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Full-Text Articles in Engineering
Relationship Between Learning In The Engineering Laboratory And Student Evaluations, Sasha Nikolic, Thomas F. Suesse, Thomas Goldfinch, Timothy J. Mccarthy
Relationship Between Learning In The Engineering Laboratory And Student Evaluations, Sasha Nikolic, Thomas F. Suesse, Thomas Goldfinch, Timothy J. Mccarthy
Faculty of Engineering and Information Sciences - Papers: Part A
BACKGROUND OR CONTEXT This study is built upon previous research that developed an instrument to measure the learning objectives of the laboratory across the cognitive, psychomotor and affective domains with research that investigated student evaluations of sessional laboratory demonstrators, laboratory experiments and facilities. This research highlighted the importance of laboratory work in engineering education, and the need to improve our understanding of how learning occurs in the laboratory. PURPOSE OR GOAL Student evaluations are heavily used in higher education, and a greater understanding is needed on how these evaluations relate to learning. APPROACH An instrument used to measure learning in …
Learning Diagnostic Diagrams In Transport-Based Data-Collection Systems, Vu Tran, Peter W. Eklund, Christopher David Cook
Learning Diagnostic Diagrams In Transport-Based Data-Collection Systems, Vu Tran, Peter W. Eklund, Christopher David Cook
Faculty of Engineering and Information Sciences - Papers: Part A
Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural model from Automatic Vehicle Location (AVL) and Automatic Passenger Counters (APC) data. Our purpose is to discover the variability of transit service attributes and their effects on traveller behaviour. A Tsrd diagram describes and helps to analyse factors affecting public transport by combining domain knowledge with statistical data.
Multiple Kernel Learning In The Primal For Multimodal Alzheimer's Disease Classification, Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
Multiple Kernel Learning In The Primal For Multimodal Alzheimer's Disease Classification, Fayao Liu, Luping Zhou, Chunhua Shen, Jianping Yin
Faculty of Engineering and Information Sciences - Papers: Part A
To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which …
Training Laboratory: Using Online Resources To Enhance The Laboratory Learning Experience, Sasha Nikolic
Training Laboratory: Using Online Resources To Enhance The Laboratory Learning Experience, Sasha Nikolic
Faculty of Engineering and Information Sciences - Papers: Part A
2014 IEEE. Technology has enabled students to search and utilize information from a diverse range of sources. One mechanism that students turn to for additional resources is the internet. This paper explores student interaction with an internet resource, called the Training Laboratory. This resource has multiple uses, including: 1) the training of laboratory teaching assistants; 2) providing students an opportunity to develop pre-requisite laboratory skills; 3) reduce the workload of developing resources when designing laboratory notes; 4) reduce the duplication of learning fundamental laboratory skills in multiple subjects; 5) provide a means to share resources to satellite campuses; and, 6) …
Collaborative Learning Through Taas: A Mobile System For Courses Over The Cloud, Geng Sun, Jun Shen
Collaborative Learning Through Taas: A Mobile System For Courses Over The Cloud, Geng Sun, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
As mobile cloud-based learning gain wide acceptance, learners engaged in online courses have more opportunities to participate in virtual teams. In this paper we present a new system, Teamwork as a Service (TaaS), which is service-oriented and emphasizes on building a smart collaborative learning context in conjunction with cloudhosting learning management systems (LMSs). Each of the five services of TaaS aims to organize a certain type of refined learning activities, so as to join together to facilitate the whole collaborative learning process. The implementation details of TaaS are demonstrated, and some typical user interfaces (UIs) are illustrated about how TaaS …
Bayesian Learning And Predictability In A Stochastic Nonlinear Dynamical Model, John Parslow, Noel Cressie, Edward P. Campbell, Emlyn Jones, Lawrence Murray
Bayesian Learning And Predictability In A Stochastic Nonlinear Dynamical Model, John Parslow, Noel Cressie, Edward P. Campbell, Emlyn Jones, Lawrence Murray
Faculty of Engineering and Information Sciences - Papers: Part A
Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied …
