Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.     

基于深度时空残差卷积神经网络的课堂教学视频中多人课堂行为识别

针对课堂教学场景遮挡严重、学生众多,以及目前的视频行为识别算法并不适用于课堂教学场景,且尚无学生课堂行为的公开数据集的问题,构建了课堂教学视频库以及学生课堂行为库,提出了基于深度时空残差卷积神经网络的课堂教学视频中实时多人学生课堂行为识别算法.首先,结合实时目标检测和跟踪,得到每个学生的实时图片流;接着,利用深度时空残...     

基于学习风格的自适应式虚拟交互方法的研究

新工科教育的改革推进了自适应学习的发展,以往的自适应方式对学习者的主观能动性的重视不足,且大部分已有研究无法提供个性化程度较高的交互体验.针对这些问题,该研究基于学习风格模型提出了一种自适应式虚拟现实交互的新方法.该方法设计了适用于虚拟交互环境的学习风格判断方式,通过分析学习者的主客观数据来判断其学习风格,由此对交互环...     

Students’ perceptions about the use of video games in the classroom

Video games are often regarded as promising teaching and learning tools for the 21st century. One of the main arguments is that video games are appealing to contemporary students. However, there are indications that video game acceptance cannot be taken for granted. In this study, a path model to examine and predict student acceptance of video games is proposed, and empirically tested by involving 858 secondary school students. The results show that students’ preference for using video games in the classroom is affected directly by a number of factors: the perceptions of students regarding the usefulness, ease of use, learning opportunities, and personal experience with video games in general. Gender effects are found as well, but appear to be mediated by experience and ease of use.     

Learning linkages: Integrating data streams of multiple modalities and timescales

Increasingly, student work is being conducted on computers and online, producing vast amounts of learning‐related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or “log data.” But log data leave out important contextual information about the learning experience. For example, a student working at a computer might be working independently with few outside influences. Alternatively, he or she might be in a lively classroom, with other students around, talking and offering suggestions. Tools that capture these other experiences have potential to augment and complement log data. However, the collection of rich, multimodal data streams and the increased complexity and heterogeneity in the resulting data pose many challenges to researchers. Here, we present two empirical studies that take advantage of multimodal data sources to enrich our understanding of student learning. We leverage and extend quantitative models of student learning to incorporate insights derived jointly from data collected in multiple modalities (log data, video, and high‐fidelity audio) and contexts (individual vs. collaborative classroom learning). We discuss the unique benefits of multimodal data and present methods that take advantage of such benefits while easing the burden on researchers' time and effort.     

Predicting students' knowledge after playing a serious game based on learning analytics data: A case study

Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires–postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict knowledge change based on in-game student interactions. We have tested this approach in a case study for which we have conducted preexperiments–postexperiments with 227 students playing a previously validated serious game on first aid techniques. We collected student interaction data while students played, using a game learning analytics infrastructure and the standard data format Experience API for Serious Games. After data collection, we developed and tested prediction models to determine whether knowledge, given as posttest results, can be accurately predicted. Additionally, we compared models both with and without pretest information to determine the importance of previous knowledge when predicting postgame knowledge. The high accuracy of the obtained prediction models suggests that serious games can be used not only to teach but also to measure knowledge acquisition after playing. This will simplify serious games application for educational settings and especially in the classroom easing teachers' evaluation tasks.     

Internet of Things-based student performance evaluation framework

In recent years, solutions based on Internet of Things (IoT) are gaining impetus in educational institutions. It is observed that student performance evaluation system in education institutions is still manual. The performance score of student in traditional evaluation system is confined to its academic achievements while activity-based performance attributes are overlooked. Moreover, the traditional system fails to capitalise information of each student related to different activities in learning environment. In relation to this context, we propose to facilitate automated student performance evaluation system by exploring ubiquitous sensing capabilities of IoT. The system deduces important results about the performance of the students by discovering daily spatial–temporal patterns. These patterns are based on the data collected by the sensory nodes (objects) in the institution learning environment. The information is generated by applying data mining algorithms for each concerned activity. The automated decisions are taken by management authority for each student using game theory. In addition, to effectively manage IoT-based activity data, tensor-based storage mechanism is proposed. The experimental evaluation compares the student performance score generated by the proposed system with the manual student performance evaluation system. The results depict that the proposed system evaluates the performance of the student efficiently.     

