Introductory Programming Research Articles

Coding Decoded: Exploring Course Achievement and Gender Disparities in an Online Flipped Classroom Programming Course

In introductory programming courses (IPCs), students encounter various difficulties that are related to low achievement and high dropout and failure rates. Technology-rich approaches that promote self-directed learning while facilitating competency development and knowledge construction through social collaboration may offer advantages in this context. The current study assesses such an instructional approach by (1) identifying antecedents and process variables related to course achievement in an online flipped classroom IPC and (2) testing for gender differences regarding antecedents, process variables, and course achievement. In the winter semester of 2020/21, a sample of 144 Austrian university students participated in a survey with measurements at different points in time. Multiple linear regression was carried out to explore factors related to course achievement. The results indicate that gender, achievement-avoidance goals, academic self-concept, engagement in asynchronous learning, and course satisfaction were positively related to achievement. In contrast, work avoidance was identified as a barrier to achievement. Additionally, multivariate analysis of variance (MANOVA) was employed to test gender differences. MANOVA revealed significant gender differences regarding learning goals, mathematical self-concept, work avoidance, and engagement in synchronous learning. There were no gender differences regarding course satisfaction or achievement. The study has implications for designing innovative programming courses that could foster course satisfaction and achievement and thus reduce dropout and failure rates.

Exploring the Predictive Potential of Complex Problem-Solving in Computing Education: A Case Study in the Introductory Programming Course

Programming is acknowledged widely as a cornerstone skill in Computer Science education. Despite significant efforts to refine teaching methodologies, a segment of students is still at risk of failing programming courses. It is crucial to identify potentially struggling students at risk of underperforming or academic failure. This study explores the predictive potential of students’ problem-solving skills through dynamic, domain-independent, complex problem-solving assessment. To evaluate the predictive potential of complex problem-solving empirically, a case study with 122 participants was conducted in the undergraduate Introductory Programming Course at the University of Maribor, Slovenia. A latent variable approach was employed to examine the associations. The study results showed that complex problem-solving has a strong positive effect on performance in Introductory Programming Courses. According to the results of structural equation modeling, 64% of the variance in programming performance is explained by complex problem-solving ability. Our findings indicate that complex problem-solving performance could serve as a significant, cognitive, dynamic predictor, applicable to the Introductory Programming Course. Moreover, we present evidence that the demonstrated approach could also be used to predict success in the broader computing education community, including K-12, and the wider education landscape. Apart from predictive potential, our results suggest that valid and reliable instruments for assessing complex problem-solving could also be used for assessing general-purpose, domain-independent problem-solving skills in computing education. Likewise, the results confirmed the positive effect of previous programming experience on programming performance. On the other hand, there was no significant direct effect of performance in High School mathematics on Introductory Programming.

A detection process to create awareness of source-code plagiarism among students using it to pass introductory programming

A detection process to create awareness of source-code plagiarism among students using it to pass introductory programming

Embedding numerical methods and MATLAB programming in a fluid mechanics course for undergraduates in engineering technology

Undergraduate students in engineering technology are typically not required to take any courses on numerical methods or computational techniques and thus have little or no knowledge of many basic numerical approaches commonly used in engineering disciplines, such as root finding, curve fitting, numerical integration, and numerical differentiation. In addition, they are only required to take one introductory level programming course and thus usually experience difficulty when working on course projects involving extensive programming. However, the industry is demanding different skillsets than the ones that were expected just a decade ago. Numerical and programming skills are becoming increasingly important. In this case study, the effectiveness of embedding numerical methods and MATLAB programming in MMET 303 Fluid Mechanics and Power, a four-credit junior-level required course offered every semester for undergraduates at the Department of Engineering Technology and Industrial Distribution at Texas A&M University, was assessed. A series of learning modules were purposefully designed and implemented as a trial test in the classes offered in the semester of Fall 2023. Instructor's observation, submitted assignments, and survey results were analyzed. The results suggested that embedding numerical methods and associated MATLAB programming into a required course enhanced students’ analytical skills of tackling practical problems, helping them become better prepared as they move on into the industrial companies or the graduate schools.

