Fraction arithmetic is a foundational skill in mathematics education, and identifying effective ways to assess and enhance this skill is crucial to promoting student success. Three Â鶹ÊÓƵ professors have been awarded a grant to develop an advanced machine learning tool to assess children's proficiency in fraction arithmetic.
The team behind the project includes Karl Kosko, Ph.D., and , both from the Â鶹ÊÓƵ School of Teaching, Learning and Curriculum Studies; and Qiang Guan, Ph.D., from the Â鶹ÊÓƵ Department of Computer Science. This collaborative effort, funded by a one-year, $101,929 grant from The Jaffe Foundation, will leverage the interdisciplinary strengths of the team.
Kosko, a prominent figure in the field of mathematics education, brings a wealth of knowledge in curriculum design and pedagogical strategies. Ferdig, known for his pioneering work at the intersection of technology and education, will contribute his expertise in lifespan development to ensure the tool aligns with the cognitive and developmental stages of young learners. Guan's deep understanding of artificial intelligence will be instrumental in designing the machine learning algorithms that will underpin the assessment tool.
The machine learning tool they are developing will have the potential to revolutionize how educators evaluate and support children's understanding of fraction arithmetic. By analyzing patterns and trends in students' responses, the tool will provide real-time insights into individual learning needs and progress. This will enable teachers to tailor their instruction more effectively, resulting in enhanced learning outcomes for students.
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