A federated learning approach for thermal comfort management

Existing thermal comfort prediction approaches by machine learning models have been achieving great success based on large datasets in sustainable Industry 4.0 environment. However, the industrial Internet of Things (IoT) environment generates small-scale datasets where each dataset may contain lots of worker’s private data. The latter is challenging the current prediction approaches as small datasets running a large number of iterations can result in overfitting. Moreover, worker’s privacy has been a public concern throughout recent years. Therefore, there must be a trade-off between developing accurate thermal comfort prediction models and worker’s privacy-preserving. To tackle this challenge, we present a privacy-preserving machine learning technique, federated learning (FL), where an FL-based neural network algorithm (Fed-NN) is proposed for thermal comfort prediction. Fed-NN departs from current centralized machine learning approaches where a universal learning model is updated through a secured parameter aggregation process in place of sharing raw data among different industrial IoT environments. Besides, we designed a branch selection protocol to solve the problem of communication overhead in federating learning. Experimental studies on a real dataset reveal the robustness, accuracy, and stability of our algorithm in comparison to other machine learning algorithms while taking privacy into consideration.     

物联网技术与应用分析

物联网是当前的研究热点,已受到了我国政府的重视。文章介绍了物联网的定义、物联网的体系结构,以及物联网的核心技术——感知识别技术、信号处理技术和架构技术,最后介绍了物联网在不停车收费系统、智能家居安防系统和手机支付等方面的应用。     

Computer support for learning mathematics: A learning environment based on recreational learning objects

In this paper, we introduce an electronic collaborative learning environment based on Interactive Instructors of Recreational Mathematics (IIRM), establishing an alternative approach for motivating students towards mathematics. The IIRM are educational software components, specializing in mathematical concepts, presented through recreational mathematics, conceived as interactive, recreation-oriented learning objects, integrated within the environment. We present the architecture of the learning environment which integrates communication services that support the interaction processes of the learning community, through instant messaging, chat rooms, and multi-player math games. Through the environment’s interface of their personal workspace, students have access to several easy-to-use mechanisms that allows them to customize its content, its layout, and its appearance. At internal levels, the functionality of IIRM is enhanced with features supported by the environment infrastructure. We evaluated different aspects of the learning environment in three short, motivation-oriented math courses given to Mexican high-school students. The results indicate that the use of the IIRM-based electronic learning environment, positively affects student attitudes towards mathematics. We believe that this approach has the potential to promote the mathematics learning process, basically on its motivational aspects.     

面向长短期混合数据的MOOC辍学预测策略研究

针对MOOC中学生行为数据的长短期混合特性,为解决辍学预测中的动态类别不平衡问题,提出一种基于深度学习的辍学预测策略。首先建立以天为时间步长、周为学习周期的新型学生行为时间序列,以捕捉每一时间步长下时间序列数据的短期依赖关系和相邻学习周期之间的长期模式和趋势。然后结合辍学定义的两种不同表达揭示MOOC辍学预测的动态类别不平衡现象。接着引入基于代价敏感的长短期时间序列深度学习模型,以实现对高辍学风险学生的精准预测。最后在KDD Cup 2015数据集上的实验证明,所提策略能够有效帮助MOOC课程教师和教学管理者追踪课程学生在不同时间步长的学习状态,从而动态监控不同学习阶段的辍学行为。     

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.     

Measuring instant emotions based on facial expressions during computer-based assessment

Emotions are very important during learning and assessment procedures. However, measuring emotions is a very demanding task. Several tools have been developed and used for this purpose. In this paper, the efficiency of the FaceReader during a computer-based assessment (CBA) was evaluated. Instant measurements of the FaceReader were compared with the researchers’ estimations regarding students’ emotions. The observations took place in a properly designed room in real time. Statistical analysis showed that there are some differences between FaceReader’s and researchers’ estimations regarding Disgusted and Angry emotions. Results showed that FaceReader is capable of measuring emotions with an efficacy of over 87% during a CBA and that it could be successfully integrated into a computer-aided learning system for the purpose of emotion recognition. Moreover, this study provides useful results for the emotional states of students during CBA and learning procedures. This is actually the first time that student’s instant emotions were measured during a CBA, based on their facial expressions. Results showed that most of the time students were experiencing Neutral, Angry, and Sad emotions. Furthermore, gender analysis highlights differences between genders’ instant emotions.     