Professional Development in Computational Thinking: A Systematic Literature Review

This paper presents a systematic literature review of professional development programs in computational thinking (CT). CT has emerged as an essential set of skills that everyone should develop to participate in a global society. However, there were no pre-service or in-service teacher programs to integrate CT into the K–12 classrooms until very recently. Thus, it is important to identify how educators and researchers address the challenges to prepare the next generation of students and what gaps persist in the current literature. We review existing work in this field from two perspectives: First, we analyze the learning outcomes, assessment methods, pedagogical approaches, and pedagogical tools used in the professional development programs in CT. Second, we examine how these programs assess the teachers’ knowledge and skills as outcomes. We used the technological pedagogical and content knowledge (TPACK) framework to characterize existing literature and identify possible gaps in the preparation of pre-service and in-service teachers in CT. Our results suggest that (1) existing evidence is limited to developed countries; (2) many studies are only focusing on teachers understanding the concepts but do not explore how the participants evaluate or create learning activities; (3) no studies look into classroom observations as part of the program, which limits our understanding to how these programs work; and (4) most programs use block-based programming languages as the tool to develop student CT. While block-based programming languages are used for introductory training programs, students are often expected to transfer their learning to more professional programming languages.

Teaching Virtual Reality Interactive Digital Narratives: A Curriculum and Case Study

Programs of study for Immersive Media are being developed and enacted at many higher education institutions. This article presents a course on Interactive Digital Narratives (IDN) in Virtual Reality (VR) that can familiarize undergraduate students of diverse backgrounds with immersive storytelling’s foundational technical, design, and development tenets. The course curriculum balances IDN design and immersive storytelling strategies with VR project management, user experience and interface design, spatial audio, digital scenography, introductory programming, and rudimentary artificial intelligence. The course connects technical and media affordances to theories of IDN to provide an introductory understanding of IDN in VR. The course ran in a remote synchronous modality in the spring of 2021 at a small liberal arts college in Chicago, Illinois. The paper presents the course’s 15-week curriculum, student insights, examples of projects, a rubric for a final project, and a Github Repo with instructor resources.

PyDex: Repairing Bugs in Introductory Python Assignments using LLMs

Students often make mistakes in their introductory programming assignments as part of their learning process. Unfortunately, providing custom repairs for these mistakes can require a substantial amount of time and effort from class instructors. Automated program repair (APR) techniques can be used to synthesize such fixes. Prior work has explored the use of symbolic and neural techniques for APR in the education domain. Both types of approaches require either substantial engineering efforts or large amounts of data and training. We propose to use a large language model trained on code, such as Codex (a version of GPT), to build an APR system -- PyDex -- for introductory Python programming assignments. Our system can fix both syntactic and semantic mistakes by combining multi-modal prompts, iterative querying, test-case-based selection of few-shots, and program chunking. We evaluate PyDex on 286 real student programs and compare to three baselines, including one that combines a state-of-the-art Python syntax repair engine, BIFI, and a state-of-the-art Python semantic repair engine for student assignments, Refactory. We find that PyDex can fix more programs and produce smaller patches on average.

“Hear” and “Play” Students Misconceptions on Concurrent Programming using Sonic Pi

Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for “playing” with concurrency and “hearing” common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students’ misconceptions regarding concurrency in Sonic Pi, and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi’s simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

Aspects of occupational safety: a survey among European cancer nurses

PurposeNurses are particularly at risk for occupational exposure to hazardous cancer drugs, risking both acute and chronic health effects. Knowledge on the implemented safety precautions into minimizing these risks is limited. MethodsThe European Cancer Nursing Index (ECNI) was developed by the European Oncology Nursing Society (EONS) to illustrate the development and status of this profession. In this study, anonymous online survey data on occupational safety reported by European cancer nurses as part of the ECNI 2022, was analysed. ResultsA total of 630 cancer nurses from 29 countries responded to the survey. A majority reported that written guidelines (n = 553, 88%) on safe handling and administration of hazardous drugs, personal protection equipment (PPE) and cytotoxic spillage kits (n = 514, 82%) were available at their workplaces. 130 (21%) nurses reported that wipe testing to assess any residual hazardous drugs on workplace surfaces were conducted systematically at their workplaces. 185 (29%) nurses reported that nurses sometimes or always continued with their regular tasks (including handling hazardous cancer drugs) during pregnancy and breast feeding. 185 (29%) also responded that nurses at their workplaces did not receive an introductory education program before handling hazardous drugs. In total, 346 (55%) of the nurses reported that their workplace had a freedom to speak-up guardian or whistle blower policy for members of staff. ConclusionsEven if most nurses report that there are safety routines in place at their workplaces, the results reveal several serious occupational risks for European nurses handling hazardous cancer drugs. Actions are needed to improve and optimize occupational safety for nursing staff.

