A conceptual framework for the cultural integration of cooperative learning : a Thai primary mathematics education perspective

The Thailand education reform adopted cooperative learning to improve the quality of education. However, it has been reported that the introduction and maintenance of cooperative learning has been difficult and uncertain because of the cultural differences. The study proposed a conceptual framework developed based on making a connection between Thai cultures and cooperative learning elements, and implemented a small-scale research project in a Thai primary mathematics class with a teacher and thirty-two Grade 4 students. The results uncovered that the three components including preparation of teachers, instructional strategies and preparation of students can be vehicles for the culture integration in cooperative learning.

A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Developing agency in the creative career : a design-based framework for work integrated learning

Research shows that approximately half of creative practitioners operate as embedded creatives by securing gainful employment within organisations located in the field beyond their core discipline. This foregrounds the significance of having the skills necessary to successfully cross the disciplinary boundaries in order to negotiate a professional role. The multiple implications of such reframing for emerging creative practitioners who navigate uncertain professional boundaries include developing a skill of identifying and successfully targeting the shifting professional and industry coordinates while remaining responsive to changes. A further implication involves creative practitioners engaging in a continuous cycle of re-negotiation of their professional identity making the management of multiple professional selves - along with creating and recreating a meaningful frame of references such as the language around their emerging practice - a necessary skill. This chapter presents a case study of a set of Work Integrated Learning subjects designed to develop in creative industries practitioners the skills to manage their emerging professional identities in response to the shifts in the professional world.

Conceptualising the learning organisation : creating a maturity framework to develop a shared understanding of the library's role in literacy and learning

Although they sit outside the formal education sector, libraries are intrinsically centres of learning where people can engage with knowledge and ideas and acquire the literacy skills that are essential for active participation in an increasingly digital society. In Australia, National and State Libraries Australasia (NSLA) has acknowledged the need to not only better understand the general concept of the library as a learning institution, but also to help the individual NSLA libraries specifically identify their capabilities in this arena. The NSLA Literacy and Learning project aimed to improve the members' organisational comprehension and practice as learning institutions and to help them conceptualise their ability to deliver literacy and learning programmes that will benefit their staff and their communities. The NSLA concept of learning institution encompassed two discrete lenses: the internal lens of the library's own organisational understanding and practice, and the external lens of the clients who engage in the literacy and learning programmes delivered by the library. The ultimate goal was to develop a matrix which could enable libraries to assess their perceived levels of maturity as learning institutions along a continuum of emerging to active capabilities. The matrix should also serve as a tool for shared understanding about the NSLA's own strategic directions in the literacy and learning space. This case study documents the evolving process of developing a learning institution maturity framework for libraries that considers individual, team and organisational learning, as well as clients' interactions with the organisation, with the goal of producing a framework that has the potential to measure the value of learning and growth in both the library's staff and the library's communities

Let's do it: A framework to investigate the affordances of experimental learning environments

BACKGROUND Experimental learning, traditionally conducted in on-campus laboratory venues, is the cornerstone of science and engineering education. In order to ensure that engineering graduates are exposed to ‘real-world’ situations and attain the necessary professional skill-sets, as mandated by course accreditation bodies such as Engineers Australia, face-to-face laboratory experimentation with real equipment has been an integral component of traditional engineering education. The online delivery of engineering coursework endeavours to mimic this with remote and simulated laboratory experimentation. To satisfy student and accreditation requirements, the common practice has been to offer equivalent remote and/or simulated laboratory experiments in lieu of the ones delivered, face-to face, on campus. The current implementations of both remote and simulated laboratories tend to be specified with a focus on technical characteristics, instead of pedagogical requirements. This work attempts to redress this situation by developing a framework for the investigation of the suitability of different experimental educational environments to deliver quality teaching and learning. PURPOSE For the tertiary education sector involved with technical or scientific training, a research framework capable of assessing the affordances of laboratory venues is an important aid during the planning, designing and evaluating stages of face-to-face and online (or cyber) environments that facilitate student experimentation. Providing quality experimental learning venues has been identified as one of the distance-education providers’ greatest challenges. DESIGN/METHOD The investigation draws on the expertise of staff at three Australian universities: Swinburne University of Technology (SUT), Curtin University (Curtin) and Queensland University of Technology (QUT). The aim was to analyse video recorded data, in order to identify the occurrences of kikan-shido (a Japanese term meaning ‘between desks instruction’ and over-the-shoulder learning and teaching (OTST/L) events, thereby ascertaining the pedagogical affordances in face-to-face laboratories. RESULTS These will be disseminated at a Master Class presentation at this conference. DISCUSSION Kikan-shido occurrences did reflect on the affordances of the venue. Unlike with other data collection methods, video recorded data and its analysis is repeatable. Participant bias is minimised or even eradicated and researcher bias tempered by enabling re-coding by others. CONCLUSIONS Framework facilitates the identification of experiential face-to-face learning venue affordances. Investigation will continue with on-line venues.

Understanding the effectiveness of ICT professional learning through the TPACK Conceptual Framework: A case study

With a significant investment in digital technologies in Australian schools, the effective integration of such technology into teaching and learning is paramount. A growing body of evidence indicates that ICT professional learning is integral to the transformation of pedagogy that will improve student learning outcomes. The question arises as to how professional learning is planned and delivered within schools to ensure that all needs are being met. The purpose of this paper is to report on the research findings of a study into professional learning and ICT integration. The TPACK conceptual framework underpins the analyses of the data and brings forth the importance of technological and pedagogical knowledge. Six key categories will be discussed and the implication for practice will be considered.

A model eliciting framework for integrating mathematics and robotics learning

Robotics is taught in many Australian ICT classrooms, in both primary and secondary schools. Robotics activities, including those developed using the LEGO Mindstorms NXT technology, are mathematics-rich and provide a fertile round for learners to develop and extend their mathematical thinking. However, this context for learning mathematics is often under-exploited. In this paper a variant of the model construction sequence (Lesh, Cramer, Doerr, Post, & Zawojewski, 2003) is proposed, with the purpose of explicitly integrating robotics and mathematics teaching and learning. Lesh et al.’s model construction sequence and the model eliciting activities it embeds were initially researched in primary mathematics classrooms and more recently in university engineering courses. The model construction sequence involves learners working collaboratively upon product-focussed tasks, through which they develop and expose their conceptual understanding. The integrating model proposed in this paper has been used to design and analyse a sequence of activities in an Australian Year 4 classroom. In that sequence more traditional classroom learning was complemented by the programming of LEGO-based robots to ‘act out’ the addition and subtraction of simple fractions (tenths) on a number-line. The framework was found to be useful for planning the sequence of learning and, more importantly, provided the participating teacher with the ability to critically reflect upon robotics technology as a tool to scaffold the learning of mathematics.

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance

[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.