This study analyzes SSCI papers on “game learning” and “artificial intelligence” in WOS (2014-2024) using CiteSpace. The study Finds that, at present, core authors have contributed a comparatively limited number of publications, while tech companies and universities have formed small-scale “industry-academia-research” collaborations. Keyword analysis indicates that “Design” and “motivation” are key research focuses, and ten main directions are identified, including AI, reinforcement learning, ICT, etc. Finally, future research is suggested to focus on four areas: improving teachers' intelligent game-based teaching competencies, balancing technological and educational priorities, strengthening tech-game synergy, and broadening research scope. These efforts aim to provide novel perspectives and strategies for game-based learning research in the context of AI. (Abstract)

Prior studies have mainly focused on testing collaborative programming learning (CPL) patterns while neglecting the exploration of the dynamic evolution of social epistemic interaction patterns among different groups. Studying the social and epistemic network nature of learner interaction is crucial to understanding the CPL process. This study aims to explore the social epistemic interaction patterns and their evolutionary path among different groups. In this quasi-experimental design, 51 high school students were randomly allocated into 17 groups. Content analysis was used to analyze online collaborative conversations and interaction contents in the early, middle, and later periods of CPL. Social epistemic network and cluster analyses revealed three interaction patterns. The results showed that groups in cluster 1 were composed of core roles, which exhibited a multi-center balanced collaboration pattern (MBCP), and their social epistemic interaction levels showed a continuous upward trend; groups in cluster 2 included core, semi-core, and edge roles, respectively, and demonstrated a hierarchical center-led coordination pattern (HLCP) that initially gained but later declined in social epistemic interaction levels; groups in cluster 3 included core and edge roles, and displayed a single-center feedback cooperation pattern (SFCP), which remained consistently low in social epistemic interaction levels. Our findings emphasize the importance of CPL’s social epistemic interactions. By recognizing these patterns, educators can better facilitate meaningful student interactions, fostering deeper learning and social development.

Blended learning intervention strategies (BLIS) play a crucial role in enhancing the effectiveness of blended learning (BL) and enriching student learning experiences. However, the lack of a comprehensive assessment system limits their broader application. This study proposes a satisfaction-oriented assessment system for BLIS. Initial indicators were identified through text analysis and refined via three Delphi Expert Consultation (DEC) rounds. The Analytic Hierarchy Process (AHP) was employed to determine indicator weights, resulting in a system comprising 4 first-level indicators, 12 s-level indicators, and 32 third-level indicators. Furthermore, five satisfaction levels-initial, advanced, growth, optimization, and maturity-were defined with corresponding observational traits. Ultimately, the practical validation confirmed that the system effectively measures students' satisfaction with BLIS. This assessment system can offer meaningful guidance for educators in designing, selecting, and adjusting BL interventions, supporting evidence-based improvement of BL implementation.

ABSTRACT Background Robot programming can simultaneously cultivate learners' computational thinking (CT) and spatial thinking (ST). However, there is a noticeable gap in research focusing on the micro‐level development patterns of learners' CT and ST and their interconnections. Objectives This study aims to uncover the intricate development patterns and interrelations between learners' CT and ST at a micro level within a robot programming environment. Methods Thirty middle school students participated in the study and completed SOLO robot programming tasks. Process data on their online programming behaviour were collected through programming platform log data, screen recordings and synchronised think‐aloud audio, while result data were obtained through CT and ST standardised tests. The data were then analysed using cluster analysis, process mining techniques, Kruskal–Wallis, and Spearman analysis. Results and Conclusions The results revealed three clusters of learner types (i.e., masters, debuggers and beginners). Masters skilfully employed both CT and ST, while debuggers effectively activated ST through feedback. Beginners, however, showed lower proficiency in both. Notably, their CT structures demonstrated bidirectional thinking, top‐down systematic programming and bottom‐up debugging methods. The intrinsic developmental structure of ST shifted from direction language to spatial behaviour. Furthermore, ST and CT were mutually enhanced and mental rotation in ST is positioned as a foundational skill for CT. Implications These findings contribute to a deeper understanding of learners' intrinsic thinking processes and offer essential guidance for optimising programming education.

Programming education is burgeoning, but it encounters hurdles in implementing thinking-based intelligence instruction. The emergence of generative artificial intelligence, through the utilization of prompt engineering, not only provides meticulous feedback but also significantly elevates the quality and efficiency of human-computer interaction (HCI), thereby nurturing computational thinking (CT). This study aimed to reveal the developmental characteristics of CT and the “black box” of the HCI process through learning analytics methods (i.e., microgenetic analysis, lag sequential analysis, cluster analysis, paired t-tests). 44 college students participated in progressive prompt-assisted programming learning. The results indicated that generative progressive prompts significantly improved students’ CT and its sub-dimensions (i.e., creativity, problem-solving, algorithmic thinking, critical thinking, and cooperativity). Moreover, algorithmic thinking was identified as the core skill in CT development. Additionally, regarding students’ HCI patterns, students with low-level CT focused on more superficial interaction patterns, such as guided and exploratory behaviors, while students with high-level CT concentrated on more in-depth interaction patterns, including debating and summarizing behaviors. Based on our findings, educators in programming should incorporate generative prompts and tailor strategies to accommodate diverse HCI patterns among students with varying CT levels.

