Entertainment Computing最新文献

Unravelling the projective identity in computer role-playing games: A narrative inquiry 揭示电脑角色扮演游戏中的投射身份：一种叙事探究

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-17 DOI: 10.1016/j.entcom.2026.101084

Fatima Zohra Benzert, Hanane Sarnou

In computer role-playing games (CRPGs), players are afforded a large scope of identificatory possibilities, allowing for the formation of multi-layered and complex player-as-avatar constructs. Framing this liminal entanglement within James Gee’s projective identity theory, this study explored indepth the composition and structure of player-as-avatar constructs. We employed a narrative inquiry approach using semi-structured interviews to collect comprehensive storied accounts of projective identity formation experiences from the subjective perspective of 14 players from different backgrounds, specifically in CRPG contexts. Findings indicated that projective identities may constitute various overlapping self-defining components across personal, social, relational, and material levels. These identities are shaped by both game affordances and players’ transgressive engagement with game design and content, which highlights the nuanced and fluid nature of identity formation in virtual spaces. This research provides new insights into the multi-layered structure of projective identities and emphasises the importance of understanding player perspectives in the study of virtual identity construction.

在电脑角色扮演游戏（crpg）中，玩家有很大的识别可能性，可以形成多层次和复杂的玩家角色结构。在James Gee的投射身份理论框架下，本研究深入探讨了玩家作为虚拟角色的构成和结构。我们采用叙述性调查方法，利用半结构化访谈，从14名来自不同背景的玩家（特别是在CRPG环境中）的主观角度，收集关于投射性身份形成经历的综合故事。研究结果表明，投射身份可能在个人、社会、关系和物质层面上构成各种重叠的自我定义成分。这些身份是由游戏场景和玩家对游戏设计和内容的过分投入所塑造的，这突出了虚拟空间中身份形成的微妙和流动性。这项研究为投射身份的多层结构提供了新的见解，并强调了在虚拟身份构建研究中理解玩家视角的重要性。

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引用次数: 0

Beyond the screen: Machine learning models for forecasting relationship compatibility after initial meetings 屏幕之外：用于预测初次见面后关系兼容性的机器学习模型

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-11 DOI: 10.1016/j.entcom.2026.101085

Tao Yan

Within this regard, the pervasiveness of the Internet and social networking websites has fully dramaturgically altered traditional cultural attitudes towards dating and relationships in the trend of establishing acquaintances through cyberspace prior to face-to-face dates. During these first dates, it is quite common that an individual must make one of life’s most grueling decisions-to pursue or foreclose an emerging relationship. This research deals with the prediction of potential partners’ compatibility and the relation trajectory after the first date in online dating. In this research, the possibility of mutual compatibility of relationships could be estimated using artificial intelligence and machine learning approaches, which include the CATC, SVC, and LRC models optimized by Giant Armadillo Optimization. By carefully studying a wide variety of input parameters and corresponding prediction results, the present study brings out that an optimized result of CATC with GAO, known as the CAGA model, derives an accuracy of 0.987 with the same level of precision at the time of training itself. Thus, in this regard, the model CAGA emerges as the ultimate best predictor and has been especially marked for their accuracy towards real-world relationships.

在这方面，互联网和社交网站的普及完全戏剧性地改变了传统文化对约会和关系的态度，人们倾向于在面对面约会之前通过网络空间建立熟人。在第一次约会中，一个人必须做出一生中最艰难的决定之一——追求或取消一段新兴的关系，这是很常见的。本研究探讨网络约会中潜在伴侣相容性的预测及初次约会后的关系发展轨迹。在本研究中，可以使用人工智能和机器学习方法来估计关系相互兼容的可能性，包括由Giant Armadillo Optimization优化的CATC、SVC和LRC模型。通过对大量的输入参数和相应的预测结果的仔细研究，本研究得出了具有GAO的CATC的优化结果，即CAGA模型，其精度为0.987，与训练时的精度水平相同。因此，在这方面，CAGA模型成为最终的最佳预测器，并因其对现实世界关系的准确性而特别突出。