Evaluations Of Heuristic Algorithms For Teamwork-Enhanced Task Allocation In Mobile Cloud-Based Learning, Geng Sun, Jun Shen, Junzhou Luo, Jianming Yong
Evaluations Of Heuristic Algorithms For Teamwork-Enhanced Task Allocation In Mobile Cloud-Based Learning, Geng Sun, Jun Shen, Junzhou Luo, Jianming Yong
Faculty of Engineering and Information Sciences - Papers: Part A
Enhancing teamwork performance is a significant issue in mobile cloud-based learning. We introduce a service oriented system, Teamwork as a Service (TaaS), to realize a new approach for enhancing teamwork performance in the mobile cloud environment. To coordinate most learners' talents and give them more motivation, an appropriate task allocation is necessary. Utilizing the Kolb's learning style (KLS) to refine learner's capabilities, and combining their preferences and tasks' difficulties, we formally describe this problem as a constraint optimization model. Two heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA), are employed to tackle the teamwork-enhanced task allocation, and their …
Teamwork As A Service: A Cloud-Based System For Enhancing Teamwork Performance In Mobile Learning, Geng Sun, Jun Shen
Teamwork As A Service: A Cloud-Based System For Enhancing Teamwork Performance In Mobile Learning, Geng Sun, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Although cloud computing helps learners to access online learning content through commonly used devices, it can be difficult to collaborate in the mobile environment. We develop a service-oriented system, ‘Teamwork as a Service’ (TaaS), to facilitate learners’ team learning activities. The five main services of TaaS facilitate a teamwork-enhanced learning flow associated with the theory of Kolb’s team learning experience, providing learners with an introduction, a ‘jigsaw classroom’, schedule planning, and mutual supervision during the whole process. Specifically, TaaS enables a rational grouping mechanism that allocates learners to their appropriate tasks in order to give their best performance. TaaS is …
Facilitating Collaborative Learning In Taas: A Mobile Cloud System For Enhancing Teamwork Performance, Geng Sun, Jun Shen
Facilitating Collaborative Learning In Taas: A Mobile Cloud System For Enhancing Teamwork Performance, Geng Sun, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Mobile cloud-based learning is a novel trend that brings many advantages to distributed learners to achieve collaborative learning, but it still lacks of mechanisms to enhance their teamwork performances. To make up that shortcoming, combining the features of cloud environment, we have identified a learning flow, a specification of workflow, based on Kolb team learning experience. This novel learning flow can be executed by our newly designed system, Teamwork as a Service (TaaS), in conjunction with cloud-hosting learning management systems, following which learners benefit from functions given by cloudbased services, separately for organizing cloud jigsaw classroom, planning and publishing tasks, …
Video Resources For Supporting Learning In Mathematics Rich Disciplines: A Teaching Perspective, Bothaina Bukhatwa, Anne L. Porter, Mark I. Nelson
Video Resources For Supporting Learning In Mathematics Rich Disciplines: A Teaching Perspective, Bothaina Bukhatwa, Anne L. Porter, Mark I. Nelson
Faculty of Engineering and Information Sciences - Papers: Part A
Video capture technology allows video to be readily recorded and edited. The distribution of videos through e-learning systems provides learning support to students. This article discusses our experiences in developing video genre resources. The "overview" video resource is found to be a useful technique for conveying the structure of a topic. Video resources can be combined in a variety of ways using concept maps, learning design maps and/or folder based approaches. We provide our perspective regarding the productio nof video resources using tablet technology tools. The relative ease and flexibility of the technology is discussed. The aim of this article …
Meta-Development Of Resources And Learning Designs: Providing Ict-Based Tools For Use By Mathematics Teachers, Maman Fathurrohman, Anne Porter, Annette Worthy
Meta-Development Of Resources And Learning Designs: Providing Ict-Based Tools For Use By Mathematics Teachers, Maman Fathurrohman, Anne Porter, Annette Worthy
Faculty of Engineering and Information Sciences - Papers: Part A
