A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Australian higher education evaluation through assurance of learning

A collaborative research project conducted by five Australian universities inquired into the philosophy and motivation for Assurance of Learning (AoL) as a process of education evaluation. Associate Deans Teaching and Learning representing Business schools from twenty-five universities across Australia participated in telephone interviews. Data was analysed using NVIVO9. Results indicated that articulated rationale for AoL was both ensuring that students had acquired the attributes and skills the universities claimed they had, and the philosophy of continuous improvement. AoL was motivated both by ritualistic objectives to satisfy accreditation requirements and virtuous agendas for quality improvement. Closing-the-loop was emphasised, but was mostly wishful thinking for next steps beyond data collection and reporting. AoL was conceptualised as one element within the larger context of quality review, but there was no evidence of comprehensive frameworks or strategic plans.

Assurance of learning – are academics and senior management singing from the same hymn sheet?

Assurance of learning (AoL) is an important process in quality education, designed to measure the accomplishment of educational aims at the core of an institution’s programs, whilst encouraging faculty to continuously develop and improve the programs and courses. This paper reports on a study of Australian business schools to investigate current AoL practices through semi structured interviews with senior faculty leaders followed by focus group interviews with groups of senior program leaders and groups of academic teaching staff. Initial findings indicate there are significant challenges in encouraging academic staff to commit to the process and recognise the benefits of assuring learning. The differences in understanding between the various leaders and the academics were highlighted through the different focus groups. Leaders’ stressed strategic issues such as staff engagement and change, while academics focussed on process issues such as teaching graduate attributes and external accreditation. Understanding the differences in the perspectives of leaders and faculty is important, as without a shared understanding between the two groups, there is likely to be limited engagement, which creates difficulties in developing effective assurance of learning processes. Findings indicate that successful strategies developed to foster shared values on assurance of learning include: strong senior leaders’ commitment; developing champions among program and unit level staff; providing professional development opportunities; promoting and celebrating success and effectiveness; and ensuring an inclusive process with academics of all levels collaborating in the development and implementation of the process.

Enabling innovation and facilitating learning in kifs: the role of HRM practices

The aim of this paper is to contribute to our understanding of the link between HR practices, learning orientation and types of innovation in knowledge-intensive firms (KIFs). To this end, we first compiled a comprehensive overview of the existing literature on HR practices aimed at supporting innovation. On the basis of this literature, we then collected and analyzed data from a qualitative study of 19 Danish KIFs recognized for their innovation performance, focusing on links between the HR practices they use to support exploratory and exploitive learning behaviors to enhance incremental and/or radical innovation. The findings from this study demonstrate that KIFs utilize a range of HR practices that enable different learning orientations, based on the type of innovation compatible with their organizational goals.

Learning communities learning series

In 2008 the introduction of the National Assessment Program – Literacy and Numeracy (NAPLAN), combined with the publication of the international comparative analyses of student achievement data (such as the Programme of International Student Assessment (PISA) developed by the Organisation for Economic Co-operation and Development (OECD) and the Trends in International Mathematics and Science Study (TIMSS) of the International Association for the Evaluation of Educational Achievement (IEA)) highlighted a significant priority for Australian education by identifying low levels of equity.

Meaningful musical engagement : the potential impact of generative music systems on learning experiences

In order to create music, the student must establish a relationship with the musical materials. In this thesis, I examine the capacity of a generative music system called jam2jam to offer individuals a virtual musical play-space to explore. I outline the development of an iteration of software development named jam2jam blue and the evolution of a games-like user interface in the research design that jointly revealed the nature of this musical exploration. The findings suggest that the jam2jam blue interface provided an expressive gestural instrument to jam and experience musicmaking. By using the computer as an instrument, participants in this study were given access to meaningful musical experiences in both solo and ensemble situations and the researcher is allowed a view of their development of a relationship with the musical materials from the perspective of the individual participants. Through an iterative software development methodology, pedagogy and experience design were created simultaneously. The research reveals the potential for the jam2jam software to be used as a reflective tool for feedback and assessment purposes. The power of access to ensemble music making is realised though the participants’ virtual experiences which are brought into their physical space by sharing their experience with others. It is suggested that this interaction creates an environment conducive to self-initiated learning in which music is the language of interaction. The research concludes that the development of a relationship between the explorer and the musical materials is subject to the collaborative nature of the interaction through which the music is experienced.