Scaffolding students' learning in EME150 introduction to learners and learning theory : on looking back

This is a reflective article on the importance scaffolding in the EME 150 unit taught in collaboration with Deakin University Australia. Being the first unit introduced in the second semester of the first academic year, students were given a lot of support to enhance their understanding and learning since this curriculum was solely developed by Deakin University and introduced for the first time in teachers education curriculum. The scaffolding tools discussed in this article enabled students to a) establish deep learning of the theory. b) engage in collaborative and engaged learning which established good ethical relations between students c) transfer learning by applying theory into practice.

Different sounds, different musics, different teaching : an Australian case study in multicultural teaching and learning

This single case study is part of a wider ongoing research project, begun in 2005, entitled Intercultural attitudes of pre-service music education students from Deakin University and Monash University, Melbourne, Australia. One participant selected from the entire cohort and reinterviewed in 2009 as it was apparent that his experience and expertise outstripped all the others. This paper explores the tensions between authentic pedagogical practice, as understood by the interviewee, in community teaching and in a school. The data generated were analysed using interpretative phenomenological analysis. Three major themes were identified: benefits of community music making, authentic learning, and reality of class music practice. The data demonstrate that authentic socio-cultural understanding is achievable in community music teaching, particularly in the honoring of what individuals bring the sharing of expertise between ensemble players and valuing community arts practice. However, as this is a case study demonstrates, at least in some schools, there is a lack of understanding of how multicultural music could and should be taught. Australian schools should encourage teachers who bring different sounds, different musics and different teaching into the classroom thus resolving to some degree, the potential mismatches between culturally developed learning styles and music teaching methods.

Teaching and learning centres : towards maturation

Approximately 70% of Australian Teaching and Learning Centres have been restructured and/or have undergone leadership changes in the last three years. The volatility of this environment reflects the number of significant challenges faced by Teaching and Learning Centres. In determining what makes Centres successful, the issues that are likely to impact on their ability to succeed were examined. It emerged that a myriad of factors influence whether or not a Centre was recognised as being an integral and valued part of the university’s teaching and learning community – a hallmark of having reached maturity. This paper identifies four factors as being critical to the ability of Centres to succeed, noting that a combination of other factors, appropriate to each unique context, must also be in place in order for Centres to maximise their value.

Developing the Deakin online teaching and learning repository

Slides for a report on the development of the Deakin online teaching and learning repository and the proposal to create a modern Deakin version of the 1991Najaden Collection of papers on collaborative learning through computer conferencing. The collection will be called Audio, Video, Disco - Look, Listen, Learn and contain 'best of breed' papers about Deakin's past and future as a distance edcuation - flexible learning university, from the Deakin online teaching and learning repository.

