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1.
L.P. Prieto K. Sharma Ł. Kidzinski M.J. Rodríguez‐Triana P. Dillenbourg 《Journal of Computer Assisted Learning》2018,34(2):193-203
The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time) on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye‐tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time‐independent and time‐aware models, achieving median F1 scores of about 0.7–0.8 on leave‐one‐session‐out k‐fold cross‐validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers. 相似文献
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Daniele Di Mitri Jan Schneider Marcus Specht Hendrik Drachsler 《Journal of Computer Assisted Learning》2018,34(4):338-349
Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science. 相似文献
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H.J. Pijeira-Díaz H. Drachsler P.A. Kirschner S. Järvelä 《Journal of Computer Assisted Learning》2018,34(4):397-408
Low arousal states (especially boredom) have been shown to be more deleterious to learning than high arousal states, though the latter have received much more attention (e.g., test anxiety, confusion, and frustration). Aiming at profiling arousal in the classroom (how active students are) and examining how activation levels relate to achievement, we studied sympathetic arousal during two runs of an elective advanced physics course in a real classroom setting, including the course exam. Participants were high school students (N = 24) who were randomly selected from the course population. Arousal was indexed from electrodermal activity, measured unobtrusively via the Empatica E4 wristband. Low arousal was the level with the highest incidence (60% of the lesson on average) and longest persistence, lasting on average three times longer than medium arousal and two times longer than high arousal level occurrences. During the course exam, arousal was positively and highly correlated (r = .66) with achievement as measured by the students' grades. Implications for a need to focus more on addressing low arousal states in learning are discussed, together with potential applications for biofeedback, teacher intervention, and instructional design. 相似文献
5.
Katerina Mangaroska Roberto Martinez-Maldonado Boban Vesin Dragan Gašević 《Journal of Computer Assisted Learning》2021,37(4):1030-1047
Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations. 相似文献
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This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or irregular behavior. The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network. Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network, including users. Therefore, the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow. In contrast, the irregular patterns can trigger an alert for a potential cyber-attack. The framework has been fully described where the evaluation metrics have also been introduced. The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1. The paper has been concluded with providing the potential directions for future improvements. 相似文献
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随着当今信息技术的飞速发展;信息的存在形式多种多样;来源也十分广泛。不同的存在形式或信息来源均可被称之为一种模态;由两种或两种以上模态组成的数据称之为多模态数据。多模态数据融合负责将多个模态的信息进行有效的整合;汲取不同模态的优点;完成对信息的整合。自然现象具有十分丰富的特征;单一模态很难提供某个现象的完整信息。面对保持融合后具有各个模态信息的多样性以及完整性、使各个模态的优点最大化、减少融合过程造成的信息损失等方面的融合要求;如何对各个模态的信息进行融合成为了多个领域广泛存在的一个新挑战。简要阐述了常见的多模态融合方法、融合架构;总结了三个常见的融合模型;简要分析协同、联合、编解码器三大架构的优缺点以及多核学习、图像模型等具体融合方法。在多模态的应用方面;对多模态视频片段检索、综合多模态信息生成内容摘要、多模态情感分析、多模态人机对话系统进行了分析与总结。指出了当前多模态融合出现的问题;并提出未来的研究方向。 相似文献
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Gerti Pishtari;María Jesús Rodríguez-Triana;Luis P. Prieto;Adolfo Ruiz-Calleja;Terje Väljataga; 《Journal of Computer Assisted Learning》2024,40(6):2399-2415
In the field of Learning Design, it is common that researchers analyse manually design artefacts created by practitioners, using pedagogically-grounded approaches (e.g., Bloom's Taxonomy), both to understand and later to support practitioners' design practices. Automatizing these high-level pedagogically-grounded analyses would enable large-scale studies on practitioners' design practices. Such an approach would be especially useful in the context of mobile learning, where practitioners' design practices are under-explored and complex (e.g., involving both formal and informal learning activities, happening between physical and digital spaces). 相似文献
