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An exploratory study on fade-in versus fade-out scaffolding for novice programmers in online collaborative programming settings

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JOURNAL OF COMPUTING IN HIGHER EDUCATION 2022年第2期34卷 489-516页

作者： Zheng, Lanqin Zhen, Yuanyi Niu, Jiayu Zhong, Lu Beijing Normal Univ Sch Educ Technol Fac Educ 19 XinJieKouWai St Beijing 100875 Peoples R China

programming skills have gained increasing attention in recent years because digital technologies have become an indispensable part of life. However, little is known about the roles of fade-in and fade-out scaffolding in online collaborative programming settings. To close this research gap, the present study aims to examine the roles of fade-in and fade-out scaffolding for novice programmers in online collaborative programming. A total of 90 undergraduate students participated in the exploratory study and were assigned to 15 fade-in groups and 15 fade-out groups. All of the participants completed the same programming task. The findings reveal that fade-in scaffolding can significantly improve collaborative knowledge building, programming skills, metacognitive behaviors, emotions, and collective efficacy. Goal setting, planning, monitoring and control, enacting strategies, and evaluation and reflection are identified as the crucial metacognitive behaviors. The main contribution of this exploratory study is to shed light on how to design and implement scaffolding for novice programmers.

关键词： Fade-in scaffolding Fade-out scaffolding collaborative programming programming skills Metacognitive behaviors Emotion

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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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Coding together - coding alone: the role of trust in collaborative programming

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INFORMATION COMMUNICATION & SOCIETY 2021年第13期24卷 1944-1961页

作者： Stephany, Fabian Braesemann, Fabian Graham, Mark Univ Oxford Oxford Internet Inst Oxford England

In the digital economy, innovation processes increasingly rely on highly specialised know-how and open-source software shared on digital platforms on collaborative programming. The information that feeds into the content on these platforms is provided voluntarily by a vast crowd of knowledgeable users from all over the world. In contributing to the platforms, users invest their time and share knowledge with strangers to add to the rising body of digital knowledge. This requires an open mindset and trust. In this study, we argue that such a mindset is not just an individual asset but determined by the local communities the users are embedded in. We, therefore, hypothesise that places with higher levels of trust should contribute more to Stack Overflow, the world's largest question-and-answer platform for programming questions. In relating the city-level contributions of 266 OECD metropolitan areas to infrastructure, economic, and trust measures, we find support for this hypothesis. In contrast, click rates to the platform are solely driven by infrastructure and economic variables, but not by trust. These findings highlight the importance of societal values in the twenty-first-century knowledge economy: if policymakers want to develop a lively local digital economy, it is not enough to provide fast Internet access and business opportunities. Instead, it is equally important to establish a trust-building environment that fosters sharing of innovative ideas, collaborations, and knowledge spillovers.

关键词： Digital knowledge economy Social data science platform innovation collaborative programming stack overflow trust

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Cognitive conflicts in collaborative programming and their impact on computational concept acquisition

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Learning and Instruction 2025年 98卷

作者： Tan, Jinbo Wu, Sisi Wu, Lei Ma, Shanshan Faculty of Education Shandong Normal University No.88 Wenhua Rd Shandong Province Jinan 250014 China Faculty of Education Beijing Normal University No.19 Xinjiekouwai St Beijing 100875 China Digital Learning California Lutheran University 60 W Olsen Rd Thousand Oaks 91360 CA United States

