# Computational Thinking Measurement of CS University Students
## 4 Discussion  
This study offers a significant addition to the literature by providing a thorough examination of the methods for gauging the differences in students' programming and computational thinking abilities by geography, gender, and university level. This paper presents UniCTcheck, an innovative approach created to evaluate the fundamentals of computational thinking in computer science students. This study used two important tools, the web application CTscore and the psychometric scale CTProg, to measure seven fundamental aspects of computational thinking and six critical programming ideas for computer science students. As a result, the following five research questions were formulated:  
RQ1: Does UniCTcheck Measure the Main Components of Computational Thinking and Programming for CS University Students?  
The results from the study indicate that UniCTcheck effectively measures the main components of computational thinking and programming for computer science university students. Through the use of CTscore and CTProg, a comprehensive assessment of pattern recognition, creative thinking, algorithmic thinking, problem solving, critical thinking, decomposition, and abstraction, as well as programming concepts like basic directions and sequences, conditionals, loops, functions, and data structures was conducted. The data reveal that the UniCTcheck method provides a robust framework for evaluating students' computational thinking abilities and programming skills accurately.  
Figure 13: Box plots for the CTscore and CTProg total scores by genders at both universities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CTScore**} & \multicolumn{3}{c}{**CTProg**} \\ \hline  & **Other** & **Female** & **Male** & **Other** & **Female** & **Male** \\ \hline n & 2 & 35 & 140 & 2 & 35 & 140 \\ Mean & 6.07 & 6.83 & 7.14 & 7.70 & 7.69 & 7.87 \\ Median & 6.07 & 7.14 & 7.14 & 7.70 & 7.83 & 8.04 \\ SD & 0.50 & 1.45 & 1.80 & 0.19 & 1.29 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Descriptive analysis for CTProg and CTscore by gender.  
RQ2: Is There a Relationship between Students' Computational Thinking Skills and Programming Abilities?  
The time expended by students in answering the programming test (CTProg) was directly related to their higher performance on it. Also, students that did well on the CTProg also did well on the CT skills test (CTScore); thus, it can be said that programming knowledge leads to CT skills acquisition. It has also been proved that students learning any type of loop leads to their learning of conditionals, and vice versa. Furthermore, students that know of more advanced programming concepts such as arrays need to have previously mastered conditionals and loops. Moreover, the comprehension of two-dimensional arrays (matrices) required the knowledge and understanding of one-dimensional arrays and for loop structures so to work effectively with them. Regarding CT skills acquisition, students with the problem-solving ability did well on the algorithmic thinking ability, and vice versa. In the same manner, students with a higher capacity of abstraction performed as well or better in other CT skills such as algorithmic thinking, decomposition, and creative thinking. As shown, students overperforming in algorithmic thinking also did well in CT skills like decomposition and pattern recognition.