Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

Co-creating stories in social learning systems : The role of community media and cultural organisations in disseminating knowledge

This paper investigates how community based media organisations are co-creative storytelling institutions, and how they learn to disseminate knowledge in a social learning system. Organisations involved in story co-creation are learning to create in fluid environments.They are project based, with a constant turnover of volunteers or staff. These organisations have to meet the needs of their funding bodies and their communities to remain sustainable. Learning is seen as dialogical, and this is also reflected in the nature of storytelling itself. These organisations must learn to meet the needs of their communities, who in turn learn from the organisation’s expertise in a facilitated setting. This learning is participatory and collaborative, and is often a mix of virtual and offline interaction. Such community-based organisations sit in the realm of a hybrid-learning environment; they are neither a formal educational institution like a college, nor do their volunteers produce outcomes in a professional capacity. Yet, they must maintain a certain level of quality outcomes from their contributors to be of continued value in their communities. Drawing from a larger research study, one particular example is that of the CitizenJ project. CitizenJ is hosted by a state cultural centre, and partnered with publishing partners in the community broadcasting sector. This paper explores how this project is a Community of Practice, and how it promotes ethical and best practice, meets contributors’ needs, emphasises the importance of facilitation in achieving quality outcomes, and the creation of projects for wider community and public interest.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Low achieving mathematics students' attitudinal and achievement changes as a result of using an integrated learning system

This paper reports on a study to measure the effectiveness of an integrated learning system (ILS) in improving mathematics achievement for low achieving Year 5 to 9 students. The study found that statistically significant gains on the integrated learning system were not supported by scores on standardised mathematics achievement tests. It also found that although student attitudes to computers decreased (significantly for some items), the students still liked the integrated learning system and felt that it had helped them to learn.

How can we exploit mobile data to customise learning based on a student’s environmental surroundings?

User-Based intelligent systems are already commonplace in a student’s online digital life. Each time they browse, search, buy, join, comment, play, travel, upload, download, a system collects, analyses and processes data in an effort to customise content and further improve services. This panel session will explore how intelligent systems, particularly those that gather data from mobile devices, can offer new possibilities to assist in the delivery of customised, personal and engaging learning experiences. The value of intelligent systems for education lies in their ability to formulate authentic and complex learner profiles that bring together and systematically integrate a student’s personal world with a formal curriculum framework. As we well know, a mobile device can collect data relating to a student’s interests (gathered from search history, applications and communications), location, surroundings and proximity to others (GPS, Bluetooth). However, what has been less explored is the opportunity for a mobile device to map the movements and activities of a student from moment to moment and over time. This longitudinal data provides a holistic profile of a student, their state and surroundings. Analysing this data may allow us to identify patterns that reveal a student’s learning processes; when and where they work best and for how long. Through revealing a student’s state and surroundings outside of schools hour, this longitudinal data may also highlight opportunities to transform a student’s everyday world into an inventory for learning, punctuating their surroundings with learning recommendations. This would in turn lead to new ways to acknowledge and validate and foster informal learning, making it legitimate within a formal curriculum.

Scaffolding an EFL (English as a foreign language) ‘effective writing’ class in a hybrid learning community

This study sought to establish and develop innovative instructional procedures, in which scaffolding can be expanded and applied, in order to enhance learning of English as a Foreign Language (EFL) writing skills in an effective hybrid learning community (a combination of face-to-face and online modes of learning) at the university where the researcher is working. Many educational experts still believe that technology has not been harnessed to its potential to meet the new online characteristics and trends. There is also an urgency to reconsider the pedagogical perspectives involved in the utilisation of online learning systems in general and the social interactions within online courses in particular that have been neglected to date. An action research design, conducted in two cycles within a duration of four months, was utilised throughout this study. It was intended not only to achieve a paradigm shift from transmission-absorption to socio-constructivist teaching/learning methodologies but also to inform practice in these technology-rich environments. Five major findings emerged from the study. First, the scaffolding theory has been extended. Two new scaffolding types (i.e., quasi-transcendental scaffolding and transcendental scafolding), two scaffolding aspects (i.e., receptive and productive) and some scaffolding actions (e.g., providing a stimulus, awareness, reminder, or remedy) for EFL writing skills in an effective hybrid learning community have been identified and elaborated on. Second, the EFL ‘Effective Writing’ students used the scaffolds implemented in a hybrid environment to enhance and enrich their learning of writing of English essays. The online activities, conducted after the F2F sessions most of the time, gave students greater opportunities to both reinforce and expand the knowledge they had acquired in the F2F mode. Third, a variety of teaching techniques, different online tasks and discussion topics utilised in the two modes bolstered the students’ interests and engagement in their knowledge construction of how to compose English-language essays. Fourth, through the scaffolded activities, the students learned how to scaffold themselves and thus became independent learners in their future endeavours of constructing knowledge. Fifth, the scaffolding-to-scaffold activities provided the students with knowledge on how to effectively engage in transcendental scaffolding actions and facilitate the learning of English writing skills by less able peers within the learning community. Thus, the findings of this current study extended earlier understandings of scaffolding in an EFL hybrid learning environment and will contribute to the advancement of future ICT-mediated courses in terms of their scaffolding pedagogical aspects.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.

