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collaborative programming as a Didactic Learning Strategy in CS1 Courses

IEEE REVISTA IBEROAMERICANA DE TECNOLOGIAS DEL APRENDIZAJE-IEEE RITA 2025年 20卷 32-38页

作者： Sanchez, Oscar Revelo Salazar, Alexander Baron Bolanos, Manuel Ernesto Univ Narino Syst Dept Pasto 52001 Colombia

Concepts and skills related to the basic constructs of programming (variables, types, expressions, assignment, simple input and output, and conditional control and iteration structures) are a key component in the education of future software developers. Initial programming courses (CS1) tend to have a high academic mortality rate. This is reflected in low student grades, indicating that students are not achieving the required academic competencies. Seeking alternative ways to improve student performance in CS1 courses, this paper proposes as a didactic learning strategy the use of collaborative programming/coding, mediated by the integrated development environment Visual Studio Code and its add-on Live Share, in solving computer programming problems or challenges. The results of a controlled experiment in which this collaborative tool was used in different CS1 courses of the Faculty of Engineering of the University of Nari & ntilde;o in San Juan de Pasto - Colombia are presented.

关键词： Collaboration programming profession Codes Real-time systems Visualization Encoding Training Teamwork Software development management Problem-solving Learning CS1 didactic strategy collaborative programming

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The Effects of Three Scaffoldings on Computer-Supported, Robot-Assisted collaborative programming in Higher Education

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第8期41卷 4987-5002页

作者： Xu, Weiqi Dong, Xinran Fan, Ouyang Zhejiang Univ Coll Educ 504Zijinggang campus Hangzhou 310058 Zhejiang Peoples R China

Computer-supported, robot-assisted collaborative programming (CSRACP) is a computer-supported collaborative learning (CSCL) mode used in programming education. CSRACP allows students to work in groups with the support of robots to achieve programming functions and complete complex tasks. This research applied multimodal learning analytics (MMLA) to examine the effects of three pedagogical scaffoldings (i.e., conceptual, task-oriented, and meta-cognitive scaffolding) on student pairs' CSRACP activities in higher education. The results revealed that students under conceptual scaffolding had the low-level cognitive engagement, task-oriented regulation, and low-level socio-emotional expression, with the lowest programming task score;students under task-oriented scaffolding had the operation-driven cognitive engagement, observation-oriented regulation, and low-level socio-emotional expression, with the highest programming task score;and students under meta-cognitive scaffolding had the communication-driven cognitive engagement, exploration-oriented regulation and high-level socio-emotional expression, with the medium programming task score. Based on these findings, pedagogical and analytical implications were proposed to promote CSRACP in higher education.

关键词： collaborative programming computer-supported collaborative learning pedagogical scaffolding multimodal learning analytics higher education

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The effect of ability grouping on students' computational thinking in shared regulation-supported collaborative programming

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EDUCATION AND INFORMATION TECHNOLOGIES 2025年第2期30卷 1417-1443页

作者： Lei, Yu Fu, Xin Zhao, Jingjie Yi, Baolin Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Peoples R China

Grouping students according to their abilities and promoting deeper interaction and moderation are key issues in improving computational thinking in collaborative programming. However, the distribution characteristics and evolving pathways of computational thinking in different groups have not been deeply explored. During the course of a twelve-week term, 30 sophomores participated in shared regulation-supported collaborative programming activities. This study employed statistical analysis, lag sequence analysis, and epistemic network analysis to examine the effects of ability grouping on students' behavioral patterns and cognitive networks of computational thinking. The study shows heterogeneous groups predominantly engaged with computational practices and ideas, while within homogeneous groups, high-ability groups concentrated on computational practices and low-ability groups on computational concepts. Analysis of behavior sequences across varying ability groups revealed that heterogeneous groups exhibited significantly more behaviors related to monitoring and evaluation, forming a cyclical correlation, unlike homogeneous groups which did not exhibit reflective collaborative effects. Moreover, in the initial phases of collaborative programming, the structure of the computational thinking network varied markedly across groups of different competencies. However, as group awareness tools were progressively employed, the cognitive network centroids of all groups began to show converging trajectories. These results underscore that both shared regulation strategies and grouping strategies can facilitate the enhancement of computational thinking, offering theoretical insights and practical guidance for the higher education sector.

