This research paper proposes the design and im-plementation of automated testing tool specifically tailored for supporting the learning and training of web application development. The tool aims to streamline the assessment process for instructors by automating code marking for web application teaching and training tasks, reducing the time-intensive manual assessment process. Unlike existing tools that primarily target static pages or monolithic source code files, our tool addresses the dynamic nature of modern web development courses and training, incorporating modern web development frameworks. Utilising Vue.js, Node.js, and MongoDB, the tool offers a robust infrastructure for automated test case generation, execution, and feedback provision. This paper contributes to the training and educational technology field by enhancing automated grading and feedback efficiency, enabling accurate differentiation between stu-dent assessment work and standard answers, thus bridging gaps in existing tools reliant on line-by-line comparison algorithms.

Understanding the relationship between technology and wellbeing is important in order to raise awareness and to improve interaction designs with digital technologies. Most studies used the time spent and frequency information of digital technology usage, very few explored the sequences and the patterns of how the activity occurs. We introduce the concept of “digital context,” a representation of activity data occurring in a short time-window. Using data from our study, we determined whether: (1) there are digital context patterns that are more frequent in a particular mood compared to other moods; and (2) in the case such patterns exist, whether they can be used to improve the performance of mood prediction models. Our results showed that a mood prediction model that include digital context features yielded an accuracy of 77.8%, which is an improvement compared with the models proposed in past studies.

Digital technology influences behaviours, moods and wellbeing. The relationships are complex, but users are increasingly interested in finding how to balance a digital life with psychological wellbeing. We present an approach for investigating the relationship between lifestyle aspects and digital technology usage patterns that combines MindGauge, a mobile app enabling users collect and analyse their moods and behaviours, with a productivity tool (RescueTime). We then report a 16-month study in which we collected computer and smartphone usage and self-reports from 72 participants. We present methods for analysing the relationship between productivity, task-switching, mood and lifestyle, and more specifically how digital technology usage associates with productivity and task-switching. Our study also investigates how lifestyle aspects (sleep quality, physical activity, workload, social interaction and alcoholic drink consumption) relate to mood, task-switching and productivity. Results show that more frequent task-switching is associated with negative moods. A few lifestyle aspects, such as sleep quality and physical activity, had a significant relationship with positive moods. We also contribute a mood detection model that utilise both digital footprints and lifestyle contexts, yielding an accuracy of 87 percent. The study provides evidence that such methods can be used to understand the impact of technology on wellbeing.

We propose a data-driven approach for predicting potential malfunction of concrete pump vehicles. Our approach is based on a novel machine learning model called Aggregate Cluster-Based Classifier (ACBC). It is comprised of several weak models, each represents a work states cluster, to learn the work status of several vehicle types and cluster them separately. The ACBC model also introduces a score voting process that decides on using a linear model with customised loss function or gradient boost decision tree to aggregate the outputs of the weak models. We evaluate our ACBC model using real data collected from IoT sensors attached to concrete piston vehicles. Our experimental analysis demonstrates that the ACBC model can achieve an overall accuracy of 68 % from all vehicle types and an accuracy above 80 % for certain vehicle types. Our experiments also shows that the proposed ACBC model consistently outperforms the LSTM model in terms of prediction accuracy and training time.

There is an urgent need to understand how technology impacts psychological health. This is challenging because the relationship between digital behaviour, emotions and wellbeing is complex, individual, and ethically sensitive. This study describes a mood detection system which solely utilises digital usage data from a commercial digital usage tracker tool. Using 813 days of digital behaviour data, and 807 mood self-reports, from 47 users, the system achieved maximum accuracies varying between 81-82%. The result indicates that digital footprints are useful as features to detect mood. We discuss ethical issues, and an approach to address them.