Cross-grade Comparison of Computational Thinking in Young Children Using Normalized Unplugged Assessment Scores
Emily Relkin · Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2 · 2022
The acquisition of Computational Thinking (CT) skills is an essential goal of Computer Science (CS) education. Assessment of CT is critical for CS teaching and research. However, there is a paucity of developmentally appropriate CT measures designed for early elementary school children. Among existing measures, there has been little attention paid to methods for comparing CT skills across grades. In the present study, I examined the use of a normalized scoring system for comparing performance on three grade-specific versions of the TechCheck CT assessment. TechCheck is a brief, unplugged, multiple-choice instrument for children 5-9 years of age that probes six domains of CT. Baseline raw score TechCheck data from 596 students showed normal distributions with significant differences in means between grades. I calculated Z-scores and percentile ranks by grade. This normalization allowed for the comparison of CT skills within and across grades in the context of a pilot longitudinal study of the CAL-ScratchJr coding curriculum. Application of the normative scoring system showed a difference in the outcome of only 4 percentile ranks between first and second graders while raw scores indicated an improbable 45% difference. This study demonstrates that normalized TechCheck scores that take into account differences in the score distributions across grades can improve the measurement of CT skills in the context of early childhood CS educational interventions.