关键词： Computational thinking collaborative programming Socially shared regulation of learning Epistemic network analysis Network development rrajectory

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The influence of scaffolding for computational thinking on cognitive load and problem-solving skills in collaborative programming

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EDUCATION AND INFORMATION TECHNOLOGIES 2025年第1期30卷 583-606页

作者： Shin, Yoonhee Jung, Jaewon Choi, Seohyun Jung, Bokmoon Hanyang Univ Coll Educ Wangsimni Ro 222 Seoul South Korea Korean Educ Dev Inst 7 Gyohak Ro Jincheon Gun Chungcheongbuk South Korea Korea Natl Univ Educ 241-20 Hanguktongsin Ro Gurye Gun Jeollanam Do South Korea

This study investigates the effects of metacognitive and cognitive strategies for computational thinking (CT) on managing cognitive load and enhancing problem-solving skills in collaborative programming. Four different scaffolding conditions were provided to help learners optimize cognitive load and improve their problem-solving abilities. A total of 110 participants were randomly assigned to one of the four groups. The four-week experiment included scaffolding, with two hours of Python programming each week to solve two real-world problems. Upon completing the learning process, participants' cognitive load and problem-solving skills were assessed. The results provide empirical evidence that using faded worked examples (WOE) combined with metacognitive scaffolding for CT effectively optimizes cognitive load and enhances problem-solving skills in collaborative programming, leading to improved efficiency and complexity in their solutions.

关键词： Higher education Computational thinking Cognitive load Problem-solving collaborative programming

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Customising a Three-Stage Fade-Out Scaffolding for collaborative programming Learning: Effects on programming Achievement, Self-Efficacy and programming Behaviour

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JOURNAL OF COMPUTER ASSISTED LEARNING 2025年第2期41卷

作者： Zhang, Yin-Rong Han, Zhong-Mei He, Tao Huang, Chang-Qin Jiang, Fan Yang, Gang Wu, Xue-Mei Zhejiang Normal Univ Zhejiang Key Lab Intelligent Educ Technol & Applic Jinhua Peoples R China Shenzhen Univ Coll Educ Shenzhen Peoples R China Guangdong Polytech Normal Univ Sch Educ Sci Guangzhou Peoples R China Wenzhou Univ Coll Educ Wenzhou Peoples R China

Background: collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance. Objectives: This study customises a three-stage fade-out scaffolding in collaborative programming to reduce scaffolding dependence and improve students' programming achievement, self-efficacy and programming behaviour. Methods: A quasi-experimental study lasting 18 weeks was conducted at a middle school. One hundred twenty-one eighth-grade students participated in the study;they were randomly assigned into the experimental group with three-stage fade-out scaffolding (N = 62) and the control group with conventional scaffolding (N = 59). Then, this study adopted an analysis of covariance (ANCOVA), independent samples t test, and lag sequential analysis to analyse the data. Results and ConclusionsThe results indicated that the programming achievement, self-efficacy, and programming behaviour patterns of the students in the experimental group outperformed those of the control group. Additionally, boy-dominated groups display more positive and active programming behaviour patterns than girl-dominated groups. Implications: This study designs a three-stage fade-out scaffolding approach for collaborative programming and provides diverse empirical evidence, offering valuable suggestions for the design of programming instruction and the analysis of learning processes.

关键词： collaborative programming fade-out scaffolding programming achievement programming behaviour self-efficacy

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Awareness support in collaborative programming tools: An evaluation based on programmer's perception and eye tracking

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 220卷

作者： Molina, Ana I. Bravo, Crescencio Gallardo, Jesus Lacave, Carmen Redondo, Miguel A. Univ Castilla La Mancha Dept Informat Technol & Syst Ciudad Real Spain Univ Zaragoza Aragon Inst Engn Res I3A Dept Comp & Syst Engn Zaragoza Spain

Groupware technology is an essential asset for organizations and a successful medium to support social meetings and teacher-learning processes when people are geographically distributed. Computer programming is a domain that can take advantage of the collaborative Work paradigm and of this technology. However, to be truly effective, groupware systems must provide suitable awareness support, i.e., be capable of informing users of the activity taking place and of the team outcome, so that they can build a setting for their work. The study of awareness mechanisms and their influence on collaborative programming processes is an open research question. In this context, this article contributes an experimental study to evaluate the mechanisms to support coordination, communication, and awareness of COLLECE, a distributed group programming system rich in awareness support. This study brings as a novel approach the combination of subjective and objective information sources, specifically eye tracking techniques, and incorporates a heuristic evaluation of awareness support based on a well-known framework. Thanks to this experiment, we have been able to verify that this blended approach, in which users participate intensively, provides a more comprehensive and deeper assessment of awareness and other coordination and communication mechanisms, and thus a better understanding of how their use can influence the collaborative programming process and outcome (program or piece of software). The study has revealed strengths, weaknesses and opportunities for improvement of the evaluated system, and has yielded concrete results as to which are the most useful awareness components of the user interface not related to the activity supported by the system (programming), these being the traffic lights;the easiest to use, such as the session panel;or the components that impose a greater cognitive load, such as the multi-scrollbar. Regarding the relationship between the supported awareness

