## 4 Results  
### Data Overview  
As part of this study, we analyzed 37 students' user interactions with LLMs to understand how they seek assistance in solving programming-related problems. The data extraction process involved collecting prompts directed towards the model, with each query representing a request for advice on a specific coding or programming issue. These prompts were then systematically categorized based on the nature of the request to facilitate a deeper understanding of user behavior. The users' question types were categorized based on the intent behind each prompt, which was inferred through manual review and tagging. To categorize the queries, a manual coding process was used in which two independent reviewers assigned tags to each prompt based on its semantic content and intended purpose. Discrepancies were discussed and resolved through consensus. To enhance consistency and reduce subjective bias, a predefined coding scheme with clear definitions for each category (e.g., Error Fixing, Code Understanding) was developed and used throughout the tagging process. The following types of prompts were identified, corresponding to distinct areas of programming assistance:  
* Error Fixing (EF): Queries that sought help identifying and resolving specific bugs or errors in the code. These requests often included error messages or descriptions of unexpected behaviors in the code. * Feature Implementation (FI): Requests for adding new features or functionalities within a codebase. These queries focused on implementing specific functionalities that the users wanted to incorporate into their projects. * Code Optimization (CO): Queries aimed at improving the code's performance, efficiency, or quality. Users sought advice on refactoring or optimizing existing code to meet higher standards of performance or readability. * Code Understanding (CU): Requests for clarification or explanation regarding how a particular piece of code, library, or framework works. These queries indicated that users sought to comprehend complex code segments or third-party tools. * Best Practices (BP): Questions about general coding standards, guidelines, or best practices. These queries focused on improving code quality through adherence to widely accepted programming conventions. * Documentation (DOC): Requests for official documentation, code examples, or references to clarify certain aspects of the code or its dependencies. These queries typically involved users seeking authoritative resources to guide their programming decisions. * Concept Clarification (CC): Queries about broader programming concepts or theoretical principles. These questions were not specific to a particular coding task but focused on understanding fundamental programming ideas. As noted earlier, prompt classification was conducted independently by two evaluators to reduce the risk of bias. Inter-rater agreement was assessed using [PERSON]'s kappa coefficient [19], which quantifies agreement for categorical data while accounting for chance. As each prompt could be assigned multiple tags, label-wise [PERSON]'s kappa was calculated for each category. The results indicated excellent agreement beyond chance for EF (k = 0.786) and CC (k = 0.778), and good agreement beyond chance for DOC (k = 0.722) [20]. Additionally, good agreement beyond chance was observed for CU (k = 0.621) and CO (k = 0.540) [20] These values demonstrate consistent and reliable classification across evaluators. To further contextualize our tagging scheme within the prompt engineering literature, we examined how each of the seven query categories identified in this study aligns with established prompt modes used in LLM research [8; 9; 10]. For example, queries labelled EF and FI often resembled instruction-based or few-shot prompts, as students frequently described the current state of their code and asked the model to make specific changes.