Cross-cultural learning experiences in architectural education

This qualitative study looks at the joint output of 20 architecture students from 2 different countries during their short respective Study Tours to each other’s country to discern the effect of cross-cultural experiences on their learning. This paper uses the students’ joint design efforts and reflective writings to investigate the outcome of this cross-cultural educational exchange. Their joint design efforts resulted in the making of small built structures, drawings and collaborative design proposals for an urban setting. In addition, a short questionnaire and personal interviews were also used as methods to gain insight into their experience and to use as a comparative study. The question is also raised in this paper of whether spontaneous friendship among students is integral to long term learning in a cross-cultural context in comparison to pre-designed learning objectives on the part of the educators. This paper also initiates the dialogue of the extent of cultural influences and universal ideas on collaborative architectural design. With increasing joint design ventures between architectural firms in different countries, there is interest in how collaborative design can be understood in a cross-cultural context. This paper examines short term cross cultural experiences and its contribution to architectural education.

Predictors of initiating and maintaining active commuting to work using transport and public health perspectives in Australia

Objective To identify predictors for initiating and maintaining active commuting (AC) to work following the 2003 Australia's Walk to Work Day (WTWD) campaign. Methods Pre- and post-campaign telephone surveys of a cohort of working age (18–65years) adults (n = 1100, 55% response rate). Two dependent campaign outcomes were assessed: initiating or maintaining AC (i.e., walk/cycle and public transport) on a single day (WTWD), and increasing or maintaining health-enhancing active commuting (HEAC) level (≥ 30min/day) in a usual week following WTWD campaign. Results A significant population-level increase in HEAC (3.9%) was observed (McNemar's χ2 = 6.53, p = 0.01) with 136 (19.0%) achieving HEAC at post campaign. High confidence in incorporating walking into commute, being active pre-campaign and younger age (< 46years) were positively associated with both outcomes. The utility of AC for avoiding parking hassles (AOR = 2.1, 95% CI: 1.2–3.6), for less expense (AOR = 1.8, 95% CI: 1.1–3.1), for increasing one's health (AOR = 2.5, 95% CI: 1.1–5.6) and for clean air (AOR = 2.2, 95% CI: 1.0–4.4) predicted HEAC outcome whereas avoiding the stress of driving (AOR = 2.6, 95% CI: 1.4–5.0) and the hassle of parking predicted the single-day AC. Conclusions Transportation interventions targeting parking and costs could be further enhanced by emphasizing health benefits of AC. AC was less likely to occur among inactive employees.

Environmental factors associated with active living in retirement village residents : findings from an exploratory qualitative enquiry

This exploratory enquiry employs qualitative methods to advance knowledge and understanding of physical environmental attributes related to active living among residents of Australian retirement villages. Six focus groups (n = 51 residents) were held and participants described how their current, and subsequently ideal, retirement village and neighborhood supported active lifestyles. Thematic analysis revealed three key environmental factors associated with active living: a positive social environment within the village; services and facilities provided in the village and wider neighborhood; and the presence of suitable pedestrian infrastructure. The unique discovery that environmental factors of both the retirement village and the surrounding neighborhood were associated with residents’ active living raises many questions for study. Findings informed the development of a survey instrument, and further understanding in this area has the potential to contribute to the design and siting practices of senior housing complexes within neighborhoods.

Systematic evaluation of GoSoapBox in tertiary education : a student response system for improving learning experiences and outcomes

