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53rd Southern African Computer Lecturers Association Meeting

作者： Venter, Marisa de Wet, Lizette Cent Univ Technol Bloemfontein Free State South Africa Univ Free State Bloemfontein Free State South Africa

ISBN: (纸本)9783031824777;9783031824784

The integration of gamification principles into programming education can bolster student motivation, engagement and learning outcomes. However, a one-size-fits-all model, which treats all students as a homogeneous entity, is not an optimal gamification design approach. Insight into player types among students offers a valuable understanding of their engagement with gamified experiences and facilitates the customisation of design elements so that they align more closely with their preferences and motivations. The current study aimed to investigate how different game elements influence the motivation of students who represent different player types in a gamified first-year internet programming module. The dynamics mechanics components model and self-determination theory were used to design the gamified learning environment of the study and the HEXAD player topology was used to categorise students into different player types. The results of interviews indicate that the philanthropist type had a preference for collaborative learning. Moreover, achievers preferred challenging tasks and solving difficult problems;leaderboards motivated them to work harder. Additionally, socialisers enjoyed socialising with other students the most and were motivated by faceto-face competition. The player type had the greatest motivational desire to be at the top of the leaderboards and also enjoyed face-to-face competition. Lastly, free spirits were motivated the most by being in control of their own learning and creative learning activities;they were also motivated by face-to-face competition in the gamified learning environment. This paper provides valuable insight into how gamified learning environments can be tailored to meet the diverse motivational needs of students.

关键词： player type gamification programming education higher education intrinsic motivation ARCS

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Does generative AI help in learning programming: Students' perceptions, reported use and relation to performance

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COMPUTERS IN HUMAN BEHAVIOR REPORTS 2025年 18卷

作者： Lepp, Marina Kaimre, Joosep Univ Tartu Inst Comp Sci Estonia Narva mnt 18 EE-51009 Tartu Estonia

In 2022, the release of ChatGPT marked a significant advancement in the use of Artificial Intelligence (AI) chatbots, particularly impacting fields like computer science and education. The ability to generate code snippets using AI chatbots has introduced new opportunities and challenges in teaching programming. However, there is limited agreement on how students integrate them into their learning processes. This study aims to explore how students utilize AI chatbots in the "Object-Oriented programming" course and examine the relationship between chatbot usage and academic performance. To address this, 231 students completed a survey assessing the frequency and manner of chatbot usage. Descriptive statistical methods were employed to analyze usage and perceptions, while Spearman's correlation was used to investigate the connection between chatbot usage and course performance. Results indicated that students primarily relied on AI chatbots for programming tasks. Interestingly, students' performance negatively correlates with the reported frequency of using these tools. These findings provide valuable insights for programming educators, offering a better understanding of students' perceptions and use of AI chatbots. This knowledge can inform strategies for integrating these tools effectively into computer science education.

关键词： Artificial Intelligence (AI) programming education Higher education Student perceptions Academic performance

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ChartCode: A Flowchart-Based Tool for Introductory programming Courses 56

ChartCode: A Flowchart-Based Tool for Introductory Programmi...

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56th Annual SIGCSE Technical Symposium on Computer Science education, SIGCSE TS 2025

作者： Xing, Guangming Salem, Tawfiq Liang, Gongbo Western Kentucky University Bowling Green KY United States Purdue University West Lafayette IN United States Texas A&M University - San Antonio San Antonio TX United States

ISBN: (纸本)9798400705328

This poster introduces ChartCode, a cloud-based tool designed for introductory programming courses that enables students to code by interacting with flowcharts. With a simple web interface, ChartCode allows users to create and modify flowcharts, translating them into code in real-time for multiple programming languages. The tool supports a wide range of statements, including simple statements and control flow structures. Additionally, ChartCode generates syntax trees for expressions, providing a deeper understanding of syntax. The tool can also be tailored for assessments focused on practicing specific programming constructs. By bridging visual learning and coding, ChartCode offers an accessible and flexible environment for beginners to learn programming. © 2025 Copyright held by the owner/author(s).

