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'.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Principals of audit: Testing, data and ‘implicated advocacy’

Historically, school leaders have occupied a somewhat ambiguous position within networks of power. On the one hand, they appear to be celebrated as what Ball (2003) has termed the ‘new hero of educational reform'; on the other, they are often ‘held to account’ through those same performative processes and technologies. These have become compelling in schools and principals are ‘doubly bound’ through this. Adopting a Foucauldian notion of discursive production, this paper addresses the ways that the discursive ‘field’ of ‘principal’ (within larger regimes of truth such as schools, leadership, quality and efficiency) is produced. It explores how individual principals understand their roles and ethics within those practices of audit emerging in school governance, and how their self-regulation is constituted through NAPLAN – the National Assessment Program, Literacy and Numeracy. A key effect of NAPLAN has been the rise of auditing practices that change how education is valued. Open-ended interviews with 13 primary and secondary school principals from Western Australia, South Australia and New South Wales asked how they perceived NAPLAN's impact on their work, their relationships within their school community and their ethical practice.

Public good provision and the welfare effects of indirect tax harmonisation

The paper considers the welfare effect of the harmonisation of indirect taxes in two open economies. The revenue from taxation is used for the production of a non-tradeable public good. The welfare levels are affected via two channels: (i) changes in the levels of public good provision, and (ii) changes in deadweight loss associated with the taxes. We develop a number of rules of harmonisation and derive conditions under which they lead to potential Pareto improvement.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.

The reproducibility of acquiring three dimensional gait and plantar pressure data using established protocols in participants with and without type 2 diabetes and foot ulcers

Background Several prospective studies have suggested that gait and plantar pressure abnormalities secondary to diabetic peripheral neuropathy contributes to foot ulceration. There are many different methods by which gait and plantar pressures are assessed and currently there is no agreed standardised approach. This study aimed to describe the methods and reproducibility of three-dimensional gait and plantar pressure assessments in a small subset of participants using pre-existing protocols. Methods Fourteen participants were conveniently sampled prior to a planned longitudinal study; four patients with diabetes and plantar foot ulcers, five patients with diabetes but no foot ulcers and five healthy controls. The repeatability of measuring key biomechanical data was assessed including the identification of 16 key anatomical landmarks, the measurement of seven leg dimensions, the processing of 22 three-dimensional gait parameters and the analysis of four different plantar pressures measures at 20 foot regions. Results The mean inter-observer differences were within the pre-defined acceptable level (<7 mm) for 100 % (16 of 16) of key anatomical landmarks measured for gait analysis. The intra-observer assessment concordance correlation coefficients were > 0.9 for 100 % (7 of 7) of leg dimensions. The coefficients of variations (CVs) were within the pre-defined acceptable level (<10 %) for 100 % (22 of 22) of gait parameters. The CVs were within the pre-defined acceptable level (<30 %) for 95 % (19 of 20) of the contact area measures, 85 % (17 of 20) of mean plantar pressures, 70 % (14 of 20) of pressure time integrals and 55 % (11 of 20) of maximum sensor plantar pressure measures. Conclusion Overall, the findings of this study suggest that important gait and plantar pressure measurements can be reliably acquired. Nearly all measures contributing to three-dimensional gait parameter assessments were within predefined acceptable limits. Most plantar pressure measurements were also within predefined acceptable limits; however, reproducibility was not as good for assessment of the maximum sensor pressure. To our knowledge, this is the first study to investigate the reproducibility of several biomechanical methods in a heterogeneous cohort.

Combinatorial Design of Secure Lightweight Data Dispersal Schemes (Work in Progress)

Dispersing a data object into a set of data shares is an elemental stage in distributed communication and storage systems. In comparison to data replication, data dispersal with redundancy saves space and bandwidth. Moreover, dispersing a data object to distinct communication links or storage sites limits adversarial access to whole data and tolerates loss of a part of data shares. Existing data dispersal schemes have been proposed mostly based on various mathematical transformations on the data which induce high computation overhead. This paper presents a novel data dispersal scheme where each part of a data object is replicated, without encoding, into a subset of data shares according to combinatorial design theory. Particularly, data parts are mapped to points and data shares are mapped to lines of a projective plane. Data parts are then distributed to data shares using the point and line incidence relations in the plane so that certain subsets of data shares collectively possess all data parts. The presented scheme incorporates combinatorial design theory with inseparability transformation to achieve secure data dispersal at reduced computation, communication and storage costs. Rigorous formal analysis and experimental study demonstrate significant cost-benefits of the presented scheme in comparison to existing methods.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.