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ERIC Number: EJ1478149
Record Type: Journal
Publication Date: 2025-Aug
Pages: 17
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0266-4909
EISSN: EISSN-1365-2729
Available Date: 2025-07-08
A Psychological Network Analysis to Examine Interdependencies between Fraction and Algebra Subtopics in an Intelligent Tutoring System
Markus W. H. Spitzer1; Lisa Bardach2; Eileen Richter1; Younes Strittmatter3; Korbinian Moeller4
Journal of Computer Assisted Learning, v41 n4 e70093 2025
Background: Many students face difficulties with algebra. At the same time, it has been observed that fraction understanding predicts achievements in algebra; hence, gaining a better understanding of how algebra understanding builds on fraction understanding is an important goal for research and educational practice. Objectives: However, a wide range of algebra subtopics (e.g., "Using formulas" and "Simplifying products in formulas") and fraction subtopics (e.g., "Adding and subtracting fractions," "Multiplying and dividing fractions") exist, and little is known about which specific fraction subtopics matter most for (i.e., best predict) which specific algebra subtopics. In addition to addressing across-topic subtopic correlations, a comprehensive understanding of within-topic subtopic correlations (i.e., among fraction subtopics and algebra topics, respectively) has not yet been achieved. Methods: Here, we leveraged a large data set (3158 students; 257,321 problem sets) from an intelligent tutoring system (ITS) and employed state-of-the-art psychological network analysis to visualise and quantify interdependencies between students' performance on different fractions and algebra subtopics. Results and Conclusions: We observed one robust correlation between a specific fraction and a specific algebra subtopic ("Fractions and the order of operations and Using formulas"). In addition, a larger number of within-topic subtopic correlations were observed. Importantly, cross-topic correlations and most within-topic correlations seemed to be driven by shared mathematical components (e.g., multiplication, operating rules or reading comprehension). Our findings advance the current understanding of mathematics learning and have implications for the design and improvement of ITSs, such as for developing automatic suggestions on which other subtopics to work on when a student encounters difficulties with a specific subtopic. Moreover, our study highlights the potential of psychological network analysis for analysing learning data from ITSs.
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: 1Martin-Luther University Halle-Wittenberg, Halle, Germany; 2Department of Psychology, University of Giessen, Giessen, Germany; 3Department of Psychology, Princeton University, Princeton, USA; 4Loughborough University, Loughborough, UK