Generative AI (GenAI) is becoming a widely adopted learning support tool for both students and instructors, as it offers benefits such as personalized tutoring and scaffolded learning. However, recent research highlights potential drawbacks such as overreliance and metacognitive issues, especially in novice programmers. Most prior work focuses on introductory programming courses, and important questions remain about the underlying mechanisms behind the negative effects of GenAI and if findings can be generalized when students learn more advanced computer science concepts. To address this gap, we conducted a mixed-methods study comparing student interactions with GenAI to two traditional learning supports in a second-year algorithms course: algorithm visualization (AV) and human live tutoring (LT). Twelve students participated in three 90-minute study sessions focusing on sorting, tree, and graph algorithms. We recorded gaze and interaction data, and each session concluded with a test assessing their conceptual understanding of the topic. Our analysis classifies when during the problem-solving process participants sought help, and compares the interaction patterns across the three learning supports. Although GenAI produced a larger increase in self-efficacy compared to live tutoring, it was associated with noticeably lower results in learning outcomes. We found that participants did not use algorithm visualizations effectively, faced usage barriers when using GenAI to learn advanced topics, and that live tutoring yielded the highest learning outcomes.
In this article, we present of two studies on diagnosing beginning students' misconceptions in tracing tasks: First, we show that tracing tasks can be used to design items that trigger misconceptions known from literature. We conducted a think-aloud study (N = 10), in which participants front two groups of no and minimal prior exposure to programming verbalized their thoughts While taking a test. The results indicate that certain incorrect answers are based on the thinking patterns of known misconceptions. The second study involving novice prognunmers consists of data from university students (N = 651) at five levels of experience who attended introductory computer science courses. Results indicate that errors made by participants vary across experience levels. Some misconceptions appear less frequently with experience, while others are more persistent, even among more advanced students. Taken together, the results of the study advance our understanding of the prevalence of known misconceptions and offer a preliminary insights into the learning processes that occur at the very beginning of programming education.
Background and Context. In many countries, K-12 curricula include programming within existing STEM subjects, in particular mathematics, based on explicit arguments that everyone should learn some programming and that programming may improve students’ mathematical understanding, and implicitly acknowledging the lack of specialized computing teachers and timetable slots. While much research in this area has focused on programming in the context of geometry, an ongoing shift in probability education from classical calculations to simulation-based approaches makes combined programming and probability learning an important area to explore. Objectives. We aim to identify behavioral patterns exhibited by students working on integrated programming and probability tasks and to assess how these patterns support/hinder students in learning programming and probability simultaneously. Method. This paper reports on an in-depth case study of a single ninth-grade student taking part in a two-month intervention where introductory Python programming and basic probabilistic reasoning were taught simultaneously and guided by a revised PRIMM framework. The relevant data material consists of video/screen recordings and observations of and discussions with the student during lessons and was analysed using Schulte’s Structure-Function based Block Model in the context of fine-grained microgenetic analysis. Findings. The analysis revealed three recurring behaviors: (1) Informed Cycle of Adjustments, a cycle of iterative code modifications, which could lead to programming constructs being reinforced through immediate feedback at the expense of superficial engagement with probability concepts, but could also lead to strong conceptual connections between programming and probability; (2) Running Program Fallacy, where a functioning program is taken as a sign that the code is correct, potentially masking underlying errors in the probabilistic reasoning, and hence obstructing probabilistic learning; and (3) Domain-Guided Program Comprehension, where probabilistic understanding is used to understand the program logic and behavior, resulting in an increased capacity to interpret and debug programs effectively. Implications. Our findings suggest that it is difficult for students to learn programming and probability from scratch at the same time and that it might be more efficient if students first learn basic programming before moving on to combining (possibly more advanced) programming when learning probability. Combining programming and probability could be advantageous for the latter in that it encourages active engagement and immediate feedback through simulations, although overall, additional scaffolding may be required to help students achieve the intended learning outcomes in both domains.
