Student perception of a new integrated anatomy practical program: Does students' prior learning make a difference?

While there is evidence that science and non-science background students display small differences in performance in basic and clinical sciences, early in a 4-year, graduate entry medical program, this lessens with time. With respect to anatomy knowledge, there are no comparable data as to the impact previous anatomy experience has on the student perception of the anatomy practical learning environment. A study survey was designed to evaluate student perception of the anatomy practical program and its impact on student learning, for the initial cohort of a new medical school. The survey comprised 19 statements requiring a response using a 5-point Likert scale, in addition to a free text opportunity to provide opinion of the perceived educational value of the anatomy practical program. The response rate for a total cohort of 82 students was 89%. The anatomy practical program was highly valued by the students in aiding their learning of anatomy, as indicated by the high mean scores for all statements (range: 4.04-4.7). There was a significant difference between the students who had and had not studied a science course prior to entering medicine, with respect to statements that addressed aspects of the course related to its structure, organization, variety of resources, linkage to problem-based learning cases, and fairness of assessment. Nonscience students were more positive compared to those who had studied science before (P levels ranging from 0.004 to 0.035). Students less experienced in anatomy were more challenged in prioritizing core curricular knowledge. © 2011 Wiley-Liss, Inc.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Learning experiences in a giant interactive environment: Insights from The Cube

In November 2012, Queensland University of Technology in Australia launched a giant interactive learning environment known as The Cube. This article reports a phenomenographic investigation into visitors’ different experiences of learning in The Cube. At present very little is known about people’s learning experience in spaces featuring large interactive screens. We observed many visitors to The Cube and interviewed 26 people. Our analysis identified critical variation across the visitors’ experience of learning in The Cube. The findings are discussed as the learning strategy (in terms of Absorption, Exploration, Isolation and Collaboration); and the content learned (in terms of Technology, Skills and Topics). Other findings presented here are dimensions of the learning strategy and the content learned, with differing perspectives on each dimension. These outcomes provide early insights into the potential of giant interactive environments to enhance learning approaches and guide the design of innovative learning spaces in higher education.

The potential benefits of divergent thinking and metacognitive skills in STEAM learning: A discussion paper

In the wake of an almost decade long economic downturn and increasing competition from developing economies, a new agenda in the Australian Government for science, technology, engineering, and mathematics (STEM) education and research has emerged as a national priority. However, to art and design educators, the pervasiveness and apparent exclusivity of STEM can be viewed as another instance of art and design education being relegated to the margins of curriculum (Greene, 1995). In the spirit of interdisciplinarity, there have been some recent calls to expand STEM education to include the arts and design, transforming STEM into STEAM in education (Maeda, 2013). As with STEM, STEAM education emphasises the connections between previously disparate disciplines, meaning that education has been conceptualised in different ways, such as focusing on the creative design thinking process that is fundamental to engineering and art (Bequette & Bequette, 2012). In this article, we discuss divergent creative design thinking process and metacognitive skills, how, and why they may enhance learning in STEM and STEAM.

Enhancing the learning experiences through Industry-Based Learning from Indonesian university perspective

This paper investigates how students’ learning experience can be enhanced by participating in the Industry-Based Learning (IBL) program. In this program, the university students coming into the industry to experience how the business is run. The students’ learning media is now not coming from the textbooks or the lecturers but from learning by doing. This new learning experience could be very interesting for students but at the same time could also be challenging. The research involves interviewing a number of students from the IBL programs, the academic staff from the participated university who has experience in supervising students and the employees of the industry who supported and supervised the students in their work placements. The research findings offer useful insights and create new knowledge in the field of education and learning. The research contributes to the existing knowledge by providing a new understanding of the topic as it applied to the Indonesian context.

Realisation of University-Industry Collaboration through Industry-Based Learning at Indonesian Higher Education

The collaboration between universities and industries has become increasingly important for the development of Science and Technology. This is particularly more prominent in the Science Technology Engineering and Mathematics (STEM) disciplines. Literature suggest that the key element of University-Industry Partnership (UIP) is the exchange of knowledge that is mutually beneficial for both parties. One real example of the collaborations is Industry-Based Learning (IBL) in which university students are coming into industries to experience and learn how the skills and knowledge acquired in the classroom are implemented in work places. This paper investigate how the University-Industry Collaboration program is implemented though Industry-Based Learning (IBL) at Indonesian Universities. The research findings offer useful insights and create a new knowledge in the field of STEM education and collaborative learning. The research will contribute to existing knowledge by providing empirical understanding of this topic. The outcomes can be used to improve the quality of University-Industry Partnership programs at Indonesian Universities and inform Indonesian higher education authorities and their industrial partners of an alternative approach to enhance their IBL programs.