Learning from episodes of degradation and recovery in variable Australian rangelands

Land-change science emphasizes the intimate linkages between the human and environmental components of land management systems. Recent theoretical developments in drylands identify a small set of key principles that can guide the understanding of these linkages. Using these principles, a detailed study of seven major degradation episodes over the past century in Australian grazed rangelands was reanalyzed to show a common set of events: (i) good climatic and economic conditions for a period, leading to local and regional social responses of increasing stocking rates, setting the preconditions for rapid environmental collapse, followed by (ii) a major drought coupled with a fall in the market making destocking financially unattractive, further exacerbating the pressure on the environment; then (iii) permanent or temporary declines in grazing productivity, depending on follow-up seasons coupled again with market and social conditions. The analysis supports recent theoretical developments but shows that the establishment of environmental knowledge that is strictly local may be insufficient on its own for sustainable management. Learning systems based in a wider community are needed that combine local knowledge, formal research, and institutional support. It also illustrates how natural variability in the state of both ecological and social systems can interact to precipitate nonequilibrial change in each other, so that planning cannot be based only on average conditions. Indeed, it is this variability in both environment and social subsystems that hinders the local learning required to prevent collapse.

Hunters & gatherers : strategies for curriculum mapping and data collection for assurance of learning

Assurance of learning is a predominant feature in both quality enhancement and assurance in higher education. Assurance of learning is a process that articulates explicit program outcomes and standards, and systematically gathers evidence to determine the extent to which performance matches expectations. Benefits accrue to the institution through the systematic assessment of whole of program goals. Data may be used for continuous improvement, program development, and to inform external accreditation and evaluation bodies. Recent developments, including the introduction of the Tertiary Education and Quality Standards Agency (TEQSA) will require universities to review the methods they use to assure learning outcomes. This project investigates two critical elements of assurance of learning: 1. the mapping of graduate attributes throughout a program; and 2. the collection of assurance of learning data. An audit was conducted with 25 of the 39 Business Schools in Australian universities to identify current methods of mapping graduate attributes and for collecting assurance of learning data across degree programs, as well as a review of the key challenges faced in these areas. Our findings indicate that external drivers like professional body accreditation (for example: Association to Advance Collegiate Schools of Business (AACSB)) and TEQSA are important motivators for assuring learning, and those who were undertaking AACSB accreditation had more robust assurance of learning systems in place. It was reassuring to see that the majority of institutions (96%) had adopted an embedding approach to assuring learning rather than opting for independent standardised testing. The main challenges that were evident were the development of sustainable processes that were not considered a burden to academic staff, and obtainment of academic buy in to the benefits of assuring learning per se rather than assurance of learning being seen as a tick box exercise. This cultural change is the real challenge in assurance of learning practice.

Increasing learners’ satisfaction/intention to adopt more e-learning

Purpose: E-learning is an organisationally risky investment given the cost and poor levels of adoption by users. In order to gain a better understanding of this problem, a study was conducted into the use of e-learning in a rail organisation. Design/methodology/approach: Using an online survey, employees of a rail-sector organisation were questioned about their use and likelihood of adoption of e-learning. This study explores the factors that affect the way in which learners experience and perceive such systems. Using statistical analysis, twelve hypotheses are tested and explored. Relationships between learning satisfaction, intention to adopt and the characteristics of e-learning systems were established. Findings: The study found that e-learning characteristics can buffer the relationship between learner characteristics and intention to adopt further e-learning in the future. Further, we found that high levels of support can compensate individuals who are low in technological efficacy to adopt e-learning. Research limitations/implications: The cross-sectional design of the study and its focus on measuring intention to adopt as opposed to actual adoption are both limitations. Future research using longitudinal design and research employing a time lag design measuring actual adoption as well as intention are recommended. Practical implications: From a practical perspective, organizations can focus on the actual content and authenticity of the learning experience delivered by the e-learning system to significantly impact how employees will perceive and use e-learning in the future. Low technological efficacy individuals tend not to adopt new technology. Instead of changing individuals’ personalities, organizations can implement supportive policies and practices which would lead to higher e-learning adoption rate among low efficacy individuals. Originality/value: The study integrates technology adoption and learning literatures in developing enablers for e-learning in organizations. Further, this study collects data from rail employees, and therefore the findings are practical to an industry.

Establishing initial trust in autonomous delay tolerant networks without centralised PKI

A Delay Tolerant Network (DTN) is one where nodes can be highly mobile, with long message delay times forming dynamic and fragmented networks. Traditional centralised network security is difficult to implement in such a network, therefore distributed security solutions are more desirable in DTN implementations. Establishing effective trust in distributed systems with no centralised Public Key Infrastructure (PKI) such as the Pretty Good Privacy (PGP) scheme usually requires human intervention. Our aim is to build and compare different de- centralised trust systems for implementation in autonomous DTN systems. In this paper, we utilise a key distribution model based on the Web of Trust principle, and employ a simple leverage of common friends trust system to establish initial trust in autonomous DTN’s. We compare this system with two other methods of autonomously establishing initial trust by introducing a malicious node and measuring the distribution of malicious and fake keys. Our results show that the new trust system not only mitigates the distribution of fake malicious keys by 40% at the end of the simulation, but it also improved key distribution between nodes. This paper contributes a comparison of three de-centralised trust systems that can be employed in autonomous DTN systems.

Systems thinking : an approach for advancing workplace information literacy

As the importance of information literacy has gained increased recognition, so too have academic library professionals intensified their efforts to champion, activate, and advance these capabilities in others. To date, however, little attention has focused on advancing these essential competencies amongst practitioner advocates.This paper helps redress the paucity of professional literature on the topic of workplace information literacy among library professionals.