Understanding how young learners conceptualize computing terms and programming stnictures is crucial for designing effective computing education. Although previous research has identified misconceptions among older students, little is known about how younger learners develop preconceptions before formal instruction. This study investigates how 34 primary school students (Grades 2-4, age: M = 8.65 years) conceptualize sequences, loops, and conditionals in unplugged, block-based (Scratch and BlockPy), and text -based (Python) programming environments. Using think-aloud interviews and qualitative content analysis, we investigated students' reasoning during code -tracing activities. The findings indicate common preconceptions that can explain the prevalence of certain misconceptions in novice programmers. Furthermore, we identified two preconceptions that were not previously reported as misconceptions. First, students repeated Scratch loops an extra time, likely due to the natural -language interpretation of 'repeat'. Second, some students added numbers in conditions to statements inside conditionals, resembling a misconception previously observed only in loops. Finally, we determined factors influencing the development of preconceptions, such as programming language features and students' mathematical abilities. These findings provide insight into the early development of misconceptions. They emphasize the role of programming language features in shaping young learners' understanding, highlighting the need for appropriate instructional strategies and programming environments that support beginners in building accurate mental models of programming concepts.
Background and Context: Programming assignments and compile-and-run prompts are designed to authentically assess programming ability by providing an integrated development environment for students to write code to solve a problem. Recent literature supports the use of compile-and-run prompts, where programming ability is assessed in a timed and secure exam environment, but a lack of investigation into student programming behaviors during exams raises questions about the validity and alignment of assignments and compile-mid-run prompts. Objectives: Our study evaluates the authenticity, validity, and alignment of programming assignments and compile-and-run prompts in a CS1 course. Specifically, we investigate differences in programming behaviors, how these behaviors relate to performance, and whether assignments effectively prepare students for compile-and-run prompts. Methods: We collect performance and programming process data from students during assignments and exams. By calculating programming behavior metrics derived from keystrokes and compilations, we observe various behaviors, such as students struggling with compiler errors and pasting code from external resources. Findings: We find significant behavioral differences between assignments and exams, with engagement behaviors during assignments correlating with cumulative course performance and behaviors associated with programming ability during exams correlating with performance. By analyzing pasting behaviors, we find that student over-reliance on external resources during assignments is linked to a lack of student preparedness for compile-and-run prompts. Implications: Based on our findings, we recommend compile-and-run prompts on exams as an authentic and secure assessment of individual programming ability and also recommend the tracking of programming process data to monitor student experience and engagement. We discuss the implications of over-reliance on external resources for progranuning assessments and propose strategies for incorporating compile-and-runpromnpts into CS1 to improve student learning and mitigate this over-reliance.
In recent years, Large Language Model-based AI (GenAI) assistants have begun to transform the computer programming process. Researchers in computing education are studying these tools by assessing their capabilities, analyzing associated risks and opportunities, and developing guidelines for their effective use. One concern that has received little attention thus far is the potentially disparate impacts of GenAI tools on computing students with unequal resources and opportunities in different regions across the globe. Is GenAI technology creating a digital divide among computing students from different regions? This research presents a comparative study between undergraduate computing students from the United States and Bangladesh with respect to their access to GenAI assistants, usage behavior, and concerns about these tools. We collected study data through a questionnaire distributed to undergraduate computing education students from multiple universities in both countries (n = 534). The study results reveal significant differences (p <.05) between the access, use, and attitudes of students from the two countries, suggesting the need to develop strategies for bridging the gap between the regions. This research aims to inform computing education researchers about GenAI disparities among computing students from different regions and to promote research to address this challenge.
Background. Academic help-seeking is a key metacognitive strategy that benefits post-secondary computing students' learning. Although recent works revealed rich relationships between students' characteristics and their help seeking, most focus on a single type of help resource, a single computing context, and/or a single characteristic at a time. Objectives. We seek to study the relation ships elween student characteristics and their behavior in course-affiliated (internal) help resources as well as their approaches in help resource selection/utilization. We then study whether any relationships are specific to a help resource or a course context and whether they persist When other related characteristics are controlled. Method. We collected (1) students' help-seeking behavioral records in two course-affiliated help resources (office hours and discussion forums) from 40 offerings of eight courses across two institutions over 3.5 years and (2) students' self-reported help-seeking approaches in their preferred order of help resource usage 171-oin 18 of these offerings. We investigated relationships between these metrics and six strident characteristics (gender, race, ethnicity, major, year/standing, and confidence coining into a course). Findings. On students' behavior, we found students belonging to the gender minority and students who felt less confident coining into a course sought help from internal resources more than their peers. On students' approaches, we found students belonging to the gender minority preferred using course-affiliated resources over external resources and vice versa for students belonging to the gender majority. We found Lati nx/e/a/o and non-CS major students relied on people outside of the course more than their peers, and first-year students prioritized course-affiliated resources less than their peers. We found the relationship between students' confidence and their help-seeking behavior is specific to office hours, and that both gender and confidence remain significantly related to students' usage of office hours when the other characteristic is controlled. Implications. Our results deepen the understanding of students' use of common internal help resources and reveal relationships between student characteristics and their intent to use external resources. These insights infUrin computing educators' help landscape design, resource allocation, and teaching staff training.
