The QUT innovation space : a trans-disciplinary learning environment for entrepreneurship education : final report

In the current climate of global economic volatility, there are increasing calls for training in enterprising skills and entrepreneurship to underpin the systemic innovation required for even medium-term business sustainability. The skills long-recognised as the essential for entrepreneurship now appear on the list of employability skills demanded by industry. The QUT Innovation Space (QIS) was an experiment aimed at delivering entrepreneurship education (EE), as an extra-curricular platform across the university, to the undergraduate students of an Australian higher education institute. It was an ambitious project that built on overseas models of EE studied during an Australian Learning and Teaching Council (ALTC) Teaching Fellowship (Collet, 2011) and implemented those approaches across an institute. Such EE approaches have not been attempted in an Australian university. The project tested resonance not only with the student population, from the perspective of what worked and what didn’t work, but also with every level of university operations. Such information is needed to inform the development of EE in the Australian university landscape. The QIS comprised a physical co-working space, virtual sites (web, Twitter and Facebook) and a network of entrepreneurial mentors, colleagues, and students. All facets of the QIS enabled connection between like-minded individuals that underpins the momentum needed for a project of this nature. The QIS became an innovation community within QUT. This report serves two purposes. First, as an account of the QIS project and its evolution, the report serves to identify the student demand for skills and training as well as barriers and facilitators of the activities that promote EE in an Australian university context. Second, the report serves as a how-to manual, in the tradition of many tomes on EE, outlining the QIS activities that worked as well as those that failed. The activities represent one measure of QIS outcomes and are described herein to facilitate implementation in other institutes. The QIS initially aimed to adopt an incubation model for training in EE. The ‘learning by doing’ model for new venture creation is a highly successful and high profile training approach commonly found in overseas contexts. However, the greatest demand of the QUT student population was not for incubation and progression of a developed entrepreneurial intent, but rather for training that instilled enterprising skills in the individual. These two scenarios require different training approaches (Fayolle and Gailly, 2008). The activities of the QIS evolved to meet that student demand. In addressing enterprising skills, the QIS developed the antecedents of entrepreneurialism (i.e., entrepreneurial attitudes, motivation and behaviours) including high-level skills around risk-taking, effective communication, opportunity recognition and action-orientation. In focusing on the would-be entrepreneur and not on the (initial) idea per se, the QIS also fostered entrepreneurial outcomes that would never have gained entry to the rigid stage-gated incubation model proposed for the original QIS framework. Important lessons learned from the project for development of an innovation community include the need to: 1. Evaluate the context of the type of EE program to be delivered and the student demand for the skills training (as noted above). 2. Create a community that builds on three dimensions: a physical space, a virtual environment and a network of mentors and partners. 3. Supplement the community with external partnerships that aid in delivery of skills training materials. 4. Ensure discovery of the community through the use of external IT services to deliver advertising and networking outlets. 5. Manage unrealistic student expectations of billion dollar products. 6. Continuously renew and rebuild simple activities to maintain student engagement. 7. Accommodate the non-university end-user group within the community. 8. Recognise and address the skills bottlenecks that serve as barriers to concept progression; in this case, externally provided IT and programming skills. 9. Use available on-line and published resources rather than engage in constructing project-specific resources that quickly become obsolete. 10. Avoid perceptions of faculty ownership and operate in an increasingly competitive environment. 11. Recognise that the continuum between creativity/innovation and entrepreneurship is complex, non-linear and requires different training regimes during the different phases of the pipeline. One small entity, such as the QIS, cannot address them all. The QIS successfully designed, implemented and delivered activities that included events, workshops, seminars and services to QUT students in the extra-curricular space. That the QIS project can be considered successful derives directly from the outcomes. First, the QIS project changed the lives of emerging QUT student entrepreneurs. Also, the QIS activities developed enterprising skills in students who did not necessarily have a business proposition, at the time. Second, successful outcomes of the QIS project are evidenced as the embedding of most, perhaps all, of the QIS activities in a new Chancellery-sponsored initiative: the Leadership Development and Innovation Program hosted by QUT Student Support Services. During the course of the QIS project, the Brisbane-based innovation ecosystem underwent substantial change. From a dearth of opportunities for the entrepreneurially inclined, there is now a plethora of entities that cater for a diversity of innovation-related activities. While the QIS evolved with the landscape, the demand endpoint of the QIS activities still highlights a gap in the local and national innovation ecosystems. The freedom to experiment and to fail is not catered for by the many new entities seeking to build viable businesses on the back of the innovation push. The onus of teaching the enterprising skills, which are the employability skills now demanded by industry, remains the domain of the higher education sector.

