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检索条件"丛书名=Lecture Notes in Computer Science"

共 300228 条记录，以下是711-720 订阅

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Analysis of the Impact on Immersive Experience: Narrative Effects in First and Third Person Perspectives 13th

Analysis of the Impact on Immersive Experience: Narrative Ef...

引用

13th International Conference on Design, User Experience and Usability (DUXU)

作者： Liu, Lin Lu, Shizhu Guo, Yuqing Huang, Qiuyu Yi, Xiaolie Zhang, Jifa Guangdong Univ Technol Guangzhou 510090 Guangdong Peoples R China

ISBN: (纸本)9783031613586;9783031613593

This study conducted a comprehensive analysis using meta-analysis methods on data extracted from 9 papers (N = 6770), aiming to explore the differences in audience experience and influencing factors related to first-person and third-person narratives. Specifically, the research focused on the effects of first-person and third-person narrative styles on potential factors influencing immersion experience, including emotional engagement and behavioral intent. The results indicate that both first-person and third-person narratives did not significantly impact the audience's emotional engagement and behavioral intent. When combining emotional engagement and behavioral intent into a single outcome variable, there was no significant difference between the effects of first-person and third-person narratives. However, when considering emotional engagement and behavioral intent as separate outcome variables, the study found that first-person narratives were more effective in eliciting emotional engagement compared to third-person narratives, with no significant difference in behavioral intent. Additionally, the research highlights that individual cognitive difference among the audience may influence memory biases, thereby affecting immersion experience. In summary, this study provides a clearer perspective on the core issue of the impact of different narrative perspectives on immersion experience. It not only lays the foundation for further research on immersive experiences in cultural heritage sites but also offers practical solutions for cultural communication and identity.

关键词： Narrative Perspective Immersive Experience Meta-Analysis

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Some Common Sense Remarks on the Tuning of String Trios or Quartet Instruments 9th

Some Common Sense Remarks on the Tuning of String Trios or Q...

引用

9th International Conference on Mathematics and Computation in Music (MCM)

作者： Hellegouarch, Yves Univ Caen Caen France

By “common sense” I mean the “scientific” common sense of physicists or chemists, not particularly the one of musicians which, nevertheless, is so important in quartet playing. © The Author(s), under exclusiv...

ISBN: (纸本)9783031606373;9783031606380

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Automatic Extraction of User-Centric Aspects for Tourist Spot Recommender Systems Using Reviews in Japanese

Automatic Extraction of User-Centric Aspects for Tourist Spo...

引用

Conference on Human Interface and the Management of Information Thematic Area (HIMI) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Uwano, Fumito Kobayashi, Ran'u Ohta, Manabu Okayama Univ 3-1-1 Tsushima NakaKita Ku Okayama Japan

ISBN: (纸本)9783031601248;9783031601255

In tourist reviews, various pieces of information are described to confirm the characteristics of tourist spots. This paper proposes a method to extract tourist spot aspects using Japanese tourist reviews automatically. The aspects of tourist spots are concepts that serve as criteria for searching the features of spots, such as "history" or "nature". Utilizing these aspects helps tourists easily find tourist spots with their characteristics. Thus, the aspects of whole spots should be readable and understandable for users. Using the Natural Language API, the proposed method extracts entities from tourist reviews on ***, a travel website. After transforming them into word vectors, the proposed method clusters them to create clusters of entities that reflect the features of the spots. Furthermore, the proposed method seeks synonyms for each cluster to extract aspects. In experiments, this paper quantitatively evaluated the automatically extracted aspects for 30 tourist spots in Okayama Prefecture in Japan. The experimental results revealed that the proposed method extracted a total of 446 aspects, of which 78.9% were deemed appropriate as aspects. In addition, the extracted aspects had been abstracted interpretably for user-centric expression to find tourist spots.

关键词： Tourism Recommender System Meta-data extraction Aspect Review analytics

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Mind the Gap: Confronting the Vast Divide Between CS Teaching and Machine Learning Pedagogy 19th

Mind the Gap: Confronting the Vast Divide Between CS Teachin...

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19th European Conference on Technology Enhanced Learning (EC-TEL)

作者： Perach, Shai Alexandron, Giora Weizmann Inst Sci 234 Herzl St IL-7610001 Rehovot Israel

ISBN: (纸本)9783031723148;9783031723155

The increasing demand for machine learning and deep learning (ML/DL) education spans K-12, college, and vocational training, highlighting the urgent need to prepare computer science (CS) teachers for these fields. This study aims to characterize the knowledge gaps and challenges experienced by CS teachers as they transition to teaching a rigorous ML curriculum. The paper attempts to achieve this in two distinct and self-contained ways: first, through a theoretical analysis conducted via a literature review examined through the lens of theoretical frameworks, and second, through an empirical qualitative analysis of a case study involving CS teachers transitioning to ML education. The empirical analysis echoes the findings of the theoretical analysis and sharpens them with additional insights. Our findings reveal significant difficulties, such as relearning mathematical foundations, adapting to new problem-solving paradigms, and developing ML-specific pedagogical content knowledge. Notably, the existing expertise of experienced CS teachers has limited relevance to ML/DL education, raising the question of why we focus mainly on CS teachers as the potential teaching workforce to train. The discussion integrates the theoretical and empirical findings to offer conclusions and recommendations for educational institutions, policymakers, and teacher training programs in enhancing ML/DL teaching capacities across various academic levels.

