Introductory Programming Course Research Articles

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.

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.

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

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.

How can unplugged approach facilitate novice students’ understanding of computational thinking? An exploratory study from a Nigerian university

Computational thinking (CT) has been relevant in teaching STEM courses, and educators are incorporating both plugged and unplugged CT activities in their modules to prepare new entrants for advanced knowledge. However, additional empirical data are needed to understand how an unplugged activities approach can lead to an improved understanding of CT concepts. Therefore, this study investigates the impact of unplugged activities on novice students' CT skills and unravels the characteristics of students' CT competency from performing unplugged activities. The experimental aspect of this study was conducted with a cohort of 210 students participating in an introductory programming course at a University in Nigeria. A mixed-method research approach and a quasi-experimental design were applied in this study to understand how the unplugged approach in teaching CT could help students improve their CT competency. A paired-sample t-test of familiarity score for each CT concept showed a significant increase in the score before and after the intervention. Furthermore, Latent Dirichlet Allocation (LDA) was used for topic modeling of the text describing students’ solutions. Topic modeling allows for more insights into the steps taken by the students to solve CT problems. Only 27.62 % of students' solution descriptions contained CT concepts, 56.90 % of the students solved the puzzle with an optimal score, and 72.41 % of students who applied CT concepts of problem abstraction and decomposition were able to solve the puzzle correctly. Indeed, CT unplugged activities significantly increased the number of students who gained CT competency. In addition, this study provides recommendation for computer science educators and highlight future direction.

A Case Study on Learning Visual Programming With TutoApp for Composition of Tutorials: An Approach for Learning by Teaching

Teaching programming is a topic that has generated a high level of interest among researchers in recent decades. In particular, multiple approaches to teaching visual programming have been explored, from the use of tools such as Scratch, robots, unplugged programming or activities for the development of computational thinking. Despite the wide range of resources used, students generally tend to perform poorly academically and perceive learning visual programming as a complex and demotivating task. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this article, the TutoApp system is proposed together with a new methodology based on “Learning by Teaching”, where students create tutorials in their mobile devices to explain programming concepts to their peers. The hypothesis of this paper is that the use of this tool improves learning outcomes and the level of student satisfaction. An experiment with a pre-post-test design has been carried out with 57 university students in an introductory programming course, 30 belonging to a control group (did not use TutoApp) and 27 belonging to the experimental group (used TutoApp). The findings indicate that the creation of tutorials with TutoApp significantly improved students' academic performance over those who did not use it, specifically in learning the loops and conditional control structures. However, it was observed that anxiety increased in all students while learning visual programming. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The results of this study open the door to the validation of the use of systems and methodologies for creating tutorials for teaching visual programming to university students.

Learning Analytics in Introductory Programming Courses: A Showcase from the Federal University of Amazonas

Introductory computing courses have a high failure rate worldwide. At the Federal University of Amazonas, this also happens and, since 2016 a group of professors decided to reformulate the course at the institution and some learning analytics initiatives have been adopted. The reformulation included a review of the course program and the use of an online judge. After all these years of research, the group has enough material and data and it is a good moment to summarize what has been done and the achievements so far. In this article, the focus will be the learning analytics in three main areas: student performance prediction, classification of difficulty of programming exercises, and gamification. Also, as a contribution, for the first time in a journal, the whole dataset is available to the community.

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

utomated grading systems (autograders) assist the process of teaching in introductory programming courses (CS1). However, the sole focus on correctness can obfuscate the assessment of other characteristics present in code. In this work, we investigated if code, deemed correct by an autograder, were developed with characteristics that indicated potential misunderstandings of the concepts taught in CS1. These characteristics were named Misconceptions in Correct Code (MC³). By analyzing 2,441 codes developed by CS1 students, we curated an initial list of 45 MC³. This list was assessed by CS1 instructors, resulting in the identification of MC³ that should be addressed in classes. We selected the 15 most severe MC³ for further investigation, including a semi-structured observation in a CS1 course and an automated detection software using static code analysis. The results suggested that students develop these MC³ either due to an incomplete comprehension of the concepts taught in CS1 course or a lack of attention while elaborating their code, with correctness being their primary goal. We believe our results can contribute to: (1) the research field of misconceptions in CS1; (2) promoting alternative approaches to complement the use of autograders in CS1 classes; and (3) providing insights that can serve as the foundation for teaching interventions involving MC³ in CS1.

