Knowledge Construction Technology through Hypermedia-Based Intelligent Conversational Channel

There have been multifarious approaches in building expert knowledge in medical or engineering field through expert system, case-based reasoning, model-based reasoning and also a large-scale knowledge-based system. The intriguing factors with these approaches are mainly the choices of reasoning mechanism, ontology, knowledge representation, elicitation and modeling. In our study, we argue that the knowledge construction through hypermedia-based community channel is an effective approach in constructing expert’s knowledge. We define that the knowledge can be represented as in the simplest form such as stories to the most complex ones such as on-the-job type of experiences. The current approaches of encoding experiences require expert’s knowledge to be acquired and represented in rules, cases or causal model. We differentiate the two types of knowledge which are the content knowledge and socially-derivable knowledge. The latter is described as knowledge that is earned through social interaction. Intelligent Conversational Channel is the system that supports the building and sharing on this type of knowledge.

Self-Questioning & Experimentation: An Index Vocabulary of Situated Interaction

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Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.

Similarity Measures for Exam Timetabling Problems

A large number of heuristic algorithms have been developed over the years which have been aimed at solving examination timetabling problems. However, many of these algorithms have been developed specifically to solve one particular problem instance or a small subset of instances related to a given real-life problem. Our aim is to develop a more general system which, when given any exam timetabling problem, will produce results which are comparative to those of a specially designed heuristic for that problem. We are investigating a Case based reasoning (CBR) technique to select from a set of algorithms which have been applied successfully to similar problem instances in the past. The assumption in CBR is that similar problems have similar solutions. For our system, the assumption is that an algorithm used to find a good solution to one problem will also produce a good result for a similar problem. The key to the success of the system will be our definition of similarity between two exam timetabling problems. The study will be carried out by running a series of tests using a simple Simulated Annealing Algorithm on a range of problems with differing levels of similarity and examining the data sets in detail. In this paper an initial investigation of the key factors which will be involved in this measure is presented with a discussion of how the definition of good impacts on this.

Similarity Measures for Exam Timetabling Problems

A large number of heuristic algorithms have been developed over the years which have been aimed at solving examination timetabling problems. However, many of these algorithms have been developed specifically to solve one particular problem instance or a small subset of instances related to a given real-life problem. Our aim is to develop a more general system which, when given any exam timetabling problem, will produce results which are comparative to those of a specially designed heuristic for that problem. We are investigating a Case based reasoning (CBR) technique to select from a set of algorithms which have been applied successfully to similar problem instances in the past. The assumption in CBR is that similar problems have similar solutions. For our system, the assumption is that an algorithm used to find a good solution to one problem will also produce a good result for a similar problem. The key to the success of the system will be our definition of similarity between two exam timetabling problems. The study will be carried out by running a series of tests using a simple Simulated Annealing Algorithm on a range of problems with differing levels of similarity and examining the data sets in detail. In this paper an initial investigation of the key factors which will be involved in this measure is presented with a discussion of how the definition of good impacts on this.

