APPLICATION OF KNOWLEDGE SPACE THEORY-BASED ADAPTIVE LEARNING PLATFORM (ALEKS) FOR IMPROVING THE STUDENTS’ MATHEMATICAL PROFICIENCY (THE CASE OF STAMFORD INTERNATIONAL UNIVERSITY)

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Anastasia Maga
Kristian Luciani

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This research addresses the value of Knowledge Space Theory (KST) based on the teaching of computational subjects to business students. The research is a quasi-experiment involving an action implemented at Stamford International University, Thailand. A KST-based adaptive learning platform (ALEKS) was introduced in teaching mathematics at undergraduate level of business education. This paper seeks to answer the question of the new platform’s efficiency in preparing students for subsequent computational courses, and especially whether this relationship is strong for underperforming students. The action thus held at Stamford International University involved a sample of 340 students studying mathematics in the academic year 2018-2019, three trimesters in total, who either took a Pre-college Algebra course powered by ALEKS (treatment group) or did mandatory revision/preparatory tests (control group), which were not based on KST-tools. We analyzed their further grades and pass-fail rates in the subsequently taken subject of College Algebra. We find the action to be successful, the application of KST-based learning in mathematics significantly improves all students’ performance in subsequent computational subjects, but this relationship proves to be stronger for underperforming students. The work connects with the stream of literature on the efficiency of the use of KST-based tools in teaching and learning computational subjects at the college level.

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