A Psychological Network Analysis to Examine Interdependencies Between Fraction and Algebra Subtopics in an Intelligent Tutoring System.
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 edu...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 4; pp. 1 - 18 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186918288&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186918288 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2025 vid: 41 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 186918288 186918288 186918288 10.1111/jcal.70093 186918288 ppf: 1 ppct: 17 formats: tig: atl: A Psychological Network Analysis to Examine Interdependencies Between Fraction and Algebra Subtopics in an Intelligent Tutoring System. aug: au: Spitzer, Markus W. H. Bardach, Lisa Richter, Eileen Strittmatter, Younes Moeller, Korbinian affil: Martin‐Luther University Halle‐Wittenberg, Halle, Germany sug: subj: Academic Performance Evaluation Mathematics Methods Psychology, Social Social Network Analysis Intelligent Systems Computer-Assisted Instruction Human Funding Source Netherlands Germany Uruguay Correlational Studies Reading Learning Methods Data Analysis Software Descriptive Statistics Confidence Intervals Students Psychosocial Factors ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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