高中编程教育中协作学习现状

随着机器人技术在各行业的广泛应用，其在教育领域的重要性日益凸显。传统机器人教学模式存在理论与实践脱节、学生参与度低、缺乏团队合作机会等问题。本文旨在探讨以项目驱动和学生主导为核心的全新机器人实践教育模式。通过阐述项目驱动学习、团队协作教学和综合实践教学的理论基础，构建了包括团队组建、项目选择、项目实施的实践教育体系，并介绍了论文撰写、比赛参与、实训课程等教育成果展示与评价方式。结合教学反思与学生反馈，提出了难度渐进、丰富团队角色、加强资源支持和教学指导等改进建议，以期完善机器人实践教育体系，培养适应未来社会发展的高素质人才。

随着计算机网络技术的飞速发展，基于云计算等技术的教学应用平台——教育云平台逐渐成为教育技术领域的研究热点，将之应用于学科教学中已是大势所趋。同时，SMART原则是目标管理中的黄金原则，而在教育教学方面的应用研究较少。本研究创新性地以SMART原则为导向、以超星学习通为教育云平台、以高中信息技术课堂教学为具体案例，设计了基于教育云平台的SMART教学模式。研究表明：以SMART原则为指导有利于构建更加高效明晰的智慧教学模式；以教育云平台为技术支撑有利于资源共享和师生互动；以具体章节为案例有利于明确教学模式的具体实施流程，及时地发现问题、解决问题，以期为改进和优化智慧教学模式提供一定的启示和有价值的参考，为技术与教育的深度融合奠定基础。

高中物理教学中，带电粒子在磁场中的运动是兼具抽象性与综合性的难点内容。本文以2024年湖北高考物理试题为例，探讨AI技术辅助物理教学的新模式。研究通过构建Python仿真，对复杂物理过程开展可视化呈现与智能分析。不仅完成了题目选项的科学性验证，更具象化呈现了传统教学中难以直观展现的物理过程图景。实践应用表明，这种融合计算思维的教学方法，能够有效提升学生的物理模型建构能力与科学探究素养。本研究为AI物理教育的创新实践提供了可借鉴的具体案例。

本文以C#课程线上线下混合式教学为例，设定课程教学目标，选取合适教学模式，依托现有线下线上课程教学资源，融入“双主体”教育思想，分线上线下两类以及各类两个阶段进行混合式教学活动设计，探索在混合式教学过程中发挥教师、学生“双主体”能动性，激发学生主体在学习过程的主动性、积极性与创造性，以及发挥教师主体引导、启发、监控教学过程的主导作用，努力达成教师教授最优化与学生学习最优化，以期达到更好的教学及学习效果。

人工智能时代正催生教育领域的变革，在当前教育数字化的背景之下，培育中小学生的人工智能能力势在必行。这不仅是对智能时代呼唤数字公民的回应，亦是促使中小学生全面融入智能协同教学新生态的必要举措。但是当前我国中小学生人工智能的培育仍面临着些许困境：学校教育理念尚待更新、中小学生人工智能能力的培育体系尚未完善、中小学生人工智能能力发展的支持系统有待健全。这些困境阻碍着中小学生人工智能能力的发展。因而学校需要从优化学校课程设置，探索基于群组的教学组织方式；强化教师人工智能能力培育；构建连续性追踪与多维度评价相结合的评价体系；创新家校社多元协同机制等方面做出努力，着力提升中小学生的人工智能能力。

计算思维作为信息科技学科的核心素养之一，受到众多学者的高度关注。本文利用知识图谱软件CiteSpace，运用文献计量方法和内容分析方法，对中国知网(CNKI)数据库中2006年至2024年北大核心、SCI、CSSCI、EI和硕博论文等文献进行统计，从发文量统计、作者、关键词引用频次、关键词突变、关键词聚类和时间线分布等可视化分析，绘制知识可视化图谱，探索国内计算思维的研究热点和研究趋势。结果表明：计算思维的关注度呈现总体上升趋势，研究热点主要集中于教学环节和人工智能领域，研究趋势也随着时代的需求而发生相应的变化。