Developing ‘design minds’ for the 21st century through a public sector initiated online design education platform

Education in the 21st century demands a model for understanding a new culture of learning in the face of rapid change, open access data and geographical diversity. Teachers no longer need to provide the latest information because students themselves are taking an active role in peer collectives to help create it. This paper examines, through an Australian case study entitled ‘Design Minds’, the development of an online design education platform as a key initiative to enact a government priority for statewide cultural change through design-based curriculum. Utilising digital technology to create a supportive community, ‘Design Minds’ recognises that interdisciplinary learning fostered through engagement will empower future citizens to think, innovate, and discover. This paper details the participatory design process undertaken with multiple stakeholders to create the platform. It also outlines a proposed research agenda for future measurement of its value in creating a new learning culture, supporting regional and remote communities, and revitalising frontline services. It is anticipated this research will inform ongoing development of the online platform, and future design education and research programs in K-12 schools in Australia.

Situating learning : (re)examining the notion of apprenticeship in coach education

The last two decades has seen a proliferation in the provision of, and importance attached to, coach education in many Western countries [1]. Pivotal to many coach education programmes is the notion of apprenticeship [2,3,4]. Increasingly, mentoring is being positioned as a possible tool for enhancing coach education and consequently professional expertise [5]. However, there is a paucity of empirical data on interventions in, and evaluations of, coach education programmes. In their recent evaluation of a coach education programme Cassidy, Potrac & McKenzie [6] conclude that the situated learning literature could provide coach educators with a generative platform for the (re)examinationof apprenticeships and mentoring in a coach education context. This paper consequently discusses the merits of using situated learning theory [7] and the associated concept of Communities of Practice (CoP) [8] to stimulate discussion on developing new understandings of the practices of apprenticeship and mentoring in coach education.

Class participation as a learning and assessment strategy in law : facilitating students’ engagement, skills development and deep learning

Assessment has widely been described as being ‘at the centre of the student experience’. It would be difficult to conceive of the modern teaching university without it. Assessment is accepted as one of the most important tools that an educator can deploy to influence both what and how students learn. Evidence suggests that how students allocate time and effort to tasks and to developing an understanding of the syllabus is affected by the method of assessment utilised and the weighting it is given. This is particularly significant in law schools where law students may be more preoccupied with achieving high grades in all courses than their counterparts from other disciplines. However, well-designed assessment can be seen as more than this. It can be a vehicle for encouraging students to learn and engage more broadly than with the minimums required to complete the assessment activity. In that sense assessment need not merely ‘drive’ learning, but can instead act as a catalyst for further learning beyond what a student had anticipated. In this article we reconsider the potential roles and benefits in legal education of a form of interactive classroom learning we term assessable class participation (‘ACP’), both as part of a pedagogy grounded in assessment and learning theory, and as a platform for developing broader autonomous approaches to learning amongst students. We also consider some of the barriers students can face in ACP and the ways in which teacher approaches to ACP can positively affect the socio-emotional climates in classrooms and thus reduce those barriers. We argue that the way in which a teacher facilitates ACP is critical to the ability to develop positive emotional and learning outcomes for law students, and for teachers themselves.

Co-creating stories in social learning systems : The role of community media and cultural organisations in disseminating knowledge

This paper investigates how community based media organisations are co-creative storytelling institutions, and how they learn to disseminate knowledge in a social learning system. Organisations involved in story co-creation are learning to create in fluid environments.They are project based, with a constant turnover of volunteers or staff. These organisations have to meet the needs of their funding bodies and their communities to remain sustainable. Learning is seen as dialogical, and this is also reflected in the nature of storytelling itself. These organisations must learn to meet the needs of their communities, who in turn learn from the organisation’s expertise in a facilitated setting. This learning is participatory and collaborative, and is often a mix of virtual and offline interaction. Such community-based organisations sit in the realm of a hybrid-learning environment; they are neither a formal educational institution like a college, nor do their volunteers produce outcomes in a professional capacity. Yet, they must maintain a certain level of quality outcomes from their contributors to be of continued value in their communities. Drawing from a larger research study, one particular example is that of the CitizenJ project. CitizenJ is hosted by a state cultural centre, and partnered with publishing partners in the community broadcasting sector. This paper explores how this project is a Community of Practice, and how it promotes ethical and best practice, meets contributors’ needs, emphasises the importance of facilitation in achieving quality outcomes, and the creation of projects for wider community and public interest.

