Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Discourse processes and products : land surveyors in Hong Kong

2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Systematic Review of User Mental Models on Applications Sustainability

In Human-Computer Interaction (HCI), a user’s mental model affects application sustainability. This study's goal is to find and assess previous work in the area of user mental models and how it relates to the sustainability of application. Thus, a systematic review process was used to identify 641 initial articles, which were then screened based on inclusion and exclusion criteria. According to the review, it has been observed that the mental model of a user has an impact on the creation of applications not only within the domain of Human-Computer Interaction (HCI), but also in other domains such as Enterprise Innovation Ecology, Explainable Artificial Intelligence (XAI), Information Systems (IS), and various others. The examined articles discussed company managers' difficulties in prioritising innovation and ecology, and the necessity to understand users' mental models to build and evaluate intelligent systems. The reviewed articles mostly used experimental, questionnaire, observation, and interviews, by applying either qualitative, quantitative, or mixed-method methodologies. This study highlights the importance of user mental models in application sustainability, where developers may create apps that suit user demands, fit with cognitive psychology principles, and improve human-AI collaboration by understanding user mental models. This study also emphasises the importance of user mental models in the long-term viability and sustainability of applications, and provides significant insights for application developers and researchers in building more user-centric and sustainable applications

A multi-agent system for administering the prescription of anti-retroviral and anti-TB drugs

Thesis (M. Tech.) -- Central University of Technology, Free State, 2007Multi-agent systems (MAS) consist of a number of autonomous agents that communicate among themselves to coordinate their activities in order to solve collectively a complex problem that cannot be tackled by any agent individually. These kinds of systems are appropriate in many domains where problems that are complex, distributed and heterogeneous require communication and coordination between separate autonomous agents, which may be running on different machines distributed over the Internet and are located in many different places. In the health care domain, MAS have been used for distributed patient scheduling, organ and tissue transplant management, community care, decision support, training and so on. One other promising area of application is in the prescription of antiretroviral and antiTB drugs. The drugs used to treat the two diseases have many and similar side effects that complicate the prescription process. These factors have to be considered when prescribing medication to a person coinfected with HIV and tuberculosis. This is usually done manually using drug recommendation tables, which are complicated to use and require a great deal of decisionmaking. The design and implementation of a multiagent system that assists health care staff in carrying out the complex task of combining antiretroviral and antiTB drugs in an efficient way is described. The system consists of a number of collaborating agents requiring the communication of complex and diverse forms of information between a variety of clinical and other settings, as well as the coordination between groups of health care professionals (doctors, nurses, counsellors, etcetera.) with very different skills and roles. The agents in the system include: patient agents, nurse agents, lab agents, medication agents and physician agents. The agents may be hosted on different machines, located in many different places distributed over the Internet. The system saves time, minimises decision errors and increases the standard of health care provided to patients