This innovative practice full paper provides an in-depth analysis of the Design and Build (D&B) module, which utilises cross-programme grouping method, within UK undergraduate engineering programmes, showcasing a unique approach to authentic assessment. It elucidates the module's significant impact on enhancing student employability and interdisciplinary collaboration, offering a novel model that integrates real-world challenges and teamwork into the academic curriculum. The distinctiveness of the D&B module lies in its branched structure, which not only reinforces technical and soft skills but also promotes innovation and practical application of knowledge. The primary aim of such modules is to enhance student employability through the development of technical expertise, problem-solving abilities, and teamwork skills. Additionally, it seeks to foster innovation and the practical application of theoretical knowledge, preparing graduates to meet the dynamic demands of the engineering industry. The study's findings reveal that the D&B module significantly contributes to student employability by enhancing technical competencies, soft skills, and the ability to engage in innovative problem-solving. Graduates from the programme demonstrate a high degree of readiness for the professional environment, showcasing the effectiveness of the module in bridging the gap between academic learning and industry requirements.

Despite decades long establishment of effective tutoring principles, no adaptive tutoring system has been developed and open-sourced to the research community. The absence of such a system inhibits researchers from replicating adaptive learning studies and extending and experimenting with various tutoring system design directions. For this reason, adaptive learning research is primarily conducted on a small number of proprietary platforms. In this work, we aim to democratize adaptive learning research with the introduction of the first open-source adaptive tutoring system based on Intelligent Tutoring System principles. The system, we call Open Adaptive Tutor (OATutor), has been iteratively developed over three years with field trials in classrooms drawing feedback from students, teachers, and researchers. The MIT-licensed source code includes three creative commons (CC BY) textbooks worth of algebra problems, with tutoring supports authored by the OATutor project. Knowledge Tracing, an A/B testing framework, and LTI support are included.

People often hold inaccurate mental models of robots. When such misconceptions regard a robot’s perceptual capabilities, they can lead to issues with safety, privacy, and interaction efficiency. This work is the first attempt to model users’ beliefs about a robot’s perceptual capabilities and make plans to improve their accuracy—i.e., to perform belief repair. We designed a new domain called the Robot Olympics, implemented it as a web-based game platform for collecting data about users’ beliefs, and developed an approach to estimating and influencing users’ beliefs about a virtual robot in that domain. We then conducted a study that collected user behavior and belief data from 240 online participants who played the game. Results revealed shortcomings in modeling the participant’s interpretations of the robot’s actions, as well as the decision making process behind their own actions. The insights from this work provide recommendations for designing further studies and improving user models to support belief repair in human-robot interaction.

In recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.