Building support for learning within a Doctor of Education programme

Professional doctorates were introduced in the 1990s for practitioners to research ‘real-world’ problems relevant to their respective workplace communities and contexts. An array of difficulties faces professional doctoral students as they transition from professionals to practitioner researchers. This study sought to understand the learning journey of a cohort of students at an Australian university and to assess whether the cohort approach provided the necessary support for students to reach their scholarly destinations. Throughout the first 18 months of the programme, focus group interviews and surveys were conducted to gauge students’ experiences and to evaluate developments for support within the programme. Utilising a socio-cultural perspective helped identify and explain the importance of shared practice in fostering learning, the development of academic and researcher identities, and the role of communities of practice. Challenges of managing time and overcoming the professional and academe divide were facilitated by the evolving developments of the programme.

A conceptual decision-making framework for the delivery of innovative change in organisations

Research found that today’s organisations are increasingly aware of the potential barriers and perceived challenges associated with the successful delivery of change — including cultural and sub-cultural indifferences; financial constraints; restricted timelines; insufficient senior management support; fragmented key stakeholder commitment; and inadequate training. The delivery and application of Innovative Change (see glossary) within a construction industry organisation tends to require a certain level of ‘readiness’. This readiness is the combination of an organisation’s ability to part from undertakings that may be old, traditional, or inefficient; and then being able to readily adopt a procedure or initiative which is new, improved, or more efficient. Despite the construction industry’s awareness of the various threats and opportunities associated with the delivery of change, research found little attention is currently given to develop a ‘decision-making framework’ that comprises measurable elements (dynamics) that may assist in more accurately determining an organisation’s level of readiness or ability to deliver innovative change. To resolve this, an initial Background Literature Review in 2004 identified six such dynamics, those of Change, Innovation, Implementation, Culture, Leadership, and Training and Education, which were then hypothesised to be key components of a ‘Conceptual Decision-making Framework’ (CDF) for delivering innovative change within an organisation. To support this hypothesis, a second (more extensive) Literature Review was undertaken from late 2007 to mid 2009. A Delphi study was embarked on in June 2008, inviting fifteen building and construction industry members to form a panel and take part in a Delphi study. The selection criterion required panel members to have senior positions (manager and above) within a recognised field or occupation, and to have experience, understanding and / or knowledge in the process of delivering change within organisations. The final panel comprised nine representatives from private and public industry organisations and tertiary / research and development (R&D) universities. The Delphi study developed, distributed and collated two rounds of survey questionnaires over a four-month period, comprising open-ended and closed questions (referred to as factors). The first round of Delphi survey questionnaires were distributed to the panel in August 2008, asking them to rate the relevancy of the six hypothesised dynamics. In early September 2008, round-one responses were returned, analysed and documented. From this, an additional three dynamics were identified and confirmed by the panel as being highly relevant during the decision-making process when delivering innovative change within an organisation. The additional dynamics (‘Knowledge-sharing and Management’; ‘Business Process Requirements’; and ‘Life-cycle Costs’) were then added to the first six dynamics and used to populate the second (final) Delphi survey questionnaire. This was distributed to the same nine panel members in October 2008, this time asking them to rate the relevancy of all nine dynamics. In November 2008, round-two responses were returned, analysed, summarised and documented. Final results confirmed stability in responses and met Delphi study guidelines. The final contribution is twofold. Firstly, findings confirm all nine dynamics as key components of the proposed CDF for delivering innovative change within an organisation. Secondly, the future development and testing of an ‘Innovative Change Delivery Process’ (ICDP) is proposed, one that is underpinned by an ‘Innovative Change Decision-making Framework’ (ICDF), an ‘Innovative Change Delivery Analysis’ (ICDA) program, and an ‘Innovative Change Delivery Guide’ (ICDG).

Understanding the role of knowledge in the practice of expert nephrology nurses in Australia

This paper, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, aims to explore the role of knowledge in expert practice. Using grounded theory methodology, the study involved 17 registered nurses who were practicing in a metropolitan renal unit in New South Wales, Australia. Concurrent data collection and analysis was undertaken, incorporating participants' observations and interviews. Having extensive nephrology nursing knowledge was a striking characteristic of a nursing expert. Expert nurses clearly relied on and utilized extensive nephrology nursing knowledge to practice. Of importance for nursing, the results of this study indicate that domain-specific knowledge is a crucial feature of expert practice.