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《Concurrency and Computation》2017,29(21)
Speculative multithreading is an effective technique to automatically parallelize sequential programs. Conventional thread partition approaches primarily include heuristic rule–based (HR‐based) and machine learning–based. Heuristic rule–based approaches are effective to parallelize one type of programs and can seldom obtain the respective optimal partitions for different programs, and existing machine learning–based approaches usually use vector‐based characterization to represent a program, but easily ignore control information among basic blocks and partitions along other paths except the critical path. This paper proposes a novel graph‐based thread partition approach to overcome these 2 bottlenecks. Our approach characterizes programs with graphs, integrating feature and control informations, extracting good partition scheme successfully, and also applies a machine‐learning algorithm to predict partition for unseen programs. Prophet, which consists of an automatic parallelization compiler and a multicore simulator, evaluates the performance of multithreaded programs. Experiment results reveal that our approach delivers a maximum performance improvement of about 55.49% on an 8 core than HR‐based approach and a maximum 97.67% performance improvement over HR‐based partition for SPEC2000 benchmarks. This result suggests that graph‐based thread partition approach is effective for thread partition in speculative multithreading. 相似文献
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J. Schneider D. Börner P. van Rosmalen M. Specht 《Journal of Computer Assisted Learning》2017,33(2):164-177
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在机器学习应用中,由于数据来源渠道多以及部分标注者水平不足,训练数据质量很难得到保证.通过深度结合机器学习和可视化技术,可视分析技术将人融入数据质量分析与提升回路中,帮助提升训练数据质量,从而提高模型性能.文中首先总结了训练数据质量问题的三大类型:标注错,覆盖窄,标注缺;然后基于这些问题类型,介绍分析了相关的可视分析工作,包括标注错误修正方法,数据集偏离纠正方法和无标注数据质量提升方法;最后深入分析了基于可视分析的训练数据质量提升面临的机遇与挑战,包括在复杂任务、大语言模型、多模态数据、流数据等场景下的数据质量提升. 相似文献
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可视化与可视分析已成为众多领域中结合人类智能与机器智能协同理解、分析数据的常见手段。人工智能可以通过对大数据的学习分析提高数据质量,捕捉关键信息,并选取最有效的视觉呈现方式,从而使用户更快、更准确、更全面地从可视化中理解数据。利用人工智能方法,交互式可视化系统也能更好地学习用户习惯及用户意图,推荐符合用户需求的可视化形式、交互操作和数据特征,从而降低用户探索的学习及时间成本,提高交互分析的效率。人工智能方法在可视化中的应用受到了极大关注,产生了大量学术成果。本文从最新工作出发,探讨人工智能在可视化流程的关键步骤中的作用。包括如何智能地表示和管理数据、如何辅助用户快速创建和定制可视化、如何通过人工智能扩展交互手段及提高交互效率、如何借助人工智能辅助数据的交互分析等。具体而言,本文详细梳理每个步骤中需要完成的任务及解决思路,介绍相应的人工智能方法（如深度网络结构）,并以图表数据为例介绍智能可视化与可视分析的应用,最后讨论智能可视化方法的发展趋势,展望未来的研究方向及应用场景。 相似文献
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借鉴聚类思想和万有引力计算方法,提出了解决基于示例学习中两个关键问题的新思路,这两个新思路分别是,利用示例邻近同类其它示例数目来描述该示例潜在预测能力,以及利用实例质量来帮助更加准确地预测新实例类别。据此构造了一种聚类型基于示例学习的新方法,并利用标准机器学习数据库中3个复杂数据样本,对所提方法的性能进行实验检测,有关的对比实验结果表明,所提方法在实例预测能力以及学习结果占用空间有效性方面,均优越其它多种基于示范学习方法。 相似文献
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随着移动应用(App)的广泛使用,移动应用的安全事件也频频发生。从数以亿计的移动应用中准确地识别出潜在的安全隐患成为了信息安全领域重要的难题之一。移动应用数量级增长的同时,也产生了海量的应用安全数据。这些数据使得移动应用的安全解析成为了可能。本文分别从用户界面解析、重打包应用检测、应用功能与安全行为一致性检测、基于上下文的恶意行为检测、终端用户应用管理和使用行为分析这五个方面介绍了移动应用安全解析学目前的成果。同时,基于以上的研究成果,对未来的研究方向进行了展望,并讨论了这些研究方向面临的挑战。 相似文献
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The increase in available data from sensors embedded in industrial equipment has led to a recent rise in the use of industrial predictive maintenance. In the aircraft industry, predictive maintenance has become an essential tool for optimizing maintenance schedules, reducing aircraft downtime, and identifying unexpected faults. Despite this, there is currently no comprehensive survey of predictive maintenance applications and techniques solely devoted to the aircraft manufacturing industry. This article is an in-depth state-of-the-art systematic literature review of the different data types, applications, projects, and opportunities for predictive maintenance in this industry. The goal of this review is to identify, and highlight the challenges and opportunities for future research in this field. This review found that the current focus of research is too biased towards aircraft engines due to a lack of publicly available data sets, and that greater automation is an important step to optimize aircraft maintenance to its full potential. 相似文献