Background: collaborative programming has become an increasingly important approach for developing computational thinking. The occurrence and resolution of cognitive conflicts have been considered an essential opportunity for students to acquire and construct the corresponding computational concepts. Managing students' cognitive conflicts might critically impact students' learning performance and experience. Aims: This study aims to analyze students' collaborative dialogue texts from the time series perspective to explore the cognitive conflict management patterns in collaborative programming activities and examine students' acquisition of computational concepts and collaborative experience in different cognitive conflict management patterns. Samples: Participants were 48 seventh-grade students. Methods: Participants were asked to attend a lecture for four weeks and then randomly assigned to 16 groups of three. Each group was asked to work on three programming tasks in three weeks collaboratively. Students' dialogue during the collaboration was audio-recorded for examining their cognitive conflict behaviors. After which, students were asked to complete a test of computational concepts and a questionnaire about their collaborative experience. Results: Four cognitive conflict management patterns were identified: leadership manipulation, consensus-seeking, relationship protection, and discussion construction. Students using the discussion construction pattern demonstrated the strongest understanding of computational concepts and had more positive collaborative experiences. Conclusion: This study highlights the importance of structured debate processes in managing cognitive conflicts and enhancing knowledge acquisition and collaborative experiences. The findings provide actionable insights for educators to guide students in effectively managing cognitive conflicts during collaborative programming. © 2025 The Authors

关键词： Cognitive conflicts management pattern collaborative experience collaborative programming Computational concept K-12 education

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Exploring the influence of students' perceptions of face-to-face collaboration on cognitive engagement and learning outcomes in collaborative programming

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ACTA PSYCHOLOGICA 2024年 248卷 104393页

作者： Liu, Zhi Gao, Ya Yang, Yuqin Kong, Xi Zhao, Liang Cent China Normal Univ Natl Engn Res Ctr Educ Big Data Wuhan Hubei Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Hubei Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Sch Educ Informat Technol Wuhan Hubei Peoples R China

collaborative programming is being increasingly used to overcome the difficulties of the individual programming process. In this study, we investigated the effect of collaborative perception on cognitive engagement and learning outcomes in collaborative programming. We used a quasi-experimental research to determine the differences in cognitive engagement and learning outcomes of three groups with different levels of collaborative perception. The findings highlight several important conclusions. First, there were significant differences in cognitive engagement and learning outcomes across collaborative perception groups. Students with high levels of collaborative perception demonstrate more comprehensive and diverse cognitive engagement, resulting in higher learning outcomes compared to those with lower perception. Second, students in the low collaborative perception group had more Clarification- Elaboration cognitive connections, and students in the high collaborative perception group had stronger Clarification- Positioning and Clarification- Verification cognitive connections. Third, collaborative perception positively moderated the relationship between cognitive engagement and learning outcomes. In particular, three cognitive engagement, Clarification , Elaboration , and Positioning , had a greater impact on performance when moderated by collaborative perceptions. These findings have practical implications for educators and course designers, emphasizing the importance of considering students' collaborative perception when forming groups and promoting effective collaborative programming.

关键词： Students' perceptions of collaboration Cognitive engagement Epistemic network analysis Learning outcomes collaborative programming

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Agent-in-the-Loop: Conversational Agent Support in Service of Reflection for Learning During collaborative programming 21st

Agent-in-the-Loop: Conversational Agent Support in Service o...

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21st International Conference on Artificial Intelligence in Education (AIED)

作者： Sankaranarayanan, Sreecharan Kandimalla, Siddharth Reddy Hasan, Sahil An, Haokang Bogart, Christopher Murray, R. Charles Hilton, Michael Sakr, Majd Rose, Carolyn Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9783030522407;9783030522391

Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent's primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used.

关键词： Conversational agents Human-AI collaboration Reflection prompts Group conversational agents Adaptive collaborative Learning Support (ACLS) collaborative programming

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Exploring the Benefits and Barriers of Using Computational Notebooks for collaborative programming Assignments 20

Exploring the Benefits and Barriers of Using Computational N...

