Are systemizing and autistic traits related to talent and interest in mathematics and engineering? Testing some of the central claims of the empathizing-systemizing theory.

In two experiments, we tested some of the central claims of the empathizing-systemizing (E-S) theory. Experiment 1 showed that the systemizing quotient (SQ) was unrelated to performance on a mathematics test, although it was correlated with statistics-related attitudes, self-efficacy, and anxiety. In Experiment 2, systemizing skills, and gender differences in these skills, were more strongly related to spatial thinking styles than to SQ. In fact, when we partialled the effect of spatial thinking styles, SQ was no longer related to systemizing skills. Additionally, there was no relationship between the Autism Spectrum Quotient (AQ) and the SQ, or skills and interest in mathematics and mechanical reasoning. We discuss the implications of our findings for the E-S theory, and for understanding the autistic cognitive profile.

Number comparison and number ordering as predictors of arithmetic performance in adults: Exploring the link between the two skills, and investigating the question of domain-specificity.

Recent evidence has highlighted the important role that number ordering skills play in arithmetic abilities (e.g., Lyons & Beilock, 2011). In fact, Lyons et al. (2014) demonstrated that although at the start of formal mathematics education number comparison skills are the best predictors of arithmetic performance, from around the age of 10, number ordering skills become the strongest numerical predictors of arithmetic abilities. In the current study we demonstrated that number comparison and ordering skills were both significantly related to arithmetic performance in adults, and the effect size was greater in the case of ordering skills. Additionally, we found that the effect of number comparison skills on arithmetic performance was partially mediated by number ordering skills. Moreover, performance on comparison and ordering tasks involving the months of the year was also strongly correlated with arithmetic skills, and participants displayed similar (canonical or reverse) distance effects on the comparison and ordering tasks involving months as when the tasks included numbers. This suggests that the processes responsible for the link between comparison and ordering skills and arithmetic performance are not specific to the domain of numbers. Finally, a factor analysis indicated that performance on comparison and ordering tasks loaded on a factor which included performance on a number line task and self-reported spatial thinking styles. These results substantially extend previous research on the role of order processing abilities in mental arithmetic.

An experiment in group learning technology: evaluating critical thinking in face-to-face and computer-supported seminars

Can learning quality be maintained in the face of increasing class size by the use of Computer Supported Co-operative Learning (CSCL) technologies? In particular, can Computer-Mediated Communication promote critical thinking in addition to surface information transfer? We compared face-to-face seminars with asynchronous computer conferencing in the same Information Management class. From Garrison's theory of critical thinking and Henri's critical reasoning skills, we developed two ways of evaluating critical thinking: a student questionnaire and a content analysis technique. We found evidence for critical thinking in both situations, with some subtle differences in learning style. This paper provides an overview of this work.

A content analysis method to measure critical thinking in face-to-face and computer supported group learning

This paper gives a detailed account of the content analysis method developed at Queen's University Belfast to measure critical thinking during group learning, as used in our controlled comparisons between learning in face-to-face and computer conference seminars. From Garrison's 5 stages of critical thinking, and Henri's cognitive skills needed in CMC, we have developed two research instruments: a student questionnaire and this content analysis method. The content analysis relies on identifying, within transcripts, examples of indicators of obviously critical and obviously uncritical thinking, from which several critical thinking ratios can be calculated.