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检索条件"丛书名=Lecture notes in artificial intelligence,"

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The Role of First Language in Automated Essay Grading for Second Language Writing 25th

The Role of First Language in Automated Essay Grading for Se...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Hwang, Haerim Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783031642982;9783031642999

The advancement of AI has paved the way for human-like performance in numerous tasks. One field that has seen significant benefits from its application is the automated essay grading (AEG). Recent AEG systems have demonstrated impressive accuracy in evaluating essay quality. However, the majority of AEG systems have been developed with a primary focus on essays produced in a first language (L1) context although there are countless second language learners around the world who need their writing skills assessed for education or evaluation reasons. More importantly, most AEG systems for L2 essays do not consider the impact of L1 which is crucial in shaping linguistic features within them, possibly limiting their applicability to some essays written by learners whose L1 is not given adequate consideration. This, in turn, may bring up concerns about the equal representation and inclusion of essays from learners with various L1 backgrounds. To investigate this possibility, we developed 11 AEG systems using the XGBoost algorithm, each based on the essay data of a specific L1 group (Arabic, Chinese, French, German, Hindi, Italian, Japanese, Korean, Spanish, Telugu, Turkish), and compared these systems' performance. Furthermore, we ascertained the relative importance of linguistic features in each system. Our study showed that the AEG systems provided with essay data from different L1 groups varied in accuracy, with slightly different linguistic features being the most crucial in the system. This suggests the necessity to include more diverse and inclusive data from learners with different L1 backgrounds in the development of AEG systems.

关键词： Automated Essay Grading XGBoost First Language Diversity

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Data Alignment and Duration Modelling in VITS 27th

Data Alignment and Duration Modelling in VITS

引用

27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Hanzlicek, Zdenek Univ West Bohemia Fac Appl Sci NTIS New Technol Informat Soc Univ 22 Plzen 30614 Czech Republic

ISBN: (纸本)9783031705656;9783031705663

The paper analyses data alignment and duration modelling in the modern end-to-end speech synthesis model VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech). The standard version of VITS utilizes the MAS (Monotonic Alignment Search) procedure to align input text/phones and corresponding speech during the training procedure;the alignment is also used to obtain phoneme durations for the stochastic duration predictor training. This study analyzes the resulting MAS alignment and compares it with a reference alignment obtained by an LSTM-based phonetic segmentation system. We also examine the performance of VITS when the reference phonetic segmentation replaces the default MAS alignment. The comparison shows that while the original VITS is still slightly preferred in terms of quality, it provides a less interpretative data alignment. The duration modelling is more transparent in the modified version, allowing better duration control and modifications. The analysis has been carried out on two Czech voices.

关键词： text-to-speech synthesis VITS MAS duration

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A Multi-task Automated Assessment System for Essay Scoring 25th

A Multi-task Automated Assessment System for Essay Scoring

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Chen, Shigeng Lan, Yunshi Yuan, Zheng Kings Coll London Dept Informat London England East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9783031642982;9783031642999

Most existing automated assessment (AA) systems focus on holistic scoring, falling short in providing learners with comprehensive feedback. In this paper, we propose a Multi-Task Automated Assessment (MTAA) system that can output detailed scores along multiple dimensions of essay quality to provide instructional feedback. This system is built on multi-task learning and incorporates Orthogonality Constraints (OC) to learn distinct information from different tasks. To achieve better training convergence, we develop a training strategy, Dynamic Learning Rate Decay (DLRD), to adapt the learning rates for tasks based on their loss descending rates. The results show that our proposed system achieves state-of-the-art performance on two benchmark datasets: ELLIPSE and ASAP++. Furthermore, we utilize ChatGPT to assess essays in both zero-shot and few-shot contexts using an ELLIPSE subset. The findings suggest that ChatGPT has not yet achieved a level of scoring consistency equivalent to our developed MTAA system and that of human raters.

关键词： Automated Essay Scoring Multi-Task Learning ChatGPT Automated Assessment Zero-Shot Learning Few-Shot Learning

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Identifying Gaps in Students' Explanations of Code Using LLMs 25th

Identifying Gaps in Students' Explanations of Code Using LLM...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Banjade, Rabin Oli, Priti Sajib, Mahmudul Islam Rus, Vasile Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9783031642982;9783031642999

This study investigates methods based on Large Language Models (LLMs) to identify gaps or missing parts in learners' self-explanations. This work is part of our broader effort to automate the evaluation of students' freely generated responses, which in this work are learners' self-explanations of code examples during code comprehension activities. We experimented with two methods and four distinct LLMs in two distinct settings. One method prompts LLMs to identify gaps in learners' self-explanations, whereas the other method relies on LLMs performing a sentence-level semantic similarity task to identify gaps. We evaluated these methods in two settings: (i) simulated data generated using LLMs and (ii) actual student data. Results revealed the semantic similarity method significantly improves task performance over the zero-shot prompting for gap identification (the holistic method), i.e., over the standard method of prompting LLMs to directly address the gap identification task.