Didactic Notes on Using Unnamed Clases, Non-Static Main Methods and Records in Introductory Object-Oriented Programming Courses with Java 21

Didactic Notes on Using Unnamed Clases, Non-Static Main Methods and Records in Introductory Object-Oriented Programming Courses with Java 21

Introductory Programming Course for Data Science in Non-STEM Disciplines

Introductory Programming Course for Data Science in Non-STEM Disciplines

Predicting Students’ Outcome in an Introductory Programming Course: Leveraging the Student Background

For a lot of beginners, learning to program is challenging; similarly, for teachers, it is difficult to draw on students’ prior knowledge to help the process because it is not quite obvious which abilities are significant for developing programming skills. This paper seeks to shed some light on the subject by identifying which previously recorded variables have the strongest correlation with passing an introductory programming course. To do this, a data set was collected including data from four cohorts of students who attended an introductory programming course, common to all Engineering programmes at a Chilean university. With this data set, several classifiers were built, using different Machine Learning methods, to determine whether students pass or fail the course. In addition, models were trained on subsets of students by programme duration and engineering specialisation. An accuracy of 68% was achieved, but the analysis by specialisation shows that both accuracy and the significant variables vary depending on the programme. The fact that classification methods select different predictors depending on the specialisation suggests that there is a variety of factors that affect a student’s ability to succeed in a programming course, such as overall academic performance, language proficiency, and mathematical and scientific skills.

Improving CS1 Programming Learning with Visual Execution Environments

Students in their first year of computer science (CS1) at universities typically struggle to grasp fundamental programming concepts. This paper discusses research carried out using a Java-based visual execution environment (VEE) to introduce fundamental programming concepts to CS1 students. The VEE guides beginner programmers through the fundamentals of programming, utilizing visual metaphors to explain and direct interactive tasks implemented in Java. The study’s goal was to determine if the use of the VEE in the instruction of a group of 63 CS1 students from four different groups enrolled in two academic institutions (based in Madrid, Spain and Galway, Ireland) results in an improvement in their grasp of fundamental programming concepts. The programming concepts covered included those typically found in an introductory programming course, e.g., input and output, conditionals, loops, functions, arrays, recursion, and files. A secondary goal of this research was to examine if the use of the VEE enhances students’ understanding of particular concepts more than others, i.e., whether there exists a topic-dependent benefit to the use of the VEE. The results of the study found that use of the VEE in the instruction of these students resulted in a significant improvement in their grasp of fundamental programming concepts compared with a control group who received instruction without the use of the VEE. The study also found a pronounced improvement in the students’ grasp of particular concepts (e.g., operators, conditionals, and loops), suggesting the presence of a topic-dependent benefit to the use of the VEE.

Exploring differences in self-regulated learning strategy use between high- and low-performing students in introductory programming: An analysis of eye-tracking and retrospective think-aloud data from program comprehension

Previous studies have reported mixed results regarding the relationship between students’ use of self-regulated learning (SRL) strategies and their performance in introductory programming courses. These studies were constrained by their reliance on self-report questionnaires as a means of collecting and analysing data. To address this limitation, this study aimed to employ eye-tracking and retrospective think-aloud techniques to identify differences in SRL strategy use for program comprehension tasks between high-performing students (N = 31) and low-performing students (N = 31) in an undergraduate programming course. All participants attended individual eye-tracking sessions to comprehend two Python program codes with different constructs. Their eye-tracking data and video-recalled retrospective think-aloud data were captured and recorded for analysis. The findings reveal that higher-order cognitive skills, such as elaboration and critical thinking, were mostly adopted by high-performing students, while basic cognitive and resource management strategy, such as rehearsal and help-seeking, were mostly employed by low-performing students when comprehending the program codes. This study not only demonstrates the design of combining eye-tracking and retrospective think-aloud data to explore students’ use of SRL strategies but also provides evidence to support the notion that program comprehension is a complex process that cannot be effectively addressed by employing merely rudimentary strategies, such as repetitively reading the same code segment. In the future, researchers could explore the possibility of using a webcam to monitor and assess students’ online programming processes and provide feedback based on their eye movements. They could also examine the effects of SRL strategies training on students’ motivation, engagement, and performance in various types of programming activities.

Project-based Iterative Teaching Model for Introductory Programming Course

Project-based Iterative Teaching Model for Introductory Programming Course

Need-Based Game Design: Developing a CT Game Based on Educational Needs

According to reports and previous research, present and future society show a high need for citizens with technical skills, such as computer programming. The need for technical competent citizens can however be challenging for higher education institutions to accommodate due to low numbers of students in higher education computer science. But also due to the high drop-out rates and low pass rates in introductory programming courses. With that said, it is important to both recruit new students to computer science and to support those who already study the subject, to meet the demands of the present and the future. An approach that has shown great promise in motivating students while still keeping focus on the learning aspects in education, is game-based learning (GBL). To facilitate support in higher education computer science, more specifically courses in fundamental programming, this study had the aim of identifying important design concepts in the development of a need-based educational game on computational thinking (CT). Skills related to programming have commonly been organized as part of the CT concept.  To identify the educational needs, e-mail interviews were conducted with teachers that teach higher education courses on computer programming in Swedish universities. Based in an on-going design science project to create a learning game on computational thinking for higher education, the study discusses and compare the identified needs with design theories, such as affordances, emotional design, and sustainable design. The conclusion is a conceptual design of the educational game with a set of recommendations for a need-based game design in educational context.