This paper is a report of a meta-development research project of mathematical learning resources and learning designs. The process of meta-development was conducted through mapping of mathematical learning resources and learning designs based on an observation of 119 participating teachers in Bojonegara Sub District, Indonesia. The outcomes of the meta-development include two prototypes that are similar in concept, yet different in function: the first prototypes can be use for mapping mathematical learning resources in the Internet, while the second prototype can be use for documenting and sharing mathematics teaching and learning experiences. The prototypes were evaluated based on trial with …
An Exploratory Study Of Personal Reflection And Collaboration Skills Using Online Collaborative Tool In Project-Based Learning, Sim Kim Lau, Wendy Meyers
An Exploratory Study Of Personal Reflection And Collaboration Skills Using Online Collaborative Tool In Project-Based Learning, Sim Kim Lau, Wendy Meyers
Faculty of Engineering and Information Sciences - Papers: Part A
By deepening our understanding of the use of Web 2.0 for reflective practice, knowledge co-construction and project based learning this paper aims to contribute to our understanding of collaborative learning. The paper investigates a case study of a post-graduate system development subject to increase student learning through the development of students' personal reflection and collaboration skills. The project aims to develop key foundational knowledge and skills identified in the IS 2010 curriculum guidelines, i.e. the ability to work collaboratively. Of particular interest was the ability of student collaboration combined with personal reflective learning to lead to negotiation of meaning and …
An Adaptive Bilateral Negotiation Model Based On Bayesian Learning, Chao Yu, Fenghui Ren, Minjie Zhang
An Adaptive Bilateral Negotiation Model Based On Bayesian Learning, Chao Yu, Fenghui Ren, Minjie Zhang
Faculty of Engineering and Information Sciences - Papers: Part A
Endowing the negotiation agent with a learning ability such that a more beneficial agreement might be obtained is increasingly gaining attention in agent negotiation research community. In this paper, we propose a novel bilateral negotiation model based on Bayesian learning to enable self-interested agents to adapt negotiation strategies dynamically during the negotiation process. Specifically, we assume that two agents negotiate over a single issue based on time-dependent tactic. The learning agent has a belief about the probability distribution of its opponent's negotiation parameters (i.e., the deadline and reservation offer). By observing opponent's historical offers and comparing them with the fitted …
A Web-Based Learning Support To Improve Students' Learning Of Statistics, Norhayati Baharun, Anne L. Porter
A Web-Based Learning Support To Improve Students' Learning Of Statistics, Norhayati Baharun, Anne L. Porter
Faculty of Engineering and Information Sciences - Papers: Part A
This study investigating the impact of a web-based learning supports particularly a Headstart program on student outcomes in statistics at the University of Wollongong. Unlike in 2010, a Headstart program was introduced to the undergraduate students enrolled in an introductory statistics subject in 2011. This program allowed students to access the first module of work which includes a set of five lecture notes, video clips, and the first assessment via the subject e-learning site approximately four weeks prior to the start of the formal session. For the assessment, the students were required to complete a draft and redraft the first …
Engineering Across Cultures: New Learning Resources For Intercultural Competency In Engineering, Thomas Goldfinch, Elyssebeth Leigh, Les Dawes, Anne Gardner, Timothy Mccarthy
Engineering Across Cultures: New Learning Resources For Intercultural Competency In Engineering, Thomas Goldfinch, Elyssebeth Leigh, Les Dawes, Anne Gardner, Timothy Mccarthy
Faculty of Engineering and Information Sciences - Papers: Part A
BACKGROUND The work described in this paper has emerged from an ALTC/OLT funded project, Exploring Intercultural Competency in Engineering. The project indentified many facets of culture and intercultural competence that go beyond a culture-as-nationality paradigm. It was clear from this work that resources were needed to help engineering educators introduce students to the complex issues of culture as they relate to engineering practice. A set of learning modules focussing on intercultural competence in engineering practice have been developed that cover the various aspects of culture in engineering identified in the project. Supporting the resources, an eBook detailing the ins and …
Learning-Based Prostate Localization For Image Guided Radiation Therapy, Luping Zhou, Shu Liao, Wei Li, Dinggang Shen