Abstract Computer programming has emerged as an important field in K‐12 science, technology, engineering, and maths (STEM) education in the AI era. However, contemporary programming education is hindered by fragmented course content, high complexity, and difficulties in maintaining engagement, impeding smooth progress. More effective collaborative learning strategies need to be explored. This study constructed jigsaw‐integrated task‐driven learning (jigsaw‐TDL) in a high school Python programming course under a STEM curriculum and verified its teaching effectiveness on students’ learning motivation, computational thinking, collaborative skills, and programming performance both quantitatively and qualitatively. Nighty‐nine high school students were randomly assigned to a jigsaw‐TDL group and a general collaborative task‐driven learning group (collaborative‐TDL). During the experiment, a Python programming course was introduced over 7 weeks. Questionnaires, programming tasks, and semistructured interviews were comprehensively applied to examine students’ learning outcomes. Finally, the jigsaw‐TDL group showed significantly better performance than the collaborative‐TDL group in learning motivation, computational thinking, and collaborative skills. However, it only led to better programming performance in the less complex tasks. The majority of students held a positive attitude toward the jigsaw‐TDL model, acknowledging its benefits in group collaboration, programming knowledge acquisition, and application. This research provides empirical evidence and potential guidance for task organization and collaborative learning support in programming courses and STEM education.

The training program aims to improve students’ ability to operate the Git/GitHub application in collaborative computer programming learning. The partners who are the subject of training activities are three Vocational High Schools of the Computer and Informatics Engineering Expertise Program (SMK TKI) in Malang City and Batu City, East Java Province, Indonesia. The training program was developed with the ASSURE model. The learning strategy used by the training team is peer-to-peer learning. Each vocational school determines 40 students as participants in the training program. Training activity success data were collected using knowledge tests, performance tests, self-evaluation sheets, and questionnaires. The analysis design uses One-Group Pretest-Posttest. The data were analyzed with the One Sample T-test technique. The Posttest value (M=74.88; SD=6.68) is higher than the pretest (M=46.62; SD=9.28). One Sample T-test analysis on the Pretest and Posttest scores yielded t(119)=26.28; p 0.01; d=3.50. The Pretest score significantly differs from the Posttest score, where the effect size score is 3.50, and the p-value is smaller than 0.05 (α).

In Education 4.0, a personalized learning process is expected, and that students are the protagonist. In this new education format, it is necessary to prepare students with the skills and competencies of the 21st-Century, such as teamwork, creativity, and autonomy. One of the ways to develop skills and competencies in students can be through block programming, which can be used with emerging technologies such as robotics and IoT and in an interdisciplinary way. Thus, block programming in High School is important because it is possible to work on aspects such as problem-solving, algorithmic thinking, among other skills (Perin et al., 2021), which are necessary in the contemporary world. Thus, our Systematic Mapping Study (SMS) aims to identify which block programming tools support of Education 4.0 in High School. Overall, 46 papers were selected, and data were extracted. Based on the results, a total of 24 identified block programming tools that can be used in high school collaboratively and playfully and with an interdisciplinary methodology. Moreover, it was possible to see that most studies address block programming with high school students, demonstrating a lack of studies that address block programming with teachers. This SMS contributed to identifying block programming tools, emerging technologies, audience (teacher or student), and learning spaces where block programming is being worked on.

Education is facing challenges to keep pace with the widespread introduction of robots and digital technologies in industry and everyday life. These challenges necessitate new approaches to impart students at all levels of education with the knowledge of smart connected robot systems. This paper presents the high-school enrichment program Intelligent Robotics and Smart Transportation, which implements an approach to teaching the concepts and skills of robot connectivity, collaborative sensing, and artificial intelligence, through practice with multi-robot systems. The students used a simple control language to program Bioloid wheeled robots and utilized Phyton and Robot Operating System (ROS) to program Tello drones and TurtleBots in a Linux environment. In their projects, the students implemented multi-robot tasks in which the robots exchanged sensory data via the internet. Our educational study evaluated the contribution of the program to students’ learning of connectivity and collaborative sensing of robot systems and their interest in modern robotics. The students’ responses indicated that the program had a high positive contribution to their knowledge and skills and fostered their interest in the learned subjects. The study revealed the value of learning of internet of things and collaborative sensing for enhancing this contribution.