Tangible programming tools have become a mainstream teaching aid in gamification programming learning (GPL) due to their interactivity and ability to enhance novice learners' computational thinking and spatial reasoning skills. However, comparing the relative efficacy of different programming tools that simultaneously support these skills was not adequately explored. This study designed and evaluated three programming tools: the tangible programming tool (TPG), which uses real touchable objects; the block programming tool (BPG), which employs virtual programming blocks and 3D game scenarios; and the paper-and-pencil programming tool (PPG), which uses paper and pen to draw. The study involved 112 seventh-grade students from three natural classes: Class A (TPG, n1=37), Class B (BPG, n2=38), and Class C (PPG, n3=37). These students completed four gamification programming tasks and CT skills, spatial reasoning skills, enjoyment, cognitive load and GPL task list measurements. The results indicated that the tangible programming tool led to lower cognitive load, significant improvement in spatial reasoning skills and better abstraction and problem decomposition skills. The block programming tool provided a more enjoyable experience and facilitated students' algorithm design and efficiency. The paper-and-pencil programming tool was found to be less effective in improving spatial reasoning skills. This study's findings can help programming educators cultivate students' thinking skills and improve their learning experience by effectively selecting the most appropriate programming tools.

Feedback is crucial during programming problem solving, but context often lacks critical and difference. Generative artificial intelligence dialogic feedback (GenAIDF) has the potential to enhance learners’ experience through dialogue, but its effectiveness remains sufficiently underexplored in empirical research. This study employed a rigorous quasi-experimental design and collected multidimensional data through mixed methods to investigate the impact of GenAIDF at different stages of programming problem-solving on high school students’ programming skills and critical thinking. One hundred seventy-two high school students from four distinct classes participated in this study. We established three experimental groups, introducing GenAIDF during the code writing (CAG, NCAG = 43), verification debugging (DAG, NDAG = 43), and both code writing and verification debugging (CDAG, NCDAG = 43) stages, and one control group, without GenAIDF introduced at any stage (NAG, NNAG = 43). The results indicated that, first, in terms of programming skills, the three experimental groups exhibited no significant difference in their programming knowledge, yet they significantly outperformed the control group. CAG excelled in programming project performance, while DAG excelled in structure. CDAG excelled in functions but had poor plagiarism scores. Second, regarding critical thinking skills, DAG performed best, followed by CAG, CDAG, and NAG, with significant differences observed among the four groups. Finally, student interviews revealed increased learning engagement, satisfaction, and critical thinking consciousness. Based on these findings, the study provides empirical recommendations for teachers on effectively utilizing GenAIDF in the future.

在数字技术爆炸式发展乃至人工智能技术涌现的时代,教师是教育数字化战略得以落地的核心和关键.教师应该具备和发展哪些素养与能力,成为国内外教育领域研究的关注重点.教师的数字素养水平直接关乎教育数字化转型进程以及人才的培养质量,关乎我国教育现代化和教育强国战略的实现.

Tangible programming combines the advantages of object manipulation with programmable hardware, which plays an essential role in improving programming skills. As a tool for ensuring the quality of projects and improving learning outcomes, the PDCA cycle strategy is conducive to cultivating reflective thinking. However, there is still a lack of empirical research on the effect of introducing the PDCA cycle strategy into programming education. In this study, using a PDCA cycle strategy, in a four-pronged model of “(P)draw up a plan, (D)assemble and programming, (C)test and debug, display and reflect (A),” and its effects on students’ programming skills and their reflective thinking were explored. There were 65 children between the ages of 7 and 8 years participated in this study. There were 31 students in each of the experimental group and the control group. A combination of qualitative and quantitative research methods was adopted in this research, and students’ programming processes and results were observed and counted. The study results revealed that after attending the ‘Magic Card Robot’ course that applied the PDCA cycle strategy, the experimental group students outperformed their counterparts in programming skills (sequencing, repetitive and conditional structures). Meanwhile, the experimental group students’ reflective thinking levels were higher than those of the control group students. These findings imply that tangible programming education using the PDCA cycle strategy in the course has potential.

Electronic homework will be a trend with the improvement of informatization, and it has proved effective by researchers. However, there still exists a problem that the amount of students’ workload does not match their knowledge level. In order to solve this problem, the study applies the connection and interaction of knowledge emphasized by connection to construct connectionism-based interactive electronic homework approach to achieve the dynamic presentation of homework. By carrying out comparative experiments, the study has discovered that the approach can improve students’ learning performance, learning motivation and learning engagement, and there is a positive correlation between learning engagement and learning performance. Therefore, a connectionism-based interactive electronic homework approach will help primary school students complete their mathematics homework in a more efficient and high-quality way.