关键词： flowchart-based coding programming education web-based programming tool

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Problem Solving-Oriented programming Knowledge Tracing from Behavior to Thought

Problem Solving-Oriented Programming Knowledge Tracing from ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Tao Tang, Linxia Yang, Huali Ou, Xinjia Hu, Shengze Geng, Jing Hu, Junjie Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Education Jianghan University Wuhan China

ISBN: (纸本)9798350368741

programming knowledge tracing (programming KT) aims to analyze the dynamic programming states in solving problems based on historical behaviors and predict future performance. In programming, a student's thought process can lead to multiple solutions for the same problem. However, current programming KT models attribute learners' responses only to knowledge mastery and ability, overlooking thought factors, which creates a contradiction between data fitting and the rationalization of the model inference process. To address this, we propose a problem solving-oriented programming KT (SPKT) method that incorporates programming knowledge, computational thinking, and solving ability to improve attribution accuracy. Specifically, we designed a dual-channel attention network based on the principle of initiative gain to retrieve knowledge. Additionally, we utilized the edit tree distance algorithm to capture fine-grained trajectory representations and employed a redress mechanism with gating to update abilities based on code information. Experiments demonstrate SPKT's superiority. © 2025 IEEE.

关键词： deep learning Knowledge tracing learner modeling problem solving programming education

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Dynamic, Randomizable, Autogradable Visual programming Simulations for Python Using PrairieLearn 56

Dynamic, Randomizable, Autogradable Visual Programming Simul...

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56th Annual SIGCSE Technical Symposium on Computer Science education, SIGCSE TS 2025

作者： Chulo, Noemi Classon, Gabriel Chiu, Ashley Garcia, Dan Fox, Armando Norouzi, Narges UC Berkeley Berkeley United States

ISBN: (纸本)9798400705328

We debut a visual program simulation tool for Python in PrairieLearn, a web-based assessment system. Our tool allows students to create program visualizations based on Python code through direct interaction with elements on a virtual canvas. Students can submit their visualizations, which are then automatically evaluated for correctness. For instructors, the integration of our tool with PrairieLearn allows problems to be randomly generated based on template problems they write. Instructors can use these randomizations to generate unique questions for each student. © 2025 Copyright held by the owner/author(s).

关键词： assessment introductory programming mastery learning programming education visual programming

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Leveraging Gamification in ICT education: Examining Gender Differences and Learning Outcomes in programming Courses

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7933页

作者： Mellado, Rafael Cubillos, Claudio Vicari, Rosa Maria Gasca-Hurtado, Gloria Pontificia Univ Catolica Valparaiso Escuela Comercio Valparaiso 2340025 Chile Pontificia Univ Catolica Valparaiso Escuela Ingn Informat Valparaiso 2340025 Chile Univ Fed Rio Grande do Sul Inst Informat BR-90010150 Porto Alegre Brazil Univ Medellin Fac Ingn Medellin 50026 Colombia

This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An experimental study was conducted with 88 university students randomly assigned to gamified and non-gamified groups. Learning gains were assessed through pre- and post-tests, motivational factors were measured via questionnaires, and usability perceptions were evaluated using the Technology Acceptance Model (TAM) questionnaire. Results revealed that women learned significantly more than men in the non-gamified condition, while men outperformed women in the gamified condition. Furthermore, men reported higher enjoyment, usefulness, and comfort with the gamified tool than women. Interestingly, both genders indicated greater satisfaction with the non-gamified version. These findings contribute nuanced insights into how gamification impacts genders differently in programming education, suggesting that gamification may hinder women's learning while modestly benefiting men. The study highlights the importance for practitioners to carefully consider gender dynamics when implementing gamified approaches, potentially offering customization options or blended techniques to optimize learning outcomes for all students in programming education.

关键词： gamification programming education gender differences motivation engagement

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Praxly: An Online IDE for the Praxis CS Test Pseudocode 56

Praxly: An Online IDE for the Praxis CS Test Pseudocode

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56th Annual SIGCSE Technical Symposium on Computer Science education, SIGCSE TS 2025

作者： Saupp, Benjamin Macmillan, Ellona Mayfield, Chris Johnson, Chris Stewart, Michael C. Hodges, Shelita James Madison University Harrisonburg VA United States Richmond Public Schools Richmond VA United States

ISBN: (纸本)9798400705328

We are collaborating with a nonprofit organization to train high school Career Technical education (CTE) teachers to teach computer science courses. In the United States, many CTE teachers are not automatically licensed to teach CS. The traditional pathway to licensure requires 18 credits of CS at a university, which is impractical for teachers while working full-time. Another pathway to licensure is taking the Praxis CS test, which in many states gives teachers an add-on endorsement in computer science. One of the biggest challenges for our teachers is learning how to program with the pseudocode used on the test. We have created an online development environment, named Praxly, that visualizes and runs the pseudocode, providing a space in which teachers can practice. Praxly supports both a text-based editor and a block-based editor, and the editors are synchronized with each other. This poster highlights Praxly's features and implementation details, outlines early research results, and discusses next steps for the project. © 2025 Copyright held by the owner/author(s).