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引用次数: 0

Prediction of MOBA game events based on In-Game Data 基于游戏内数据的MOBA游戏事件预测

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-22 DOI: 10.1016/j.entcom.2026.101091

Michail Vardakis , George Margetis , Ioannis Chatzakis , Konstantinos C. Apostolakis , Constantine Stephanidis

The growth of esports as a widely enjoyed activity for entertainment and social engagement has positioned it as one of the prominent domains of entertainment, gaming, and sports, attracting considerable interest from the research community. Considering that esports generate rich, real-time telemetry data, which can constitute the critical mass needed for researching data analysis and forecasting, a significant potential arises, especially in providing more engaging viewing and commentary. In this work, we study short-horizon event prediction in League of Legends, focusing on predicting imminent player elimination events (”deaths”) instead of overall outcome prediction during professional matches. We propose a time-series forecasting approach based on Temporal Fusion Transformers that uses multi-modal in-game data formulated as time-series to forecast near-term elimination events. Our system is designed to facilitate esports sportscasters, directors, producers, and content creators, attempting to surface likely high-impact moments and support proactive narrative cues. Trained in less than 200 matches, for a 5.0-second forecasting horizon and based on 10.0 s worth of historical data, our model achieves a

F_{1}

score of around 0.6, which constitutes its performance comparable to similar work. This study demonstrates that short-horizon event forecasting is feasible using in-game data and transformer-based temporal models for the esports domain, thereby introducing a novel approach addressing a research topic that is rarely explored in esports analytics literature. Its findings suggest practical improvements for real-time support of esports’ storytelling experience and open avenues for future research on anticipatory analytics in interactive and spectator-driven digital sports.

电子竞技作为一种广泛受欢迎的娱乐和社交活动，其发展使其成为娱乐、游戏和体育的突出领域之一，吸引了研究界的极大兴趣。考虑到电子竞技产生丰富的实时遥测数据，这些数据可以构成研究数据分析和预测所需的临界质量，因此产生了巨大的潜力，特别是在提供更具吸引力的观看和评论方面。在这项工作中，我们研究了《英雄联盟》中的短期事件预测，重点是预测即将到来的玩家淘汰事件（“死亡”），而不是预测职业比赛中的整体结果。我们提出了一种基于时间融合变压器的时间序列预测方法，该方法使用多模态游戏内数据作为时间序列来预测近期淘汰事件。我们的系统旨在为电子竞技赛事主持人、导演、制作人和内容创作者提供便利，试图呈现可能具有高影响力的时刻，并支持主动叙事线索。在不到200场比赛的训练中，在5.0秒的预测范围内，基于10.0秒的历史数据，我们的模型获得了大约0.6的F1分数，这使得它的性能与类似的工作相当。本研究表明，在电子竞技领域，使用游戏内数据和基于转换器的时间模型进行短期事件预测是可行的，从而引入了一种新颖的方法来解决电子竞技分析文献中很少探索的研究主题。其研究结果为电子竞技故事体验的实时支持提供了切实可行的改进，并为未来在互动和观众驱动的数字体育中进行预期分析的研究开辟了道路。

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引用次数: 0

Avatar identification in online games: the moderating role of presence 网络游戏角色认同：在场的调节作用

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-18 DOI: 10.1016/j.entcom.2026.101089

Zhenglin Zhang, Chenyan Li

With the Internet’s growth and evolving lifestyle needs, social activities have extended into virtual online environments. We investigate factors affecting avatar identification in online games and their consequences, to guide game design and prevent internet addiction. This study analyzes the impact of avatar similarity and attachment styles on self-avatar and avatar-avatar identification through questionnaires, also examining presence’s moderating and substitutive roles. Using Smart PLS 4.0, we constructs a structural equation model showing that similarity enhances avatar identification; anxious attachment positively and avoidant attachment negatively influence it; avatar identification promotes game engagement; presence and avatar similarity mutually influence and substitute in avatar identification; Presence weakens the avoidant attachment-avatar identification link. This paper advises game developers to conduct market research for user profiling, offer customizable avatars, minimize user-avatar differences, and enhance immersion. It also encourages gamers to balance real and virtual identities, promoting enjoyable avatar identification and personal development.