Perceptual Learning Modules (PLM) in CS1: a Negative Result and a Methodological Warning

Perceptual Learning Modules (PLMs) is a variation of Perceptual Learning based on multiple-choice questionnaires. There exists successful research of the use of PLMs in math and flight training. The possibility of designing and adopting PLMs in Introductory Programming Courses (CS1) is still an open area of study. The goal of this study is to test whether students that received a PLM training on recognising segments of programs will perform better at writing programs. Two PLM interventions were administered to students. The first intervention was a nonrandom controlled experiment, in which students opted to answer the PLM questionnaire (N=40), while the control group consisted of students that did not answer it (N=629). The second intervention was a randomized controlled experiment with a placebo, in which students were randomly assigned to perform either the PLM questionnaire (N=51) or another a placebo activity (N=51). The different forms of analysis of the first experiment results yielded Cohen&amp;rsquo;s d ranging from 0.23 to 0.34 in favor of the PLM intervention. For the second experiment, the effect size was d = -0.11 against the PLM intervention, but the two results were significant. We believe that the cautious conclusion is that there is a null effect in using a PLM activity as part of a CS1 course. The paper is also of interest because of the methodological decisions and techniques used.

Supporting Co-Regulation and Motivation in Learning Programming in Online Classrooms

Self-regulation of learning in programming has been extensively investigated, emphasising an individual's metacognitive and motivational regulation components. However, learning often happens in socially situated contexts, and little emphasis has been paid to studying social modes of regulation in programming. We designed Thyone, a collaborative Jupyter Notebook extension to support learners' programming regulation in an online classroom context with the overall aim to foster their intrinsic motivation toward programming. Thyone's salient features - Flowchart, Discuss and Share Cell - incorporate affordances for learners to co-regulate their learning and drive their motivation. In an exploratory quasi-experimental study, we investigated learners' engagement with Thyone's features and assessed its influence on their learning motivation in an introductory programming course. We found that Thyone facilitated the co-regulation of programming learning and that the users' engagement with Thyone appeared to positively influence components of their motivation: interest, autonomy, and relatedness. Our results inform the design of technological interventions to support co-regulation in programming learning.

Editorial

Dear Readers,&amp;nbsp; It gives me great pleasure to announce the ninth regular issue of 2023. I would like to thank all the authors for their sound research and our editorial board and guest reviewers for the extremely valuable reviews and suggestions for improvement. These contributions, together with the generous support of the consortium members, enable us to run our journal successfully and maintain its quality.&amp;nbsp; Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends.&amp;nbsp; As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation by your institution, we would be very grateful for any kind of support.&amp;nbsp; In this regular issue, I am very pleased to introduce five accepted papers involving 22 authors from 10 different countries: Austria, Brazil, Estonia, France, Lebanon, Malaysia, Spain, The Netherlands, United Kingdom, and Vietnam.&amp;nbsp; In a collaborative research effort, Jan Bergstra from The Netherlands and John V. Tucker from the United Kingdom introduce the Naive Fracterm Calculus, which is a new perspective on elementary arithmetic and can be described as naive when compared to a variety of algebraic and logical, axiomatic formalisations of elementary arithmetic. Ricardo Caceffo, Jacques Wainer, Guilherme Gama, Islene Garcia, and Rodolfo Azevedo from Brazil conducted a study based on perceptual learning modules, more specifically a variation of perceptual learning based on multiple-choice questionnaires to be used in an introductory programming course, and report on related issues. In a collaborative research work between colleagues from Malaysia, Lebanon and France, Mohammad Kchouri, Norharyati Harum, Hussein Hazimeh, and Ali Obeid suggest a new technique to detect falls by combining Fuzzy Logic and Support Vector Machine, achieving an overall accuracy of about 99.87% in detecting the fall function. In an international research collaboration between Estonia, Austria, Spain, Luis P. Prieto, Gerti Pishtari, Yannis Dimitriadis, Mar&amp;iacute;a Jes&amp;uacute;s Rodr&amp;iacute;guez-Triana, Tobias Ley, and Paula Odriozola-Gonz&amp;aacute;lez propose and explore single-case learning analytics, which defines a process in which doctoral students, researchers, and computational elements collaborate to extract insights into a single learner&amp;rsquo;s experience and learning process. Last but not least, also in a joint research work by researchers&amp;nbsp;from Vietnam and the United Kingdom, Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, and Pham Thi Kim Thoa present their approach PlantKViT for forest plants classification, which is based on a combination model of Vision Transformer and KNN and achieves a 93% accuracy.&amp;nbsp; Enjoy Reading!&amp;nbsp; Cordially,&amp;nbsp; Christian G&amp;uuml;tl, Managing Editor&amp;nbsp; Graz University of Technology, Graz, Austria