Background and Context: Integrated computing (i.e., activities that use computing in non-CS classrooms) increasingly serve as K-12 students’ first encounter with CS in formal education, laying the foundation for future learning and fostering essential computational thinking skills. Objective: This study examined integrated computing activities to identify trends among non-CS topics and programming concepts taught. Method: Using existing, open-source datasets, we analyzed the differences in non-CS topics and programming concepts taught based on each activity’s grade level, programming paradigms, and source (i.e., in-service teachers or researchers/curriculum designers). We also conducted a moderator/mediator analysis to examine how features of the classroom, teacher, and school affected what programming concepts were included in the activities. Findings: Our analysis revealed that integrated computing activities encompass a wide range of non-CS topics from both technical and creative disciplines. This flexibility indicates that computing is being applied across various non-CS topics, not limited to a narrow set of activities. Most importantly, we found significant differences between activities provided by teachers and researchers/designers, including differences in non-CS topics, grade levels, and programming concepts taught in the activities. Implications: These findings suggest that integrated computing activities and curricula created by designers largely do not align with the activities used by teachers in their classrooms. We discuss the strengths and weaknesses of current integrated computing activities as early CS experiences for students and suggestions for future work.
Background and Context Misconceptions in programming have been studied extensively, but most research focuses on uncovering and assessing misconceptions in students. When teachers are involved, it is usually only to elicit their perspective on misconceptions in their students. However, there is no guarantee that teachers do not hold misconceptions themselves. Detecting the possible presence of misconceptions in teachers is a crucial step for improving their content knowledge and pedagogical content knowledge, which benefits hundreds of students each year. Objectives The study aims to answer the following research questions: Which programming misconceptions do teachers themselves hold? Are teachers aware of these misconceptions, do they observe them in their students, and do they consider them important? Are there differences in the teachers’ perspectives, depending on whether they hold misconceptions themselves? Which strategies do teachers employ to deal with the misconceptions in their students? Method We conducted an extensive, 55-page-long survey of upper-secondary informatics teachers who teach programming in Python. The survey focused on 16 Python misconceptions reported in prior research that involve concepts covered in the teachers’ upper-secondary courses. The first part of the survey assessed whether the teachers held misconceptions, probing their knowledge with two related questions for each misconception and asking for mandatory explanations. The second part of the survey asked teachers whether they previously knew about the misconceptions, how prevalent they were in their students, how important they believe them to be, and how they could tell that their students hold the misconceptions. Findings The number of teachers who gave incorrect answers on programming misconception questions varies considerably by misconception, ranging from 3 % to 40 %. Most teachers report being familiar with the misconceptions that were part of the study, consider them rather important, and have observed them at least once in their students. Teachers who answered correctly consistently rate misconceptions as more important and more prevalent among their students. Strategies to deal with the misconceptions include ways to prevent, detect, and fix them. Implications When teachers hold misconceptions, all of their students can be affected. This study highlights the importance of professional development for teachers so that they can both correct their own misconceptions and recognize them in their students, ultimately leading to better programming education. Our results also caution computing education researchers against assuming that teachers are free from misconceptions. We recommend that future studies include an assessment of the participants’ knowledge, to ensure that findings are properly contextualized.
Background and Motivation. Live coding is a common pedagogical technique where instructors write code in real time during lectures. For students, the main drawbacks of live coding are that it can feel too fast and it can be difficult to take notes. Objectives. Our work seeks to improve the student experience in live coiling lectures by: (1) understanding how instructors expect students to take notes and what challenges students face in doing so; and (2) investigating whether a specialized note-taking tool can help students keep up with the pace of the lecture and take better notes. Methods. Based on interviews h instructors who use live coding (n=10), we designed a simple note -taking interface consisting of a rich text editor Which allows students to take snapshots of the instructor's code. We conducted a within-subjects lab experiment (n=57) comparing our interface with a traditional code editor during two 15 -minute live coding lectures. We used quizzes and surveys to assess learning, mental workload, and student perceptions, and analyzed students' notes to determine how much information was captured from the lecture. Findings. In the experimental condition, NASA-TLX surveys indicated a significantly lower mental workload and students reported that they could more easily keep up with the lecture. Additionally, students perceived their notes to be more useful and our analysis revealed that the notes had significantly more information from the lecture and provided more context for copied code. Despite these benefits, we did not see a significant difference in learning between the two conditions. Implications. Our results show that during live coding lectures, we can decrease student mental workload arid increase the quality of notes by providing an interface which (1) allows capturing the instructor's code without having to type it out; and (2) maintains a clear visual distinction between code snippets and other text. Future work may examine if such an interface can lead to learning gains over long-term use in the classroom.