关键词： Self Explanation Large Language Models Automated Assessment Scaffolding Code Comprehension

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Competency Evaluation of Chinese Pilots Based on Human Factors Analysis Model 21st

Competency Evaluation of Chinese Pilots Based on Human Facto...

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21st International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE)

作者： Luo, Min Chen, Yanqiu Zhang, Yuan Engn & Tech Res Ctr Civil Aviat Safety Anal & Pre Acad Civil Aviat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9783031607271;9783031607288

Pilot competencies are used to effectively predict and evaluate the level of pilot performance, which can be manifested and observed through the use of relevant knowledge, skills and attitudes to perform activities or tasks under specific conditions. This study (Supported by the Fundamental Research Funds of CASTC(X242060302102)) optimized the Human Factor Analysis and Classification System (HFACS), and then established the corresponding relationship between the three competencies of pilots and the dimensions of model analysis. Finally, the study used the optimized HFCAS model to analyze the typical occurrences of China in the past five years. In the end, the common concrete manifestations of the three major competency deficiencies are obtained to provide data support for industry risk identification and flight training direction.

关键词： Core competency psychological competency Style competency Optimized HFACS

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Aspect-Based Semantic Textual Similarity for Educational Test Items 25th

Aspect-Based Semantic Textual Similarity for Educational Tes...

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25th International Conference on artificial intelligence in Education (AIED)

作者： Do, Heejin Lee, Gary Geunbae POSTECH Grad Sch AI Pohang South Korea POSTECH Dept CSE Pohang South Korea

ISBN: (纸本)9783031642982;9783031642999

In the educational domain, identifying the similarity among test items provides various advantages for exam quality management and personalized student learning. Existing studies mostly relied on student performance data, such as the number of correct or incorrect answers, to measure item similarity. However, nuanced semantic information within the test items has been overlooked, possibly due to the lack of similarity-labeled data. Human-annotated educational data demands high-cost expertise, and items comprising multiple aspects, such as questions and choices, require detailed criteria. In this paper, we introduce a task of aspect-based semantic textual similarity for educational test items (aSTS-EI), where we assess the similarity by specific aspects within test items and present an LLM-guided benchmark dataset. We report the baseline performance by extending the STS methods, setting the groundwork for future aSTS-EI tasks. In addition, to assist data-scarce settings, we propose a progressive augmentation (ProAug) method, which generates step-by-step item aspects via recursive prompting. Experimental results imply the efficacy of existing STS methods for a shorter aspect while underlining the necessity for specialized approaches in relatively longer aspects. Nonetheless, markedly improved results with ProAug highlight the assistance of our augmentation strategy to overcome data scarcity.

关键词： Educational Item Similarity Semantic Textual Similarity Dataset Aspect-based Similarity Natural Language Processing

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Trust Transfer in Autonomous Vehicles: The Role of Warm Brand Image Amid Automation Failure 21st

Trust Transfer in Autonomous Vehicles: The Role of Warm Bran...

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21st International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE)

作者： Qin, Shaotian Li, Chihao Qi, Yue Renmin Univ China Dept Psychol Beijing 100872 Peoples R China Renmin Univ China Lab Dept Psychol Beijing 100872 Peoples R China

ISBN: (纸本)9783031607301;9783031607318

As automated driving technology becomes an integral part of daily life, the significance of consumer trust in autonomous systems grows. While existing research has explored human-machine trust, the role of brand image has been overlooked. Trust transfer theory posits that initial trust in new products often originates from existing brand trust. Given the limited awareness of self-driving products, this study investigates how attitudes toward current brands may transfer into trust in autonomous systems. This paper utilized CARLA for simulating autonomous driving scenarios and conducted three studies to explore the impact of brand image (warmth/competence) on human-machine trust and its influence on automation failure. Study 1 analyzed the brand images of 39 Chinese autonomous driving companies, revealing a predominant focus on competence. In Study 2, a one-way, three-level online experiment demonstrated that both competence-based and warmth-based brand images significantly enhance initial human-machine trust. Study 3 explored the impact of automation failure on trust and the moderating effect of brand image, using a 2 (automation failure: high vs. low) x 2 (brand image: competence vs. warmth) x 2 (measurement time: pretest vs. post-test) experimental design. The findings expand current understanding of human-machine trust and provide empirical support for the integrated model of human trust in AI. The research also offers strategic insights for self-driving brands, suggesting that adopting awarmth-based brand image could help maintain human-machine trust despite potential automation failures.