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The assumption that social skills are necessary ingredients of collaborative learning is well established but rarely empirically tested. In addition, most theories on collaborative learning focus on social skills only at the personal level, while the social skill configurations within a learning group might be of equal importance. Using the integrative framework, this study investigates which social skills at the personal level and at the group level are predictive of task‐related e‐mail communication, satisfaction with performance and perceived quality of collaboration. Data collection took place in a technology‐enhanced long‐term project‐based learning setting for pre‐service teachers. For data collection, two questionnaires were used, one at the beginning and one at the end of the learning cycle which lasted 3 months. During the project phase, the e‐mail communication between group members was captured as well. The investigation of 60 project groups (N = 155 for the questionnaires; group size: two or three students) and 33 groups for the e‐mail communication (N = 83) revealed that personal social skills played only a minor role compared to group level configurations of social skills in predicting satisfaction with performance, perceived quality of collaboration and communication behaviour. Members from groups that showed a high and/or homogeneous configuration of specific social skills (e.g., cooperation/compromising, leadership) usually were more satisfied and saw their group as more efficient than members from groups with a low and/or heterogeneous configuration of skills. 相似文献
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Kwok Tai Chui Ryan Wen Liu Miltiadis D. Lytras Mingbo Zhao 《Behaviour & Information Technology》2019,38(9):940-949
ABSTRACTThe study of patient behaviours (vital sign, physical action and emotion) is crucial to improve one’s quality of life. The only solution for handling and managing millions of people’s behaviours and health would be big data and IoT technology because most of the countries are lack of medical professionals. In this paper, a big data and IoT-based patient behaviour monitoring system have proposed. Qualitative studies are carried out on the selected behaviours analytics, cardiovascular disease identification and fall detection. At last, authors have summarised the general challenges like trust, privacy, security and interoperability as well as special challenges in various sectors: government, legislators, research institutions, information technology companies and patients. 相似文献
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Multimodal machine learning(MML)aims to understand the world from multiple related modalities.It has attracted much attention as multimodal data has become increasingly available in real-world application.It is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each other.However,it is a key challenge to fuse the multi-modalities in MML.Different from previous work,we further consider the side-information,which reflects the situation and influences the fusion of multi-modalities.We recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the instance.Accordingly,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature representation.Moreover,two versions of MLDL are proposed to deal with the sequential data.Experiments on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods. 相似文献
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While most Systems Engineering Management (SEM) applications use some subset of traditional Project Management (PM) methods and tools, the actual practice of systems engineering management involves continuous cognitive zigzagging between systems engineering—the product domain—and project management—the project domain. Focusing on seven PM methods, we examine two research questions regarding systems engineering practitioners: (1) While conducting SEM, do they perceive a notion of a project domain, a product domain, and a combined project‐product domain? (2) What is the extent to which, and ways by which, systems engineering practitioners deem PM methods as effective for supporting SEM? Using analysis of structured questionnaires among 24 participants, we verified that project and product are indeed viewed as two complementary facets of SEM, and that certain PM methods address both domains better than others with respect to particular examined factors. © 2011 Wiley international, Inc. 相似文献
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Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model. 相似文献