Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses
Abstract
:1. Introduction
2. Theoretical Perspectives
2.1. Computational Thinking and STEM Courses
2.2. Computational Thinking and Language Courses
2.3. CT Assessment Tool
2.4. Research Objectives
- Is our research tool adequate for estimating students’ CT levels?
- Is there a correlation between students’ CT levels and their performance in STEM and language courses?
- Is there a detectable correlation between students’ CT levels and their choice of field of study?
3. Method
3.1. Settings and Participants
3.2. Research Tool for Assessing CT Skills
3.3. The Strategy of Data Analysis
4. Results
5. Discussion
6. Limitations and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- Alice is sitting directly opposite David.
- Henry is sitting between Greta and Eugene.
- Franny is not next to Alice or David.
- There is one person between Greta and Claire.
- Eugene is sitting immediately to David’s left.
- Some points on the star.
- A number indicating if a line from a point is drawn to the nearest point (1), the second closest point (the number 2), etc.
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Computational Thinking (Related to Computer Science) | |
---|---|
Bar and Stephenson (2011) [2] | Grover and Pea (2013) [6] |
Data collection | Abstractions and pattern generalizations |
Data representation and analysis | Systematic processing of information |
Abstraction | Symbol systems and representations |
Analysis and model validation | Algorithmic notions of flow of control |
Automation | Structured problem decomposition (modularizing) |
Testing and verification | Iterative, recursive, and parallel thinking |
Algorithms and procedures | Conditional logic |
Problem decomposition | Efficiency and performance constraints |
Control structures | Debugging and systematic error detection |
Parallelization | |
Simulation |
Computational Thinking (Related to Computer Science and STEM) | ||
---|---|---|
Yadav et al. (2014) [41] | ISTE and CSTA (2011) [61] | Weintrop et al. (2016) [12] |
Problem identification and decomposition | Formulating problems in a way that enables us to use a computer and other tools to help solve them | Data practices |
Abstraction | Logically organizing and analyzing data | Modelling and simulation practices |
Logical thinking | Representing data through abstractions, such as models and simulations | Problem-solving practices |
Algorithms | Automating solutions through algorithmic thinking (a series of ordered steps) | System thinking practices |
Debugging | Identifying, analyzing, and implementing possible solutions to achieve the most efficient and effective combination of steps and resources | |
Generalizing and transferring the problem-solving process to a wide variety of problems |
Log-Likelihood | x2 | Δx2 | Δdf | p-Value | |
---|---|---|---|---|---|
1PL | −622.48 | 1244.96 | |||
2PL | −605.35 | 1210.70 | 34.26 | 14 | 0.002 |
3PL | −598.02 | 1196.04 | 14.66 | 14 | 0.402 |
Parameters | Fit Indices of Every Element | |||
---|---|---|---|---|
Elements | Difficulty | Distinction | x2 | Pr (>x2) |
q1 | 1.266 | 0.324 | 4.0136 | 0.9208 |
q2 | −0.426 | 1.114 | 6.4867 | 0.6337 |
q3 | −1.030 | 0.605 | 6.0765 | 0.6436 |
q4 | −0.136 | 1.269 | 5.5186 | 0.8515 |
q5 | −3.020 | 0.705 | 9.9105 | 0.2772 |
q6 | −0.707 | 0.750 | 5.6962 | 0.7426 |
q7 | −1.245 | 2.033 | 8.0635 | 0.3168 |
q8 | −2.208 | 1.238 | 7.3209 | 0.4653 |
q9 | 1.047 | 0.773 | 7.2294 | 0.6436 |
q10 | −1.200 | 1.290 | 8.1537 | 0.5149 |
q11 | 0.162 | 0.952 | 9.5822 | 0.3663 |
q12 | −0.122 | 4.272 | 5.3639 | 0.495 |
q13 | −0.486 | 2.250 | 9.2992 | 0.2673 |
q14 | 0.166 | 2.018 | 10.1331 | 0.3564 |
95% Confidence Interval | |||||
---|---|---|---|---|---|
Ν | r | Lower CI | Upper CI | ||
2PL_scores | Mathematics | 51 | 0.270 | 0.045 | 0.500 |
Physics | 33 | 0.514 | 0.208 | 0.729 | |
Informatics | 32 | −0.046 | −0.389 | 0.307 | |
Biology | 13 | 0.806 | 0.459 | 0.940 | |
Greek Language | 80 | 0.272 | 0.056 | 0.464 |
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Bounou, A.; Lavidas, K.; Komis, V.; Papadakis, S.; Manoli, P. Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses. Educ. Sci. 2023, 13, 1101. https://doi.org/10.3390/educsci13111101
Bounou A, Lavidas K, Komis V, Papadakis S, Manoli P. Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses. Education Sciences. 2023; 13(11):1101. https://doi.org/10.3390/educsci13111101
Chicago/Turabian StyleBounou, Aikaterini, Konstantinos Lavidas, Vassilis Komis, Stamatis Papadakis, and Polyxeni Manoli. 2023. "Correlation between High School Students’ Computational Thinking and Their Performance in STEM and Language Courses" Education Sciences 13, no. 11: 1101. https://doi.org/10.3390/educsci13111101