An exploratory investigation of the practice of assessment for learning in Vietnamese higher education: Three case studies of lecturers' practice

In response to limited research conducted on the practice of assessment for learning (AfL) in higher education and in Asian educational settings, this qualitative study, using sociocultural theories of learning and a multiple case study approach, investigates how AfL was implemented by three lecturers in one Vietnamese university. Findings revealed that the lecturers engaged with AfL principles and practices to some extent. However, despite the lecturers' significant efforts, Vietnamese sociocultural factors such as respect for harmony, hierarchy, and examination-oriented learning, impacted on their practice of AfL. This study therefore argues that AfL requires adaptation for it to be effective in the Vietnamese tertiary context.

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.

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.

Activities of daily living in children with developmental coordination disorder: Performance, learning, and participation

Background Children with developmental coordination disorder (DCD) face evident motor difficulties in daily functioning. Little is known, however, about their difficulties in specific activities of daily living (ADL). Objective The purposes of this study were: (1) to investigate differences between children with DCD and their peers with typical development for ADL performance, learning, and participation, and (2) to explore the predictive values of these aspects. Design. This was a cross-sectional study. Methods In both a clinical sample of children diagnosed with DCD (n=25 [21 male, 4 female], age range=5-8 years) and a group of peers with typical development (25 matched controls), the children’s parents completed the DCDDaily-Q. Differences in scores between the groups were investigated using t tests for performance and participation and Pearson chi-square analysis for learning. Multiple regression analyses were performed to explore the predictive values of performance, learning, and participation. Results Compared with their peers, children with DCD showed poor performance of ADL and less frequent participation in some ADL. Children with DCD demonstrated heterogeneous patterns of performance (poor in 10%-80% of the items) and learning (delayed in 0%-100% of the items). In the DCD group, delays in learning of ADL were a predictor for poor performance of ADL, and poor performance of ADL was a predictor for less frequent participation in ADL compared with the control group. Limitations A limited number of children with DCD were addressed in this study. Conclusions This study highlights the impact of DCD on children’s daily lives and the need for tailored intervention.

The logic of data-sense: Thinking through Learning Personalisation

Big Data and Learning Analytics’ promise to revolutionise educational institutions, endeavours, and actions through more and better data is now compelling. Multiple, and continually updating, data sets produce a new sense of ‘personalised learning’. A crucial attribute of the datafication, and subsequent profiling, of learner behaviour and engagement is the continual modification of the learning environment to induce greater levels of investment on the parts of each learner. The assumption is that more and better data, gathered faster and fed into ever-updating algorithms, provide more complete tools to understand, and therefore improve, learning experiences through adaptive personalisation. The argument in this paper is that Learning Personalisation names a new logistics of investment as the common ‘sense’ of the school, in which disciplinary education is ‘both disappearing and giving way to frightful continual training, to continual monitoring'.

Dancing practices from multiple perspectives: the increasingly complex role of the contemporary dancer

This paper will discuss the complexities of the role of contemporary dancer in this current epoch, with a particular focus on the multiple identities dancers embody within dance practice and how these accumulate to form a creative self-in-process or ‘moving identity’. Wider issues, such as training will be explored questioning how technical skills can be imparted alongside autonomous learning approaches to ensure that dancers are prepared to negotiate the entrepreneurial ecology of various dance sectors. Furthermore, the paper will examine the shifting relationship between choreographer and dancer from hierarchical to co-creative including how, in spite of the often collaborative nature of dance creation, the marketplace continues to celebrate the singular authorial position of the choreographer. Each of these elements will reflect back the complex issues of agency and creative self-hood that dancers must negotiate in an increasingly diverse and changeable arts environment.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

The role of microcomputer-based laboratory display in supporting the construction of new understanding in kinematics

Teachers' failure to utilise MBL activities more widely may be due to not recognising their capacity to transform the nature of laboratory activities to be more consistent with contemporary constructivist theories of learning. This research aimed to increase understanding of how MBL activities specifically designed to be consistent with a constructivist theory of learning support or constrain student construction of understanding. The first author conducted the research with his Year 11 physics class of 29 students. Dyads completed nine tasks relating to kinematics using a Predict-Observe-Explain format. Data sources included video and audio recordings of students and teacher during four 70-minute sessions, students' display graphs and written notes, semi-structured student interviews, and the teacher's journal. The study identifies the actors and describes the patterns of interactions in the MBL. Analysis of students' discourse and actions identified many instances where students' initial understanding of kinematics were mediated in multiple ways. Students invented numerous techniques for manipulating data in the service of their emerging understanding. The findings are presented as eight assertions. Recommendations are made for developing pedagogical strategies incorporating MBL activities which will likely catalyse student construction of understanding.