Programming Proficiency Research Articles

Goal-Setting, Active Practice, Self-Monitoring: a Web-Based System to Improve Programming Proficiency

Mastering programming fundamentals poses a significant challenge for students, especially in demanding higher education environments. This pilot study investigates the effectiveness of a web-based system, SoftwareSkills, designed to enhance programming proficiency among second-year bachelor students. SoftwareSkills leverages, active practice, and self-monitoring to support programming practice. Thirty-six students (n=36) participated in a quasi-experimental design, divided into independent exploration and instructional session groups. User interaction data, exercise performance, and behavioural patterns were analysed. The study revealed four distinct learning strategies: reviewing skill masteries, dedicating sufficient time to each question, striving for mastery, and adapting spaced practice. However, some students exhibited rapid responses and minimal practice, suggesting a need for deeper learning strategies. These findings highlight the suitability of self-regulated learning and deliberate practice in programming education. Future work will involve first and second-year programming students and integrate AI-driven personalised learning features to optimise the learning experience. This research contributes insights into effective practices and the potential of technology-aided learning to support student success in programming education.

Exploring Learning Dynamics: A Comparative Study of IDEs and Command-Line Interfaces for Learning Java Programming language in the Department of Computer Science, Jigawa State College of Education and Legal Studies Ringim

This study explored the learning dynamics associated with Integrated Development Environments (IDEs) and Command-Line Interfaces (CLIs) for teaching and learning java programming language in the Department of Computer Science at the College of Education and Legal Studies, Ringim. The study evaluates the effectiveness of different programming tools (IDEs and CLIs) in enhancing the learning experience of Java programming students. It seeks to understand how these tools impact learners' confidence, skill development, and overall programming proficiency. The results revealed distinct advantages and challenges associated with each environment, underscoring the importance of context and learner preferences in selecting the appropriate tool for Java programming education. The study provides valuable recommendations for educators on optimizing programming instruction to better meet the needs of diverse learners.

Implementation of Project-Based Visual Programming Modules on Problem-Solving Skills Information Technology Education students to Support the SDG’s

Students in the Information Technology Education program need to master programming languages as an essential part of their competencies to support educational goals. One of the courses that require programming proficiency is Visual Programming or desktop-based programming. Problem-solving skills are crucial in the development of desktop-based applications. This study aims to analyze the visual programming problem-solving abilities of PTI students in supporting the SDGs through the implementation of the project-based visual programming module. This research employs a quantitative approach with a pre-experimental design of the One-Group Pretest-Posttest type. The sampling technique used is purposive sampling, involving 15 students currently taking the object-oriented programming course. The data used in this study consists of the test results of problem-solving abilities through the final project. Based on the analysis, the project-based visual programming module has been proven effective in improving students' problem-solving skills, as indicated by the paired sample t-test with a significance value of 0.002 < 0.05. In conclusion, there is a significant difference in students' problem-solving abilities before and after using the project-based module. The improvement in students' problem-solving skills through this module is considered high, with an N-Gain score of 0.839.

Factors that influence computer programming proficiency in higher education

Factors that influence computer programming proficiency in higher education

Expowo: An R package for mining global plant diversity and distribution data

AbstractPremiseData on plant distribution and diversity from natural history collections and taxonomic databases are increasingly becoming available online as exemplified by the Royal Botanic Gardens, Kew's Plants of the World Online (POWO) database. This growing accumulation of biodiversity information requires an advance in bioinformatic tools for accessing and processing the massive data for use in downstream science. We present herein expowo, an open‐source package that facilitates extracting and using botanical data from POWO.Methods and ResultsThe expowo package is implemented in R and designed to handle the entire vascular plant tree of life. It includes functions to readily distill taxonomic and distributional information about all families, genera, or species of vascular plants. It outputs a complete list of species in each genus of any plant family, with the associated original publication, synonyms, and distribution, and plots global maps of species richness at the country and botanical country levels, as well as graphs displaying species‐discovery accumulation curves and nomenclatural changes over time. To demonstrate expowo's strengths in producing easy‐to‐handle outputs, we also show empirical examples from a set of biodiverse countries and representative species‐rich and ecologically important angiosperm families.ConclusionsBy harnessing bioinformatic tools that accommodate varying levels of R programming proficiency, expowo functions assist users who have limited R programming expertise in efficiently distilling specific botanical information from online sources and producing maps and graphics for the further interpretation of biogeographic and taxonomic patterns.

Engaging students of color in physiological computing with insights from eye-tracking

Computing involving physiological data such as heart rate and electrodermal activity has been used as a way to enrich K-12 students’ computing learning experiences. This study explored a novel way to engage elementary students of color as “developers” through a series of physiological computing lessons during a summer learning program. Learners used real-time physiological data (i.e. muscle energy) as computer inputs to build computer programs. The study used an explanatory mixed method by combining multi-dimensional eye-tracking metrics data with qualitative investigation to reveal the visual attention of students with different programming proficiency levels. The regression analysis revealed three statistically significant predictors of students’ programming performances. Subsequent qualitative case studies provided additional insights into students’ problem-solving processes. Interpretations and caveats are presented and discussed.

