A conceptual model of personalized virtual learning environments

The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs. (c) 2005 Elsevier Ltd. All rights reserved

Knowledge Engineering in Agent Oriented Business Process Management

The challenge of dynamic environment requires managing business processes with the ability to adapt to changes and to collaborate in activities. As a promising technology to process management, agent technology with its flexible, distributed and intelligent features has been studied in numerous studies. However, most existing approaches are special and ad-hoc. They have not looked much into the nature and characteristic of agents and their rational behaviours in process management. This paper intends to investigate the mechanism how to build intelligent agents in dynamic process management from the view of knowledge engineering. An agent-oriented approach to dynamic process management with its knowledge engineering is discussed, and a three-layer knowledge model of intelligent agents is proposed. By exploiting the knowledge involved in dynamic process management and transforming it into a computational model, this work provides an essential support of developing agent-oriented approaches to business process management

The Design of Agents Oriented Collaboration in SCM

In today\u27s global marketplace, individual firms no longer compete as independent entities but rather as integral part of supply chain links. In order to cater for the increasing demand on collaboration between supply chain partners, the technology of intelligent agent has gained increased interest in supply chain management. However fewer researches have clearly investigated the mechanism about agent applications in this area. In this paper we are to study the way how to incorporate intelligent agents into supply chain management from the perspective of agent-oriented system analysis and design. A multi-agent framework for collaborative planning, forecasting and replenishment in supply chain management is developed, in which supply chain collaboration models are composed from software components that represent types of supply chain agent, their constituent control elements, and their interaction protocols

New insights into probabilistic pattern formation of embryonic stem cells using agent-based modelling

Embryonic stem cells (ESCs) hold great potential for developing future therapies for a wide range of diseases. However, the mechanisms of pattern formation during embryonic development remain poorly understood. ESCs in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach to identify biologically plausible rules acting at the mesoscale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours including exploring effects due to neighbour interactions. We introduced a new metric, the stem cell aggregate pattern distance (SCAPD), to assess the deviation between the probabilistic experimental pattern formation (used as ground truth) and the probabilistic simulated outcome. We demonstrated our models can produce broadly realistic pattern formation (when compared to experimental data) with a quantified level of uncertainty. The best of our models improve fitness, evaluated by SCAPD, by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement, respectively. Collectively, our findings provide compelling arguments that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Furthermore, our work also defines a region of the parameter space that is compatible with patterning, which assists future studies in the field of cell engineering. We envisage that the novel approaches explored within this work will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments

Quantifying Health Inequalities Induced by Data and AI Models

AI technologies are being increasingly tested and applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI might do more harm than good, potentially leading to the widening of underlying inequalities. This paper proposes a generic allocation-deterioration framework for detecting and quantifying AI induced inequality. Specifically, AI induced inequalities are quantified as the area between two allocation-deterioration curves. To assess the framework’s performance, experiments were conducted on ten synthetic datasets (N>33,000) generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showing its ability to accurately detect and quantify inequality proportionally to controlled inequalities. Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios. Results showed that compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted to HiRID ICUs. All four AI models assessed were shown to induce significant inequalities (2.45% to 43.2%) for non-White compared to White patients. The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of which was >9 times worse

Improving the learning of clinical reasoning through computer-based cognitive representation

Objective: Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods: Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results: A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions: The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction

Ontology-Based Intelligent Agents in Workplace eLearning

Despite the ever increasing practices of e-learning, most workplace e-learning applications fail to meet the learners’ needs and ultimately fail to serve the organization’s quest for success. The dominance of technology-oriented approaches makes e-learning applications less goal-effective, and makes them perceived to be of poor quality and design. To solve this problem, a performance oriented approach is presented in this study. This approach aims to align the individual learning needs vis-à-vis the organizational goals and makes learning connected with work performance. Based on the approach, a prototype system has been developed that uses intelligent agent and ontology technology. A set of experiments have been conducted to demonstrate the effectiveness of the approach