In this paper we present PearProgram, a hybrid learning and research tool that helps introductory Computer Science (CS) students learn how to pair program, including in remote learning environments. Grounded in theory from the Learning Sciences, the tool -- a collaborative, online IDE -- has two primary goals: 1) to help introductory CS students achieve pair programming success; and 2) to research what factors contribute to pairs that have beneficial outcomes. We present our learnings from the use of PearProgram in three remote introductory CS courses: a CS1 course, and two large international courses, including one for high school students. Teacher and student users responded positively to PearProgram, and use of the tool was associated with beneficial learning outcomes in these online learning environments. Our research opens many future research directions for (remote) pair programming, and indicates practices that may prove useful for CS educators at all levels.

The purpose of this study was to analyze the effectiveness of STEM-PjBL learning on students' critical thinking, creative thinking, communication, and collaboration (4C) skills. This research is quantitative with a one-group pretest-posttest design. The research samples were 106 class XI State Senior High School 16 Semarang students. Quantitative data on critical thinking skills were analyzed using the N-gain test, while other data were analyzed descriptively. The results showed increased students' critical thinking skills based on the N-gain test. The paired sample t-test results in the SPSS 28 program obtained a significance value/Sig. (2-tailed) of 0.001, there were differences in critical thinking skills before and after STEM-PjBL learning. STEM-PjBL learning effectively trained students' creative, collaborative, and communicative thinking skills with high categories. This learning effectively trained students' critical thinking, creative thinking, communication, and collaboration skills

Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students’ efficiency of programming learning and development of computational thinking. To address the above issues, this study introduces generative AI into human-computer collaborative programming learning and proposes a dialogue-negotiated human-computer collaborative programming learning method based on generative AI. The method focuses on the problems-solving process and constructs multiple agents through Prompt design, which enable students to improve their computational thinking and master programming skills in the process of human-computer interaction for problem-solving. Finally, a quasi-experiment was conducted to verify the effectiveness of the proposed method in a 10th grade computer programming course in a high school. 43 students in the experimental group learned with the proposed method, while 42 students in the control group adopted the traditional computer-supported human-computer collaborative programming learning method. The experimental results showed that the proposed method more significantly improved students’ computational thinking, programming learning attitudes, and learning achievement. This study provides theoretical foundations and application reference for future generative AI-assisted human-computer collaborative teaching.

STEM learning communities provide a structure for social interactions among students, their peers and STEM professionals that benefit students to acquire computational literacy and persistence in science disciplines. Focusing on STEM skills development at secondary education level, a female Computer Science teacher at the 7th public Junior High school of Athens Greece, founded the voluntary Coding Club “GreekCodersK12”. This K-12 education initiative ran for 4 years (2015-2019), formed a learning community based on creativity, innovation and digital skills enhancement and collaborated with researchers, professionals, scientists who contributed voluntarily in the Club’s projects. This paper presents the experiences gained and the lessons learned during this four-year period and throws light on the role of a potential Coding Club founder. The results of this learning path, indicate that a Coding Club is an inclusive after-school programme that can function as an umbrella for innovative activities like programming, educational robotics, STEM, STEAM and ESTEAM (Entrepreneurship – Science – Technology – Engineering – Art – Mathematics).

Abstract This systematic analysis examines effectiveness research on online and blended learning from schools, particularly relevant during the Covid‐19 pandemic, and also educational games, computer‐supported cooperative learning (CSCL) and computer‐assisted instruction (CAI), largely used in schools but with potential for outside school. Eight research databases were searched. Studies which were non‐school, before 2000, not in English, without data and duplicates were removed, leaving 1355 studies: online 7%, blended 13%, CSCL 7%, games 26% and CAI 47%. Overall, digital technology was more effective (better) than regular instruction in 85% of studies, 8% the same and 3% worse. Blended learning was considerably better than online learning. CAI was the most effective, with games and CSCL coming after blended learning, but of course CAI was not searched for and these were not widely used outside of schools. Primary and early years/kindergarten were most effective (87% better) and secondary/high next (80%). Although science and mathematics were the most popular subjects, English as a foreign language interventions were the most effective, then writing and STEM, thinking, arts/music, humanities, health and science, reading and maths, foreign languages and English in that order. Overall, females did better than males. ‘Low ability’ children and second language learners did especially well. Disadvantaged and special educational needs/disabled students did slightly worse. Self‐efficacy was highly related to better outcomes. The limitations/strengths of the research were discussed and linked back to previous literature, a critical analysis made, and detailed implications for practitioners, policy makers and future researchers outlined. Digital technology's main advantage may be the possibility for enhanced task flexibility and learner autonomy, encouraging greater self‐regulation. However, this may not be an advantage for all students.

