Thesis (Ph.D.)--Victoria University (Melbourne, Vic.), 2009.

Preparing engineering students for interdisciplinary practice in the workplace requires a meaningful understanding of interdisciplinary learning in engineering practice. Such an understanding could help to address the ongoing issues and concerns of the interdisciplinary learning of engineering students. The review of literature on interdisciplinary engineering education raises a major concern of the speculative approach to formulating learning outcomes of interdisciplinary engineering education, which results from the lack of understanding of how practising engineers engage in interdisciplinary learning in their workplaces. This thesis directly addresses this concern by providing the empirical evidence for a number of learning outcomes, and by identifying the associated learning practices found in three cases of interdisciplinary collaborations between engineers and life science practitioners. It also enhances the understanding of interdisciplinary learning in engineering practice by providing a detailed explanation of why engineers are more likely to engage in those learning practices and how they are more likely to achieve the learning outcomes. The main contribution of this thesis is in assembling the identified learning outcomes and the associated learning practices into one theoretical framework that embodies both the description and the explanation of interdisciplinary learning in engineering practice for a particular subclass – engineering for the life sciences. The framework describes interdisciplinary learning in terms of four epistemic practices and four learning outcomes. Additionally, it includes a contingent causal explanation for those practices and outcomes by validating the underlying causal relationships. The findings of this research could inform the formulation of learning outcomes and the deployment of learning practices in interdisciplinary engineering curricular. In addition, the generalisation of the findings to the education domain suggests practices that can help university students in their intellectual development.

The unique contribution to knowledge of this research is the study of the development of judgement capacity in apprentice and undergraduate engineering learners in Activity Led Learning (ALL) environments. Four case studies of engineering students investigated the learners' experiences of making judgements in various engineering undergraduate and apprenticeship programmes. A phenomenological research methodology was used to infer the learner's judgements from the learners' dialogues and actions that were observed during the learning activity. The findings of the study indicate that the experience and incidence of the learners' exertion of judgement is dependent upon the construct of the ALL environment to provide a problem space with potential for disjuncture, and the intentionality of the learners. The learners did not solve problems by a linear progression but repeatedly re-activated experiences and knowledge, exercising judgements until the states of disjuncture were satisfied leading to the conclusion of the problem. Heuristic judgements that may result in decision making errors tended to dominate the problem spaces though their incidence did not appear to be influenced by the technical or socio-technical demands of the project problem spaces. This thesis concludes that in ALL environments, projects of sufficient length and complexity similar to realistic professional practice, may enable students to acquire the practice of better judgement through disjuncture and by re-activating learning experiences and importing analogies into new problem spaces. However, to acquire skills and knowledge to improve judgement capacity, requires specific and purposeful interventions within ALL that enable the learner to know when heuristic judgements are reliable or otherwise unreliable, and acquiring reasoning strategies to compensate for the effects. It is proposed that in such interventions the learner learns to record their own judgements as they are exerted and to reflect critically on those judgements and their consequences. It also requires that any ALL project that aims to promote judgement capacity has in place assessment instruments that specifically consider the learner effort in the self-development of judgement.

This thesis reports a study of the impact of work placements on the transferable skills of engineering students. The thesis provides a review of the theoretical and empirical literature in the field of student work placements and transferable skills and provides a discussion of the measurement of impact in this field. It also describes the design of the study, methods of data collection and the data analyses used. The research project was carried out at Loughborough University from 2005 – 2008. The data was collected from 247 students and 5 DIS (Diploma in Industrial Studies) tutors from three engineering departments (Chemical Engineering, Civil Engineering and the Institute of Polymer Technology and Materials Engineering (IPTME)) and 26 line managers from 19 different companies which take students on placements. The results shows that the overwhelming majority of the students valued work placements as a way of developing transferable skills and identified the transferable skills which work placements were most likely and least likely to develop. There was close agreement on these matters between students who had experienced placements and those that had not. All DIS tutors and 87% of the line managers interviewed considered that a work placement had a very strong or strong impact upon the transferable skills of the students. Triangulation of the responses by students, tutors and line managers revealed close agreement on these matters. Students, tutors and line managers had mixed opinions whether work placements would improve degree results. In fact, work placement students performed significantly better in degree examinations than non work placement students. The tutors and line managers stressed particularly that work placements increased the confidence and maturity of the students. They suggested holiday work, summer work, team based projects as a part of the University degree courses as alternative ways of helping the students who are not doing work placements to acquire and improve their transferable skills, although they did not think that these suggested alternatives will be as effective as the one year placement. They considered that the duration of the work experience period is a key factor in improving transferable skills.

There has been a significant increase in the level of remote or distance learning using the Internet, often referred to as e-learning or online education. E-learning is often combined with classroom instruction and on-the-job training and this is referred to as blended learning. The purpose of this research is to investigate the impact blended learning has in improving engineering training in the engineering field of industrial automation. This is especially in improving the reaction, achievement and return on investment of learners compared to that of only the traditional classroom or e-learning approaches. One of the gaps in current research is the examination of the impact of blended learning in improving engineering training. The research revealed significant growth in the use of e-learning for engineers and technicians. There would however appear to be a large number of engineers and technicians who were disappointed with their experiences of e-learning. Significant concerns were also identified in the efficacy of e-learning and the lack of hands-on experience in this form of training for engineers and technicians. Suggestions are made as a result of the research into addressing these issues.

Sub-optimal automated transportation control systems incur high mobility, human health and environmental costs. With society reliant on its transportation systems for the movement of individuals, goods and services, minimizing these costs benefits many. Intersection traffic signal controllers are an important element of modern transportation systems that govern how vehicles traverse road infrastructure. Many types of traffic signal controllers exist; fixed time, actuated and adaptive. Adaptive traffic signal controllers seek to minimize transportation costs through dynamic control of the intersection. However, many existing adaptive traffic signal controllers rely on heuristic or expert knowledge and were not originally designed for scalability or for transportation’s big data future. This research addresses the aforementioned challenges by developing a scalable system for adaptive traffic signal control model development using deep reinforcement learning in traffic simulation. Traffic signal control can be modelled as a sequential decision-making problem; reinforcement learning can solve sequential decision-making problems by learning an optimal policy. Deep reinforcement learning makes use of deep neural networks, powerful function approximators which benefit from large amounts of data. Distributed, parallel computing techniques are used to provide scalability, with the proposed methods validated on a simulation of the City of Luxembourg, Luxembourg, consisting of 196 intersections. This research contributes to the body of knowledge by successfully developing a scalable system for adaptive traffic signal control model development and validating it on the largest traffic microsimulator in the literature. The proposed system reduces delay, queues, vehicle stopped time and travel time compared to conventional traffic signal controllers. Findings from this research include that using reinforcement learning methods which explicitly develop the policy offers improved performance over purely value-based methods. The developed methods are expected to mitigate the problems caused by sub-optimal automated transportation signal controls systems, improving mobility and human health and reducing environmental costs. / Thesis / Doctor of Philosophy (PhD) / Inefficient transportation systems negatively impact mobility, human health and the environment. The goal of this research is to mitigate these negative impacts by improving automated transportation control systems, specifically intersection traffic signal controllers. This research presents a system for developing adaptive traffic signal controllers that can efficiently scale to the size of cities by using machine learning and parallel computation techniques. The proposed system is validated by developing adaptive traffic signal controllers for 196 intersections in a simulation of the City of Luxembourg, Luxembourg, successfully reducing delay, queues, vehicle stopped time and travel time.