随着互联网的发展和生活方式需求的演变，社交活动已经扩展到虚拟的在线环境中。研究影响网络游戏角色识别的因素及其后果，以指导游戏设计和预防网络成瘾。本研究通过问卷调查的方式，分析虚拟化身相似度和依恋类型对自我虚拟化身和虚拟化身-虚拟化身认同的影响，并考察存在的调节和替代作用。利用Smart PLS 4.0构建了一个结构方程模型，表明相似性增强了头像识别；焦虑型依恋对其有积极影响，回避型依恋对其有消极影响；角色识别提升了游戏粘性；虚拟形象识别中的存在感与相似性相互影响、相互替代；存在削弱了回避型依恋-化身识别的联系。本文建议游戏开发者针对用户特征进行市场调查，提供可定制的角色，最小化用户角色差异，并增强沉浸感。它还鼓励玩家平衡真实和虚拟身份，促进愉快的角色识别和个人发展。

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引用次数: 0

The antecedents and consequences of Cross-platform Media credibility in an emerging country: The integration of News-Finds-Me Perception and Bandwagon Effect 新兴国家跨平台媒体可信度的前因后果：新闻发现与从众效应的整合

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-16 DOI: 10.1016/j.entcom.2026.101087

Doan Viet Phuong Nguyen , Thanh-Binh Phung , Angelina Nhat-Hanh Le

The relationship between various media types is a complex and fascinating research topic. The ascent of online media has undoubtedly contributed to the decline of traditional media consumption. Despite their competition, traditional media can benefit from the social media user base. In the mentioned relationship, it is important to consider the effects of the social media users’ characteristics, such as the Bandwagon and News-finds-me perception, in accordance with the essential factors, including media credibility, using motivation, and re-use intention in the context of cross-platform, with television and SNS participation. Using the PLS-SEM procedure, this study evaluates the intricate relationship between the aforementioned factors. The findings indicate complementary effects between the two collaborative platforms, as the media users’ perceptions may influence the collaborative mediums’ perceived credibility and motivations, which in turn influence users’ re-use intention.

各种媒介类型之间的关系是一个复杂而有趣的研究课题。网络媒体的崛起无疑导致了传统媒体消费的下降。尽管存在竞争，但传统媒体可以从社交媒体的用户群中获益。在上述关系中，根据媒体可信度、使用动机、跨平台背景下的重复使用意愿等要素，在电视和SNS参与的情况下，考虑社交媒体用户特征（例如Bandwagon和news - findme perception）的影响是很重要的。利用PLS-SEM程序，本研究评估了上述因素之间的复杂关系。研究结果表明，两个协作平台之间存在互补效应，媒体用户的感知可能影响协作媒体的感知可信度和动机，进而影响用户的重复使用意愿。

引用次数: 0

Determining the effects of League of Legends ranked tiers on outcome prediction models 确定《英雄联盟》等级对结果预测模型的影响

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-12 DOI: 10.1016/j.entcom.2026.101088

Artin Lafrance, Ratvinder Grewal

League of Legends (LoL) is a popular Multiplayer Online Battle Arena (MOBA) video game with a competitive ranked system. This study trained XGBoost, Random Forest, and Logistic Regression models on Bronze and Diamond tier datasets in order to predict match outcomes as well as observe differences in model performance on Bronze to Diamond tiers. Exactly, 20,781 matches were collected using the Riot API with the intended goal of having 5000 matches for Bronze and Diamond tiers and 1500 for the remaining tiers. Datasets were created for 10, 15, 20 and 25 min for each tier. XGBoost outperformed the other models, as it achieved an accuracy of 72.7% at 10 min for both Bronze and Diamond-trained tiers. At the 15 min interval, the XGBoost model achieved an accuracy of 77.1% trained on Bronze and 76.9% trained on Diamond. At the 20 min interval, it reached an accuracy of 79.3% for the Bronze-trained model and 80.1% on the Diamond-trained model. At the 25 min interval, this accuracy increased to 82.1% for Bronze-trained and 82.8% for Diamond-trained. The Diamond test datasets between 10 to 25 min were more accurately predicted compared to other tiers.