关键词： AI Education Machine Learning Education Deep Learning Education Teacher Professional Development

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Can a Conversational Agent Pass Theory-of-Mind Tasks? A Case Study of ChatGPT with the Hinting, False Beliefs, and Strange Stories Paradigms 2nd

Can a Conversational Agent Pass Theory-of-Mind Tasks? A Case...

引用

2nd International Conference on Human and Artificial Rationalities (HAR)

作者： Brunet-Gouet, Eric Vidal, Nathan Roux, Paul Ctr Hosp Versailles Serv Hosp Univ Psychiat Adultes & Addictol Le Chesnay France Univ Versailles St Quentin En Yvelines Univ Paris Saclay INSERM DisAPDevPsyCESPUMR1018 F-94807 Villejuif France

ISBN: (纸本)9783031552441;9783031552458

We investigate the possibility that the recently proposed OpenAI's ChatGPT conversational agent could be examined with classical theory-of-mind paradigms. We used an indirect speech understanding task, the hinting task, a new text version of a False Belief/False Photographs paradigm, and the Strange Stories paradigm. The hinting task is usually used to assess individuals with autism or schizophrenia by requesting them to infer hidden intentions from short conversations involving two characters. In a first experiment, ChatGPT 3.5 exhibits quite limited performances on the Hinting task when either original scoring or revised rating scales are used. We introduced slightly modified versions of the hinting task in which either cues about the presence of a communicative intention were added or a specific question about the character's intentions were asked. Only the latter demonstrated enhanced performances. No dissociation between the conditions was found. The Strange Stories were associated with correct performances but we could not be sure that the algorithm had no prior knowledge of the test. In the second experiment, the most recent version of ChatGPT (4-0314) exhibited better performances in the Hinting task, although they did not match the average scores of healthy subjects. In addition, the model could solve first and second order False Beliefs tests but failed on items with reference to a physical property like object visibility or more complex inferences. This work offers an illustration of the possible application of psychological constructs and paradigms to a conversational agent of a radically new nature.

关键词： large language model ChatGPT theory-of-mind indirect speech False Beliefs

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Uncovering Students' Processing Tactics Towards ChatGPT's Feedback in EFL Education Using Learning Analytics 17th

Uncovering Students' Processing Tactics Towards ChatGPT's Fe...

引用

17th International Conference on Blended Learning. Intelligent Computing in Education (ICBL)

作者： Su, Hanyu Tong, Yixin Zhang, Xinyi Fan, Yizhou Peking Univ Grad Sch Educ Beijing 100871 Peoples R China

ISBN: (纸本)9789819744411;9789819744428

ChatGPT, a promising chatbot program based on generative artificial intelligence (GAI), shows significant promise for its potential use in English as a Foreign Language (EFL) education. By engaging in dialogue with students, it provides personalized support for their reading and writing, which are fundamental aspects of EFL instruction. Although research on students' interaction with ChatGPT is emerging, there is a lack of focus on their Processing Tactic towards ChatGPT's Feedback (PTCF) and its impact on learning outcomes. To mitigate this gap, we employed a learning analytic method combining cluster analysis and process mining to analyze multimodal data generated during their reading and writing tasks. The analysis identified three clusters of students' PTCF: (1) Silence with no response, (2) Passive dependence, and (3) Active construction. By conducting sequence analysis on these behavior patterns, it was found that learners adopted three learning modes when using ChatGPT, which are Comprehension-focused, Tool-inspired, and Thinking-alienator. The findings also revealed differences in learning gains among groups of students with varying learning modes, specifically in the improvement of domain knowledge. Implications of these findings for both practical application and future research were also discussed later.

关键词： ChatGPT Feedback Learning Analytics Processing Tactics Artificial Intelligence

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Gamified Wearable EEG Technology to Support Controlling of Cognitive Load After Brain Injury 12th

Gamified Wearable EEG Technology to Support Controlling of C...

