Critical thinking is a core educational goal and an essential competency in higher education. The rapid rise of generative artificial intelligence (GenAI) has redefined the context in which critical thinking develops. While GenAI can act as a cognitive partner that fosters engagement, it can also function as a cognitive shortcut, encouraging reliance and surface-level thinking. Emerging studies report mixed findings on whether GenAI use strengthens or diminishes critical thinking. This study aims to investigate how GenAI influences students' critical thinking in computing education, focusing on the mechanisms and conditions that determine when it supports or hinders critical thinking. The proposed research seeks to inform the design of AI-supported learning environments that sustain critical engagement rather than replace it.
Background and Context: Computer science students must learn the skill of selecting an appropriate algorithm design technique for a given algorithm design problem. Despite the topic's significance, there is little research to improve student performance. Objective: We are studying the student thinking process while selecting an appropriate algorithm for a given algorithm design problem. In this paper, we studied student behaviors while finding a solution to an algorithm design problem. Method: We conducted an hour-long think-aloud interview with students who had taken an Advanced Algorithms course and observed their behavior while generating a solution for a given problem. Findings: Analyzing the interviews, we found twelve common behaviors students engaged in while selecting an algorithm for a given problem. Our results show that most students experimented with given examples and thought of existing techniques for given questions. The behaviors most strongly associated with success were identifying sub-problems and connecting to similar problems, but students did not engage in these behaviors as often as they should. Implications: We proposed the three steps that could be useful for students to follow, which could be used by the instructor to teach students in a systematic way to identify the correct algorithm. Our findings show which behaviors are mostly strongly associated with success, and which seem useful are insufficient on their own for success.
Introductory programming (i.e., CS1) is a key subject in any computing degree, as students who do not succeed are significantly more likely to discontinue their studies. As such, maintaining motivation and engagement is critical to increasing student's chance of success. In this paper we explore the potential for increasing introductory programming student's motivation and engagement by offering multiple assessment options. These options are designed to cover the same concepts, and be of equivalent difficulty, but are themed differently. Our goal is to increase the likelihood that each student will find at least one option personally relatable and motivating. In order to achieve this, we also investigate methods for estimating the difficulty of a programming task based on an exemplar solution, so that we can ensure the assessment options are equivalent. We found that students reacted positively to being given assessment options, and that there were no negative impacts to student's learning outcomes based on the assessment theme they chose. We also demonstrate the potential of several static code metrics in estimating the difficulty of introductory programming tasks.
Randomisation in programming assessment is a popular way to create many versions of the same question or exam. However, the technique of question pooling, writing multiple distinct questions and presenting students a random one, raises issues of fairness, as it is challenging for experienced programmers, such as tertiary assessors, to determine if two questions are actually of a similar difficulty. Some randomisation techniques, such as simple substitution, only require the question author to write a template, and provide a list of keywords that they want substituted into each part of the question. As these questions are generated by programmatically substituting flavour text, and do not actually change how the student should write their solution at an algorithmic level, it seems reasonable to assume that this type of randomisation is perfectly fair. This paper outlines our first steps in researching whether or not this type of randomisation is actually fair, by analysing 22 questions, with over 17,000 real student responses from an introductory programming course. A cursory analysis, looking solely at student marks, reveals that in some assessments, students perform differently based on what question variant they get. However, a further statistical analysis using one-way ANOVA showed that these differences were not practically meaningful, with no statistical significance between question variant difficulty. Overall, the results suggest that variant randomization did not materially affect student outcomes and that the assessment design was robust and fair across versions. This indicates to us that this form of randomisation is not unfairly discriminating against students, and gives confidence in our continued use of the technique.
In computer science, assessment often takes the form of asynchronous programming assignments. Generative AI is pushing assessment into the classroom. As a result, handwritten study aids such as cheat sheets are being revisited as a tool to increase learning and reduce student anxiety. In this study, we research how preparing and using cheat sheets affects students' performance on weekly, short, paper-and-pencil quizzes in a computing course. Across the semester, we examine three versions of cheat sheet preparation and accessibility. For the first three quizzes, students were invited to create and use handwritten cheat sheets. For the next three, they received extra credit for creating a cheat sheet, but it was withheld from use during the quiz. For the remaining five, no incentives were provided and cheat sheets could not be used. We immediately surveyed students after each quiz to understand how they prepared and to gauge their perceived quiz anxiety. We assigned each cheat sheet scores for organization and density, correlated these scores vs. quiz grades, and analyzed student-reported anxiety levels vs. cheat sheet preparation. For the 35 participants, we found that after the first quiz, individual quiz grades are positively correlated with cheat sheet organization and that having a cheat sheet reduced anxiety. The effect held even when cheat sheets were withheld.