Older Australians: Structural barriers to learning in later life

Learning in older age is associated with a wide range of benefits including increases in skills, social interactions, self-satisfaction, coping ability, enjoyment, and resilience to age-related changes in the brain. It is also recognized as being a fundamental component of active ageing and if active ageing objectives are to be met for the growing ageing population, barriers to learning for this group need to be fully understood so that they can be properly addressed. This paper reports on findings from a study aimed at determining the degree that structural factors deter older people aged 55 years and older from engaging in learning activities relative to other factors, based on survey (n=421) and interview (n=40) data. Quantitative and qualitative analyses revealed that factors related to educational institutions as well as infrastructure were commonly cited as barriers to participation in learning. The implications of these and other findings are discussed.

Ageing and learning in Australia: Arguing an evidence base for informed and equitable policy

Given Australia’s population ageing and predicted impacts related to health, productivity, equity and enhancing quality of life outcomes for senior Australians, lifelong learning has been identified as a pathway for addressing the risks associated with an ageing population. To date Australian governments have paid little attention to addressing these needs and thus, there is an urgent need for policy development for lifelong learning as a national priority. The purpose of this article is to explore the current lifelong learning context in Australia and to propose a set of factors that are most likely to impact learning in later years.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Ageing and learning as conceptualized by senior adults in two cultures: Hong Kong and Australia

This paper is about a study aimed to understand what successful ageing and later life learning mean to older adults in two cultures: Hong Kong and Australia. Findings from the study were reported in this paper to shed light on: (1) the meaning of ageing and learning as conceptualized by elders in Hong Kong and Australia; (2) the reasons for participation in later life learning, as well as, barriers for non-participation; (3) their learning interests and instructional preferences, and finally (4) the correlation between learning and successful ageing, and between learning and other well-being variables, including health, happiness and satisfaction of elders in Hong Kong and Australia. Two large samples of elders from Hong Kong (n=519) and Queensland, Australia (n=421) participated in the study. Within group analysis of the data from the two locations indicated that there are more similarities, rather than differences, between elders in Hong Kong and Australia with respect to background characteristics, meanings of ageing and learning, reasons for participation, barriers for non-participation, learning interests and instructional preferences.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.

Enhancing Physics Education in the National Defence University : A design based research approach in the production of learning material

This research is connected with an education development project for the four-year-long officer education program at the National Defence University. In this curriculum physics was studied in two alternative course plans namely scientific and general. Observations connected to the later one e.g. student feedback and learning outcome gave indications that action was needed to support the course. The reform work was focused on the production of aligned course related instructional material. The learning material project produced a customized textbook set for the students of the general basic physics course. The research adapts phases that are typical in Design Based Research (DBR). The research analyses the feature requirements for physics textbook aimed at a specific sector and frames supporting instructional material development, and summarizes the experiences gained in the learning material project when the selected frames have been applied. The quality of instructional material is an essential part of qualified teaching. The goal of instructional material customization is to increase the product's customer centric nature and to enhance its function as a support media for the learning process. Textbooks are still one of the core elements in physics teaching. The idea of a textbook will remain but the form and appearance may change according to the prevailing technology. The work deals with substance connected frames (demands of a physics textbook according to the PER-viewpoint, quality thinking in educational material development), frames of university pedagogy and instructional material production processes. A wide knowledge and understanding of different frames are useful in development work, if they are to be utilized to aid inspiration without limiting new reasoning and new kinds of models. Applying customization even in the frame utilization supports creative and situation aware design and diminishes the gap between theory and practice. Generally, physics teachers produce their own supplementary instructional material. Even though customization thinking is not unknown the threshold to produce an entire textbook might be high. Even though the observations here are from the general physics course at the NDU, the research gives tools also for development in other discipline related educational contexts. This research is an example of an instructional material development work together the questions it uncovers, and presents thoughts when textbook customization is rewarding. At the same time, the research aims to further creative customization thinking in instruction and development. Key words: Physics textbook, PER (Physics Education Research), Instructional quality, Customization, Creativity

A reinforcement learning based algorithm for finite horizon Markov decision processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.