《英雄联盟》（LoL）是一款流行的多人在线竞技游戏（MOBA），带有竞争性排名系统。本研究在铜级和钻石级数据集上训练XGBoost、随机森林和逻辑回归模型，以预测比赛结果，并观察模型在铜级和钻石级上的性能差异。准确地说，使用Riot API收集了20,781场比赛，目标是为青铜和钻石级别收集5000场比赛，为其余级别收集1500场比赛。为每一层分别创建10、15、20和25分钟的数据集。XGBoost优于其他模型，因为它在10分钟内达到了72.7%的准确率，无论是铜级还是钻石级。在15分钟的间隔时间内，XGBoost模型在青铜和钻石上的准确率分别达到77.1%和76.9%。在间隔20 min时，bronze训练模型的准确率为79.3%，diamond训练模型的准确率为80.1%。在25分钟的间隔中，青铜训练的准确率提高到82.1%，钻石训练的准确率提高到82.8%。与其他层相比，Diamond测试数据集在10到25分钟之间的预测更准确。

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引用次数: 0

A recognition method for hitting movements of table tennis players based on fuzzy decision support system 基于模糊决策支持系统的乒乓球运动员击球动作识别方法

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-05-01 Epub Date: 2026-01-22 DOI: 10.1016/j.entcom.2026.101090

Xin Liu , Fangxian Yi

Due to table tennis’s speed and complexity, movement analysis and planning are essential. The pace of these actions and the athletes’ playing styles make conventional analytical approaches difficult. This work introduces a fuzzy decision-support system for the analysis of table tennis striking movements. Fuzzy Inference and Signal Processing for Table Tennis Striking Movement Analysis is the method. Preprocessing includes noise reduction and signal segmentation using preset criteria. Statistical and frequency metrics may be extracted from motion data via feature extraction. To improve accuracy, the hybrid technique employs an Echo State Network (ESN) classifier to combine expert knowledge with empirical data. The suggested approach can recognize striking motions with over 99.7% accuracy across various skill levels. Due to its versatility and real-time performance, this system is ideal for automated sports analytics, coaching, and officiating. Since the research provides a clear and flexible framework for analyzing table tennis motions, it may be applied to other racket sports. The suggested study offers a complete table tennis evaluation and improvement tool.

由于乒乓球运动的速度和复杂性，运动分析和计划是必不可少的。这些动作的速度和运动员的比赛风格使得传统的分析方法变得困难。本文介绍了一种用于乒乓球击球动作分析的模糊决策支持系统。模糊推理与信号处理是乒乓球击球动作分析的一种方法。预处理包括降噪和使用预设标准的信号分割。统计和频率度量可以通过特征提取从运动数据中提取出来。为了提高准确率，混合技术采用回声状态网络（ESN）分类器将专家知识与经验数据相结合。建议的方法可以识别击打动作，准确率超过99.7%，跨越不同的技能水平。由于其多功能性和实时性能，该系统是自动化体育分析，教练和裁判的理想选择。本研究为分析乒乓球运动提供了一个清晰而灵活的框架，可以应用于其他球拍运动。建议的研究提供了一个完整的乒乓球评估和改进工具。

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引用次数: 0

Educational games for learning vocabulary in English as a Foreign Language 学习英语作为外语词汇的教育游戏

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1016/j.entcom.2025.101070

Anna Tonda , Ricardo Pardo , Inmaculada Remolar , Veronica Rossano

Today’s immigration challenges underscore the importance of learning English as a key to accessing better opportunities and integrating into society, given its status as a standard language for communication. A common challenge in studying English as a foreign language is the lack of commitment to courses, resulting in inconsistent progress and difficulty achieving fluency. This study explores the impact of a Virtual Reality game on English vocabulary acquisition among learners of English as a Foreign Language. The game simulates a kitchen environment where users reproduce food recipes, an everyday topic universally relatable across cultures.