This study aims to examine the effectiveness of Computer-Supported Collaborative Learning (CSCL) on academic achievement. The study was conducted using the meta-analysis method. In the present study, a total of 40 studies that were carried out between 2010 and 2020 and met the inclusion criteria were subjected to meta-analysis. In the present study, the values of the effect size and combined effect size of each study included in the meta-analysis were calculated using Comprehensive Meta-Analysis (CMA) software. The sample of the study consists of 3474 participants. The results of the study revealed that the studies were usually conducted at the university stage with a medium sample size, and published as articles. It was also revealed that most of the studies were carried out in the field of sciences and social sciences. Considering the intervention durations, it was observed that the studies were particularly carried out within a period between 1 and 4 weeks (37.8%). According to the results of the analysis, the average effect size was calculated as 0.523. Considering the results, it can be stated that CSCL has a positive and moderate effect on academic achievement. Also, the results of the moderator analysis revealed that the effect of CSCL on academic achievement did not change by the learning stage, domain subject, and the sample size but it changed by the intervention duration.

Orchestrating collaborative learning (CL) is difficult for teachers as it involves being aware of multiple simultaneous classroom events and intervening when needed. Artificial intelligence (AI) technology might support the teachers’ pedagogical actions during CL by helping detect students in need and providing suggestions for intervention. This would be resulting in AI and teacher co-orchestrating CL; the effectiveness of which, however, is still in question. This study explores whether having an AI assistant helping the teacher in orchestrating a CL classroom is understandable for the teacher and if it affects the teachers’ pedagogical actions, understanding and strategies of coregulation. Twenty in-service teachers were interviewed using a Wizard-of-Oz protocol. Teachers were asked to identify problems during the CL of groups of students (shown as videos), proposed how they would intervene, and later received (and evaluated) the pedagogical actions suggested by an AI assistant. Our mixed-methods analysis showed that the teachers found the AI assistant useful. Moreover, in multiple cases the teachers started employing the pedagogical actions the AI assistant had introduced to them. Furthermore, an increased number of coregulation methods were employed. Our analysis also explores the extent to which teachers’ expertise is associated with their understanding of coregulation, e.g., less experienced teachers did not see coregulation as part of a teacher’s responsibility, while more experienced teachers did.

The development of the Metaverse has drawn much attention in education. Roblox, as an important platform in the Metaverse, attracts millions of young users, which raises the question of how its effectiveness as a learning environment can be maximized. This study aims to synthesize the available evidence to provide an overview of the current research on learning in Roblox by exploring its benefits, challenges, and existing gaps. In line with PRISMA and assisted by LDA topic modeling, we included 40 studies that were analyzed to answer our questions. The research findings show that: (1) Roblox could be combined with social interactive learning or collaborative learning environments, provide a VR environment that supports learning, and be of benefit to programming in STEM education; (2) the use of Roblox in learning has the advantages of attracting a large number of student users, eliciting the positive attitudes of students, and promoting students’ cognitive and noncognitive learning abilities; and (3) there are also challenges such as cyberbullying, cybersecurity, lack of adequate teaching design, etc. Empirical studies on this topic have only begun to emerge, and more future research is needed into different pedagogical scenarios to explore the effects, factors, outcomes, designs, etc.

