Individual differences in the process of relational reasoning. (April 2016)

• Record Type: Journal Article
• Authors: Grossnickle, Emily M.; Dumas, Denis; Alexander, Patricia A.; Baggetta, Peter
• Is Part Of: Learning and instruction. Volume 42(2016:Apr.)
• Page Start: 141
• Page End: 159
• Publication Date: 2016-04
• Subjects: Relational reasoning -- Bayesian network analysis -- Componential model -- Analogical reasoning -- Working memory; Learning -- Periodicals; Teaching -- Periodicals; Apprentissage -- Périodiques; Enseignement -- Périodiques; Learning; Teaching; Periodicals; Electronic journals; 370.1
• DOI: 10.1016/j.learninstruc.2016.01.013
• Languages: English
• ISSNs: 0959-4752

Abstract

Abstract: The ability to discern meaningful patterns—relational reasoning—has been identified as a process important for student learning and cognition. Yet, research has typically investigated performance over processing, particularly when examining the role of factors such as working memory capacity. Moreover, studies have focused on analogical reasoning to the exclusion of other identified relational forms (i.e., anomaly, antinomy, antithesis). Study 1 investigates the role of individual differences in relational reasoning performance across four relational forms. Then, Study 2 identifies the highest and lowest-performing students from Study 1 to examine the probability that undergraduate students reach each of four sequential component processes of reasoning and the degree to which significant individual differences from Study 1 (i.e., visuospatial working memory) play a role in each process. Results indicate that low performing students experience particular difficulties in identifying relevant inferences and in mapping those inferences. This was due in part to the relation between working memory capacity and the processes of inferring and mapping. The outcomes from this study contribute to understandings of the sources of success and failure in reasoning for students at different levels, and identify potential entry points for intervention research. Highlights: Bayesian network analyses examined how relational reasoning processes sequentially unfold for multiple forms of relations. Low-performing students experienced difficulty identifying inferences and mapping higher-order relations. Working memory predicted relational reasoning performance and was critical during inferring and mapping phases. Results identify possible entry points for improving relational reasoning in students.