To validate the virtual environment, tests were conducted with 40 participants divided equally into experimental and control groups. Pre and post-tests measured vocabulary acquisition and listening comprehension, while a usability questionnaire assessed participants’ interaction with the VR system. Results showed that the experimental group achieved significant vocabulary improvements, with p-values of 0.02 and 0.01, compared to more modest gains in the control group. The usability test indicated high satisfaction and engagement levels, highlighting the VR tool as a compelling method for vocabulary learning in EFL learners.

鉴于英语作为交流的标准语言的地位，今天的移民挑战强调了学习英语作为获得更好机会和融入社会的关键的重要性。将英语作为外语学习的一个共同挑战是缺乏对课程的投入，导致进步不一致，难以达到流利。本研究探讨虚拟现实游戏对英语作为外语学习者英语词汇习得的影响。这款游戏模拟了一个厨房环境，用户在其中复制食物食谱，这是一个跨文化普遍相关的日常话题。为了验证虚拟环境，将40名参与者平均分为实验组和对照组进行了测试。前后测试测量词汇习得和听力理解，而可用性问卷评估参与者与VR系统的互动。结果显示，实验组取得了显著的词汇进步，p值分别为0.02和0.01，而对照组的进步较为温和。可用性测试表明，虚拟现实工具的满意度和参与度很高，这表明虚拟现实工具是英语学习者学习词汇的一种有吸引力的方法。

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引用次数: 0

A comprehensive review and ensemble CNN approach for Bharatanatyam single-hand gesture classification Bharatanatyam单手手势分类的综合综述和集成CNN方法

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-01-01 Epub Date: 2025-12-20 DOI: 10.1016/j.entcom.2025.101069

Kokul Thanikasalam, Amirthalingam Ramanan, Pavithra Kanmanirajah

Bharatanatyam, the oldest Indian classical dance form, relies on hand gestures to convey meanings and narratives. Recognizing these gestures is important for performance analysis and for supporting novice learners. This study presents the first comprehensive review of classification methods for Bharatanatyam single-hand gestures (Asamyukta Hastas), identifying key limitations in existing work. To address the lack of comprehensive datasets and their limited generalization, this study introduces a robust benchmark dataset collected from a large number of dancers. Previous methods, which relied solely on either deep or hand-crafted features, struggled to capture the fine-grained details of complex gestures. To overcome this, an ensemble Convolutional Neural Network (CNN) based model is proposed to classify 30 Asamyukta Hastas using both deep and hand-crafted features. From each raw hand-gesture image, a hand-landmark skeleton image is generated to capture finger positions while removing extraneous details. In addition, an embedded hand-landmark image is produced to provide landmark cues alongside the raw visual features. Three individual CNN models are trained using raw hand gesture images, hand-landmark skeleton images, and embedded hand-landmark images, as each modality provides complementary information. The performance of each CNN is further enhanced using an attention module, and their outputs are ultimately combined through a majority-voting ensemble strategy. The proposed model achieved an average accuracy of 98.28% and an average F1-score of 97.18% on the test set, with a mean recognition time of 325 ms. The source code and dataset for this work are publicly available at https://doi.org/10.5281/zenodo.11514705.