How Do First-Year Engineering Students’ Emotions Change while Working on Programming Problems?

Emotions are a complex multi-faceted phenomenon. To assess the complexity of emotions from different facets, multi-modal approaches are necessary. However, multi-modal approaches are rarely used for assessing emotions, especially in the context of computer programming. This study adopts a multi-modal approach to understand the changes in students’ perception of emotions before and after working on programming problems. Understanding these changes in students’ perceptions may enable educators to devise interventions that help students adjust their perceptions and regulate their emotions as per their skills. We conducted a one-on-one programming session and retrospective think-aloud interview with 17 students from an introductory programming course. During the programming session, students filled surveys and performed four programming tasks. While working on these tasks, students’ eye gaze, video of face and screen, and electrodermal activity data were also collected using a non-invasive device. The data collection for this study was multi-modal, with a mix of both qualitative and quantitative data collection methods. Data analysis was primarily qualitative, with additional triangulation of qualitative and biometric data for select exemplars. The findings of this study suggest that students experience changes in emotions because of many reasons, for instance, they encountered repeated errors, they set high standards for their performance, or they could not manage time. For some students, negative emotions changed to positive emotions when they solved errors without any external help or achieved more than what they expected going into the task. Moreover, the triangulation of qualitative and biometric data of two participants provides a fine-grained analysis of their emotions and behaviors and confirmed the change in the perception of their emotions while performing the programming tasks.

How to design a broad mindfulness program: One approach to introducing mindfulness to a STEM community within a university setting.

Mindfulness has become popular in recent decades as a tool for psychological well-being. However, mindfulness has yet to find a solid footing as a routine practice within the Science, Technology, Engineering, and Mathematics (STEM) field. Here, we discuss the design of an introductory mindfulness program that provides the STEM community with a broad overview of various mindfulness methods. We also discuss delivery implementation methods and other considerations when designing mindfulness programming. This article provides resources for those interested in incorporating mindfulness into the STEM field, especially within the university setting.

Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

This research paper explores the effectiveness of live coding as an active learning methodology in teaching programming, particularly in the context of diverse learning styles. Live coding, characterized by real-time coding demonstrations by instructors, has been increasingly adopted to enhance the learning experience in programming education. It offers immediate feedback, demonstrates problem-solving in action, and allows instructors to incorporate student suggestions, making it a dynamic and engaging teaching tool. However, its effectiveness varies among students with different learning preferences. This study investigates the impact of various learning style dimensions, as defined by the Felder–Silverman model, on the effectiveness of live coding in an introductory object-oriented programming course. The study was conducted at Aalborg University, Denmark, with students from the BSc Software program. It aims to provide empirical evidence on how different learning style dimensions influence student preferences and the effectiveness of live coding, offering insights to educators for tailoring active learning methodologies in programming courses to diverse learner needs.

What Learning Strategies are Used by Programming Students? A Qualitative Study Grounded on the Self-regulation of Learning Theory

Self-regulation of learning (SRL) is an essential ability for academic success in multiple educational contexts, including programming education. However, understanding how students regulate themselves during programming learning is still limited. This exploratory research aimed to investigate the regulatory strategies externalized by 51 students enrolled in an introductory programming course. The objective was to identify the SRL strategies used by these students during multiple phases of the learning process and compare the SRL behavior of high and low-performers. The following research questions guided this investigation: (RQ1) What regulation of learning strategies are used by programming students?; and (RQ2) How do the SRL strategies used by high and low-performing students differ?. The findings demonstrate that learning to program involves complex psychological resources (e.g., cognition, metacognition, behavior, motivation, and emotion) and that students present heterogeneity in their SRL repertoire. In addition, high and low-performing students showed significant differences in how they regulate, which can contribute to understanding the factors that may contribute to learning programming. Lastly, we argue that for analyzing SRL strategies, it is necessary to consider the specificities of programming education, which motivated the development of a conceptual framework to describe the identified strategies and regulatory phases in this learning domain.

