Can playing Monopoly™ enhance learning for property students

This paper outlines the initial results from a pilot study into the educational use of the board game Monopoly City™ in a first year property economics unit. This game play was introduced as a fun and interactive way of achieving a number of desired outcomes including: enhanced engagement of first year students; introduction of foundational threshold concepts in property education; introduction of problem solving and critical analysis skills; early acculturation of property students to enhance student retention; and early team building within the Property Economics cohort, all in an engaging and entertaining way. Preliminary results in this research project are encouraging. The students participating in this initial cycle have demonstrated explicit linkages between their Monopoly City™ experiences and foundation urban economic and valuation theories. Students are also recognising the role strategy and chance play in the property sector. However, linking Monopoly City™ activities to assessment has proved important in student attendance and hence engagement.

Predicting reduced driver alertness on monotonous highways : a driving simulator study

Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

A holistic model for blended learning

While the studio environment has been promoted as an ideal educational setting for project-based disciplines, few qualitative studies have been undertaken in a comprehensive way (Bose, 2007). This study responds to this need by adopting Grounded Theory methodology in a qualitative comparative approach. The research aims to explore the limitations and benefits of a face-to-face (f2f) design studio as well as a virtual design studio (VDS) as experienced by architecture students and educators at an Australian university in order to find the optimal combination for a blended environment to maximize learning. The main outcome is a holistic multidimensional blended model being sufficiently flexible to adapt to various setting, in the process, facilitating constructivist learning through self-determination, self-management, and personalization of the learning environment.

Collaborative learning in the rural Indonesian craft industry

The research explores the potential for participatory and collaborative approaches in working with the Indonesian glass-bead rural craft industry, which currently struggles to sustain its business. Contextual inquiry and participatory action research were used to understand the local context, including motivations, barriers and opportunities and to collaboratively develop strategies for advancement and innovation. The study documents participatory design projects undertaken to make, sell and promote hedonic products. It identifies the importance of understanding local context and individual craftsperson aspirations in designing collaborative support programs. It also provides an in depth insight into the Indonesian rural craft industry.

Socially inclusive learning in a community setting

People with mental health problems, learning difficulties and poor literacy and numeracy are at risk of social exclusion including homelessness. They are often disconnected from the Vocational Education and Training (VET) system, with few opportunities for education and employment. Academic research has demonstrated a link between literacy and numeracy and social inclusion, however, the pathways to enact this are not well understood. This report presents insights into how a community based adult literacy program in West End, Brisbane, was first established and has since evolved into a successful model of what we are calling ‘socially inclusive learning’. The research informing the report was conducted as a twelve-month study from April 2013 to April 2014 funded through a partnership involving Anglicare Southern Queensland, A Place to Belong and the School of Public Health and Social Work, Queensland University of Technology. The research was conducted by Greg Marston and Jeffrey Johnson- Abdelmalik. The aim of the study was to clarify the principles, practice and methodology of the Reading and Writing group (hereafter RAW) and identify the characteristics of RAW that support the social inclusion of the individual being provided literacy learning. The questions guiding the research included: • What are the key principles and practices of the RAW model? • How does the acquisition of literacy and numeracy skills contribute to the recovery of people with mental health and other issues? • How can the acquisition of literacy and numeracy skills support people’s social inclusion, including achieving employment and further education outcomes? A related aim of the project was to document how this community based literacy and numeracy program operates so that other organisations with an interest in addressing similar needs can learn from the model, particularly organisations that co-locate support and education and organisations that adopt a recovery approach when working with people with mental health challenges and intellectual and psychiatric disabilities. The report highlights the background to RAW, the learning philosophy of RAW, the profile of the participants in the program and the various roles and responsibilities and structure that supports the work of RAW. The report presents perspectives from the teachers, student participants and tutors on how the group is designed and the principles implemented.

Transforming the core business of teaching and learning in classrooms through ICT

Information and Communication Technology (ICT) has become an integral part of societies across the globe. This study demonstrates how successful technology integration by 10 experienced teachers in an Australian high school was dependent on teacher-driven change and innovation that influenced the core business of teaching and learning. The teachers were subject specialists across a range of disciplines, engaging their Year Eight students (aged 12–14 years) in the Technology Rich Classrooms programme. Two classrooms were renovated to accommodate the newly acquired computer hardware. The first classroom adopted a one-to-one desktop model with all the computers with Internet access arranged in a front-facing pattern. The second classroom had computers arranged in small groups. The students also used Blackboard to access learning materials after school hours. Qualitative data were gathered from teachers mainly through structured and unstructured interviews and a range of other approaches to ascertain their perceptions of the new initiative. This investigation showed that ICT was impacting positively on the core business of teaching and learning. Through the support of the school leadership team, the built environment was enabling teachers to use ICT. This influenced their pedagogical approaches and the types of learning activities they designed and implemented. As a consequence, teachers felt that students were motivated and benefited through this experience.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