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12th International Conference on Games and Learning Alliance (GALA)

作者： Merilampi, Sari Jyrakoski, Taina Poberznik, Anja Karttunen, Nina Seessalo, Toni Virkki, Johanna Lipping, Tarmo Satakunta Univ Appl Sci Pori Finland Tampere Univ Fac Informat Technol & Commun Sci Pori Finland

ISBN: (纸本)9783031490644;9783031490651

Traumatic brain injury (TBI) results from an injury to the head. Depending on the severity of the injury and the affected regions of the brain, consequences vary a lot. Apart from physical challenges, a person with TBI may have various cognitive deficits, which affect the cognitive load of the person in different situations. The paper presents an early paper prototype for a wearable cognitive load measurement device design using biomarkers from an electroencephalogram (EEG). The paper discusses how the rehabilitation and daily tasks could be adjusted and optimized for individuals' needs by using such a cognitive load measurement tool. Designing this kind of system requires skills from various disciplines. Thus, the early product design was created in a multidisciplinary workshop. As everyday EEG-based tools are still in their early development phase, it is very important to pay a lot of attention to technical development. However, in order to facilitate the implementation of such technology, it is also crucial to concentrate on easy-to-wear, user-friendly, and fashionable product design.

关键词： EEG cognitive load traumatic brain injury product design

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Using Word Games as Facilitator to Awareness Raising Communication in Public Spaces 30th

Using Word Games as Facilitator to Awareness Raising Communi...

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30th International Conference on Collaboration Technologies and Social Computing (CollabTech)

作者： Nishide, Shinya Nishida, Takeshi Kobe Univ Grad Sch Intercultural Studies Kobe Hyogo Japan Senri Int Sch Kwansei Gakuin Osaka Japan

ISBN: (纸本)9783031679971;9783031679988

In public spaces, where individuals with diverse values converge, discrepancies in the perception of acceptable behavior, such as appropriate speaking volumes, are common. Creating a common understanding requires continuous and proactive communication, including outreach to those unfamiliar, which can impose a significant psychological burden. This study proposes an awareness communication method that facilitates forming a common understanding in public spaces through participatory word games. The paper explores the potential of the proposed method by implementing a prototype in a library setting, leading to system design and implementation.

关键词： Public Space Word Games Awareness-Raising Communication

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Generative AI Chatbot in PyramidApp: Students' Behaviors and Design Principles 30th

Generative AI Chatbot in PyramidApp: Students' Behaviors and...

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30th International Conference on Collaboration Technologies and Social Computing (CollabTech)

作者： Gutierrez-Ferre, Aldric Hernandez-Leo, Davinia Sanchez-Reina, J. Roberto Univ Pompeu Fabra ICT Dept TIDE Barcelona Spain

ISBN: (纸本)9783031679971;9783031679988

Generative Artificial Intelligence (GenAI) offers new opportunities to implement useful features within computer Supported Collaborative Learning (CSCL) environments. Despite these growing prospects, there is still limited research concerning the application ofGenAI in learning environments. Thiswork in progress aims to evaluate the mediation of a masked GenAI chatbot in the setting of the CSCL web application PyramidApp. A quasi-experimental within-subjects study was designed to assess the effect of GenAI chatbot intervention within the environment of PyramidApp. In the setting of 9 online activities, we evaluated the effect of the GenAI chatbot activity in 105 conversational chat rooms. The findings revealed that the GenAI chatbot provides useful feedback as students rate the chatbot's answers higher than their peers' answers (M-Chatbot = 4.11, MStudents = 3.91). The presence of the chatbot has an effect on group communication with the length of messages increased in chat rooms where the chatbot was present. Moreover, chatbot behavior to rate the students' answers was correlated with the students' behavior. The present study offers valuable insights into the optimal strategies for integrating a GenAI Large Language Model into educational tools and computer supported learning.

关键词： computer-Supported Collaborative Learning Collaborative Learning Flow Pattern ChatGPT Human-AI Collaboration PyramidApp

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Can Machines and Humans Use Negation When Describing Images? 2nd

Can Machines and Humans Use Negation When Describing Images?

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2nd International Conference on Human and Artificial Rationalities (HAR)

作者： Sato, Yuri Mineshima, Koji Ochanomizu Univ Tokyo Japan Keio Univ Tokyo Japan

ISBN: (纸本)9783031552441;9783031552458

Can negation be depicted? It has been claimed in various areas, including philosophy, cognitive science, and AI, that depicting negation through visual expressions such images and pictures is challenging. Recent empirical findings have shown that humans can indeed understand certain images as expressing negation, whereas this ability is not exhibited by machine learning models trained on image data. To elucidate the computational ability underlying the understanding of negation in images, this study first focuses on the image captioning task, specifically the performance of models pre-trained on large linguistic and image datasets for generating text from images. Our experiment demonstrates that a state-of-the-art model achieves some success in generating consistent captions from images, particularly in photographs rather than illustrations. However, when it comes to generating captions containing negation from images, the model is not as proficient as humans. To further investigate the performance of machine learning models in a more controlled setting, we conducted an additional analysis using a Visual Question Answering (VQA) task. This task enables us to specify where in the image the model should focus its attention when answering a question. As a result of this setting, the model's performance was improved. These results will shed light on the disparities in the attentional focus between humans and machine learning models.

关键词： negation grounding image captioning visual question answering machine learning cognitive science

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