Book Review: "Language learning beyond the classroom"

Language learning beyond the classroom is part of a growing body of literature focused on teaching and learning in contexts that are informal and unstructured. Areas include so-called shadow education and informal pedagogies. Shadow education refers to the privatised tutoring supplementing school curricular that is a pervasive feature of education in parts of Asia (Bray & Lykins, 2012) and increasingly evident in Australia. Informal pedagogies refers to teaching in informal contexts and was the focus of a Special Interest Group (SIG) at the recent American Educational Research Association (AERA) annual conference in Chicago. Presentations in the SIG included designing tools for supporting learning in science classes after school and in sites such as zoos...

The introduction of an Inter-professional learning unit: staff and student perspectives

Background The School of Clinical Sciences comprises a number of health disciplines including podiatry, paramedic science, pharmacy, medical imaging and radiation therapy. A new inter-professional unit was introduced in 2014, which covered key introductory learnings applicable for future health practitioners. This study examined teaching staff and student perspectives about their experience with the new unit for first year students. Methods Qualitative interviews with teaching staff (n=9) and focus group interviews with students (5 groups which ranged in size from 4-30) were conducted. Extensive notes were taken during the interviews Issues emerging from the interviews were identified and organised according to themes and subthemes. Results Four major themes were identified namely: Something new; To be or not to be that is the question; Advantages of the new unit; and Areas for improvement. Previous staff experience with inter-professional learning (IPL) had been ad-hoc, whereas the new unit brought together several disciplines in a planned and deliberate way. There was strong philosophical agreement about the value of IPL but some debate about the extent to which the unit provided IPL experience. The unit was seen as assisting students’ social and academic adjustment to university and provided opportunity for professional socialisation, exposure to macro and micro aspects of the Australian health care system and various types of communication. For podiatry students it was their first opportunity to formally meet and work with other podiatry students and moved their identity from ‘university student’ to ‘podiatry student’. Other positives included providing the opportunity for staff and students to interact at an early stage with the perceived benefit of reducing attrition. Areas for unit improvement included institutional arrangements, unit administration aspects and assessment. Conclusion The unit was seen as beneficial by staff and students however, students were more polarised in their views than staff. There was a tension between feeling apart of and learning about one's own profession and feeling apart of and learning about the roles of other health professionals in relation to patient care and the health care system.

Learning Temporal Alignment Uncertainty for Efficient Event Detection

In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks.

Literacy teaching and learning in a digital world: Challenges of a new era

Teachers the world over are aware of the range of new challenges that arise from this new era. One challenge is the role of digital technologies in literacy learning. Despite its reputation for being engaging, digital technologies do not always enhance learning outcomes. Whilst the concerns vary across time and place, the core issue of what is a highly sought after literacy learning outcome in this new era warrants consideration. This paper introduces Kalantzis and Cope’s (2005) theorisation of eight knowledge processes for literacy learning. They claim that experiencing the known, conceptualising by naming, analysing functionally and applying appropriately, whilst necessary, are not on their own sufficient for the development of high level literacy practices. It is their contention that students must also be able to experience the new, conceptualise by theorising, analyse creatively and apply critically. This theorisation forms an auditing framework for considering the outcomes of different uptakes of digital technologies in a Social Studies and a Science unit.

Investigating the effectiveness of an ecological approach to learning design in a first year mathematics for engineering unit

This is presentation of the refereed paper accepted for the Conferences' proceedings. The presentation was given on Tuesday, 1 December 2015.

The Google Online Marketing Challenge: A Transnational Comparison of Classroom Learning with Real Clients, Real Money, and Real Advertising Campaigns

In 2008, a collaborative partnership between Google and academia launched the Google Online Marketing Challenge (hereinafter Google Challenge), perhaps the world’s largest in-class competition for higher education students. In just two years, almost 20,000 students from 58 countries participated in the Google Challenge. The Challenge gives undergraduate and graduate students hands-on experience with the world’s fastest growing advertising mechanism, search engine advertising. Funded by Google, students develop an advertising campaign for a small to medium sized enterprise and manage the campaign over three consecutive weeks using the Google AdWords platform. This article explores the Challenge as an innovative pedagogical tool for marketing educators. Based on the experiences of three instructors in Australia, Canada and the United States, this case study discusses the opportunities and challenges of integrating this dynamic problem-based learning approach into the classroom.