Factors Influencing the Social Help-seeking Behavior of Introductory Programming Students in a Competitive University Environment

Collaboration is an important aspect of computing. In a classroom setting, working with others can increase a student’s motivation to attempt more challenges, reduce the difficulty of complicated concepts, and bring about greater overall success. Despite extensive research in other domains, there has been minimal exploration within computing on what impacts a student’s decision to seek social assistance in highly competitive university environments. To understand what affects introductory programming students’ social help-seeking behavior in this context, we conducted 32 semi-structured interviews with students and performed thematic analysis and qualitative coding on the ensuing transcripts. Our qualitative analysis revealed 18 significant factors. We noticed that the decision to seek social help involved a two-fold process: first, the decision to engage in social help-seeking, and subsequently, the decision of who to ask for help. Furthermore, we found that help-seeking in computing is not fundamentally different from other disciplines, although some of the factors were unique to the topic of computing and the specific environment of this study. Factors related to communication style, the type of question being asked, and the school’s cheating policy were central when discussing code, an integral part of computing. Regarding the environment, students repeatedly reported that the competitive major, the explicit and implicit class standards, and feelings of intimidation, among others, influenced them. These findings suggest that understanding both steps and the sociocultural context is important in order to effectively lower the barriers to asking for help.

Detecting AI assisted submissions in introductory programming via code anomaly

Detecting AI assisted submissions in introductory programming via code anomaly

Matematik Destekli Programlamaya Giriş Eğitiminin Bilgi İşlemsel Düşünme Üzerindeki Etkisi

The study aims to research the effect of an introductory programming course equipped with math-based programming activities on computational thinking skills and self-efficacy. A static-groups pre-test post-test quasi-experimental design was used. One hundred seventy-six 6th-grade students of a public school participated in the study. Eighty-nine of these students were in the experimental group, and 87 were in the control group. While the students in the experimental group received introductory programming education with Math-supported activities, the students in the control group received programming education with traditional course activities. Equivalent programming activities were carried out in both groups. Data were collected via two scales; Computational Thinking Test and Self-Efficacy Perception Scale for Computational Thinking Skills. After the study was completed, post-test scores were analyzed by ANCOVA analysis by controlling pre-test scores. The findings indicated no difference between the two groups regarding computational thinking test performance. Similarly, no conclusion stated a difference between the groups’ perceptions of self-efficacy of computational thinking. According to these results, evaluations regarding the positive and negative effects of using mathematics and programming together in an elementary programming education, which is thought to be related to Computational Thinking Skills, were reached at the skills of the study.

Belonging in Engineering for Black, Latinx, and Indigenous Students: Promising Results From an Educational Intervention in an Introductory Programming Course

Belonging in Engineering for Black, Latinx, and Indigenous Students: Promising Results From an Educational Intervention in an Introductory Programming Course

A Multi-institutional Study of Peer Instruction in Introductory Computing

Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. Over the past five years, there have been a number of research articles regarding the value of PI in computer science. The present work adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, we provide evidence that introductory computing instructors can successfully implement PI in their classrooms. We find encouraging minimum (74%) and average (92%) levels of success as measured through student valuation of PI for their learning. This work also documents and hypothesizes reasons for comparatively poor survey results in one course, highlighting the importance of the choice of grading policy (participation vs. correctness) for new PI adopters.

Reproducing Predictive Learning Analytics in CS1

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

Computing Education in the Era of Generative AI

Challenges and opportunities faced by computing educators and students adapting to LLMs capable of generating accurate source code from natural-language problem descriptions.