Interpreting Practice: Dilthey, Epistemology, and the Hermeneutics of Historical Life

This paper explores Dilthey’s radical transformation of epistemology and the human sciences through his projects of a critique of historically embodied reason and his hermeneutics of historically mediated life. Answering criticisms that Dilthey overly depends on epistemology, I show how for Dilthey neither philosophy nor the human sciences should be reduced to their theoretical, epistemological, or cognitive dimensions. Dilthey approaches both immediate knowing and theoretical knowledge in the context of a hermeneutical phenomenology of historical life. Knowing is not an isolated activity but an interpretive and self-interpretive practice oriented by situated reflexive awareness and self-reflection. As embedded in an historical relational context, knowing does not only consist of epistemic validity claims about representational contents but is fundamentally practical, involving all of human existence. Empirically informed Besinnung, with its double reference to sense as meaning and bodily awareness, orients Dilthey’s inquiry rather than the “irrationalism” of immediate intuition or the “rationalism” of abstract epistemological reasoning

Contelog: A Formal Declarative Framework for Contextual Knowledge Representation and Reasoning

Context-awareness is at the core of providing timely adaptations in safety-critical secure applications of pervasive computing and Artificial Intelligence (AI) domains. In the current AI and application context-aware frameworks, the distinction between knowledge and context are blurred and not formally integrated. As a result, adaptation behaviors based on contextual reasoning cannot be formally derived and reasoned about. Also, in many smart systems such as automated manufacturing, decision making, and healthcare, it is essential for context-awareness units to synchronize with contextual reasoning modules to derive new knowledge in order to adapt, alert, and predict. A rigorous formalism is therefore essential to (1) represent contextual domain knowledge as well as application rules, and (2) efficiently and effectively reason to draw contextual conclusions. This thesis is a contribution in this direction. The thesis introduces first a formal context representation and a context calculus used to build context models for applications. Then, it introduces query processing and optimization techniques to perform context-based reasoning. The formal framework that achieves these two tasks is called Contelog Framework, obtained by a conservative extension of the syntax and semantics of Datalog. It models contextual knowledge and infers new knowledge. In its design, contextual knowledge and contextual reasoning are loosely coupled, and hence contextual knowledge is reusable on its own. The significance is that by fixing the contextual knowledge, rules in the program and/or query may be changed. Contelog provides a theory of context, in a way that is independent of the application logic rules. The context calculus developed in this thesis allows exporting knowledge inferred in one context to be used in another context. Following the idea of Magic sets from Datalog, Magic Contexts together with query rewriting algorithms are introduced to optimize bottom-up query evaluation of Contelog programs. A Book of Examples has been compiled for Contelog, and these examples are implemented to showcase a proof of concept for the generality, expressiveness, and rigor of the proposed Contelog framework. A variety of experiments that compare the performance of Contelog with earlier Datalog implementations reveal a significant improvement and bring out practical merits of current stage of Contelog and its potential for future extensions in context representation and reasoning of emerging applications of context-aware computing

Exploring the development of clinical reasoning skills among doctors-in-training

Clinical reasoning is complex, difficult to conceptualise and learn, and important as it is closely linked with medical expertise. Learning clinical reasoning skills is primarily an unguided and subconscious process for doctors-in-training, and there is a need for an evidence based, explicit approach to support the learning of these core skills. The focus of this research is the process by which doctors-in-training learn clinical reasoning skills within the context of General Medicine in north Queensland. The literature to date has been extensive but has struggled to identify a practical framework for doctors-in-training which clearly supports their learning of clinical reasoning skills. This program of research investigated four factors identified in the literature as influencing the development of clinical reasoning skills: the metacognitive awareness levels of doctors-in-training; the learning climate of Intern doctors in their first year of clinical work; the influence of Consultants; and the role of Interns as learners. The first factor was investigated by exploring whether metacognitive awareness correlated with performance in medical undergraduate examinations, and whether there was an increase in metacognitive awareness from the first to the fifth-year of the undergraduate medical course. Volunteer medical students completed the Metacognitive Awareness Inventory (MAI), as well as consenting to give access to their examination scores for this study. For the first-year undergraduate doctors-in-training there were correlations between the Knowledge of Cognition domain of the MAI and their end of year examination results, but not with the Regulation of Cognition domain. For fifth-year students there were correlations between both the Knowledge and Regulation of Cognition domains and their end of year examination results. This study found that the overall MAI scores were not significantly different between first and fifth-year undergraduates in this sample. The Regulation of Cognition domain and its sub-domains, regarded as key factors in clinical reasoning skill development, did not significantly differ between first and fifth-year undergraduate doctors-in-training. The second factor investigated was whether the learning climate of Intern doctors-in-training was conducive to learning. The validated Dutch Resident Educational Climate Test (D-RECT) was used, and written responses invited to the question 'What three aspects of the junior doctor learning environment would you alter?' The Coaching and Assessment and the Relations between Consultants domains were identified as significantly lower in General Medicine than for other units, triangulating the written comments provided by the Interns. The third factor investigated Consultant Physicians as role models for doctors-in-training learning clinical reasoning skills. The focus of the semi-structured interviews explored how the Physicians understood clinical reasoning, their understanding of how they had acquired these skills, and the ways they sought to foster these skills among their doctors-in-training. The seven Consultants described their journey to gaining clinical reasoning expertise as being unguided, generally subconscious and seldom discussed. Most Consultants spoke of being unaware of their own journey to gaining clinical reasoning expertise, and did not regard themselves as role models for doctors-in-training. Most Consultants indicated that acquiring clinical knowledge and learning to think about their decision-making processes (metacognition), were crucial for acquiring expertise, but very few Consultants explained how they could intentionally foster these skills. The final factor was explored by investigating how Intern doctors-in-training understood their own development of clinical reasoning skills. At the start of their General Medicine term, Interns were presented with basic information about clinical reasoning. At the end of that term, participating Interns were interviewed. A paper copy of the presentation given at the start of the term was used to stimulate Intern reflections on their learning during the General Medicine term. The 27 Interns interviewed identified that learning clinical reasoning was a tacit, personal journey influenced by enabling and inhibitory factors. The Interns attributed the differences between their clinical reasoning skills and those of their Consultants as being primarily due to the experience and superior clinical knowledge of the Consultants. A multi-methods research design was used to answer the research questions across the four studies. The first two factors were investigated using quantitative methods, while qualitative methods were employed for the last two. The multi-methods approach enabled findings from the separate studies to be triangulated, supporting confidence in the trustworthiness of the synthesised outcomes and reducing an over-dependence on any individual study. The Synthesis and Proposed Framework chapter initially integrates the findings from the four studies to provide an overall understanding of how clinical reasoning skills are currently fostered in north Queensland. These synthesised results are then used to propose an evidence-based learning model and a method for its implementation at the teaching hospital. The modified Cognitive Apprenticeship Learning Model (mCALM) could help to make expert thinking visible by explicitly supporting constructivist learning practices, metacognitive skills, deliberate practice and a conducive learning climate. The mCALM appears well suited to explicitly fostering the learning of clinical reasoning skills for doctors-in-training in north Queensland

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201