Museum development and cross-cultural learning in the Kelabit Highlands, Borneo

The article discusses an aspect of the first phase of the Kelabit Highlands Museum Development Project. Deakin University and the Rurum Kelabit Sarawak collaborated in a field school for post-graduate cultural heritage and museums studies students that was held in Bario in June 2012. The article provides details about the learning framework and research activities that were designed to facilitate exchange and cross-cultural learning between the students and local participants.

Project-oriented design-based learning: aligning students’ views with industry needs

This paper focuses on the alignment of students' views on project-oriented design-based learning (PODBL) with today's industrial needs. A Collaborative relationship between academic institutions and industrial expectations is a significant process towards analytical thinking (linking the theory and practice). Improving students' knowledge as well as the students' transition into industry, requires efficient joint ventures by both learning institutions and industry partners. Project-based learning (PBL) is well developed and implemented in most engineering schools and departments around the world. What requires closer attention is the focus on design within this project-based learning framework. Today design projects have been used to motivate and teach science in elementary, middle, and high school classrooms. They are also used to assist students with possible science and engineering careers. For these reasons, design-based learning (DBL) is intended to be an effective approach to learning that is centered on a design problem-solving structure adopted for a problem-oriented project-based education. Based on an industry design forum, which the authors conducted in Melbourne, Australia in 2012, a research study was performed to investigate the industry and academic requirements for students focusing on achieving design skills. To transform the present situation in the academic teaching and learning environment and to fulfill industry needs, this research study also investigated the students' views on design skills.

Motivation, self-assessment and tacit knowledge in e-learning context selection

Formal consideration of the links between students' Motivation, Self-Assessment abilities and Tacit Knowledge is shown in this paper to provide a useful model (MSATK) for planning postgraduate, Web-based education. The design of effective e-Learning courses requires a Learning Framework that emphasizes contextanalysis within knowledge-mediated processes. Contextual analysis ensures that self-assessment will be effective in complex domains that rely on Web sources of experiential knowledge, usually accessible as Professional practice models that employ diagnostic tools for scenario simulation processes. Demographic trends now facing Japan and Western countries, and the knowledge management support requirements of global e-Learning initiatives are challenging the value of current selfassessment processes. Building a Culture of Critique is highly desirable, but the lack of an Learning Framework that reflects student ownership of their learning process has obscured the need for tools to correctly interpret domain contexts, or for student freedom to drive the need for modified scenarios. The value of a Learning Framework that links motivation, tacit knowledge, selfassessment practice and contextual analysis is examined in this paper with consequential implications for Web support.

An integrated framework for suicide risk prediction

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Munkahelyi tanulást befolyásoló tényezők – humán-számítógép együttműködés vizsgálata (Influencing factors of on-the-job learning – Analysis of human-computer collaboration)

A komplex, dinamikus, tudásalapú társadalomban nemcsak a tanulás formái, hanem a tanulás helyszínei is módosulnak, s a munkahely tanulásban betöltött szerepe felértékelődött. A munkahelyi környezet is számos átalakuláson esett át, az információs és kommunikációs technológiák (IKT) fejlődésével egyidejűleg lehetővé vált többek között a távmunka, jelentősen átformálva a munkavégzés és a munkahelyi interakciók módját. A kutatók arra keresték a választ kutatásukban, hogy a szervezeten belül milyen tényezők támogatják vagy gátolják a munkahelyi tanulást. A kutatás fő üzenete, hogy a tanulás keretrendszere, az egyéni képességek és az észlelt tanulási szituáció együttesen határozza meg a munkahelyi tanulást. A kutatók eredményüket kvalitatív kutatással feltárt három esettanulmányon keresztül ismertetik. ____ In a knowledge-based society not only the forms of learning have been changed but also the places of learning. The role of workplace in the learning process is becoming more important. Meantime, there has been a substantial change in the working conditions as the development in information and communication technologies (ICTs) makes it possible to telecommute transforming remarkably the way of working and the interactions at the workplace. The central question of the research is which intra-organizational factors support or hinder onthe- job learning. The main message of the research is that the learning framework, the individual cognitive competences and the perceived learning situation influence collectively on-the-job learning. Authors present the results of the qualitative research though three case studies.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

HRM and Innovation: A Multi-level Organizational Learning Perspective

Drawing on the 4I organizational learning framework (Crossan et al., 1999), this article develops a model to explain the multi-level and cross-level relationships between HRM practices and innovation. Individual, team, and organizational level learning stocks are theorized to explain how HRM practices affect innovation at a given level. Feed-forward and feedback learning flows explain how cross-level effects of HRM practices on innovation take place. In addition, we propose that HRM practices fostering individual, team, and organizational level learning should form a coherent system to facilitate the emergence of innovation. The article is concluded with discussions on its contributions and potential future research directions.

A perspective on supporting STEM academics with blended learning at an Australian university

Design-based educational research can aid in providing a lens into understanding the complexities around imaginative methods, while also creating an avenue to share personal insights to support the solving of teaching and learning problems to direct future efforts. In this study, the ‘I’ narrative was extensively utilised in the form of an autoethnography perspective. This was achieved by incorporating three self-report methods within a design experiment, in order to explore the messiness associated with showcasing the creation and modification of a faculty-wide blended learning framework for STEM teachers. Data generation procedures from three sources provided the evidential basis for investigating this process: (1) self-reflection, (2) key literature findings, and (3) critical discussions from a community of inquiry. The findings identified three particular features of the process of change that were challenging, for which STEM academics required support: educators’ professional context; finding models to support change in practice; and identifying the change agent. The paper argues for the program of a personal and complex methodology to inform practice, providing insights into the change process, because process is just as important as product.

What do clients learn from counseling?

Thirty-five clients who received counseling participated in this exploratory study by completing a letter to a friend that described in as much detail as possible what they had learned from counseling. The participants’ written responses were analyzed using a content analysis approach. The analysis indicated that the data were best categorized in terms of three broad areas of learnings (Self, Relations with Others, and the Process of Learning and Change). The Self taxonomy was found to consist of six hierarchical levels. The Relations with Others taxonomy consisted of five hierarchical levels, while the Process of Learning and Change taxonomy consisted of five hierarchical levels. The results suggested that these three taxonomies offer a promising and exciting way to view the impact of counseling within a learning framework. If these taxonomies are found to be stable in future research and clients are easily classified using the taxonomies then this approach may have implications for counseling. It may well be that to maximise the learnings counselors could use specific strategies and techniques to enhance their clients’ learning in the three areas.

Tracking rail safety : incident and accident investigation in Australia

Scoping Project: Currently no national or structured learning framework available in Aus or NZ Current Project: Develop a national training program & capability framework for rail incident investigators - Establish the potential market demand - Define the curricula for a multi-level national training program - Explore training providers & delivery options