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51st ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE)

作者： Borowski, Marcel Zagermann, Johannes Klokmose, Clemens N. Reiterer, Harald Radle, Roman Aarhus Univ Aarhus Denmark Univ Konstanz Constance Germany

ISBN: (纸本)9781450367936

programming assignments in computer science courses are often processed in pairs or groups of students. While working together, students face several shortcomings in today's software: The lack of real-time collaboration capabilities, the setup time of the development environment, and the use of different devices or operating systems can hamper students when working together on assignments. Text processing platforms like Google Docs solve these problems for the writing process of prose text, and computational notebooks like Google Colaboratory for data analysis tasks. However, none of these platforms allows users to implement interactive applications. We deployed a web-based literate programming system for three months during an introductory course on application development to explore how collaborative programming practices unfold and how the structure of computational notebooks affect the development. During the course, pairs of students solved weekly programming assignments. We analyzed data from weekly questionnaires, three focus groups with students and teaching assistants, and keystroke-level log data to facilitate the understanding of the subtleties of collaborative programming with computational notebooks. Findings reveal that there are distinct collaboration patterns;the preferred collaboration pattern varied between pairs and even varied within pairs over the course of three months. Recognizing these distinct collaboration patterns can help to design future computational notebooks for collaborative programming assignments.

关键词： computational notebooks collaborative programming application development programming assignments

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Analysing computational thinking in collaborative programming: A quantitative ethnography approach

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JOURNAL OF COMPUTER ASSISTED LEARNING 2019年第3期35卷 421-434页

作者： Wu, Bian Hu, Yiling Ruis, A. R. Wang, Minhong East China Normal Univ Dept Educ Informat Technol Shanghai Peoples R China Univ Wisconsin Wisconsin Ctr Educ Res Madison WI USA Univ Hong Kong Fac Educ KM&EL Lab Hong Kong Peoples R China

Computational thinking (CT), the ability to devise computational solutions for real-life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high-performing and a low-performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high-performing team exhibited CT that was systematic, whereas the CT of the low-performing team was characterized by tinkering or guess-and-check approaches. However, the low-performing group's CT development trajectory ultimately converged towards the high-performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem-solving contexts.

关键词： network model X-Ray Computed Tomography Discourse Analysis educators Ethnography Problem solving collaborative programming tinkering

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Global networks in collaborative programming

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REGIONAL STUDIES REGIONAL SCIENCE 2019年第1期6卷 371-373页

作者： Braesemann, Fabian Stoehr, Niklas Graham, Mark Univ Oxford Oxford Internet Inst Oxford England UCL Dept Comp Sci London England

To understand the dynamics of the digital knowledge economy, it is crucial to reveal the geography of global flows of knowledge on digital platforms. This article visualizes a key form of knowledge production in the digital economy: mapping the joint collaborations of users from different cities on Stack Overflow, the world's most popular question-and-answer website for programming questions. The network map reveals that users from only a limited number of places are actively taking part in the exchange of programming knowledge. While Stack Overflow access and participation are theoretically unrestricted, contributions are clustered in metropolitan regions in North America, Western Europe, and South Asia.

关键词： Internet Geography Knowledge Geographies Digital Economy Global Information Flows Networks collaborative programming Stack Overflow

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The Use of Wiki in Teaching programming: Effects Upon Achievement, Attitudes, and collaborative programming Behaviors

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INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES 2018年第3期16卷 18-45页

作者： Lin, Yu-Tzu Wu, Cheng-Chih Chiu, Chiung-Fang Natl Taiwan Normal Univ Grad Inst Informat & Comp Educ Taipei Taiwan Natl Chi Nan Univ Grad Inst Curriculum Instruct & Technol Puli Township Taiwan

This article explores the feasibility of employing cooperative program editing tools in teaching programming. A quasi-experimental study was conducted, in which the experimental group coedited the programs with peers using the wiki. The control group co-edited the programs with peers using only the face-to-face approach. The findings show that the co-editing platform was effective in assisting collaborative learning of programming, especially for program implementation. By observing editing histories, students could compare programs and then reflect more deeply about programming. The use of the wiki history tool also helped to illuminate nonlinear and dynamic procedures utilized in programming. Students who engaged more in the collaborative programming or interacted more with partners on the wiki showed greater program implementation achievements. The major benefit of using the wiki was the enhanced ability to observe the dynamic programming procedure and to encounter programming conflicts, which contributed to the process of procedural knowledge acquisition and elaboration.

关键词： collaborative Learning collaborative programming Computer programming Co-Writing Social Learning Wiki

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