Can computational talent be detected? Predictive validity of the Computational Thinking Test

摘要

Computational thinking (CT) is arising as a set of problem-solving skills that must be acquired by the new generations of students to fully understand and participate in our computer-based world. However, from a psychometric approach, we are still at an early stage regarding the definition and assessment of CT as a psychological variable. One way to advance in this area is to investigate whether 'computationally talented' students (i.e., 'computational top thinkers') can be detected even before learning to code; and, if so, how to teach them properly to fully develop their high-computational ability. This paper presents several empirical concatenated studies about the predictive validity of the Computational Thinking Test (CTt), which is administered on a sample of 314 middle school Spanish students (n = 314). We report the predictive validity of the CTt, conducted at the beginning of the quarter, with respect to academic performance (Informatics, Mathematics, and Language) and learning analytics in a Code.org course collected at the end of the quarter. We also analyze the predictive validity of the CTt to early distinguish between 'computational regular thinkers' and 'computational top thinkers' (i.e., those who spontaneously accelerated from the 'block-based' programming environment of Code.org to the 'text-based' one of Khan Academy). Finally, we perform a case study over two of the students categorized as 'computational top thinkers', in which one of their coding products written in Processing JavaScript is described. Our results demonstrate that 'computationally talented' students can be detected in middle school, and that these subjects have the ability to accelerate in the Computer Science Education standards between 1 and 2 years compared to the regular learners. This could have major implications on the emerging computing curricula, which should take into account these individual differences in computational ability and 'learning-how-to-code' speed to ensure an appropriate progression for every student.