Learning-Based Prostate Localization For Image Guided Radiation Therapy, Luping Zhou, Shu Liao, Wei Li, Dinggang Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions, thus filtering out features that are salient in image appearance, but irrelevant to prostate localization. Furthermore, an image similarity function is learned to make the image appearance distance consistent with the underlying prostate alignment. The efficacy of …
A Scalable Algorithm For Learning A Mahalanobis Distance Metric, Junae Kim, Chunhua Shen, Lei Wang
A Scalable Algorithm For Learning A Mahalanobis Distance Metric, Junae Kim, Chunhua Shen, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
A distance metric that can accurately re°ect the intrinsic characteristics of data is critical for visual recognition tasks. An e®ective solution to de¯ning such a metric is to learn it from a set of training sam- ples. In this work, we propose a fast and scalable algorithm to learn a Ma- halanobis distance. By employing the principle of margin maximization to secure better generalization performances, this algorithm formulates the metric learning as a convex optimization problem with a positive semide¯nite (psd) matrix variable. Based on an important theorem that a psd matrix with trace of one can always be represented …
A Multi-Resolution Approach To Learning With Overlapping Communities, Lei Tang, Xufei Wang, Huan Liu, Lei Wang
A Multi-Resolution Approach To Learning With Overlapping Communities, Lei Tang, Xufei Wang, Huan Liu, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
The recent few years have witnessed a rapid surge of par- ticipatory web and social media, enabling a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we attempt to harness the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes one product, whether he/she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in mul- tiple dierent communities with each regulating the actor's behavior in varying degrees, and …
E-Learning Barriers In The United Arab Emirates: Preliminary Results From An Empirical Investigation, Lejla Vrazalic, Robert C. Macgregor, D Behl, Jean Fitzgerald
E-Learning Barriers In The United Arab Emirates: Preliminary Results From An Empirical Investigation, Lejla Vrazalic, Robert C. Macgregor, D Behl, Jean Fitzgerald
Faculty of Engineering and Information Sciences - Papers: Part A
E-learning is relatively new to the United Arab Emirates. Most tertiary institutions have allocated ICT resources to provide alternatives to the previously used teacher-centred "chalk and talk" approach to learning and teaching. However we have not yet developed a comprehensive understanding of the application of e-learning methods and resources in the tertiary education sector in the UAE. This paper describes a collaborative research project which empirically investigated the perceived barriers to e-learning for students studying at tertiary institutions in the UAE using an online questionnaire. The paper analyses the associations between e-learning barriers and students' age and gender. The ease …
Psdboost: Matrix-Generation Linear Programming For Positive Semidefinite Matrices Learning, Chunhua Shen, Alan Welsh, Lei Wang
Psdboost: Matrix-Generation Linear Programming For Positive Semidefinite Matrices Learning, Chunhua Shen, Alan Welsh, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part A
In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue is how to preserve positive semidefiniteness during the course of learning. Our algorithm is mainly inspired by LPBoost [1] and the general greedy convex optimization framework of Zhang [2]. We demonstrate the essence of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing on a few different applications in machine learning. The proposed PSDBoost algorithm extends traditional Boosting algorithms in that its parameter is a positive semidefinite matrix with trace being one instead of a classifier. PSDBoost is based on the observation that any …
Learning Texture Similarity With Perceptual Pairwise Distance, Yan Gao, Lei Wang, Kap Luk Chan, Wei-Yan Yau
Learning Texture Similarity With Perceptual Pairwise Distance, Yan Gao, Lei Wang, Kap Luk Chan, Wei-Yan Yau
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning similarity helps to alleviate this perceptual inconsistency. For textures, psychological experiments were shown to be able to construct perceptual pairwise distance matrix. We are going to show how this distance information could be utilized in learning similarity by Support Vector Machines for efficient texture classification and retrieval.