Bharatanatyam是印度最古老的古典舞蹈形式，依靠手势来传达意义和叙述。识别这些手势对于性能分析和支持初学者非常重要。本研究首次全面回顾了Bharatanatyam单手手势（Asamyukta Hastas）的分类方法，确定了现有工作中的关键局限性。为了解决缺乏全面的数据集及其有限的泛化问题，本研究引入了一个从大量舞者中收集的鲁棒基准数据集。以前的方法仅仅依赖于深层特征或手工特征，很难捕捉到复杂手势的精细细节。为了克服这个问题，提出了一种基于集成卷积神经网络（CNN）的模型，使用深度和手工特征对30个浅yukta hasas进行分类。从每个原始手势图像中，生成一个手部地标骨架图像来捕捉手指位置，同时去除无关的细节。此外，还生成了一个嵌入的手地标图像，以在原始视觉特征旁边提供地标线索。使用原始手势图像、手地标骨架图像和嵌入的手地标图像训练三个单独的CNN模型，因为每种模态都提供了互补的信息。使用注意力模块进一步增强每个CNN的性能，并最终通过多数投票集成策略将其输出组合起来。该模型在测试集上的平均准确率为98.28%，平均f1分数为97.18%，平均识别时间为325 ms。这项工作的源代码和数据集可在https://doi.org/10.5281/zenodo.11514705上公开获得。

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引用次数: 0

Beyond pre-scripted interactions: mapping the integration of LLMs in digital game-based learning – a scoping review 超越预先编写的互动：绘制法学硕士在基于数字游戏的学习中的整合-范围审查

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

Entertainment Computing

Pub Date : 2026-01-01 Epub Date: 2026-01-08 DOI: 10.1016/j.entcom.2026.101082

Yu-lin Gong , Min-kai Wang , Yun-Fang Tu , Chang-qin Huang , Di Zhang

Digital Game-Based Learning (DGBL) has demonstrated effectiveness in fostering engagement and academic achievement but faces challenges in adaptability, real-time feedback, and personalized scaffolding. Large Language Models (LLMs) offer promising solutions by enabling interactive learning experiences, dynamic assessments, and adaptive instructional support. This scoping review systematically examines the integration of LLMs in DGBL, assessing their impact on student engagement, learning outcomes, and pedagogical effectiveness. Following PRISMA-ScR guidelines, seven peer-reviewed studies published between 2024 and 2025 were identified from Web of Science, Scopus, ERIC, and PubMed. Thematic analysis revealed that LLM-enhanced DGBL primarily supports three functional roles: (1) conversational AI for interactive scaffolding, facilitating real-time student-NPC interactions; (2) adaptive learning support, personalizing feedback and guiding problem-solving strategies; and (3) automated assessment, evaluating student performance and providing instructional interventions. Findings indicate that LLM-driven DGBL enhances student motivation, cognitive engagement, and academic performance while reducing cognitive load. However, key challenges persist, including AI over-reliance, transparency concerns, and the need for ethical safeguards. Future research should explore longitudinal effects, interdisciplinary applications, and AI literacy strategies to ensure responsible and effective integration of LLMs in game-based learning.

数字游戏学习（Digital Game-Based Learning， DGBL）在促进参与和学术成就方面已经证明了有效性，但在适应性、实时反馈和个性化框架方面面临挑战。大型语言模型（llm）通过实现交互式学习体验、动态评估和适应性教学支持，提供了有前途的解决方案。这一范围审查系统地检查了法学硕士在DGBL中的整合，评估了他们对学生参与、学习成果和教学效率的影响。遵循PRISMA-ScR指南，从Web of Science、Scopus、ERIC和PubMed中确定了2024年至2025年间发表的7项同行评议研究。专题分析显示，llm增强的DGBL主要支持三个功能角色：(1)用于交互式脚手架的会话AI，促进实时学生与npc的交互；(2)自适应学习支持、个性化反馈和指导性问题解决策略；(3)自动评估，评估学生表现并提供教学干预。研究结果表明，llm驱动的DGBL提高了学生的学习动机、认知投入和学习成绩，同时减少了认知负荷。然而，关键挑战依然存在，包括对人工智能的过度依赖、对透明度的担忧以及对道德保障的需求。未来的研究应该探索纵向效应、跨学科应用和人工智能素养策略，以确保法学硕士在基于游戏的学习中负责任和有效地整合。

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引用次数: 0