# Computational Thinking Measurement of CS University Students
## 6 Conclusions  
This study significantly advances the understanding of how to measure differences in computational thinking (CT) and programming abilities among computer science (CS) students by geography, gender, and university level. The innovative UniCTcheck method, incorporating the CTScore web application and CTProg psychometric scale, effectively evaluates seven fundamental CT components and six critical programming concepts.  
UniCTcheck Effectiveness: UniCTcheck was validated as a robust framework for assessing CT components and programming skills in CS students. The comprehensive evaluation demonstrated its effectiveness in measuring essential skills such as pattern recognition, algorithmic thinking, and problem solving, as well as programming concepts including loops, functions, and data structures.  
Relationship Between CT Skills and Programming Abilities: A significant correlation was found between students' CT skills and programming abilities. Higher performance in the CTProg programming test was directly related to better scores in the CTScore assessment, indicating that strong programming knowledge facilitates CT skills acquisition.  
Variation by University Level: The study observed minimal differences in CT skills and programming abilities across university levels. However, slight increases in median scores for advanced programming concepts were noted in fourth-year students, particularly at Rey Juan Carlos University (URJC), where there was an increased dispersion in both the CTscore and CTProg results.  
Variation by University Location: Significant differences were found between universities, with URJC students outperforming those at Atlantic Technological University (ATU) on both CTScore and CTProg. URJC students displayed higher mean and median scores with a lower dispersion. As explained in the limitation section, this may be due to the difference in admission requirements in between the two programmes.  
Variation by Gender: No statistically significant differences were found in CT skills and programming abilities between genders. Both male and female students demonstrated similar levels of competency, indicating that these skills are not inherently gender-specific.  
In summary, this study provides valuable insights into the factors influencing CS students' CT and programming abilities. The use of UniCTcheck, CTScore, and CTProg offers a comprehensive assessment framework that can inform tailored educational interventions to enhance students' computational competencies effectively.  
**Author Contributions:** Conceptualisation, R.H.-N., C.P., J.F., D.P.-A. and E.C.; methodology, R.H.-N., C.P., J.F., D.P.-A. and E.C.; software, R.H.-N., D.P.-A. and E.C.; validation, R.H.-N., C.P., J.F. and E.C.; formal analysis, R.H.-N. and C.P.; investigation, R.H.-N., C.P. and J.F.; resources, R.H.-N., D.P.-A. and E.C.; data curation, R.H.-N. and C.P.; writing--original draft preparation, R.H.-N. and C.P.; writing--review and editing, R.H.-N., C.P. and J.F.; visualisation, R.H.-N., C.P., D.P.-A. and E.C.; supervision, R.H.-N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by research grants PID2022-137849 OB-I00 funded by MI-CIU/AEI/10.13039/501100011033 and by the ERDF, EU.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by Research Ethics Committee of Rey Juan Carlos University with internal registration number 190920233223, 9 October 2023.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author due to ethical reasons.  
The authors declare no conflicts of interest.