Background and Context: Learning programming syntax is challenging for novice programmers. One promising approach to teaching syntax in introductory computer science (CS) courses is assigning syntax exercises: simple, repetitive problems where students are asked to make a small modification to code or practice recalling and typing syntax. Prior work has shown that syntax exercises, as an addition to a course, are beneficial for all students, and perhaps especially helpful for students without prior CS experience. Still, it is unclear how syntax exercises compare to other types of practice problems. Objective: The goal of our study was to investigate whether syntax exercises are a beneficial intervention when compared to other programming practice problems. We evaluated if syntax exercises impact students’ (1) average grade on programming problems, (2) syntax skills, (3) CS self-efficacy, or (4) beliefs about CS, and if they had a differential benefit for students without prior CS experience. Method: We conducted a randomized-controlled trial in an undergraduate introductory CS course. As part of their weekly homework, all students completed practice programming problems. Students in the treatment group were also assigned to complete syntax exercises, whereas students in the control group were assigned to complete additional practice programming problems. Findings: When controlling for students’ prior CS experience, we did not find any statistically significant differences between students assigned to syntax exercises versus practice programming problems on (1) average grade on programming problems, (2) syntax skills, (3) CS self-efficacy, or (4) beliefs about CS. We also did not find any differential benefit of syntax exercises for students without prior CS experience. Implications: Our findings suggest that syntax exercises may not provide additional benefits compared to practicing programming problems. The results also shed light on how design choices in syntax exercises and other course factors may affect outcomes. Our work introduces a framework to describe variation in the broader class of syntax-focused practice activities, which can guide future research of interventions to support students’ early learning of programming skills.
Computer science (CS) is gradually being recognized as a crucial component in secondary education around the world, prompting researchers and educators to work towards uncovering effective CS pedagogics, learning barriers, and student attitudes towards CS. However, prior research has yet to explicitly include the voices of secondary students in this conversation, and ask them what they want the pedagogy of their CS education to look like. In this study, we ask (1) what perceptions do secondary students have of CS education, and (2) what pedagogical desires do secondary students have for CS education? We investigated these questions with a cohort of 18 secondary students through co -design and speculative design workshops. Our analysis shows that students believe CS pedagogy should: have distributed responsibility and accountability, value the whole individual, foreground autonomy, community, and collaboration, as well as extend outside the classroom. Additionally, students believe CS pedagogy should support learning and be engaging and relevant. This work points to the urgency and importance of re-examining, disrupting, re -imagining, arid redesigning What secondary CS classrooms, environments, and pedagogies educators should adopt in order to appeal to more students and and provide experiences for students to thrive.
Computer science students often struggle with abstract theoretical concepts, particularly in introductory courses on theoretical computer science. One such challenge is understanding context-free languages and their various representations. In this study we investigate factors that influence the difficulty of constructing context-free grammars and pushdown automata for context-free languages. We propose two potential difficulty generating factors targeting how a language is presented to students: representation in natural language and as a verbose set notation. Furthermore, we propose two factors targeting the structure of the given context-free language: nesting of constructs and insertion of multiplicities. We conducted a controlled experiment using within-subject randomization in an interactive learning system, testing the proposed difficulty factors for constructing context-free grammars and pushdown automata. Our results suggest that three of the four factors significantly influence students' objective performance in solving exercises for constructing context-free grammars, while students' perceived difficulties only partly align with the objective performance measures. The findings for pushdown automata tasks differed markedly from those for context-free grammar tasks. Our variations either had negligible effects or, in some cases, even reduced difficulty. Thus, no robust statistical conclusions can be made for pushdown automata tasks. The results lay foundations for learning systems that adaptively choose appropriate exercises for individual students.