关键词： collaborative programming Awareness Groupware Eye tracking Software evaluation

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collaborative programming of robotic task decisions and recovery behaviors

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AUTONOMOUS ROBOTS 2023年第2期47卷 229-247页

作者： Eiband, Thomas Willibald, Christoph Tannert, Isabel Weber, Bernhard Lee, Dongheui German Aerosp Ctr DLR Inst Robot & Mechatron Wessling Germany Tech Univ Munich TUM Dept Elect & Comp Engn Munich Germany

programming by demonstration is reaching industrial applications, which allows non-experts to teach new tasks without manual code writing. However, a certain level of complexity, such as online decision making or the definition of recovery behaviors, still requires experts that use conventional programming methods. Even though, experts cannot foresee all possible faults in a robotic application. To encounter this, we present a framework where user and robot collaboratively program a task that involves online decision making and recovery behaviors. Hereby, a task-graph is created that represents a production task and possible alternative behaviors. Nodes represent start, end or decision states and links define actions for execution. This graph can be incrementally extended by autonomous anomaly detection, which requests the user to add knowledge for a specific recovery action. Besides our proposed approach, we introduce two alternative approaches that manage recovery behavior programming and compare all approaches extensively in a user study involving 21 subjects. This study revealed the strength of our framework and analyzed how users act to add knowledge to the robot. Our findings proclaim to use a framework with a task-graph based knowledge representation and autonomous anomaly detection not only for initiating recovery actions but particularly to transfer those to a robot.

关键词： Learning from demonstration programming by demonstration collaborative programming Interactive programming Anomaly detection Recovery behavior Task-graph Conditional task Force-based tasks Execution monitoring

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collaborative programming for Work-Relevant Learning: Comparing programming Practice With Example-Based Reflection for Student Learning and Transfer Task Performance

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2022年第5期15卷 594-604页

作者： Sankaranarayanan, Sreecharan Kandimalla, Siddharth Reddy Bogart, Christopher A. Murray, R. Charles Hilton, Michael Sakr, Majd F. Rose, Carolyn P. Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Amazon Inc Seattle WA 98109 USA Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Software Res Pittsburgh PA 15213 USA Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA 15213 USA

Computer science pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this article, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computer science education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future.

关键词： programming profession Task analysis Problem-solving Reflection Collaboration Computer science education Software collaborative learning tools collaborative programming computer science education computer-supported collaborative learning conversational agent-based support ensemble programming Mob programming project-based learning worked examples

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collaborative programming by Demonstration in a Virtual Environment

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IEEE INTELLIGENT SYSTEMS 2012年第2期27卷 14-17页

作者： Sung, Yunsick Cho, Kyungeun Dongguk Univ Dept Multimedia Engn Seoul South Korea

collaborative learning for a robot, a software agent, and a human subject in a virtual environment reduces time, labor, and cost compared to real-life approaches.

关键词： Virtual Reality Control Engineering Computing Groupware Robots Software Agents collaborative Learning collaborative programming Virtual Environment Robot Software Agent Human Subject Human Factors Robots Virtual Environments collaborative Work programming Software Agents Human Robot Interaction Human Computer Interaction Robot Interaction collaborative programming By Demonstration collaborative Learning Iterative Learning Human X 2013

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Constructing Knowledge Graphs for Online collaborative programming

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IEEE ACCESS 2021年 9卷 117969-117980页

作者： Zhen, Yuanyi Zheng, Lanqin Chen, Penghe Beijing Normal Univ Sch Educ Technol Fac Educ Beijing 10875 Peoples R China Beijing Normal Univ Adv Innovat Ctr Future Educ Beijing 100875 Peoples R China

This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class reached 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.

关键词： Collaboration collaborative work Text recognition programming profession Text categorization Knowledge engineering Tagging Knowledge graph entity recognition relation extraction deep neural network model online collaborative learning collaborative programming

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