Ezine and iRadio as knowledge creation metaphors for scaffolding learning in physical and virtual learning spaces

eZine and iRadio represent metaphors for multimedia communication on the Internet. Participating students experience a simulated Internet publishing environment in both their classroom and virtual learning environment. This chapter presents an autoethnographic account highlighting the voices of the learning designer and the teacher and provides evidence of the planning and implementation of two tertiary music elective courses over three iterations of each course. A blended learning environment was incorporated within each elective music course and a collaborative approach to development between lecturers, tutors, learning and technological designers using an iterative research design. The research suggests that learning design which provides real world examples and resources integrating authentic task design into their unit can provide meaningful and engaging experiences for students. The dialogue between learning designers and teachers and iterative review of the learning process and student outcomes, we believe, has engaged students meaningfully to achieve transferable learning outcomes.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Implicit online learning

Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

The WIL (Work Integrated Learning) report : a national scoping study [Final Report]

This report provides an account of the first large-scale scoping study of work integrated learning (WIL) in contemporary Australian higher education. The explicit aim of the project was to identify issues and map a broad and growing picture of WIL across Australia and to identify ways of improving the student learning experience in relation to WIL. The project was undertaken in response to high levels of interest in WIL, which is seen by universities both as a valid pedagogy and as a means to respond to demands by employers for work-ready graduates, and demands by students for employable knowledge and skills. Over a period of eight months of rapid data collection, 35 universities and almost 600 participants contributed to the project. Participants consistently reported the positive benefits of WIL and provided evidence of commitment and innovative practice in relation to enhancing student learning experiences. Participants provided evidence of strong partnerships between stakeholders and highlighted the importance of these relationships in facilitating effective learning outcomes for students. They also identified a range of issues and challenges that face the sector in growing WIL opportunities; these issues and challenges will shape the quality of WIL experiences. While the majority of comments focused on issues involved in ensuring quality placements, it was recognised that placements are just one way to ensure the integration of work with learning. Also, the WIL experience is highly contextualised and impacted by the expectations of students, employers, the professions, the university and government policy.

Assessment for learning - Why the theory needs the practice

Assessment for Learning is a pedagogical practice with anticipated gains of increased student motivation, mastery and autonomy as learners develop their capacity to monitor and plan their own learning progress. Assessment for Learning (AfL) differs from Assessment of learning in its timing, occurring within the regular flow of learning rather than end point, in its purpose of improving student learning rather than summative grading and in the ownership of the learning where the student voice is heard in judging quality. Since Black and Wiliam (1998) highlighted the achievement gains that AfL practices seem to bring to all learners in classrooms, it has become part of current educational policy discourse in Australia, yet teacher adoption of the practices is not a straightforward implementation of techniques within an existing classroom repertoire. As can be seen from the following meta-analysis, recent research highlights a more complex interrelationship between teacher and student beliefs about learning and assessment, and the social and cultural interactions in and contexts of the classroom. More research is needed from a sociocultural perspective that allows meaning to emerge from practice. Before another policy push, we need to understand better the many factors within the assessment relationship. We need to hear from teachers and students through long-term AfL case studies both to inform AfL theory and to shed light on the complexities of pedagogical change for enhancing learner autonomy.

What it means to become a public relations professional: student perceptions of professional identity through real-world learning

Public relations educators need new solutions to prepare students to become tomorrow's practitioner today. Managers and employers in the new creative workforce (McWilliam, 2008) expect graduates to be problem solvers, critical and creative thinkers, reflective, and self reliant (Barrie, 2008; David, 2004). Enabling students to develop these attributes requires a collaborative and creative approach to pedagogy (Jeffrey & Craft, 2001, 2004). A model for the next generation of public relations education was developed to integrate industry partnerships as a way to bridge pedagogy and professional practice. The model suggests (a) that industry partnerships be embedded in learning activities, (b) that assessment items be considered on a continuum and delivered incrementally across a course of study, and (c) that connections between classroom and workplace activities are clearly signposted for students.