Negation has been studied extensively in the fields of linguistics, psychology, and logic. However, it has been almost entirely overlooked in the realm of code comprehension research and the teaching of programming. Negations in code are interesting for several reasons. First, negations can be expressed either using logic operators (like ! or !=) or else by words embedded in variable names (as in notDone). Second, different types of negations can be combined together in the same expression. To explore whether using different negative expressions affects code comprehension, we conducted a controlled experiment involving 268 participants. The task was to understand short code snippets containing various logical expressions and types of negations. The results showed significant differences between the comprehension of different code snippets, both in terms of time needed and in terms of the correctness achieved. This illustrates a cognitive complexity that has important implications for writing inure readable code and for guiding refactoring practices. In particular, we suggest that students be taught to avoid negations if possible, e.g. by using len > 0 rather than len != 0 to verify that an array is not empty.
As computer security threats continue to grow, it is critical that all computer science (CS) students develop foundational security principles, including the complex but essential concept of a security mindset. However, much of the existing security education literature lacks grounding in the learning sciences, often portraying students as passive recipients of facts rather than active co-constructors of knowledge. To address this gap, we conducted a qualitative study to examine evidence of a security mindset in situ, laying the foundation for future research on when and how this mindset emerges. We analyzed think-aloud coding sessions modeled on the Build-It phase of the Build-It, Break-It, Fix-It (BIBIFI) competition. Despite limited or no prior exposure to computer security, participants exhibited core aspects of a security mindset, including secure design practices and threat perception. These findings suggest that students can demonstrate meaningful security reasoning even without formal coursework, highlighting opportunities for low-overhead interventions to cultivate a security mindset. Our results inform future research and pedagogical design targeting foundational security thinking in undergraduate CS education.
Background and Context. Prior work has explicitly called on undergraduate computer science (CS) programs to better prepare students for the demands of professional software development. While students in university courses primarily work on programming projects from scratch ("greenfield development") and create small coding projects, professional developers in industry are expected to comprehend and modify large, existing code bases ("brownfield development"). As a result, CS graduates enter the workforce with little to no instruction on how to comprehend and modify a large code base. Objectives. We aim to identify the variety of struggles that final-year undergraduate students experience when comprehending and modifying a large code base so that software engineering instruction can adapt to address students' needs. Methods. We conducted a think-aloud protocol with 13 undergraduates in their final year of a CS degree at a public, four-year university in North America. In the protocol, students modified an existing feature in an open-source code base with roughly 60,000 lines of code. Using Information Foraging Theory to analyze students' code navigation and program comprehension processes, we identified ineffective student behaviors related to their thought processes and comprehension strategies. Findings. We found a variety of ineffective behaviors among students, which we categorized into four concrete struggles. Students were unable to 1) effectively use documentation to get started on the task, 2) use methodical, structurally-guided comprehension processes, 3) find all parts of the relevant code, and 4) abandon irrelevant lines of reasoning. Implications. Our study not only shows that students experience sonic of the same struggles as professional developers, but also identifies struggles that are unique to students with limited experience working on large code bases, such as relying on opportunistic search strategies and over-investigating irrelevant code. We suggest pedagogical recommendations to address these struggles, such as explicitly teaching students about code comprehension techniques for large code bases and designing tasks in which tudents find, comprehend, and modify code across multiple files in a code base.
The significance of spatial skills for success in STEM fields has been established through several interdisciplinary studies. In the study presented in this paper, we investigated how comprehensive training in programming using turtle graphics or educational robotics can positively impact high school (MAge = 15.31 years) novices’ spatial skills. In addition, we examined whether prior knowledge (spatial abilities pretest scores), cognitive abilities, math grade, gender, or age explained variation in intervention effects. In a quasi-experimental classroom intervention study with N = 602 students, we differentiated between two experimental groups (turtle graphics and robotics) and a control group studying other CS topics without programming content. Mixed-effects models revealed that turtle programming and robotics training positively improved students’ skills in spatial visualization and spatial orientation. Moreover, female students in the educational robotics group showed significantly greater improvements in their spatial visualization skills than male students.
Background: It is unclear how to best meet the growing demand for high school computer science (CS) teachers. In many cases, current high-school teachers, without CS expertise, are trained to teach CS. However, such training may open avenues for teachers to transition into the computing industry and drive Leacher attrition. Purpose: This study explores whether high school CS teachers are at a higher risk of attrition compared to teachers of other subjects. Methods: We use Cox-Proportional Hazards models to determine the likelihood of a CS teacher leaving compared to teachers of other subjects in North Carolina, a state that annually employs over 20,000 high school teachers. Findings: Our results indicate that CS teachers have a lower of attrition than teachers in other subject areas, while also controlling for factors commonly related to attrition such as a teacher's education level and demographics. Implications: This lower rate of attrition among CS teachers is encouraging because teacher attrition negatively impacts students, incurs significant costs, and contributes to a current teacher stalling shortage in the US. CS Leachers may have lower rates of attrition due to being experienced teachers: 48% of CS teachers in our dataset began teaching CS with more than five years of teaching experience. While there may be concerns about retraining current teachers to teach CS, training dedicated and experienced teachers to teach CS colic] continue to support growing efforts to expand CS education. This work alleviates concerns that such retraining could lead to teacher attrition and retraining may prove a more viable approach than training individuals with CS expertise to be teachers.