Digital Identity 3.0: The Platform for People

Developed economies are moving from an economy of corporations to an economy of people. More than ever, people produce and share value amongst themselves, and create value for corporations through co-creation and by sharing their data. This data remains in the hands of corporations and governments, but people want to regain control. Digital identity 3.0 gives people that control, and much more. In this paper we describe a concept for a digital identity platform that substantially goes beyond common concepts providing authentication services. Instead, the notion of digital identity 3.0 empowers people to decide who creates, updates, reads and deletes their data, and to bring their own data into interactions with organisations, governments and peers. To the extent that the user allows, this data is updated and expanded based on automatic, integrated and predictive learning, enabling trusted third party providers (e.g., retailers, banks, public sector) to proactively provide services. Consumers can also add to their digital identity desired meta-data and attribute values allowing them to design their own personal data record and to facilitate individualised experiences. We discuss the essential features of digital identity 3.0, reflect on relevant stakeholders and outline possible usage scenarios in selected industries.

The Google Online Marketing Challenge: A Transnational Comparison of Classroom Learning with Real Clients, Real Money, and Real Advertising Campaigns

In 2008, a collaborative partnership between Google and academia launched the Google Online Marketing Challenge (hereinafter Google Challenge), perhaps the world’s largest in-class competition for higher education students. In just two years, almost 20,000 students from 58 countries participated in the Google Challenge. The Challenge gives undergraduate and graduate students hands-on experience with the world’s fastest growing advertising mechanism, search engine advertising. Funded by Google, students develop an advertising campaign for a small to medium sized enterprise and manage the campaign over three consecutive weeks using the Google AdWords platform. This article explores the Challenge as an innovative pedagogical tool for marketing educators. Based on the experiences of three instructors in Australia, Canada and the United States, this case study discusses the opportunities and challenges of integrating this dynamic problem-based learning approach into the classroom.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Lead exposure through gestation-only caused long-term learning/memory deficits in young adult offspring

Numerous observations in clinical and preclinical studies indicate that the developing brain is particular sensitive to lead (Pb)'s pernicious effects. However, the effect of gestation-only Pb exposure on cognitive functions at maturation has not been studied. We investigated the potential effects of three levels of Pb exposure (low, middle, and high Pb: 0.03%, 0.09%, and 0.27% of lead acetate-containing diets) at the gestational period on the spatial memory of young adult offspring by Morris water maze spatial learning and fixed location/visible platform tasks. Our results revealed that three levels of Pb exposure significantly impaired memory retrieval in male offspring, but only female offspring at low levels of Pb exposure showed impairment of memory retrieval. These impairments were not due to the gross disturbances in motor performance and in vision because these animals performed the fixed location/visible platform task as well as controls, indicating that the specific aspects of spatial learning/memory were impaired. These results suggest that exposure to Pb during the gestational period is sufficient to cause long-term learning/memory deficits in young adult offspring. (C) 2003 Elsevier Inc. All rights reserved.

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Experiences during three generations of web based learning. Six years of web based communication

This paper tells the story of how a set of university lectures developed during the last six years. The idea is to show how (1) content, (2) communication and (3) assessment have evolved in steps which are named “generations of web learning”. The reader is offered a stepwise description of both didactic foundations of university lectures and practical implementation on a widely available web platform. The relative weight of directive elements has gradually decreased through the “three generations”, whereas characteristics of self-responsibility and self-guided learning have gained in importance. -Content was in early times presented and expected to be learned but in later phases expected to be constructed for examples of case studies. -Communication meant in early phases to deliver assignments to the lecturer but later on to form teams, exchange standpoints and review mutually. -Assessment initially consisted in marks invented and added up by the lecturer but was later enriched by peer review, mutual grading and voting procedures. How much “added value” can the web provide for teaching, training and learning? Six years of experience suggest: mainly insofar as new (collaborative and selfdirected) didactic scenarios are implemented! (DIPF/Orig.)