Abstract Background Instructor scaffolding is proved to be an effective means to improve collaborative learning quality, but empirical research indicates discrepancies about the effect of instructor scaffoldings on collaborative programming. Few studies have used multimodal learning analytics (MMLA) to comprehensively analyze the collaborative programming processes from a process-oriented perspective. This research conducts a MMLA research to examine the immediate and delayed effects of instructor scaffoldings on small groups’ collaborative programming in K-12 education context with an aim to provide research, analytics, and pedagogical implications. Results The results indicated that the instructor provided five types of scaffoldings from the social, cognitive, and metacognitive dimensions, and groups had seven types of responses (i.e., immediate uptake and delayed use) to five instructor scaffoldings, ranging from the low-to-medium and high level of cognitive engagement. After the scaffolding was faded, groups used the content from the high-control cognitive scaffolding frequently to solve problems in a delayed way, but groups did not use the instructor’s scaffolding content from the social and low-control cognitive scaffoldings from the pedagogical perspective, instructors should consider scaffolding types, group states and characteristics, as well as the timing of scaffolding to better design and facilitate collaborative programming. From an analytical perspective, MMLA was proved to be conducive to understand collaborative learning from social, cognitive, behavioral, and micro-level dimensions, such that instructors can better understand and reflect on the process of collaborative learning, and use scaffoldings more skillfully to support collaborative learning. Conclusions Collaborative programming is encouraged to be integrated in STEM education to transform education from the instructor-directed lecturing to the learner-centered learning. Using MMLA methods, this research provided a deep understanding of the immediate and delayed effects of instructor scaffoldings on small groups’ collaborative programming in K-12 STEM education from a process-oriented perspective. The results showed that various instructor scaffoldings have been used to promote groups’ social and cognitive engagement. Instructor scaffoldings have delayed effects on promoting collaborative programming qualities. It is highly suggested that instructors should integrate scaffoldings to facilitate computer programming education and relevant research should apply MMLA to reveal details of the process of collaboration.

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

In 2018 and 2019, Infusing Computing offered face-to-face summer PD workshops to support middle and high school teachers in integrating computational thinking into their classrooms through week-long summer PD workshops and academic-year support. Due to COVID-19, 151 teachers attended the Summer 2020 PD workshops in a week-long virtual conference format. In this paper, we describe Virtual Pivot: Infusing Computing, which employed emerging technology tools, pre-PD training, synchronous and asynchronous sessions, Snap! pair programming, live support, and live networking. Drawing on findings from participant interviews and post-PD surveys, we argue that three categories of changes (digital tools, formats, and supports for teacher engagement and collaboration) were effective in increasing participants' self-efficacy in teaching CT, supporting collaboration, and enabling participants to design CT-infused content-area lessons. We conclude by discussing how elements of this virtual PD can be replicated to increase teacher and student access to CT practices in middle and high school classrooms

In the past decade, governments worldwide have incorporated programming education in primary and secondary schools as a crucial initiative to cultivate technical talent and enhance international competitiveness. Against this backdrop, this paper examines the efficacy and implementation strategies of pair programming in Chinese high school information technology curricula. Pair programming is an effective learning approach that fosters computational thinking, communication and collaboration skills, confidence and self-efficacy, innovative thinking, and problem-solving abilities while simultaneously augmenting students' programming expertise and practical experience. To optimize the implementation of pair programming instruction, this paper proposes several recommendations, including defining students' pair programming roles, supplying essential programming tools and resources, judiciously allocating time, encouraging student sharing and interaction, emphasizing class cohesion, offering personalized guidance, and continually refining teaching methodologies.

This study seeks to deepen the understanding of the direct and indirect effects of human–computer dialogic interaction programming activities, facilitated by ChatGPT, on student engagement. Data were collected from 109 Chinese high school students who engaged in programming tasks using either ChatGPT-driven dialogic interaction or traditional pair programming. A quasi-experimental analysis revealed that ChatGPT-based programming activities remarkably boost student engagement, outperforming pair programming in behavioral, cognitive, and emotional dimensions. Results demonstrated that such activities help minimize off-task behaviors, promote higher-order cognitive skills, and foster greater interest in programming. Additionally, these interactions enhance students’ self-efficacy and reduce learning anxiety. The findings underscore the potential of ChatGPT-driven dialogic interaction in programming education. This study offers practical recommendations to enhance student engagement in programming learning.