Some Solutions to Improve Programming Skills for Information Technology Students at Tan Trao University

In the rapidly evolving field of Information Technology, proficient programming skills are paramount for students aspiring to excel in their careers. This study investigates the current state of programming competencies among IT students at Tan Trao University, identifies the challenges they face, and proposes targeted solutions to enhance their programming capabilities. Utilizing a mixed-methods approach, we gathered data through surveys, interviews, and performance assessments, revealing significant gaps in foundational knowledge, practical application, and problem-solving skills. Based on these findings, we recommend a multifaceted strategy that includes curriculum enhancement, hands-on coding workshops, peer mentoring programs, and the integration of industry-relevant projects. These solutions aim to create a robust learning environment that not only improves technical skills but also fosters critical thinking and collaboration. The implementation of these strategies is expected to significantly elevate the programming proficiency of IT students at Tan Trao University, preparing them to meet the demands of the tech industry and contribute innovatively to their future workplaces. This research underscores the urgent need for educational institutions to adapt and evolve their teaching methodologies to cultivate the next generation of skilled programmers.

ProgEdu4Web: An automated assessment tool for motivating the learning of web programming course

AbstractThe expanding software industry has increased the demand for proficient programming professionals. However, more than technical expertise is needed for successful software development; code quality assurance and effective teamwork are also indispensable. Unfortunately, recent graduates often exhibit deficiencies in programming, code quality assurance, and teamwork skills, highlighting the need for improvements in these areas. In response to these challenges, this study presents ProgEdu4Web, an innovative Automated Programming Assessment System designed to evaluate code quality and teamwork proficiency in web programming. The system integrates automatic source code evaluation tools for static code analysis, ensuring timely feedback on coding quality issues. To assess the effectiveness of ProgEdu4Web, a questionnaire and hypothesis model was employed, surveying a cohort of 111 students enrolled in web programming classes. The study's findings demonstrate that the system significantly alleviated instructor workload while motivating students to enhance their web programming with good code quality and teamwork abilities.

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

QML Powered Interface for Diffusion Imaging

In the field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.

Analyzing novice and competent programmers' problem-solving behaviors using an automated evaluation system

Background and ContextIn today's tech-driven world, programming courses are crucial. Yet, teaching programming is challenging, leading to high student failure rates. Understanding student learning patterns is key, but there's a lack of research utilizing tools to automatically collect and analyze interaction data for insights into student performance and behaviors. ObjectivesStudy aims to compare problem-solving behaviors of novice and competent programmers during coding tests, identifying patterns and exploring relationships with program correctness. MethodWe built an online system with programming challenges to collect behavior data from novice and competent programmers. Our system analyzed data using various metrics to explore behavior-program correctness relationships. FindingsAnalysis showed distinct problem-solving behavior patterns. Competent programmers had fewer syntax errors, spent less time fixing bugs, and had higher program correctness. Novices made more syntax errors and spent more time fixing coding errors. Both groups used tabs for code structure, but competent programmers introduced unfamiliar variables more often and commented on them afterward. Emphasizing iterative revisions and active engagement enhances problem-solving skills and programming proficiency. Radar charts are effective for identifying improvement areas in teaching programming. The relationship between behavior and program correctness was positively correlated for competent programmers but not novices. ImplicationsStudy findings have implications for programming education. Radar charts help teachers identify course improvement areas. Novices can learn from competent programmers' behavior. Instructors should encourage continuous skill improvement through revisions and engagement. Identified unfamiliar programming aspects offer insights for targeted learning.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

GAME-BASED LEARNING: A REVOLUTION IN THE TEACHING OF SOFTWARE DEVELOPMENT

This study explores the profound influence of Gamified education on the enhancement of programming abilities and strategic cognition, with a specific focus on the real-time strategy massively multiplayer online game "Screeps." The research had 50 participants who already had expertise in JavaScript programming. The objective was to evaluate the impact of "Screeps" on coding skill and strategic decision-making. The study used a thorough methodology that included assessments before and after the game. It examined several facets, including programming proficiency, strategic cognition, game involvement, scripting intricacy, community participation, and real-time adaptation. The study's results indicate that "Screeps" offers an engaging and difficult environment, successfully improving participants' skill in JavaScript programming and strategic thinking. The participants, who had prior knowledge of JavaScript programming, were fully engaged in the MMO environment, where their coding proficiency and strategic cognition were comprehensively assessed. The pre- and post-game evaluations enabled a sophisticated comprehension of the influence of "Screeps" on the enhancement of players' skills. The study revealed enhancements in coding proficiency and strategic cognition, highlighting the effectiveness of Gamified education in promoting essential abilities in software development. The captivating and demanding character of "Screeps" was specifically acknowledged, highlighting its potential as a powerful instrument for honing and implementing coding expertise in a dynamic professional environment. The study focuses on quantifying different aspects, such as the complexity of scripting, interaction within the community, and the ability to adapt in real-time. This approach offers a comprehensive perspective on how game-based learning, specifically through "Screeps," can contribute to the diverse field of software development education. The immersive aspect of the MMO not only catered to individual learning requirements but also functioned as a strategic tool for promoting teamwork, a highly esteemed talent in the software development sector. Keywords: Game-Based Learning, Software Development Education, JavaScript Programming Skills, Strategic Thinking in Gaming Massively Multiplayer Online (MMO) games, Screeps.