随着智能技术与教育的融合,通过有效的评估方式促进学生素养提升成为当前研究的核心.沉浸式学习环境以其认知性、关联性和情境性催生出未来学习的新场域,为智能时代学生核心素养的评估提供了新方向.针对当前隐形性评估存在的缺乏对过程性数据的采集与分析且评估结果效度较低等问题,研究采用文献分析法将沉浸式学习环境的典型特征与隐形性评估中"以证据为中心的设计"相弥合,深入探究两者相契合的机理以论证隐形性评估嵌入沉浸式学习环境的可行性.通过确定培养目标、建立能力模型,选择沉浸环境、设计任务模型,搜集过程数据、关联证据模型,学习动态反馈、实时精准推送等不同环节的迭代优化构建沉浸式学习环境隐形性评估实施框架.最后,通过虚拟游戏《死亡蜜蜂》案例分析,展现了实施框架的应用方式,并验证了其具有良好的可操作性和有效性.

● 论文选题是期刊论文发表过程中非常关键的部分.确定选题的方法有很多种:题目要反映研究的新颖性;题目要反映主题的学术性及研究价值;研究热点话题,包括各类基金项目和期刊当年的选题方向;领导和工作团队指定;由目前的文献找出研究遗留的问题;提出不同的方法和应用等.判断一个选题的可行性,从学术的角度看,要考虑选题解决什么问题,如何进行论证,选题是基于哪些学科的知识体系和理论方法,以及选题的局 限性.

In recent years, the experiment of the hybrid teaching model in colleges and universities has been carried out in an orderly manner, coupled with the guidance of national policies, has gradually formed a more diversifi ed evaluation method. However, despite the many attempts made by colleges and universities in recent years, the understanding of blended teaching at home and abroad is still in a period of confusion and confusion, and the various evaluation index systems proposed still have areas for improvement. Based on this, this article will sort out the evaluation index system of blended teaching and investigate the current evaluation status. The improvement of the evaluation indicators for the majors with more experimental courses will have important signifi cance and value for ensuring the comprehensive and eff ective promotion of blended teaching activities.

As a key module of the adaptive learning system, open learner models attracted wide attention in the academic world. A deep understanding of open learner models is of great significance for the implementation and optimization of personalized learning support in adaptive learning system. However, the current domestic research on open learner models is not enough. Therefore, this paper analyzed the contents of 33 foreign documents, reviews the research status of design elements of open learner models, and analyzes the empirical research characteristics on the application effect of the open learner models. Based on this, problems were summarized such as the lack of diversity in user groups, relatively limited effect verification methods, practical application mainly serving higher education, and so on. This paper then puts forward improvement suggestions from three perspectives of open content design, open form design and access design, in order to effectively promote the localization application of adaptive learning system research based on the open learner model.

游戏化学习作为一种新型学习模式,能有效发挥游戏的教育价值并兼顾教学的知识传递性与学习趣味性的特点,提高学习者的积极性和参与度.文章基于概念支架理论,针对"装机模拟器(PC Building Simulator)"游戏设计了图文和视频两种外部概念支架,采用准实验研究法研讨不同支架条件下的学习者在游戏化学习过程中的心流体验和学习效果.结果表明:在游戏化学习环境中,外部概念支架能够帮助学习者在游戏内容和学科知识之间建立联系;同时,外部概念支架与无支架相比,学习者在游戏化学习过程中的学习效果存在显著差异;图文版外部概念支架使学习者在游戏化学习过程中的心流体验显著提高,并且学习者的心流体验与学习效果之间存在正相关性.

在信息社会中,计算思维的培养显得日益重要.文章首先阐述了计算思维的内涵,并以Korkmaz设计开发的CTS作为评价学生计算思维的工具.之后,文章深入分析了严肃游戏的优势,结合Kolb体验式学习模型,将学生的学习过程描述为一个游戏周期体验过程,并将计算思维的五个维度作为核心培养内容,构建了"基于游戏的体验式学习计算思维培养模型".为验证此模型的有效性,文章选用教育类Flash编程游戏Lightbot,进行了此模型的教学应用.通过问卷调查和半结构化访谈,文章发现:基于游戏的体验式学习计算思维培养模型能有效提升学习者的计算思维.最后,文章针对模型应用效果提出相应的改进建议,以期为相关教育工作者培养学习者的计算思维提供参考.

课程思政是积极落实"三全育人"的关键举措,是对高校课程建设的改革与创新.教育学类课程落实课程思政有其固有的优势,在此背景下,对课程思政元素的挖掘、实践活动的探索以及教师意识的深化是实现构建教育学特色课程思政育人格局的重要手段.

加强对科技创新人才的培养是我国实现人才强国战略的重要任务之一,终身学习是科技创新人才成长的重要路径.采用问卷调查法,辅以访谈法,探究北京中关村科技园区科技创新人才的终身学习现状发现,科技创新人才具有提升职业能力的学习动机,偏爱混合式学习方式,善于通过在线社群联通知识,在线资源付费意愿强烈,体现了科技创新人才身份符号下的成长需求、技术影响下的学习转变以及知识价值下的理性消费.针对研究结果,从政府、企业及个人角度提出了针对科技创新人才终身学习的发展建议.

本研究根据知识分类研究的最新成果,构建知识分类视角的线上线下融合的教学模式,尝试探索疫情期间开展精准教学的新模式,助力疫情期间线上教学的开展.