Rina PY Lai’s research while affiliated with University of Cambridge and other places

Computational thinking (CT) is an emerging and multifaceted competence important to the computing era. However, despite the growing consensus that CT is a competence domain, its theoretical and empirical account remain scarce in the current literature. To address this issue, rigorous psychometric evaluation procedures were adopted to investigate the structure of CT competency, as measured by Computational Thinking Challenge (Lai, 2021a), in a large sample of 1,130 British secondary school students ( M age = 14.14 years, SD age = 1.45). Based on model comparison from an exploratory multidimensional item response theory approach, the results supported the multidimensional operationalization of CT competency. A confirmatory bi-factor item response theory model further suggested CT competency is comprised of a general CT competency factor and two specific factors for programming and non-programming problem-solving. Despite the multidimensionality, the common variance is largely explained by a primary general factor of CT competency, thus the use of a single scale score is recommended. Psychometric evaluation from the bi-factor model indicated good psychometric properties of the assessment tool. Overall, the bi-factor model provides a useful approach to investigating CT competency and serves as a robust test validation tool.

Computational thinking (CT) is an emerging and multifaceted competence important tothe computing era. However, despite the growing consensus that CT is a competence domain, its theoretical and empirical account remain scarce in the current literature. To address this issue, rigorous psychometric evaluation procedures were adopted to investigate the structure of CT competency, as measured by Computational Thinking Challenge (Lai, 2021b), in a large sample of 1,130 British secondary school students (Mage = 14.14 years, SDage = 1.45). Based on model comparison from an exploratory multidimensional item response theoryapproach, the results supported the multidimensional operationalization of CT competency. A confirmatory bi-factor item response theory model further suggested CT competency is comprised of a general CT competency factor and two specific factors for programming and non-programming problem-solving. Despite the multidimensionality, the common variance is largely explained by a primary general factor of CT competency, thus the use of a single scale score is recommended. Psychometric evaluation from the bi-factor model indicated good psychometric properties of the assessment tool. Overall, the bi-factor model provides a useful approach to investigating CT competency and serves as a robust test validation tool.

Executive function (EF) and metacognition are two cognitive predictors with well-established connections to academic performance. Despite sharing several theoretical characteristics, their overlap or independence concerning multiple academic outcomes remain under-researched. To address this gap, the present study applies a latent-variable approach to test a novel theoretical model that delineates the structural link between EF, metacognition, and academic performance. In whole-class sessions, 469 children aged 9 to 14 years (M = 11.93; SD = 0.92) completed four computerized EF tasks (inhibition, working memory, cognitive flexibility, and planning), a metacognition questionnaire, and four standardized tests of academic ability. There is a small effect size negative correlation between EF and metacognition in this sample, but they have both shared and unique contributions to academic performance. The findings are important for elucidating the role between two domain-general cognitive skills (EF and metacognition) and domain-specific academic skills.