关键词： brand image automation failure trust in automation human-AI trust warmth

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Sleep Deprivation-Induced Alterations in Mood States Correlate with Changes in Spontaneous Brain Activity 21st

Sleep Deprivation-Induced Alterations in Mood States Correla...

引用

21st International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE)

作者： Wang, Jiatao Zhou, Qianxiang Liu, Zhongqi Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Beijing 102402 Peoples R China

ISBN: (纸本)9783031607271;9783031607288

Modern warfare places soldiers under unique conditions, requiring them to operate on irregular schedules in extreme environments, resulting in unprecedented physical and psychological challenges. Prolonged duty, nighttime operations, intense combat, and dynamic work demands often reduce sleep duration and quality. Additionally, battlefield noise and harsh environmental conditions contribute to difficulty falling asleep and maintaining sleep. Consequently, numerous soldiers endure severe sleep deprivation;some even develop sleep disorders. Within military training, sleep deprivation can engender many adverse consequences, with mood variability being one of the prominent outcomes. Soldiers frequently manifest sensations of profound fatigue, heightened irritability, acute anxiety, and pronounced despondency, all of which correlate with insufficient sleep. Sleep insufficiency undermines mood stability and diminishes the capacity to cope effectively with conflicts and stressors, constituting a latent threat to soldiers' psychological well-being and work performance. Sleep is crucial in mood regulation and its impact on psychological health. In-depth research into the mechanisms underlying the relationship between sleep deprivation and mood can offer profound insights into improving soldiers' mood states and work efficiency. Furthermore, it can provide valuable data support for human-computer interaction design. This study aimed to investigate the impact of sleep deprivation on the mood states of healthy participants and the correlational relationship between alterations in mood states under the influence of sleep deprivation and spontaneous electroencephalographic (EEG) activity. The research involved a 36-h complete sleep deprivation experiment conducted on 30 male participants aged between 19 and 30. Mood states were assessed at baseline, 24 h into sleep deprivation, and 36 h into sleep deprivation using the Profile of Mood States (POMS) questionnaire, and concurrent r

关键词： Mood POMS EEG sleep deprivation relative power

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Construction of Knowledge Base of Non-Core Argument Based on Serial Verb Construction 24th

Construction of Knowledge Base of Non-Core Argument Based on...

引用

24th Chinese Lexical Semantics Workshop (CLSW)

作者： Zhou, Shufan Wang, Guirong Rao, Gaoqi Xun, Endong Beijing Language & Culture Univ Beijing Peoples R China

ISBN: (纸本)9789819705856;9789819705863

Non-core argument, an additional component of a sentence, plays an important role in semantic analysis, which can expand the basic predicate structure and form complex propositions. Non-core arguments usually need to be cited by case markers, including not only prepositions but also verbs. This study focuses on verb-guided non-core arguments, as well as an investigation of the expression of non-core argument role relationships based on serial verb construction, which is different from the previous method that mainly focused on preposition-guided non-core arguments. In this study, a new set of non-core argument role classification systems is proposed. At the same time, newspapers and periodicals corpus containing structural tree information in BCC corpus are chosen for obtaining serial verb collocation data. Through disambiguation and non-core argument role identification, the presented method eventually achieved 25517 examples of triple collocations and constructed a verb-guided non-core argument knowledge base, which provides data support for natural language processing and language theory research.

关键词： Serial Verb Construction Non-core Argument Role Verbal Guide Words Knowledge Base

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Evaluating User Experience for Intelligent Connected Vehicles: A Qualitative Study 21st

Evaluating User Experience for Intelligent Connected Vehicle...

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21st International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE)

作者： He, Mengxia Zhang, Jingyu Zhang, Liang Zhang, Rong Inst Psychol CAS Key Lab Behav Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Dept Psychol Beijing 100101 Peoples R China LiaoningNormal Univ Dalian Peoples R China

ISBN: (纸本)9783031607301;9783031607318

Intelligent and Connected Vehicles (ICV) are rapidly advancing and have become the strategic focus of global automotive enterprises. Gaining an in-depth understanding of the psychological experiences of ICV users is crucial to ensure widespread user acceptance and recognition of product development and iterations. However, most user experience studies still primarily focus on the features of traditional vehicles. This study attempts to initially develop a comprehensive evaluative framework to evaluate the user experience of ICVs. By utilizing literature analysis, user interviews, and expert evaluations, we extracted 28 key sub-dimensions of the psychological experiences of ICV users. The findings from this research will provide a framework to evaluate the user experience of ICVs. Future studies may develop the ICV user psychological experience questionnaires based on this study and conduct further validation studies. This, in turn, will assist automotive enterprises in optimizing industrial design for enhanced user experiences.

关键词： Intelligent and Connected Vehicles(ICVs) User Experience literature analysis user interviews

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