There is currently a wide range of research into the recent introduction of student response systems in higher education and tertiary settings (Banks 2006; Kay and Le Sange, 2009; Beatty and Gerace 2009; Lantz 2010; Sprague and Dahl 2009). However, most of this pedagogical literature has generated ‘how to’ approaches regarding the use of ‘clickers’, keypads, and similar response technologies. There are currently no systematic reviews on the effectiveness of ‘GoSoapBox’ – a more recent, and increasingly popular student response system – for its capacity to enhance critical thinking, and achieve sustained learning outcomes. With rapid developments in teaching and learning technologies across all undergraduate disciplines, there is a need to obtain comprehensive, evidence-based advice on these types of technologies, their uses, and overall efficacy. This paper addresses this current gap in knowledge. Our teaching team, in an undergraduate Sociology and Public Health unit at the Queensland University of Technology (QUT), introduced GoSoapBox as a mechanism for discussing controversial topics, such as sexuality, gender, economics, religion, and politics during lectures, and to take opinion polls on social and cultural issues affecting human health. We also used this new teaching technology to allow students to interact with each other during class – both on both social and academic topics – and to generate discussions and debates during lectures. The paper reports on a data-driven study into how this interactive online tool worked to improve engagement and the quality of academic work produced by students. This paper will firstly, cover the recent literature reviewing student response systems in tertiary settings. Secondly, it will outline the theoretical framework used to generate this pedagogical research. In keeping with the social and collaborative features of Web 2.0 technologies, Bandura’s Social Learning Theory (SLT) will be applied here to investigate the effectiveness of GoSoapBox as an online tool for improving learning experiences and the quality of academic output by students. Bandura has emphasised the Internet as a tool for ‘self-controlled learning’ (Bandura 2001), as it provides the education sector with an opportunity to reconceptualise the relationship between learning and thinking (Glassman & Kang 2011). Thirdly, we describe the methods used to implement the use of GoSoapBox in our lectures and tutorials, and which aspects of the technology we drew on for learning purposes, as well as the methods for obtaining feedback from the students about the effectiveness or otherwise of this tool. Fourthly, we report cover findings from an examination of all student/staff activity on GoSoapBox as well as reports from students about the benefits and limitations of it as a learning aid. We then display a theoretical model that is produced via an iterative analytical process between SLT and our data analysis for use by academics and teachers across the undergraduate curriculum. The model has implications for all teachers considering the use of student response systems to improve the learning experiences of their students. Finally, we consider some of the negative aspects of GoSoapBox as a learning aid.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

E-motion diaries : blogging emotional responses to online learning in higher education

Emotions are inherently social, and are central to learning, online interaction and literacy practices (Shen, Wang, & Shen, 2009). Demonstrating the dynamic sociality of literacy practice, we used e-motion diaries or web logs to explore the emotional states of pre-service high school teachers’ experiences of online learning activities. This is because the methods of communication used by university educators in online learning and writing environments play an important role in fulfilling students’ need for social interaction and inclusion (McInnerney & Roberts, 2004). Feelings of isolation and frustration are common emotions experienced by students in many online learning environments, and are associated with the success or failure of online interactions and learning (Su, et al., 2005). The purpose of the study was to answer the research question: What are the trajectories of pre-service teachers’ emotional states during online learning experiences? This is important because emotions are central to learning, and the current trend toward Massive Open Online Courses (MOOCs) needs research about students’ emotional connections in online learning environments (Kop, 2011). The project was conducted with a graduate class of 64 high school science pre-service teachers in Science Education Curriculum Studies in a large Australian university, including males and females from a variety of cultural backgrounds, aged 22-55 years. Online activities involved the students watching a series of streamed live lectures for the first 5 weeks providing a varied set of learning experiences, such as viewing science demonstrations (e.g., modeling the use of discrepant events). Each week, students provided feedback on learning by writing and posting an e-motion diary or web log about their emotional response. Students answered the question: What emotions did you experience during this learning experience? The descriptive data set included 284 online posts, with students contributing multiple entries. Linguistic appraisal theory, following Martin and White (2005), was used to regroup the 22 different discrete emotions reported by students into the six main affect groups – three positive and three negative: unhappiness/happiness, insecurity/security, and dissatisfaction/satisfaction. The findings demonstrated that the pre-service teachers’ emotional responses to the streamed lectures tended towards happiness, security, and satisfaction within the typology of affect groups – un/happiness, in/security, and dis/satisfaction. Fewer students reported that the streamed lectures triggered negative feelings of frustration, powerlessness, and inadequacy, and when this occurred, it often pertained to expectations of themselves in the forthcoming field experience in classrooms. Exceptions to this pattern of responses occurred in relation to the fifth streamed lecture presented in a non-interactive slideshow format that compressed a large amount of content. Many students responded to the content of the lecture rather than providing their emotional responses to this lecture, and one student felt “completely disengaged”. The social practice of online writing as blogs enabled the students to articulate their emotions. The findings primarily contribute new understanding about students' wide range of differing emotional states, both positive and negative, experienced in response to streamed live lectures and other learning activities in higher education external coursework. The is important because the majority of previous studies have focused on particular negative emotions, such as anxiety in test taking. The research also highlights the potentials of appraisal theory for studying human emotions in online learning and writing.

