Guided inquiry-based learning (GIL) is a teaching approach where students actively explore and investigate concepts through structured guidance. When applied to computational thinking, GIL can have a significant positive impact on students' development of computational thinking skills, which include problem-solving, algorithmic thinking, decomposition, abstraction, and pattern recognition. Here's how

1. Problem-solving and Critical Thinking: Guided inquiry encourages students to actively solve problems by engaging in hands-on activities. This aligns with computational thinking, as students need to break down complex problems into smaller, manageable components (decomposition), and then apply algorithms to solve them.

2. Algorithmic Thinking: Through inquiry-based learning, students often create their own solutions or algorithms. Teachers guide the process with prompts and feedback, helping students recognize patterns and develop step-by-step instructions to solve problems—key elements of computational thinking.

3. Decomposition and Abstraction: As students work through inquiry-based tasks, they are often required to decompose large problems into smaller parts. This process helps them understand abstraction by focusing on the essential elements while ignoring irrelevant details, which is central to computational thinking.

4. Collaboration and Reflection: Guided inquiry often involves collaborative learning, which encourages peer discussions. These exchanges allow students to reflect on their thought processes and computational strategies, improving their problem-solving and computational thinking.

5. Engagement with Real-World Applications: By using guided inquiry in real-world contexts or simulations, students can connect theoretical concepts with practical applications, enhancing their understanding of how computational thinking skills can be used in various fields like computer science, engineering, and data analysis.

Overall, guided inquiry promotes an active learning environment that enhances students' computational thinking by encouraging them to ask questions, explore solutions, and develop a deeper understanding of problem-solving strategies.

To explore the thesis topic on the "Effect of Guided Inquiry on Computational Thinking Skills," you should start by defining both guided inquiry and computational thinking (CT). Guided inquiry is an educational strategy where students are led through a structured process of questioning and discovery, promoting critical and analytical thinking. Computational thinking, on the other hand, involves conceptualizing and formulating problems and their solutions in ways that a computer can understand, encompassing skills like abstraction, algorithm design, decomposition, and pattern recognition.

Your literature review should delve into existing research to establish a foundation for your study. You can look for studies that have already investigated similar intersections; for instance, research indicates that guided inquiry can significantly influence students' critical thinking skills, which could be a precursor to developing computational thinking abilities. For example, a study on the effects of guided inquiry combined with problem-solving processes showed positive impacts on students' critical thinking, which might parallel improvements in computational thinking skills.

For your methodology, consider designing an experimental study where one group receives instruction through guided inquiry while another group does not, allowing for a comparative analysis of their computational thinking skills. You could use pre-test and post-test scenarios with assessments designed to measure different aspects of computational thinking. Tools like the Bebras Challenge or other validated CT assessments could be useful.

When collecting data, ensure that your instruments are reliable and valid for measuring computational thinking. You might also incorporate qualitative data through student interviews or reflections to capture insights into how guided inquiry might be influencing their thought processes.

In analyzing your data, you should look for statistical significance in the development of computational thinking skills between the experimental and control groups. Techniques like ANOVA or t-tests could be applied if your data follows a normal distribution, or non-parametric tests if it does not.

Your discussion should link your findings back to the theoretical framework, discussing how guided inquiry as a teaching strategy might foster or hinder the development of computational thinking. You could explore whether certain aspects of guided inquiry, like the formulation of questions or the process of experimentation, have a more pronounced effect on specific components of computational thinking.

Lastly, your conclusion should summarize the key findings, suggest implications for educational practices, and propose directions for future research. Consider limitations such as sample size or study duration, and suggest how these could be addressed in subsequent studies.

Ensure that your thesis is well-referenced, citing relevant research in the field, including but not limited to the sources mentioned here. This approach will provide a comprehensive examination of how guided inquiry can impact computational thinking skills.