Designing informal learning experiences for early career academics using a knowledge ecosystem model

This article presents a ‘knowledge ecosystem’ model of how early career academics experience using information to learn while building their social networks for developmental purposes. Developed using grounded theory methodology, the model offers a way of conceptualising how to empower early career academics through 1) agency (individual and relational) and 2) facilitation of personalised informal learning (design of physical and virtual systems and environments) in spaces where developmental relationships are formed including programs, courses, events, community, home and social media. It is suggested that the knowledge ecosystem model is suitable for use in designing informal learning experiences for early career academics.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Informed learning in the undergraduate classroom: The role of information experiences in shaping outcomes

This research aimed to inform the design of effective information literacy lessons in higher education. Phenomenography, a research approach designed to study human experience, was used to explore the experiences of a teacher and undergraduate students using information to learn about language and gender issues. The findings show that the way learners use information influences content-focused learning outcomes, and reveal an instructional pattern for enabling students to use information while becoming aware of the topic they are investigating. Based on the findings, a design model is offered in which learning outcomes are realized through targeted information literacy activities.

Reflecting on Reggio: An evaluation of design intent in an early childhood learning environment

Reggio Emilia is an educational philosophy that encourages teachers, students and their parents to collaborate and actively engage with the environment. This study investigates how the Reggio Emilia design approach was translated architecturally for a kindergarten in an Australian context, and provides insights into the operation of this Reggio kindergarten and the impact that it is now having on the occupants. It evaluates the original architectural design intent of the Reggio Emilia early childhood learning environment against its spatial provision. The relationship that the Reggio Emilia approach facilitates between students and the environment, and the contribution that this approach has on their learning, are also explored. Several key themes emerging from the Reggio values were identified in the literature. These were then used to inform an exploration of the kindergarten spaces and places.. Architects, teachers and a sustainability manager of the kindergarten were interviewed with their experiences constituting the primary data of this study. Using a Grounded Theory methodology, systematic data coding and analysis were then conducted. Themes and concepts that emerged from this process include: differing interpretations of the Reggio Emilia philosophy; motivations for neglect of traditional external structures and play equipment; the impact of education for sustainability; and the positive effects that Reggio Emilia is having on the rest of the institution’s development.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Building personas of students accessing a peer-facilitated support for learning program

The modern student represents a change from the traditional learner. More than ever before, additional resources are available online and yet personalised learning and peer-assistance programs are becoming an essential part of tertiary education delivery. This paper presents the first stage in a user-centred design approach to the analysis of the completeness and efficacy of such a personalised, peer-based support for learning program. This approach used an iterative design methodology based on contextual interview, workshops and focus groups to develop personas representing students visiting the program. Initial uses of these developed personas have included training of new personnel as well as the evaluation of the program. Overall the use of this user-centred approach and iterative persona development methodology has yielded an invaluable resource for the design of support for learning programs across the higher education industry within Australia and beyond.

Learning with symmetric label noise: The importance of being unhinged

Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2008] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the unhinged loss’ SLN-robustness.

Transitions to K–12 Education Systems: Experiences from Five Case Countries

Currently more than 140 countries offer, or are in transition to, what has become the international norm for pre-tertiary education, namely a kindergarten through grade 12 (K–12) school education system—kindergarten because of the preponderance of research asserting the long-term learning and social benefits of school readiness programs; and 12 years of primary and secondary schooling due to the time needed to acquire the knowledge and skills sets necessary for 21st century university education, postsecondary training, or decent1 work. This desk study2 conveys the experiences of four countries and one province in preparing and implementing a transition to a K–12 school education system: Mongolia, Ontario (Canada), the Philippines, Poland, and Turkey. Looking at K–12 transition in countries and systems that vary as broadly as this set enables common threads to stand out and divergent options to be noted.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Towards vision-based deep reinforcement learning for robotic motion control

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.

Recipe for success — Lessons learnt from using xAPI within the Connected Learning Analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

Can teacher collaboration overcome barriers to interdisciplinary learning in a disciplinary university? A case study using climate change

A teacher network was formed at an Australian university in order to better promote interdisciplinary student learning on the complex social-environmental problem of climate change. Rather than leaving it to students to piece together disciplinary responses, eight teaching academics collaborated on the task of exposing students to different types of knowledge in a way that was more than the summing of disciplinary parts. With a part-time network facilitator providing cohesion, network members were able to teach into each other’s classes, and share material and student activities across a range of units that included business, zoology, marine science, geography and education. Participants reported that the most positive aspects of the project were the collegiality and support for teaching innovation provided by peers. However, participants also reported being time-poor and overworked. Maintaining the collaboration beyond the initial one year project proved difficult because without funding for the network facilitator, participants were unable to dedicate the time required to meet and collaborate on shared activities. In order to strengthen teacher collaboration in a university whose administrative structures are predominantly discipline-based, there is need for recognition of the benefits of interdisciplinary learning to be matched by recognition of the need for financial and other resources to support collaborative teaching initiatives.

Enterprise education: Learning through personal experience

This paper outlines the development of a reflective process through which student feelings are brought to the surface to advance their learning outcomes. A key notion in relation to the capacity of student development is the ability of students to alter their collective habits of thought and in turn the nature of the learning environment. The paper examines student reflection in the context of students' heightened awareness of self and their potential influence on their environment. The notion of 'surfacing feelings' is used to describe the outcome of individual and group reflection through which several parties become aware of the active reflections of individual students, thereby increasing the potential depth of overall reflection. Finally, the author considers the transferability of the ideas discussed to other educational contexts.

Creating the reasonable adventurer: The co-evolution of student and learning environment

Purpose: This paper seeks to address the issue of how are graduate skills developed. The focus is not on which skills, but rather what type of learning environments is required within Higher Education to support the development of skills valued and demanded by SMEs within Australia. Approach: This paper takes a step back to consider the underlying issue of how an individual student's habits of thought are altered. In doing so, the past works of Morgan, Dewey, Whitehead, and Tyler are synthesized with the modern work of Baxter Magolda, Heath, and Biggs. Findings: It is argued that that without the development of a student-centred learning environment, most graduates will not develop the types of skills demanded by SMEs in a meaningfully way. That the failure to treat knowledge and skills as equal drivers of curriculum design will result in an imbalance that relegates skill development to a secondary learning outcome. Practical Implications: By removing the distraction of what skills should be developed, a clearer focus is possible regarding how educators should assist students to develop a broad array of generic graduate skills. From this perspective, skills can be viewed as an essential element of the educational process, rather than a new element that must be squeezed in between content. Value of Paper: This paper extends recent discussion of skills development through the use of an evolutionary perspective. Viewed as a process of creating social change, education becomes increasingly connected to a world that lays beyond institutional boundaries, thus promoting the notion of developing graduates for the world that awaits them.

When did you last predict a good idea? Exploring the case of assessing creativity through learning outcomes

It has been noted elsewhere that an idea is acknowledged to be creative if it is novel, or surprising and adaptive. So how does that fit with education's desire to measure student performance against fixed, consistent and predicted learning outcomes? This study explores practical measures and theoretical constructs that address the dearth of teaching, learning and assessment strategies to enhance creative capacity in enterprise and entrepreneurship education. It is argued that inappropriate assessment strategies can be significant inhibitors of the creativity of students and teachers. Referring to the broader discipline of 'design', as defined by Bruce and Besant (2002) – the application of human creativity to a purpose – both broad employer satisfaction with education and fast growing economic success are found (DCMS, 2014). As predictable assessment outcomes equal predictable students, these understandings can inform educators who wish to map and develop enhanced creative endeavours such as opportunity recognition, communication and innovation.