaged with understanding assignment requirements and relating them to structural components (sentence cards).

Conclusions:

• Monsakun effectively supports learning by problem posing in arithmetic, addressing efficiency concerns.
• The software encourages students to think critically about the structure of arithmetic word problems.
• The process of understanding requirements and mapping them to problem components is crucial for deep learning, especially for young students.