Evaluate Chat‐GPT's programming capability in Swift through real university exam questions

AbstractIn this study, we evaluate the programming capabilities of OpenAI's GPT‐3.5 and GPT‐4 models using Swift‐based exam questions from a third‐year university course. The results indicate that both GPT models generally outperform the average student score, yet they do not consistently exceed the performance of the top students. This comparison highlights areas where the GPT models excel and where they fall short, providing a nuanced view of their current programming proficiency. The study also reveals surprising instances where GPT‐3.5 outperforms GPT‐4, suggesting complex variations in AI model capabilities. By providing a clear benchmark of GPT's programming skills in an academic context, our research contributes valuable insights for future advancements in AI programming education and underscores the need for continued development to fully realize AI's potential in educational settings.

Methods and Practices in C Language Teaching

The C language holds significance as a pivotal programming language with wide-ranging applications. This paper delves into the methodologies and practical approaches employed in teaching C language as a programming tool. Through an examination of student learning requirements, comparison of various teaching methodologies, and analysis of practical cases, this paper delineates the pivotal factors contributing to success in C language instruction. By devising tailored pedagogical plans and integrating programming exercises into real-world projects, student proficiency in programming can be enhanced, resulting in favorable educational outcomes.

Investigating the reliability of aggregate measurements of learning process data: From theory to practice

AbstractBackgroundLearning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors.ObjectivesThis study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements.MethodsThis study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course.Results and ConclusionAggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient.Major TakeawaysThe proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation.

Exploring the Students’ Linear Programming Proficiency at Zambia’s Munkuye Secondary School: Hermeneutic Perspective: A Delve at the Library

Exploring the Students’ Linear Programming Proficiency at Zambia’s Munkuye Secondary School: Hermeneutic Perspective: A Delve at the Library

3DPL: A VIRTUAL REALITY BASED 3D PROGRAMMING LANGUAGE TO TEACH PRIMARY SCHOOL STUDENTS

This research paper introduces "3DPL," a Virtual Reality (VR) based 3D programming language designed to address the inherent challenges of teaching computer programming to primary school students. Traditional programming languages often create cognitive barriers for young learners, impeding their understanding of fundamental computational concepts. In response, 3DPL leverages VR technology to provide an immersive and intuitive coding environment. By seamlessly integrating VR with Zero Coded Chatbots and the MIT Scratch Application, 3DPL offers a solution that not only mitigates the learning curve for students but also enhances their overall pedagogical experience. The research employs rigorous quantitative and qualitative analyses to measure the computational and programming proficiency of primary school students using 3DPL. The findings highlight how this innovative approach fosters creativity and engagement among students. The results also contribute insights into the strengths and weaknesses of 3DPL, facilitating a comprehensive analysis of its potential shortcomings. Key Words: Virtual Reality, 3D Programming Language, Zero Coded Chatbots, Mixed Reality, Metaverse, Teaching Assistant, Drag and Drop MIT App

Curricula for teaching end-users to kinesthetically program collaborative robots.

Non-expert users can now program robots using various end-user robot programming methods, which have widened the use of robots and lowered barriers preventing robot use by laypeople. Kinesthetic teaching is a common form of end-user robot programming, allowing users to forgo writing code by physically guiding the robot to demonstrate behaviors. Although it can be more accessible than writing code, kinesthetic teaching is difficult in practice because of users' unfamiliarity with kinematics or limitations of robots and programming interfaces. Developing good kinesthetic demonstrations requires physical and cognitive skills, such as the ability to plan effective grasps for different task objects and constraints, to overcome programming difficulties. How to help users learn these skills remains a largely unexplored question, with users conventionally learning through self-guided practice. Our study compares how self-guided practice compares with curriculum-based training in building users' programming proficiency. While we found no significant differences between study participants who learned through practice compared to participants who learned through our curriculum, our study reveals insights into factors contributing to end-user robot programmers' confidence and success during programming and how learning interventions may contribute to such factors. Our work paves the way for further research on how to best structure training interventions for end-user robot programmers.

A Recommendation Method for an Online Programming Portal

An online programming portal is a valuable resource for Information Technology students to enhance their programming skills. It offers an environment where educators and learners can create problems, generate test data, program, and automatically evaluate tests. Numerous universities, both domestic and international, have achieved remarkable success by developing digital content for these portals. However, a common challenge for learners is locating problems that align with their expertise in the vast database covering various topics. In this paper, we propose a collaborative filtering recommendation approach integrated into the Online Programming Portal. This method aims to suggest a suitable set of problems based on each user’s programming proficiency. Experiments conducted on the Online Programming Portal at the Post & Telecommunications Institute of Technology (PTIT) demonstrate a significant enhancement in students’ online problem-solving results.