关键词： k-12 cs teacher training online development environment programming education

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LGS-KT: Integrating logical and grammatical skills for effective programming knowledge tracing

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NEURAL NETWORKS 2025年 185卷 107164页

作者： Sun, Xinjie Liu, Qi Zhang, Kai Shen, Shuanghong Zhuang, Yan Guo, Yuxiang Liupanshui Normal Univ Sch Comp Sci Liupanshui Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei Peoples R China State Key Lab Cognit Intelligence Hefei Peoples R China

Knowledge tracing (KT) estimates students' mastery of knowledge concepts or skills by analyzing their historical interactions. Although general KT methods have effectively assessed students' knowledge states, specific measurements of students' programming skills remain insufficient. Existing studies mainly rely on exercise outcomes and do not fully utilize behavioral data during the programming process. Therefore, we integrate a L ogical and G rammar S kills K nowledge T racing ( LGS-KT ) model to enhance programming education. This model integrates static analysis and dynamic monitoring (such as CPU and memory consumption) to evaluate code elements, providing a thorough assessment of code quality. By analyzing students' multiple iterations on the same programming problem, we constructed a reweighted logical skill evolution graph to assess the development of students' logical skills. Additionally, to enhance the interactions among representations with similar grammatical skills, we developed a grammatical skills interaction graph based on the similarity of knowledge concepts. This approach significantly improves the accuracy of inferring students' programming grammatical skill states. The LGS-KT model has demonstrated superior performance in predicting student outcomes. Our research highlights the potential application of a KT model that integrates logical and grammatical skills in programming exercises. To support reproducible research, we have published the data and code at https://***/xinjiesun-ustc/LGS-KT, encouraging further innovation in this field.

关键词： Knowledge tracing Data mining Logical skill Grammatical skill programming education

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DeepScratch: Scratch programming Language Extension for Deep Learning education

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第7期11卷 642-650页

作者： Alturayeif, Nora Alturaief, Nouf Alhathloul, Zainab King Fahd Univ Petr & Minerals Dept Comp Sci Dhahran Saudi Arabia

Visual programming languages make programming more accessible for novices, which open more opportunities to innovate and develop problem-solving skills. Besides, deep learning is one of the trending computer science fields that has a profound impact on our daily life, and it is important that young people are aware of how our world works. In this study, we partially attribute the difficulties novices face in building deep learning models to the used programming language. This paper presents DeepScratch, a new programming language extension to Scratch that provides powerful language elements to facilitate building and learning about deep learning models. We present the implementation process of DeepScratch, and explain the syntactical definition and the lexical definition of the extended vocabulary. DeepScratch provides two options to implement deep learning models: training a neural network based on built-in datasets and using pre-trained deep learning models. The two options are provided to serve different age groups and educational levels. The preliminary evaluation shows the usability and the effectiveness of this extension as a tool for kids to learn about deep learning.

关键词： Deep learning visual programming languages programming education formal language definitions neural networks

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A Fault Location Method for Student Homework Program

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计算机教育 2025年第3期 111-121页

作者： Li Zhang Zheyu Yang Hao Li Jing Jiang Zian Sun the State Key Laboratory of Complex&Critical Software Environment Beihang UniversityBeijing100191China

In order to solve the code debugging difficulties faced by students and relieve the pressure of manual personalized tutoring,this paper proposes a method for locating faults in student code,called SCFL(student code fault location).This method utilizes a historical correct code repository composed of correct codes submitted by previous students in the same *** standardizes the erroneous code and historical correct code variables simultaneously and calculates the abstract syntax change ***,by establishing the mapping between the abstract syntax change tree and the student assignment code,the fault location results of the student assignment are *** evaluation experiments show that the SCFL method has a result of 9.25 in the cumulative inspection statement count and 15.9%in the fault localization cost *** indicators are better than the three currently commonly used spectrum-based baseline methods.

关键词： programming education Fault localization Personalized tutoring programming practice

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