Different in vitro activation methods for latent transforming growth factors (TGF)–β : considerable exogenous factors to promote higher mesenchymal-origin cell proliferation in a bioprocessing platform

Regenerative medicine includes two efficient techniques, namely tissue-engineering and cell-based therapy in order to repair tissue damage efficiently. Most importantly, huge numbers of autologous cells are required to deal these practices. Nevertheless, primary cells, from autologous tissue, grow very slowly while culturing in vitro; moreover, they lose their natural characteristics over prolonged culturing period. Transforming growth factors-beta (TGF-β) is a ubiquitous protein found biologically in its latent form, which prevents it from eliciting a response until conversion to its active form. In active form, TGF-β acts as a proliferative agent in many cell lines of mesenchymal origin in vitro. This article reviews on some of the important activation methods-physiochemical, enzyme-mediated, non-specific protein interaction mediated, and drug-induced- of TGF-β, which may be established as exogenous factors to be used in culturing medium to obtain extensive proliferation of primary cells.

Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Promoting active lifestyles in young children: investigating mothers’ decisions about their child’s physical activity and screen time behaviours

Objectives Given increasing trends of obesity being noted from early in life and that active lifestyles track across time, it is important that children at a very young age be active to combat a foundation of unhealthy behaviours forming. This study investigated, within a theory of planned behaviour (TPB) framework, factors which influence mothers’ decisions about their child’s 1) adequate physical activity (PA) and 2) limited screen time behaviours. Methods Mothers (N = 162) completed a main questionnaire, via on-line or paper-based administration, which comprised standard TPB items in addition to measures of planning and background demographic variables. One week later, consenting mothers completed a follow-up telephone questionnaire which assessed the decisions they had made regarding their child’s PA and screen time behaviours during the previous week. Results Hierarchical multiple regression analyses revealed support for the predictive model, explaining an overall 73% and 78% of the variance in mothers’ intention and 38% and 53% of the variance in mothers’ decisions to ensure their child engages in adequate PA and limited screen time, respectively. Attitude and subjective norms predicted intention in both target behaviours, as did intentions with behaviour. Contrary to predictions, perceived behavioural control (PBC) in PA behaviour and planning in screen time behaviour were not significant predictors of intention, neither was PBC a predictor of either behaviour. Conclusions The findings illustrate the various roles that psycho-social factors play in mothers’ decisions to ensure their child engages in active lifestyle behaviours which can help to inform future intervention programs aimed at combating very young children’s inactivity.

Learner autonomy in language learning : teachers' beliefs

This case study research investigated the extent to which Vietnamese teachers understood the concept of learner autonomy and how their beliefs about this concept were applied in their teaching practices. Data were collected through two phases of the study and revealed that teachers generally lacked understanding about learner autonomy; there was an alignment between this lack of understanding and teachers' actual teaching practices regarding learner autonomy. The findings of this study will provide teachers and policy-makers new insights into learner autonomy against the backdrop of educational reforms in Vietnam.

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.

Using visualization tool to help engineering students learning dynamics

Dynamics is an essential core engineering subject. It includes high level mathematical and theoretical contents, and basic concepts which are abstract in nature. Hence, Dynamics is considered as one of the hardest subjects in the engineering discipline. To assist our students in learning this subject, we have conducted a Teaching & Learning project to study ways and methods to effectively teach Dynamics based on visualization techniques. The research project adopts the five basic steps of Action Learning Cycle. It is found that visualization technique is a powerful tool for students learning Dynamics and helps to break the barrier of students who perceived Dynamics as a hard subject.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.