Perceptions of online learning in an Australian university: an international students’ (Asian Region) perspective – enjoyment

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. To delve into these aspects further, this study set out to investigate the kinds of learning difficulties encountered by the international students and how they actually coped with online learning. The modified Online Learning Environment Survey (OLES) instrument was used to collect data from the sample of 109 international students at a university in Brisbane. A smaller group of 35 domestic students was also included for comparison purposes. Contrary to assumptions from previous research, the findings revealed that there were only few differences between the international Asian and Australian students with regards to their perceptions of online learning. Recommendations based on the findings of this research study were made for Australian universities where Asian international students study online. Specifically the recommendations highlighted the importance of upskilling of lecturers’ ability to structure their teaching online and to apply strong theoretical underpinnings when designing learning activities such as discussion forums, and for the university to establish a degree of consistency with regards to how content is located and displayed in a learning management system like Blackboard.

The why dimension - opening frontiers for digital learning

Digital learning has come a long way from the days of simple 'if-then' queries. It is now enabled by countless innovations that support knowledge sharing, openness, flexibility, and independent inquiry. Set against an evolutionary context this study investigated innovations that directly support human inquiry. Specifically, it identified five activities that together are defined as the 'why dimension' – asking, learning, understanding, knowing, and explaining why. Findings highlight deficiencies in mainstream search-based approaches to inquiry, which tend to privilege the retrieval of information as distinct from explanation. Instrumental to sense-making, the 'why dimension' provides a conceptual framework for development of 'sense-making technologies'.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

An exploration into retrofitting cities with natural design elements - learning from the city of Berlin, Germany

Natural design features in the built environment or biophilic elements are emerging as a potential response to the challenges of climate change, urbanisation and population pressures which have invited issues such as rising urban heat island effect, rising pollution, increased congestion, among others. This concept of living cities was made popular by Professor Tim Beatley in his book titled ‘Biophilic Urbanism’. Evidence of biophilic urbanism can be seen in some cities from around the globe since decoupling environmental pressures from future development is a priority on many agendas. Berlin is an example of a modern economy that has adopted an ecological sustainable development approach to reduce environmental degradation while driving innovation and employment.

An alternate learning approach for destructive testing in civil engineering - Benefits from remote laboratory experimentation

An alternative learning approach for destructive testing of structural specimens in civil engineering is explored by using a remote laboratory experimentation method. The remote laboratory approach focuses on overcoming the constraints in the hands-on experimentation without compromising the understanding of the students on the concepts and mechanics of reinforced concrete structures. The goal of this study is to evaluate whether or not the remote laboratory experimentation approach can become a standard in civil engineering teaching. The teaching activity using remote-laboratory experimentation is presented here and the outcomes of this activity are outlined. The experience and feedback gathered from this study are used to improve the remote-laboratory experimentation approach in future years to other aspects of civil engineering where destructive testing is essential.