Perceptions of online learning in an Australian university : Malaysian students' perspectives

Several researchers have reported that cultural and language differences can affect online interactions and communications between students from different cultural backgrounds. Other researchers have asserted that online learning is a tool that can improve teaching and learning skills, but, its effectiveness depends on how the tool is used. Therefore, this study aims to investigate the kinds of challenges encountered by the international students and how they actually cope with online learning. To date little research exists on the perceptions of online learning environments by international Asian students, in particular Malaysian students who study in Australian Universities; hence this study aims to fill this gap. A mixed-method approach was used to collect quantitative and qualitative data using a modified Online Learning Environment Survey (OLES) instrument and focus group interviews. The sample comprised 76 international students from a university in Brisbane. Thirty-five domestic Australian students were included for comparison. Contrary to assumptions from previous research, the findings revealed that there were few differences between the international Asian students from Malaysia and Australian students with regard to their perceptions of online learning. Another cogent finding that emerged was that online learning was most effective when included within blended learning environments. The students clearly indicated that when learning in a blended environment, it was imperative that appropriate features are blended in and customised to suit the particular needs of international students. The study results indicated that the university could improve the quality of the blended online learning environment by: 1) establishing and maintaining a sense of learning community; 2) enhancing the self motivation of students; and 3) professional development of lecturers/tutors, unit coordinators and learning support personnel. Feedback from focus group interviews, highlighted the students‘ frustration with a lack of cooperative learning, strategies and skills which were expected of them by their lecturers/tutors in order to work productively in groups. They indicated a strong desire for lecturers/tutors to provide them prior training in these strategies and skills. The students identified four ways to optimise learning opportunities in cross-cultural spaces. These were: 1) providing preparatory and ongoing workshops focusing on the dispositions and roles of students within student-centred online learning environments; 2) providing preparatory and ongoing workshops on collaborative group learning strategies and skills; 3) providing workshops familiarising students with Australian culture and language; and 4) providing workshops on strategies for addressing technical problems. Students also indicated a strong desire for professional development of lecturers/tutors focused on: 1) teacher attributes, 2) ways to culturally sensitive curricula, and 3) collaborative learning and cooperative working strategies and skills, and 4) designing flexible program structures. Recommendations from this study will be useful to Australian universities where Asian international students from Malaysia study in blended learning environments. An induction program (online skills, collaborative and teamwork skills, study expectations plus familiarisation with Australian culture) for overseas students at the commencement of their studies; a cultural awareness program for lecturers (cultural sensitivity, ways to communicate and a better understanding of Asian educational systems), upskilling of lecturers‘ ability to structure their teaching online and to apply strong theoretical underpinnings when designing learning activities such as discussion forums, and consistency with regards to how content is located and displayed in a learning management system like Blackboard. Through addressing the research questions in this study, the researcher hopes to contribute to and advance the domain of knowledge related to online learning, and to better understand how international Malaysian students‘ perceive online learning environments. These findings have theoretical and pragmatic significance.

Hunters & gatherers : strategies for curriculum mapping and data collection for assurance of learning

Assurance of learning is a predominant feature in both quality enhancement and assurance in higher education. Assurance of learning is a process that articulates explicit program outcomes and standards, and systematically gathers evidence to determine the extent to which performance matches expectations. Benefits accrue to the institution through the systematic assessment of whole of program goals. Data may be used for continuous improvement, program development, and to inform external accreditation and evaluation bodies. Recent developments, including the introduction of the Tertiary Education and Quality Standards Agency (TEQSA) will require universities to review the methods they use to assure learning outcomes. This project investigates two critical elements of assurance of learning: 1. the mapping of graduate attributes throughout a program; and 2. the collection of assurance of learning data. An audit was conducted with 25 of the 39 Business Schools in Australian universities to identify current methods of mapping graduate attributes and for collecting assurance of learning data across degree programs, as well as a review of the key challenges faced in these areas. Our findings indicate that external drivers like professional body accreditation (for example: Association to Advance Collegiate Schools of Business (AACSB)) and TEQSA are important motivators for assuring learning, and those who were undertaking AACSB accreditation had more robust assurance of learning systems in place. It was reassuring to see that the majority of institutions (96%) had adopted an embedding approach to assuring learning rather than opting for independent standardised testing. The main challenges that were evident were the development of sustainable processes that were not considered a burden to academic staff, and obtainment of academic buy in to the benefits of assuring learning per se rather than assurance of learning being seen as a tick box exercise. This cultural change is the real challenge in assurance of learning practice.