The aim of this study is to evaluate the reliability of a crowd simulation model developed by the authors by reproducing Dyer et al.'s experiments (published in Philosophical Transactions in 2009) on human leadership and consensus decision making in a computer-based environment. The theoretical crowd model of the simulation environment is presented, and its results are compared and analysed against Dyer et al.'s original experiments. It is concluded that the simulation results are largely consistent with the experiments, which demonstrates the reliability of the crowd model. Furthermore, the simulation data also reveals several additional new findings, namely: 1) the phenomena of sacrificing accuracy to reach a quicker consensus decision found in ants colonies was also discovered in the simulation; 2) the ability of reaching consensus in groups has a direct impact on the time and accuracy of arriving at the target position; 3) the positions of the informed individuals or leaders in the crowd could have significant impact on the overall crowd movement; and 4) the simulation also confirmed Dyer et al.'s anecdotal evidence of the proportion of the leadership in large crowds and its effect on crowd movement. The potential applications of these findings are highlighted in the final discussion of this paper.
This paper presents a crowd model which aims to simulate the individual behaviour and increase the heterogeneity in crowd simulation thus to achieve a more realistic simulation result. This model builds upon the physics foundation by referencing both Reynolds' Boids model and Helbing's Social Force model. The mechanism of behaviour processing is based on the Reynolds' steering behaviours model. The basic individual behaviours such as seek, wander and avoid obstacles are identified and serve as the basis to achieve the more complex behaviours, such as follow the leader (grouping), queue at doorway (clogging) and so on. Fine network model is used to obtain the precise positions of each individual and the multi-agent system approach is adopted to support the decision making process. A set of key personal parameters (both physical and psychological) are identified for the individuals, which act as independent intelligent agents and conduct their own decision and behaviour. The group/collective behaviour can also be observed through the individuals which react to the surrounding crowd (those personal parameters also determine how they would response). All the individual parameters are configurable in order to provide the ability to fine-tune the model to fit various needs and scenarios. A series of test simulations with various individual parameters have been conducted during the research and the results are presented in the paper.
Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.
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