Student and staff perceptions of learning and teaching online, 2005 data

The dataset consists of data gathered from Deakin University staff and students.

Staff-derived data consists of qualitative data relating to advantages and disadvantages of teaching online; manifestation of cultural diversity in online learning environments; strategies to accommodate cultural diversity online; and using online environments to support cultural diversity

Student-derived data consists of quantitative and qualitative data relating to student perceptions of online learning; student demographics; student expectations of their university experience; students' approach to learning and online learning; perceptions of online learning and online team work; and perceptions of student and teacher roles at university.

Material world: learning and teaching chemistry

 In the Australian National Curriculum, the science understanding of overarching ideas of matter and energy covers science topics in the conceptual area of chemistry, such as the properties, forms and uses of different materials, the states of matter (solid, liquid and gas), and energy, such as forces, movement and electricity. This chapter focusses on explaining the abstract science ideas related to matter and energy through the use of appropriate vocabulary, examining ways of organising knowledge and linking scientific models and theories to observations and experiences. The particle model of matter is used to explain common observations, demonstrating the value of scientific inquiry and the role of models and representations in scientific thinking. A directed inquiry teaching approach in which there is a focus on the use of representations is recommended for these abstract topics. Representations are a vital component of communicating the abstract ideas of matter and energy. The use of the pedagogical approach in which students construct and evaluate representations of scientific ideas is used in the negotiation and development of their understandings.

Code-switching and teaching Chinese as a second language

This book aims to explore the nature of code-switching. The purpose is to find out how this works and thereby inform language-teaching strategies. It focuses on Chinese / English bilinguals with special emphasis on younger students living in two linguistic worlds (Chinese and English). The book examines code-switching in relation to several aspects: grammatical structures, tonal facilitation, contextual factors, speakers' social background aspects and their participation in school language programs.

Learning how to teach chemistry with technology: pre-service teachers’ experiences with integrating technology into their learning and teaching

The Australian Government initiative, Teaching Teachers for the Future (TTF), was a targeted response to improve the preparation of future teachers with integrating technology into their practice. This paper reports on TTF research involving 28 preservice teachers undertaking a chemistry curriculum studies unit that adopted a technological focus. For chemistry teaching the results showed that technological knowledge augmented the fundamental pedagogical knowledge necessary for teaching chemistry content. All the pre-service teachers demonstrated an understanding of the role of technology in teaching and learning and reported an increased skill level in a variety of technologies, many they had not used previously. Some students were sceptical about this learning when schools did not have technological resources available. This paper argues that teacher education courses should include technological skills that match those available in schools, as well as introduce new technologies to support a change in the culture of using technology in schools.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.

Identifying pedagogy and teaching strategies for achieving nationally prescribed learning outcomes

With the globalisation of university education, national frameworks are commonly used to prescribe standardised learning outcomes and achieve accountability. However, these frameworks are generally not accompanied by guiding pedagogy to support academics in adjusting their teaching practices to achieve the set outcomes. This paper reports the results of a scoping review of health science literature aimed at identifying pedagogy and teaching strategies relevant to achieve the learning outcomes specified by the Australian Qualifications Framework at a master's degree level. Eight practical teaching messages emerged from the review and three broad pedagogical trends were identified: the need to use authentic disciplinary-based learning activities; ensure that students are able to discover different perspectives about future practice and bring student reflection about their own knowledge into curricula. More critically, the review highlights that academics attempting to translate national learning outcome frameworks into their teaching practices face a complex and time-consuming task which may involve searching beyond their own disciplinary focus to identify practical teaching strategies to meet prescribed learning outcomes.