Master’s Thesis | Learning Sciences & Technologies
Author
Luisa Perez Lacera
Institution
Worcester Polytechnic Institute
Date
April 2022
Project Overview
Computational Thinking (CT) is widely promoted as a foundational skill for problem solving across disciplines, yet there is no single, agreed-upon definition of what CT entails. This ambiguity creates challenges for instruction, assessment, and curriculum design—particularly when students’ own interpretations of CT may not align with established theoretical frameworks.
This master’s thesis explored how college students conceptualize and reason about computational thinking by examining their responses to a series of carefully designed, real-world vignettes. The study aimed to surface student preconceptions, identify recurring themes in their reasoning, and assess the alignment between student judgments and researcher-intended definitions of CT.
This master’s thesis explored how college students conceptualize and reason about computational thinking by examining their responses to a series of carefully designed, real-world vignettes. The study aimed to surface student preconceptions, identify recurring themes in their reasoning, and assess the alignment between student judgments and researcher-intended definitions of CT.
Research Questions
- Do student judgments about whether an example represents computational thinking align with researcher-designed intentions?
- What themes emerge in student explanations for examples with the highest and lowest agreement?
Study Design
Vignette-Based Approach
- 12 total vignettes:
- 8 researcher-intended computational thinking examples
- 4 researcher-intended non-computational thinking examples
- Vignettes spanned four core CT domains (Weintrop et al., 2016):
- Data practices
- Modeling
- Systems thinking
- Computational problem solving
- Each CT domain included both technology-related and non-technology-related scenarios
Participants responded to each vignette by:
- Selecting Yes / No to indicate whether they believed the scenario represented CT
- Providing a written explanation justifying their decision
Participants & Procedure
- 37 undergraduate participants
- Data collected over a 7-week period in Spring 2021
- Survey administered via Qualtrics using the SONA system
- No demographic data collected (intentional design choice)
Qualitative Analysis
Coding Process
- Grounded theory approach to identify emergent themes
- Multi-stage, collaborative coding process:
- Initial individual coding
- Group meetings over five months (2–3 times per week)
- Development of an initial code list (58 codes)
- Iterative refinement to a final set of 38 validated codes
- Full re-coding and consensus discussions
This process ensured analytic rigor, reliability, and conceptual clarity.
Key Findings
Alignment with Researcher Intent
- Student agreement was high for some CT-intended vignettes, particularly those involving:
- Data analysis
- Explicit problem solving
- Iterative reasoning
- Agreement was much lower for non-CT vignettes, indicating ambiguity in how students define CT boundaries
Emergent Themes
Commonly cited concepts across student explanations included:
- Problem solving
- Analyzing information
- Iteration
- Decomposition
- Abstraction
- Data use
Students frequently relied on outcome-based reasoning (e.g., “solving a problem”) rather than explicitly referencing established CT frameworks.
Interpretation
Findings suggest that students:
- Often recognize CT in contexts involving data and decision-making
- Struggle to distinguish CT from general problem solving
- Frequently associate CT with technology use, even when non-technological examples clearly demonstrate CT principles
These results echo broader challenges in CT education around definitional ambiguity and instructional alignment.
Why This Matters
This thesis contributes to the learning sciences literature by:
- Providing a student-centered lens on computational thinking
- Highlighting gaps between theoretical definitions and learner perceptions
- Informing the design of CT instruction and assessment that more clearly communicates core concepts
- Supporting the use of vignettes as a research and pedagogical tool for probing conceptual understanding
Understanding how students conceptualize CT is a necessary step toward designing instruction that is both accurate and accessible.
Skills Demonstrated
- Qualitative research design and grounded theory analysis
- Development and validation of coding schemes
- Vignette-based assessment design
- Research synthesis and academic writing
- Translating theory into actionable instructional insights