Learning about and taking action for the environment : child and teacher experiences in a preschool water education program

Water education and conservation programs have grown exponentially in Australian primary and secondary schools and, although early childhood services have been slower to respond to the challenges of sustainability, they are catching up fast. One early program targeted at preschools was the Water Aware Centre Program in northern New South Wales developed by the local water supply authority. This paper reports on a qualitative study of children’s and teachers’ experiences of the program in three preschools. The study’s aim was to identify program attributes and pedagogies that supported learning and action taking for water conservation, and to investigate if and how the program influenced children’s and teachers’practices. Data were collected through an interview with the program designer, conversations with child participants of the program, and a qualitative survey with early childhood staff. A three-step thematic analysis was conducted on the children’s and teachers’ data. Findings revealed that the program expanded children and teachers’ ideas about water conservation and increased their water conservation practices. The children were found to influence the water conservation practices of the adults around them, thus changing practices at school and at home.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A multiple case study of NAPLAN numeracy testing of Year 9 students in three Queensland secondary schools

This thesis reports on a multiple case study of the actions of three Queensland secondary schools in the context of Year 9 NAPLAN numeracy testing, focusing on their administrative practices, curriculum, pedagogy and assessment. It was established that schools have found it both challenging and costly to operate in an environment of educational reform generally, and NAPLAN testing in particular. The lack of a common understanding of numeracy and the substantial demands of implementing the Australian Curriculum have impacted on schools' ability to prepare students appropriately for NAPLAN numeracy tests. It was concluded that there is scope for schools to improve their approaches to NAPLAN numeracy testing in a way that maximises learning as well as test outcomes.

Learning Chinese as a heritage language in Australia and beyond : the role of capital

The benefits of learning and retaining heritage languages are well documented in the literature. Chinese heritage language learners’ commitment to their heritage language learning has gained significant research ground in social psychological and post-structural schools, with empirical evidence predominantly emerging from the North American contexts. There is scant sociological examination of similar problems conducted outside North America. The current study aims to make a contribution in this regard. The use of Bourdieu's sociological notion of capital in the Australian context complements the social psychological and post-structural work predominantly produced in the North American contexts. The initial quantitative sub-study analyses the impacts of various resources on the Chinese heritage language proficiency of 230 snowball-sampled Chinese Australian respondents to an online survey. The subsequent qualitative sub-study explores the profits of learning Chinese heritage language through interviews with a subset of the survey sample. Findings indicate that cultural, social, and symbolic capital significantly positively contributes to Chinese Australians’ Chinese heritage language proficiency, which, in return, produces profits in different forms of capital. The study suggests a reciprocal relationship between ‘capital’ and Chinese heritage language proficiency.

“I was proud of myself that I didn’t give up and I did it”: Experiences of pride and triumph in learning science

The role that specific emotions, such as pride and triumph, play during instruction in science education is an under-researched field of study. Emotions are recognized as central to learning yet little is known about the way in which they are produced in naturalistic settings, how emotions relate to classroom learning during interactions, and what antecedent factors are associated with emotional experiences during instruction. Data sources for the study include emotion diaries, student written artifacts, video recordings of class interactions, and interviews. Emotions produced in the moment during classroom interactions are analyzed from video data and audio data through a novel theoretical framework related to the sociology of human emotions. These direct observations are compared with students’ recollected emotional experiences reported through emotion diaries and interviews. The study establishes links between pride and triumph within classroom interactions and instructional tasks during learning episodes in a naturalistic setting. We discuss particular classroom activities that are associated with justified feelings of pride and triumph. More specifically, classroom events associated with these emotions were related to understanding science concepts, social interactions, and achieving success on challenging tasks.

Changing views of culture, learning, teaching, and knowledge : Fiji in-service primary teachers

This paper explores in-service primary teachers' views and beliefs about culture, learning, teaching and knowledge. Fifty Bachelor of Education in-service primary teachers at a university in Fiji participated in the study. The analysis reveals a mix of old and new beliefs about culture, learning teaching and knowledge. The influence of globalization of technology is a contributing factor towards changing views. To keep the indigenous epistemologies alive and valued like the Western epistemologies, more effort is needed to integrate these in formal education. This has implications for the future professional preparation of pre-service and in-service teachers.