Introductory Programming Course Research Articles

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.

Potentiality of generative AI tools in higher education: Evaluating ChatGPT's viability as a teaching assistant for introductory programming courses

Potentiality of generative AI tools in higher education: Evaluating ChatGPT's viability as a teaching assistant for introductory programming courses

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

Learning problem generator for introductory programming courses

Learning problem generator for introductory programming courses

A Syllabi Analysis of CS1 Courses from Brazilian Public Universities

The design and administration of methodological interventions is a possible way to address dropout and failure rates in undergraduate introductory programming courses (CS1). However, to implement such strategies, it is required to identify how CS1 courses are organized and offered to students. In this work, we analyzed 225 syllabi from CS1 courses distributed in 95 Brazilian public universities. We collected these syllabi from Brazilian undergraduate programs with focus on Computer Science aspects. We report context information regarding the most covered topics, most common CS1 course names, when undergraduates take the course, total class hours, and the programming paradigms and languages taught. The results indicate that the Brazilian scenario has its own characteristics that differs from those presented in related work conducted in other countries or world regions. In Brazil, we identified 90% of the analyzed CS1 courses teaches the procedural paradigm, with C as the most common programming language (53% of the total). These results differ from those conducted in other countries where Java and object-oriented are the most common languages and paradigm taught in CS1 courses. We believe that our results can be used to: (1) provide an update to those interested in the Brazilian scenario of CS1 courses; (2) support future interventions in teaching and learning of CS1; and (3) support the Brazilian community in the development of future CS1 syllabi.

Empirical Evaluation of a Differentiated Assessment of Data Structures: The Role of Prerequisite Skills

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities.

Learning by facilitating: A project-based interdisciplinary approach

Teamwork skills are an important part of business school curriculum typically taught using project-based experiential methods. This paper presents best practices for teaching teamwork skills to Gen Z students using a project-based learning, peer mentoring approach. With experiential learning theory as a guide, an interdisciplinary process was developed between an introductory programming course and a capstone business project management course to improve experiential learning and overcome issues like the free-rider problem. The opinions and sentiments of peer mentors were examined using text analysis and sentiment analysis. Peer mentoring between two interdisciplinary courses was found to reduce the free-rider problem and was beneficial for both classes.

Strategy for learning microcontroller programming—a graphical or a textual start?

Microcontroller programming competencies contribute to the sustainable employability of engineering graduates of both higher and secondary education. To develop the required programming skills, one of the challenges for educators is to determine which programming environments should be implemented in introductory programming courses. Conceptually, graphical (e.g. iconic or diagrammatic) environments appear to be very different from textual environments. Our study focused on a programming course in a mechatronics vocational training programme at the secondary school level in Slovenia. To investigate the expectations of potential employers towards our graduates, we surveyed local companies. Out of 104 respondents, 90 (86.5%) expected graduates to be able to use various programming environments, including upcoming ones. In our study, we divided 114 students into two groups of equal prior knowledge. Group A started the course with a textual programming environment and switched to a graphical one in the second sequence, while group B followed a reverse sequence. Group A achieved better test results after both sequences. Knowledge transfer, as measured by normalised learning gains, was also in favour of group A. We concluded that it is more efficient to start with the textual environment and then continue with the graphical environment. The results of this study open up some challenges for further research to investigate the effectiveness of introductory programming courses based on programmable electronics with students of similar ages. Further research should consider the specific requirements of the different courses but should also the effectiveness of knowledge transfer between different programming environments.

Effects of students’ perception on self-efficacy in programming courses at the tertiary institution

Programming is a difficult subject to learn and teach. When it comes to students learning basic programming information and skills, university-level introductory programming courses (Java, C++, Visual Basic, and Python) are critical. Students' achievement is negatively impacted by a negative attitude about programming. As a result, the study discovered the impact of students' perceptions in university computer programming courses. The study covered students studying Computer Science from the University of Ghana. A survey descriptive design with a quantitative technique was used in this investigation. The population of the study was 2,030 with 368 sample size. Purposive sampling was utilized to choose University of Ghana, Legon as the study's location. The study's participants were chosen using a stratified random sampling technique. Closed-ended questionnaire was used for data collection. The SPSS version 26 and PROCESS Macro were used to analyze the data. Respondents’ data were examined applying both inferential and descriptive statistics. The study revealed that students see programming as unfamiliar was the highest perception of programming to students. The study found that students see programming as easy with dedication was the lowest perception of programming to students. In conclusion, the significant impact of perception of students in Computer Programming account for 84% of the contribution of factors that influence self-efficacy.