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1.
    
Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the \"black box\" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.  相似文献   

2.
    
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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4.
在多模态机器学习领域,为特定任务而制作的人工标注数据昂贵,且不同任务难以进行迁移,从而需要大量重新训练,导致训练多个任务时效率低下、资源浪费。预训练模型通过以自监督为代表的方式进行大规模数据训练,对数据集中不同模态的信息进行提取和融合,以学习其中蕴涵的通用知识表征,从而服务于广泛的相关下游视觉语言多模态任务,这一方法逐渐成为人工智能各领域的主流方法。依靠互联网所获取的大规模图文对与视频数据,以及以自监督学习为代表的预训练方法的进步,视觉语言多模态预训练模型在很大程度上打破了不同视觉语言任务之间的壁垒,提升了多个任务训练的效率并促进了具体任务的性能表现。本文总结视觉语言多模态预训练领域的进展,首先对常见的预训练数据集和预训练方法进行汇总,然后对目前最新方法以及经典方法进行系统概述,按输入来源分为图像—文本预训练模型和视频—文本多模态模型两大类,阐述了各方法之间的共性和差异,并将各模型在具体下游任务上的实验情况进行汇总。最后,总结了视觉语言预训练面临的挑战和未来发展趋势。  相似文献   

5.
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   

6.
    
Although activity recognition is an emerging general area of research in computer science, its potential in construction engineering and management (CEM) domain has not yet been fully investigated. Due to the complex and dynamic nature of many construction and infrastructure projects, the ability to detect and classify key activities performed in the field by various equipment and human crew can improve the quality and reliability of project decision-making and control. In particular to simulation modeling, process-level knowledge obtained as a result of activity recognition can help verify and update the input parameters of simulation models. Such input parameters include but are not limited to activity durations and precedence, resource flows, and site layout. The goal of this research is to investigate the prospect of using built-in smartphone sensors as ubiquitous multi-modal data collection and transmission nodes in order to detect detailed construction equipment activities which can ultimately contribute to the process of simulation input modeling. A case study of front-end loader activity recognition is presented to describe the methodology for action recognition and evaluate the performance of the developed system. In the designed methodology, certain key features are extracted from the collected data using accelerometer and gyroscope sensors, and a subset of the extracted features is used to train supervised machine learning classifiers. In doing so, several important technical details such as selection of discriminating features to extract, sensitivity analysis of data segmentation window size, and choice of the classifier to be trained are investigated. It is shown that the choice of the level of detail (LoD) in describing equipment actions (classes) is an important factor with major impact on the classification performance. Results also indicate that although decreasing the number of classes generally improves the classification output, considering other factors such as actions to be combined as a single activity, methodologies to extract knowledge from classified activities, computational efficiency, and end use of the classification process may as well influence one’s decision in selecting an optimal LoD in describing equipment activities (classes).  相似文献   

7.
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   

8.
    
For primary school students, mathematical word problems are often more difficult to solve than straightforward number problems. Word problems require reading and analysis skills, and in order to explain their situational contexts, the proper mathematical knowledge and number operations have to be selected. To improve students' ability in solving word problems, the problem solving process could be supported by procedural and content specific guidance or with only procedural support.. This paper evaluates the effect of two types of hints, procedural only and content‐procedural, provided by a computer programme presented in two versions. Students of grade 6 were randomly assigned to these two versions, which offered five lesson units consisting of eight word problems each. The results indicate that on average the students in the procedural‐content hints group (n = 54) finished about just as many problems in the programme as their counterparts in the procedural‐only condition (n = 51). However, the participants in the first group solved more problems correctly and improved their problem‐solving skills more as indicated by the scores on the problem‐solving post‐test. Apart from presenting our analysis of the findings of this study, also its limitations and its possible implications for future research are discussed in this paper.  相似文献   

9.
The term Classroom Proxemics refers to how teachers and students use classroom space, and the impact of this and the spatial design on learning and teaching. This study addresses the divide between, on the one hand, substantial work on proxemics based on classroom observations and, on the other hand, emerging work to design automated feedback that helps teachers identify salient patterns in their use of the classroom space. This study documents how digital analytics were designed in service of a senior teacher's practice-based inquiry into classroom proxemics. Indoor positioning data from four teachers were analysed, visualized and used as evidence to compare three distinct learning designs enacted in a physics classroom. This study demonstrates how teachers can make effective use of such visualizations, to gain insight into their classroom practice. This is evidenced by (a) documenting teachers' reflections on visualizations of positioning data, both their own and that of peers and (b) identifying the types of indicator (operationalized as analytical metrics) that foreground the most useful information for teachers to gain insight into their practice.  相似文献   

10.
    
ABSTRACT

The 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   

11.
无损检测设备可以在不破坏对象结构的情况下,检测其内部缺陷,在文物、建筑、大型土木工程中应用广泛,对结构监测和修复起着重要作用。其中,超声无损检测由于其穿透力强、指向性好,在无损检测中占据重要地位。但对于超声无损检测设备,检测不同的材料和缺陷类型时判断规则并不通用,从而导致检测对象有限,或者检测精度太低。对此提出一种基于支持向量机原理的超声无损检测处理方法,该方法具有机器学习能力,通过有限的学习过程,理论上可以完成对任何类型材料及任何类型内部缺陷的的准确识别。针对该方法,搭建了超声无损检测试验台,通过实验验证了该信号处理方法的有效性。  相似文献   

12.
    
In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.  相似文献   

13.
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   

14.
社会发展的同时带来大量数据的产生,不平衡成为众多数据集的显著特点,如何使不平衡数据集得到更好的分类效果成为了机器学习的研究热点。基于此,对目前存在的不平衡数据集分类方法进行综述研究,从不平衡数据采样方法、基于机器学习的改进算法以及组合方法三个层面对目前存在的方法进行全面的梳理与总结,对各方面方法所解决的问题、算法思想、应用场景以及各自的优缺点进行归纳和分析,同时对不平衡数据集分类方法存在的问题和未来研究方向提出一些总结和展望。  相似文献   

15.
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   

16.
    
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   

17.
    
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   

18.
多传感器数据融合的稳健处理方法   总被引：27，自引：0，他引：27  
对多传感器测定数据的综合处理，是一个关系到如何正确应用所得信息的实际问题。本文利用数理统计的方法理论和矩阵特征向量的稳定理论，将各传感器的可靠性程度模糊化，进而给出众多传感器测得数据的综合结果，从方法的理论基础和对实例的模拟运用来看，本方法克服了以往方法中对可靠性程序划分的主观影响，并提高了信息的利用程度，所得结论更加客观，且具有较好的稳定性，是一种较为实用的方法。  相似文献   

19.
Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence-related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them.  相似文献   

20.
    
Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.  相似文献   

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