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There are various ways that teachers manage student disengagement levels during their class lessons, and managing disengagement can be both stressful and challenging, especially since each student is unique. Methods and techniques utilised are specific to teachers’ own experience level, subject knowledge, and teaching styles. We report on the techniques and methods teachers utilise to identify, mitigate, and measure student disengagement during class lessons; the paper presents the results of a mixed-methods, multisession study design comprising gathered qualitative and quantitative data to enable a greater understanding. Eight educators who were full-time educators with varying years of experience from three different schools, who taught or had taught English, maths, and science subjects at the primary school level, participated in this study. The study also observed that teachers used three AR applications and collected valuable feedback on their perspectives by using analytics generated by AR applications to help manage student disengagement. A postsession survey tool was used to gather the perceived importance and ranking of the techniques and methods discussed by the teachers during the previous sessions. The results showed that the majority of teachers deemed spending “Time on Tasks” and giving “Feedback/Reflections” most suited for measuring disengagement, and encouraging “Movement” and use of “Technology” emerged as the most favoured for mitigating disengagement. For utilising AR enhanced analytics in mitigating and measuring student disengagement, the data suggested a difference in perspectives based on teachers’ teaching levels, especially concerning conversations and the use of technology devices. The study did not find conclusive evidence of differences based on teachers’ teaching subjects and there was a notable distinction in building positive relationships among English teachers. This leads to the suggestion that subject-specific pedagogy might influence the perceived effectiveness of using AR-generated analytics in mitigating and measuring student disengagement.

The transition from kindergarten to primary school involves preparing students for a more structured classroom-based learning environment, which is typically different from the play-based model in kindergartens. Building on the Forest Room concept, which connects restless and disengaged students to nature as a calming medium, this case study describes the design of a combined storybook and augmented reality application to provide a literacy primer that integrates this concept. The design case study is presented relative to three frameworks that review the support for educational content, motivation and engagement mechanisms, and features of the AR application. This serves to validate the design process relative to these criteria and identifies opportunities for enhancement, including opportunities for meaningful interaction. The resulting application demonstrates appropriate design strategies to support its target age group and focus. It provides a stimulating and flexible learning activity that can be readily integrated into the classroom and that supports the kindergarten transition to appropriate classroom behaviour by encouraging active engagement and collaboration, blending aspects of both outdoor and classroom-based activities.

Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of students’ click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.

Usability is a principal aspect of the system development process to improve and augment system facilities and meet users’ needs and necessities in all domains. It is no exception for cultural heritage. Usability problems of the interactive technology practice in cultural heritage museums should be recognized thoroughly from the viewpoints of experts and users. This paper reports on a two-phase empirical study to identify the usability problems in audio guides and websites of cultural heritage museums in Vietnam, as a developing country, and Australia, as a developed country. In phase one, five-user experience experts identified usability problems using the set of usability heuristics, and proposed suggestions to mitigate these issues. Ten usability heuristics identified a total of 176 problems for audio guides and websites. In phase two, we conducted field usability surveys to collect the real users’ opinions to detect the usability issues and examine the negative-ranked usability. The outstanding issues for audio guides and websites were pointed out. Identification of relevant usability issues and users’ and experts’ suggestions for these technologies should be given immediate attention to helping organizations and interactive service providers improve technologies’ adoptions. The paper’s findings are reliable inputs for our future study about the preeminent UX framework for interactive technology in the CH domain.

Cybersecurity attacks can arise from internal and external sources. The attacks perpetrated by internal sources are also referred to as insider threats. These are a cause of serious concern to organizations because of the significant damage that can be inflicted by malicious insiders. In this paper, we propose an approach for insider threat classification which is motivated by the effectiveness of pre-trained deep convolutional neural networks (DCNNs) for image classification. In the proposed approach, we extract features from usage patterns of insiders and represent these features as images. Hence, images are used to represent the resource access patterns of the employees within an organization. After construction of images, we use pre-trained DCNNs for anomaly detection, with the aim to identify malicious insiders. Random under sampling is used for reducing the class imbalance issue. The proposed approach is evaluated using the MobileNetV2, VGG19, and ResNet50 pre-trained models, and a benchmark dataset. Experimental results show that the proposed method is effective and outperforms other state-of-the-art methods.

Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.

Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.