Learning from large data : bias, variance, sampling, and learning curves

One of the fundamental machine learning tasks is that of predictive classification. Given that organisations collect an ever increasing amount of data, predictive classification methods must be able to effectively and efficiently handle large amounts of data. However, it is understood that present requirements push existing algorithms to, and sometimes beyond, their limits since many classification prediction algorithms were designed when currently common data set sizes were beyond imagination. This has led to a significant amount of research into ways of making classification learning algorithms more effective and efficient. Although substantial progress has been made, a number of key questions have not been answered. This dissertation investigates two of these key questions. The first is whether different types of algorithms to those currently employed are required when using large data sets. This is answered by analysis of the way in which the bias plus variance decomposition of predictive classification error changes as training set size is increased. Experiments find that larger training sets require different types of algorithms to those currently used. Some insight into the characteristics of suitable algorithms is provided, and this may provide some direction for the development of future classification prediction algorithms which are specifically designed for use with large data sets. The second question investigated is that of the role of sampling in machine learning with large data sets. Sampling has long been used as a means of avoiding the need to scale up algorithms to suit the size of the data set by scaling down the size of the data sets to suit the algorithm. However, the costs of performing sampling have not been widely explored. Two popular sampling methods are compared with learning from all available data in terms of predictive accuracy, model complexity, and execution time. The comparison shows that sub-sampling generally products models with accuracy close to, and sometimes greater than, that obtainable from learning with all available data. This result suggests that it may be possible to develop algorithms that take advantage of the sub-sampling methodology to reduce the time required to infer a model while sacrificing little if any accuracy. Methods of improving effective and efficient learning via sampling are also investigated, and now sampling methodologies proposed. These methodologies include using a varying-proportion of instances to determine the next inference step and using a statistical calculation at each inference step to determine sufficient sample size. Experiments show that using a statistical calculation of sample size can not only substantially reduce execution time but can do so with only a small loss, and occasional gain, in accuracy. One of the common uses of sampling is in the construction of learning curves. Learning curves are often used to attempt to determine the optimal training size which will maximally reduce execution time while nut being detrimental to accuracy. An analysis of the performance of methods for detection of convergence of learning curves is performed, with the focus of the analysis on methods that calculate the gradient, of the tangent to the curve. Given that such methods can be susceptible to local accuracy plateaus, an investigation into the frequency of local plateaus is also performed. It is shown that local accuracy plateaus are a common occurrence, and that ensuring a small loss of accuracy often results in greater computational cost than learning from all available data. These results cast doubt over the applicability of gradient of tangent methods for detecting convergence, and of the viability of learning curves for reducing execution time in general.

Building interactive relationships : the risks, dilemmas and learning initiatives associated with partnerships with 'real' purpose

Many teachers already do all kinds of interesting and potentially useful data collection with their students but in a fridge door context the data is rarely kept, the analyses are rarely shared beyond the classroom (except on a family’s fridge door) and it is very rare that data is stored and added to over time. (Bigum, 2002)

A systems analysis method for online teaching and learning systems

A Method for Educational Analysis and Design (MEAD) was developed to analyse and design online teaching and learning systems. The method is based upon a participational design approach focused on the requirements of the users (students)

Self access and learning English as a second language

A study of the development and implementation of a self-access centre in the University of Khon Kaen to help Thai students learn English as a foreign language. Problems in the teaching of English relate to the nature of Thai culture and the isolated location of the university. Explores the advantages and disadvantages of working in a research team in a department where the principles of action research are still novel.

Australian teaching and learning centres through the eyes of their directors : characteristics, capacities and constraints

This paper is based on research to identify common factors that contribute to the effective strategic leadership of teaching and learning centres. The second of three phases of data collection involved a survey of Directors of Australian teaching and learning centres. The data collected were quantitatively analysed using a range of descriptive, parametric and non-parametric techniques. Based on a response rate of 81.6 percent, we present a contemporary, comprehensive and representative quantitative snapshot of Australian teaching and learning centres, as seen through the eyes of their Directors. The time since last restructure, incumbency of the current Director and total Directorship experience of the current Centre Director all have mean values of ‘sometime in the previous one to three years’. Most Centres would consider their work in the areas of ‘recognition and reward’ and ‘professional development of staff’ as high impact functions, and they would be pleased with their efforts in the former area, and wish to perform better on the latter. The principal constraint identified by Centres was ‘lack of staff time’, both in the Faculties and in the Centre, to engage in teaching and learning improvement activities. Overall, Centres feel well included in relevant university committees and other activities.

Online discussion in engineering education : student responses and learning outcomes

A ubiquitous and widely used feature of online learning environments is the asynchronous discussion board. This chapter presents a case study of the introduction and evaluation of student use of an online discussion in an engineering management study unit. We introduced an assessable assignment task based on student use of an online discussion, in response to falling student unit evaluation results after we initially moved the unit to wholly online delivery mode. Both quantitative and qualitative unit evaluation data suggest that students perceive value in the online discussion activities. A regression analysis based on discussion usage data suggests that students derived significant learning outcome benefit toward their final unit grade from making reflective postings in the online discussion.