Monitoring student progress using virtual appliances: A case study

The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. The increasing use of information and communication technology allows these interactions to be recorded so that analytic or mining techniques are used to gain a deeper understanding of the learning process and propose improvements. But with the increasing variety of tools being used, monitoring student progress is becoming a challenge. The paper answers two questions. The first one is how feasible is to monitor the learning activities occurring in a student personal workspace. The second is how to use the recorded data for the prediction of student achievement in a course. To address these research questions, the paper presents the use of virtual appliances, a fully functional computer simulated over a regular one and configured with all the required tools needed in a learning experience. Students carry out activities in this environment in which a monitoring scheme has been previously configured. A case study is presented in which a comprehensive set of observations were collected. The data is shown to have significant correlation with student academic achievement thus validating the approach to be used as a prediction mechanism. Finally a prediction model is presented based on those observations with the highest correlation.     

How to improve collaborative learning with video tools in the classroom? Social vs. cognitive guidance for student teams

Digital video technologies offer a variety of functions for supporting collaborative learning in classrooms. Yet, for novice learners, such as school students, positive learning outcomes also depend centrally on effective social interactions. We present empirical evidence for the positive effects of instructive guidance on performance and on learning of students who use web-based video tools during a short collaborative-design task in their history lesson. In an experiment with 16-year old learners (N?=?148) working on a history topic, we compared two contrasting types of guidance for student teams?? collaboration processes (social-interaction-related vs. cognitive-task-related guidance). We also compared two types of advanced video tools. Both types of guidance and tools were aimed at supporting students?? active, meaningful learning and critical analysis of a historical newsreel. Results indicated that social-interaction-related guidance was more effective in terms of learning outcomes (e.g., the students?? history skills) than cognitive-task-related guidance. The different tools did not yield consistent results. The implications of these findings are discussed.     

电子书包支持的体验学习活动设计与应用研究

旨在研究电子书包支持的体验学习的活动模型和应用效果。首先通过理论分析提出了电子书包支持的体验学习活动模型,并设计了《校园植物知多少》等3个教学专题,之后在某小学进行了试点实验研究。研究结果表明,电子书包支持的体验学习模型确实有助于提高学生的认知体验、行为体验和情感体验的参与度。     

Guide on the Side: Testing the Tool and the Tutorials

Guide-on-the-Side (GOTS) open source software is emerging as a popular new platform for library tutorials. Unlike video tutorials, GOTS tutorials provide an active learning experience for students. This research sought to determine student preference for passive video screencast tutorials versus interactive GOTS tutorials. In addition, the study compared creation time for GOTS versus video screencast tutorials, an important consideration in the adoption of this technology. Findings suggest that students are evenly split on tutorial preference, largely based on their individual learning styles. Furthermore, results showed that GOTS tutorials take significantly longer to create than simple screencasts, but may save time in the long-run because they are easily edited.     

Content-free collaborative learning modeling using data mining

Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future.     

Virtual reality for collaborative e-learning

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.     

A case study-based investigation of students’ experiences with social software tools

The term “social software” covers a range of tools which allow users to interact and share data with other users, primarily via the web. Blogs, wikis, podcasts and social networking websites are some of the tools that are being used in educational, social and business contexts. We have examined the use of social software in the UK further and higher education to collect evidence of the effective use of social software in student learning and engagement. We applied case study methodology involving educators and students from 26 initiatives. In this paper, we focus on the student experience: educational goals of using social software; benefits to the students; and the challenges they experience. Our investigations have shown that social software supports a variety of ways of learning: sharing of resources; collaborative learning; problem-based and inquiry-based learning; and reflective learning. Students gain transferable skills of team working, negotiation, communication and managing digital identities. Although these tools enhance a student's sense of community, the need to share and collaborate brings in additional responsibility and workload, which some students find inflexible and “forced”. Our findings show that students have concerns about usability, privacy and the public nature of social software tools for academic activities.