Using Self-Regulated Learning To Manage The Discomfort Of Becoming Fluent With Information Technology, Victoria M. Neville, Sue Bennett
Using Self-Regulated Learning To Manage The Discomfort Of Becoming Fluent With Information Technology, Victoria M. Neville, Sue Bennett
Faculty of Engineering and Information Sciences - Papers: Part A
The technologically complex and changing world of the twenty first century requires teachers who are both knowledgeable and skilled in using information technology in their pedagogical practices. The changing nature of information technology means that teachers need to be flexible in how they use information technology in their teaching, adaptable to the changes in technological developments, problem solvers in unfamiliar circumstances, and continuing learners throughout their professional life. These ideas are encapsulated in the concept of fluency with information technology, or FITness (Committee on Information Technology Literacy, 1999). This research study, in progress, uses an interpretive, qualitative methodological approach to …
Improving Adaboost For Classification On Small Training Sample Sets With Active Learning, Lei Wang, Xuchun Li, Eric Sung
Improving Adaboost For Classification On Small Training Sample Sets With Active Learning, Lei Wang, Xuchun Li, Eric Sung
Faculty of Engineering and Information Sciences - Papers: Part A
Recently, AdaBoost has been widely used in many computer vision applications and has shown promising results. However, it is also observed that its classification performance is often poor when the size of the training sample set is small. In certain situations, there may be many unlabelled samples available and labelling them is costly and time-consuming. Thus it is desirable to pick a few good samples to be labelled. The key is how. In this paper, we integrate active learning with AdaBoost to attack this problem. The principle idea is to select the next unlabelled sample base on it being at …
Image Retrieval With Svm Active Learning Embedding Euclidean Search, Lei Wang, Kap Luk Chan, Yap Peng Tan
Image Retrieval With Svm Active Learning Embedding Euclidean Search, Lei Wang, Kap Luk Chan, Yap Peng Tan
Faculty of Engineering and Information Sciences - Papers: Part A
Image retrieval with relevance feedback suffers from the small sample problem. Recently, SVM active learning has been proposed to tackle this problem, showing promising results. However, a small but sufficient number of initially labelled samples are still required to ensure the subsequent active learning efficient and good retrieval performance. In the existing method, the user is asked to label more images before active learning starts. In this paper, a method of embedding Euclidean search into SVM active learning is proposed. With the help of Euclidean search, not only the adverse effect on retrieval performance due to lack of initially labelled …
Bootstrapping Svm Active Learning By Incorporating Unlabelled Images For Image Retrieval, Lei Wang, Kap Luk Chan, Zhihua Zhang
Bootstrapping Svm Active Learning By Incorporating Unlabelled Images For Image Retrieval, Lei Wang, Kap Luk Chan, Zhihua Zhang
Faculty of Engineering and Information Sciences - Papers: Part A
The performance of image retrieval with SVM active learning is known to be poor when started with few labelled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labelled images and the unlabelled images randomly selected from the image database. Both theoretical analysis and experimental results show that by incorporating unlabelled images in the bootstrapping, the efficiency of SVM active learning can be improved, and thus improves the overall retrieval performance.
Learning Kernel Parameters By Using Class Separability Measure, Lei Wang, Kap Luk Chan
Learning Kernel Parameters By Using Class Separability Measure, Lei Wang, Kap Luk Chan
Faculty of Engineering and Information Sciences - Papers: Part A
Learning kernel parameters is important for kernel based methods because these parameters have significant impact on the generalization abilities of these methods. Besides the methods of Cross-Validation and Leave-One-Out, minimizing some upper bounds on the generalization error, such as the radius-margin bound, was also proposed to more efficiently learn the optimal kernel parameters. In this paper, a class separability criterion is proposed for learning kernel parameters. The optimal kernel parameters are regarded as those that can maximize the class separability in the induced feature space. With this criterion, learning the kernel parameters in SVM can avoid solving the quadratic programming …