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.

The influence of a novel simulated learning environment upon student clinical subjective refraction performance: A pilot study

Background: Optometry students are taught the process of subjective refraction through lectures and laboratory based practicals before progressing to supervised clinical practice. Simulated learning environments (SLEs) are an emerging technology that are used in a range of health disciplines, however, there is limited evidence regarding the effectiveness of clinical simulators as an educational tool. Methods: Forty optometry students (20 fourth year and 20 fifth year) were assessed twice by a qualified optometrist (two examinations separated by 4-8 weeks) while completing a monocular non-cycloplegic subjective refraction on the same patient with an unknown refractive error simulated using contact lenses. Half of the students were granted access to an online SLE, The Brien Holden Vision Institute (BHVI®) Virtual Refractor, and the remaining students formed a control group. The primary outcome measures at each visit were; accuracy of the clinical refraction compared to a qualified optometrist and relative to the Optometry Council of Australia and New Zealand (OCANZ) subjective refraction examination criteria. Secondary measures of interest included descriptors of student SLE engagement, student self-reported confidence levels and correlations between performance in the simulated and real world clinical environment. Results: Eighty percent of students in the intervention group interacted with the SLE (for an average of 100 minutes); however, there was no correlation between measures of student engagement with the BHVI® Virtual Refractor and speed or accuracy of clinical subjective refractions. Fifth year students were typically more confident and refracted more accurately and quickly than fourth year students. A year group by experimental group interaction (p = 0.03) was observed for accuracy of the spherical component of refraction, and post hoc analysis revealed that less experienced students exhibited greater gains in clinical accuracy following exposure to the SLE intervention. Conclusions: Short-term exposure to a SLE can positively influence clinical subjective refraction outcomes for less experienced optometry students and may be of benefit in increasing the skills of novice refractionists to levels appropriate for commencing supervised clinical interactions.

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.

Collaborative learning approach to introduce computational fluid dynamics

This paper presents the design, implementation and evaluation of a collaborative learning activity designed to replace traditional face-to-face lectures in a large classroom. This activity aims to better engage the students with their learning and improve the students’ experience and outcomes. This project is implemented in the Fluid Mechanics unit of the Mechanical Engineering degree at the Queensland University of Technology to introduce students with the concept, terminology and process of Computational Fluid Dynamics (CFD). The approach integrates a constructive collaborative assignment which is a key element in the overall quality of teaching and learning, and an integral component of the students’ experience. A detailed survey, given to the students, showed an overall high level of satisfaction. However, the results also highlighted the gap between students’ expectations both for contents and assignment and teacher expectations. Discussions to address this issue are presented in the paper based on a critical reflection.

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.

Designing rich information experiences to shape learning outcomes

Students in higher education typically learn to use information as part of their course of study, which is intended to support ongoing academic, personal and professional growth. Informing the development of effective information literacy education, this research uses a phenomenographic approach to investigate the experiences of a teacher and students engaged in lessons focused on exploring language and gender topics by tracing and analyzing their evolution through scholarly discourse. The findings suggest that the way learners use information influences content-focused learning outcomes, and reveal how teachers may enact lessons that enable students to learn to use information in ways that foster a specific understanding of the topic they are investigating.

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

More equal than others?: Meeting the professional development needs of rural primary and secondary science teachers

It is often assumed that teachers in rural and remote schools are at a disadvantage when it comes to accessing professional development. But is there sufficient evidence to support this assumption? This paper reports findings from two national surveys comparing the professional development priorities of primary and secondary science teachers from metropolitan, provincial and remote schools. The research found that while teachers' unmet needs for some PD opportunities increased significantly with school remoteness, this was not the case for all opportunities. In teasing out the different PD priorities of primary and secondary science teachers, the paper provides evidence to help education authorities and professional organisations address the specific needs of teachers in different locations.

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.