Background: Context personalization, the practice of adapting learning materials to students’ personal interests, has been shown to increase student learning and engagement. Within computer science education, research has found that LLMs can generate high-quality contextualized introductory programming exercises. Objective: In this paper, we evaluate the capability of LLMs to generate technically correct and thematically integrated contextualized algorithm design problems. Methods: In a series of three iterative studies, we use LLMs to generate contextualized algorithm design problems from a given base problem and theme, evaluating over 500 generated problems for technical and thematic alignment. Results: We find that LLM-generated algorithm design problems exhibit significantly more issues than prior work has found for introductory programming problems. We identify issues specific to the algorithm design context and then mitigate these issues with prompt engineering techniques and model choice. With these adjustments, we produce LLM-generated contextualized algorithm design problems that are technically strong, deeply themed, and largely realistic, though realism drops with more culturally and locally specific themes. Implications: We demonstrate a viable workflow for generating contextualized algorithm design problems using LLMs, including prompt design, model selection, and identification of specific issues to review for.

Algorithms are a prominent theme in computer science. However, there is little research to improve students' ability to select which algorithm to use, given an algorithm problem. We aim to analyze student behavior while selecting a standard algorithm for a given problem. We did an hour-long think-aloud interview with students at Utah State University to observe their behavior attempting to pick an algorithm. We found 12 common student behaviors where students who can identify the given problem with similar problems seen before, and those who correctly identify the subproblem, have a better chance of solving a given problem. The findings from this study helped us to form a definitive strategy to select an algorithm, which could be beneficial in solving algorithmic problems. We also developed 28 multiple-choice questions that could be used to evaluate students' ability to pick the correct algorithm design technique. During the question generation process for algorithm problems, we found different strategy that needs to be followed, like asking students to select all possible algorithms for a given problem and not using keywords like path and graph for Graph Modeling problems. The multiple-choice questions help us to scale the assessment for a randomized controlled trial, where we will test the effectiveness of educational interventions designed to help students know how to select an algorithm design technique.

Introductory Programming courses teach multiple skills such as 1) explaining the purpose of code, 2) the ability to arrange lines of code in correct sequence, and 3) the ability to trace through the execution of a program, and 4) the ability to write code from scratch. Knowing if a programming skill is a prerequisite to another would assist instructors in organizing their materials such that students encounter and learn new topics using optimal skill sequences. In this study, we used the conviction measure from association rule mining to perform pair-wise comparisons of five skills: Write, Trace, Reverse trace, Sequence, and Explain code. We used the data from four exams with more than 600 participants in each exam from a public university in the United States, where students solved programming assignments of different skills for several programming topics. Our findings matched the previous finding that tracing is a prerequisite for students to learn to write code. But, contradicting the previous claims, our analysis suggested that writing code is a prerequisite skill to explaining code and that sequencing code is not a prerequisite to writing code. Our research can help instructors by systematically arranging the skills students exercise when encountering a new topic.