Background. Goal congruity for CS (i.e., perceptions of what types of personal goals CS affords compared to the types one endorses) can drive students’ intent to pursue CS both in K-12 classrooms and beyond high school. However, goal congruity has, to date, not been sufficiently explored among middle and high school students within a CS context. Research Questions. 1) Do students’ perceived goal affordances related to CS predict their intentions to pursue a CS job above and beyond their prior CS experience, gender, race/ethnicity, and their need for support for learning differences?Do students’ agentic goal endorsements predict their intentions to pursue a CS job above and beyond their prior CS experience, gender, race/ethnicity, and their need for support for learning differences, and if so, is this moderated by their perceptions of agentic goal affordances provided by CS?Does gender moderate the relationship between students’ perceptions of goal affordances provided by CS and their intentions to pursue a CS job? Methodology. We used a cross-sectional survey to collect data focused on communal and agentic goal affordances for CS, communal and agentic goal endorsements, intentions to pursue a CS job, and prior experience learning CS in school. Participants (n = 207) included middle school students (n = 100) and high school students (n = 107) in the U.S. We conducted moderated multiple regression to answer our three research questions. Key Findings. Results indicated that students’ agentic goal affordances for CS (i.e., the extent to which they viewed CS jobs as allowing them to pursue self-oriented personal goals) was the strongest predictor of their intentions to pursue a CS job after high school, and students’ personal endorsements of agentic goals did not affect this predictive relationship. We also found that boy students rated their intentions to pursue a CS job significantly higher than girl students, controlling for all other factors. Finally, students’ prior experiences with CS, race/ethnicity, or need for support due to learning differences did not predict their intentions above and beyond gender and agentic goal affordances. Implications. Middle and high school students’ intentions to pursue a CS job depend on their perceptions of CS as a job and their gender. Students who view CS as allowing them to pursue agentic goals (e.g., autonomy, achievement, receiving recognition for their work) and who identified as boys report higher intentions to pursue a CS job after high school.
Background and Context: While ample research has examined undergraduate students' participation in Computer Science I (CS1), far less attention has been paid to Computer Science II (CS2) outcomes. Inspired by self-efficacy, Object-Oriented Programming (OOP) and CS2, we expand our understanding of the traditional computer programming curriculum sequence in CS curricula guidelines (CS1, CS2, and data structures and algoritluns). Objectives: This research aims to design, develop, and provide preliminary validity and reliability evidence of a measure of self-efficacy in OOP concepts for undergraduate students who completed a CS2 course. We present a conceptual framework at the intersection of CS2, Object-Oriented Programming (OOP), and the notion of self efficacy. We also analyze the impact of self efficacy on the overall course performance. Method: Following systematic procedures inspired by classical test theory and our conceptual framework, we created a 27-item instrtunent to measure self-efficacy in OOP among undergraduate CS students who already took CS2. A total of n = 292 undergraduate students enrolled in a data structures and algorithms course at two public universities in the same state in the southeastern United States (U.S.) completed the survey at the start of a traditional 16-week academic semester. We ran multiple linear regression models to predict undergraduate students' final grades in a data structures and algoritluns course as predictive validity evidence. Findings: Using Exploratory Factor Analysis, we examined the underlying structure of the instrument, which resulted in four distinct and internally consistent factors: 1) Class Design and Data Manipulation, 2) Flow of Control, 3) Class Hierarchy and Inheritance, and 4) Class Behavior and Method. The four-factor model accounted for approximately 64% of the variability in these data, and there were little to no cross-loadings in the pattern matrix. Implications: We discuss the findings in light of our study's limitations and delimitations and provide actionable implications for CS educational researchers and educators. The SES-OOP shows promising preliminary evidence of validity and internal consistency reliability for low-stakes purposes, though additional validation such as test -retest reliability and confirmatory analyses is needed.
