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.

Action learning in partnership with Landcare and catchment management groups to support increased pasture sowings in southern inland Queensland

The incorporation of sown pastures as short-term rotations into the cropping systems of northern Australia has been slow. The inherent chemical fertility and physical stability of the predominant vertisol soils across the region enabled farmers to grow crops for decades without nitrogen fertiliser, and precluded the evolution of a crop–pasture rotation culture. However, as less fertile and less physically stable soils were cropped for extended periods, farmers began to use contemporary farming and sown pasture technologies to rebuild and maintain their soils. This has typically involved sowing long-term grass and grass–legume pastures on the more marginal cropping soils of the region. In partnership with the catchment management authority, the Queensland Murray–Darling Committee (QMDC) and Landcare, a pasture extension process using the LeyGrain™ package was implemented in 2006 within two Grain & Graze projects in the Maranoa-Balonne and Border Rivers catchments in southern inland Queensland. The specific objectives were to increase the area sown to high quality pasture and to gain production and environmental benefits (particularly groundcover) through improving the skills of producers in pasture species selection, their understanding and management of risk during pasture establishment, and in managing pastures and the feed base better. The catalyst for increasing pasture sowings was a QMDC subsidy scheme for increasing groundcover on old cropping land. In recognising a need to enhance pasture knowledge and skills to implement this scheme, the QMDC and Landcare producer groups sought the involvement of, and set specific targets for, the LeyGrain workshop process. This is a highly interactive action learning process that built on the existing knowledge and skills of the producers. Thirty-four workshops were held with more than 200 producers in 26 existing groups and with private agronomists. An evaluation process assessed the impact of the workshops on the learning and skill development by participants, their commitment to practice change, and their future intent to sow pastures. The results across both project catchments were highly correlated. There was strong agreement by producers (>90%) that the workshops had improved knowledge and skills regarding the adaptation of pasture species to soils and climates, enabling a better selection at the paddock level. Additional strong impacts were in changing the attitudes of producers to all aspects of pasture establishment, and the relative species composition of mixtures. Producers made a strong commitment to practice change, particularly in managing pasture as a specialist crop at establishment to minimise risk, and in the better selection and management of improved pasture species (particularly legumes and the use of fertiliser). Producers have made a commitment to increase pasture sowings by 80% in the next 5 years, with fourteen producers in one group alone having committed to sow an additional 4893 ha of pasture in 2007–08 under the QMDC subsidy scheme. The success of the project was attributed to the partnership between QMDC and Landcare groups who set individual workshop targets with LeyGrain presenters, the interactive engagement processes within the workshops themselves, and the follow-up provided by the LeyGrain team for on-farm activities.

Collaborative Design in a Virtual Learning Environment : Three Design Experiments in Textile Teacher Education

The aim of the study was to analyze and facilitate collaborative design in a virtual learning environment (VLE). Discussions of virtual design in design education have typically focused on technological or communication issues, not on pedagogical issues. Yet in order to facilitate collaborative design, it is also necessary to address the pedagogical issues related to the virtual design process. In this study, the progressive inquiry model of collaborative designing was used to give a structural level of facilitation to students working in the VLE. According to this model, all aspects of inquiry, such as creating the design context, constructing a design idea, evaluating the idea, and searching for new information, can be shared in a design community. The study consists of three design projects: 1) designing clothes for premature babies, 2) designing conference bags for an international conference, and 3) designing tactile books for visually impaired children. These design projects constituted a continuum of design experiments, each of which highlighted certain perspectives on collaborative designing. The design experiments were organized so that the participants worked in design teams, both face-to-face and virtually. The first design experiment focused on peer collaboration among textile teacher students in the VLE. The second design experiment took into consideration end-users needs by using a participatory design approach. The third design experiment intensified computer-supported collaboration between students and domain experts. The virtual learning environments, in these design experiments, were designed to support knowledge-building pedagogy and progressive inquiry learning. These environments enabled a detailed recording of all computer-mediated interactions and data related to virtual designing. The data analysis was based on qualitative content analysis of design statements in the VLE. This study indicated four crucial issues concerning collaborative design in the VLE in craft and design education. Firstly, using the collaborative design process in craft and design education gives rise to special challenges of building learning communities, creating appropriate design tasks for them, and providing tools for collaborative activities. Secondly, the progressive inquiry model of collaborative designing can be used as a scaffold support for design thinking and for reflection on the design process. Thirdly, participation and distributed expertise can be facilitated by considering the key stakeholders who are related to the design task or design context, and getting them to participate in virtual designing. Fourthly, in the collaborative design process, it is important that team members create and improve visual and technical ideas together, not just agree or disagree about proposed ideas. Therefore, viewing the VLE as a medium for collaborative construction of the design objects appears crucial in order to understand and facilitate the complex processes in collaborative designing.

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.

A cooperative game of a pair of learning automata

A cooperative game played in a sequential manner by a pair of learning automata is investigated in this paper. The automata operate in an unknown random environment which gives a common pay-off to the automata. Necessary and sufficient conditions on the functions in the reinforcement scheme are given for absolute monotonicity which enables the expected pay-off to be monotonically increasing in any arbitrary environment. As each participating automaton operates with no information regarding the other partner, the results of the paper are relevant to decentralized control.

Multiaction learning automata possessing ergodicity of the mean

Multiaction learning automata which update their action probabilities on the basis of the responses they get from an environment are considered in this paper. The automata update the probabilities according to whether the environment responds with a reward or a penalty. Learning automata are said to possess ergodicity of the mean if the mean action probability is the state probability (or unconditional probability) of an ergodic Markov chain. In an earlier paper [11] we considered the problem of a two-action learning automaton being ergodic in the mean (EM). The family of such automata was characterized completely by proving the necessary and sufficient conditions for automata to be EM. In this paper, we generalize the results of [11] and obtain necessary and sufficient conditions for the multiaction learning automaton to be EM. These conditions involve two families of probability updating functions. It is shown that for the automaton to be EM the two families must be linearly dependent. The vector defining the linear dependence is the only vector parameter which controls the rate of convergence of the automaton. Further, the technique for reducing the variance of the limiting distribution is discussed. Just as in the two-action case, it is shown that the set of absolutely expedient schemes and the set of schemes which possess ergodicity of the mean are mutually disjoint.

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.

The Connected Learning Analytics toolkit

This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.