Arquitectura de Comportamientos Reactivos para Agentes Robóticos basada en CBR

En los últimos tiempos se ha demostrado la importancia del aprendizaje en la Inteligencia humana, tanto en su vertiente de aprendizaje por observación como a través de la experiencia, como medio de identificar situaciones y predecir acciones o respuestas a partir de la información adquirida. Dado este esquema general de la Inteligencia Humana, parece razonable imitar su estructura y características en un intento por diseñar una arquitectura general de inteligencia aplicada a la Robótica. En este trabajo, inspirados por las teorías de Hawkins en su obra On Intelligence, hemos propuesto una arquitectura jerárquica de inteligencia en el que los diversos módulos se implementan a partir de Razonamiento basado en Casos ¿Case Based Reasoning (CBR)¿, una herramienta de IA especialmente apta para la adquisición de conocimiento a través del aprendizaje y para la predicción basada en similitud de información. Dentro de esta arquitectura la presente tesis se centra en las capas inferiores, las de tipo reactivo, expresadas en forma de comportamientos básicos, que implementan conductas sencillas pero indispensables para el funcionamiento de un robot. Estos comportamientos han sido tradicionalmente diseñados de forma algorítmica, con la dificultad que esto entraña en muchos casos por el desconocimiento de sus aspectos intrínsecos. Además, carecen de la capacidad de adaptarse ante nuevas situaciones no previstas y adquirir nuevos conocimientos a través del funcionamiento del robot, algo indispensable si se pretende que éste se desenvuelva en ambientes dinámicos y no estructurados. El trabajo de esta tesis considera la implementación de comportamientos reactivos con capacidad de aprendizaje, como forma de superar los inconvenientes anteriormente mencionados consiguiendo al mismo tiempo una mejor integración en la arquitectura general de Inteligencia considerada, en la cual el aprendizaje ocupa el papel principal. Así, se proponen y analizan diversas alternativas de diseño de comportamientos reactivos, construidos a través de sistemas CBR con capacidad de aprendizaje. En particular se estudia i) la problemática de selección, organización, y representación de la información como recipiente del conocimiento de los comportamientos;ii) los problemas asociados a la escalabilidad de esta información; iii) los aspectos que acompañan al proceso de predicción mediante la recuperación de la respuesta de experiencias previas similares a la presentada; iv) la identificación de la respuesta no solo con la acción a tomar por parte del comportamiento sino con un concepto que represente la situación presentada; y v) la adaptación y evaluación de la respuesta para incorporar nuevas situaciones como nuevo conocimiento del sistema. También se analiza la organización de comportamientos básicos que permite obtener, a través de sus interacciones, comportamientos emergentes de nivel superior aún dentro de un alcance reactivo. Todo ello se prueba con un robot real y con un simulador, en una variante de un escenario de aplicación clásico en Robótica, como es la competición Robocup. La elaboración de esta tesis ha supuesto, además de los aspectos puramente investigadores, un esfuerzo adicional en el desarrollo de las herramientas y metodología de pruebas necesarias para su realización. En este sentido, se ha programado un primer prototipo de marco de implementación de comportamientos reactivos con aprendizaje, basados en CBR, para la plataforma de desarrollo robótico Tekkotsu.

A multiagent based strategy for detecting attacks in databases in a distributed mode

This paper presents a distributed hierarchical multiagent architecture for detecting SQL injection attacks against databases. It uses a novel strategy, which is supported by a Case-Based Reasoning mechanism, which provides to the classifier agents with a great capacity of learning and adaptation to face this type of attack. The architecture combines strategies of intrusion detection systems such as misuse detection and anomaly detection. It has been tested and the results are presented in this paper.

An Artificial Intelligence Approach to Dyscalculia

Dyscalculia stands for a brain-based condition that makes it hard to make sense of numbers and mathematical concepts. Some adolescents with dyscalculia cannot grasp basic number concepts. They work hard to learn and memorize basic number facts. They may know what to do in mathematical classes but do not understand why they are doing it. In other words, they miss the logic behind it. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work focuses on the development of an Intelligent System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming framework to Knowledge Representation and Reasoning, complemented with a Case-Based problem solving approach to computing, that allows for the handling of incomplete, unknown, or even contradictory information.

Analogical reasoning in case-based design

A computer can assist the process of design by analogy by recording past designs. The experience these represent could be much wider than that of designers using the system, who therefore need to identify potential cases of interest. If the computer assists with this lookup, the designers can concentrate on the more interesting aspect of extracting and using the ideas which are found. However, as the knowledge base grows it becomes ever harder to find relevant cases using a keyword indexing scheme without knowing precisely what to look for. Therefore a more flexible searching system is needed.

If a similarity measure can be defined for the features of the designs, then it is possible to match and cluster them. Using a simple measure like co-occurrence of features within a particular case would allow this to happen without human intervention, which is tedious and time- consuming. Any knowledge that is acquired about how features are related to each other will be very shallow: it is not intended as a cognitive model for how humans understand, learn, or retrieve information, but more an attempt to make effective, efficient use of the information available. The question remains of whether such shallow knowledge is sufficient for the task.

A system to retrieve information from a large database is described. It uses co-occurrences to relate keywords to each other, and then extends search queries with similar words. This seems to make relevant material more accessible, providing hope that this retrieval technique can be applied to a broader knowledge base.

Using ‘think aloud’ as a strategy for learning clinical reasoning in high fidelity case-based simulation for undergraduate nursing students

AIMS This paper reports on the implementation of a research project that trials an educational strategy implemented over six months of an undergraduate third year nursing curriculum. This project aims to explore the effectiveness of ‘think aloud’ as a strategy for learning clinical reasoning for students in simulated clinical settings. BACKGROUND Nurses are required to apply and utilise critical thinking skills to enable clinical reasoning and problem solving in the clinical setting [1]. Nursing students are expected to develop and display clinical reasoning skills in practice, but may struggle articulating reasons behind decisions about patient care. For students learning to manage complex clinical situations, teaching approaches are required that make these instinctive cognitive processes explicit and clear [2-5]. In line with professional expectations, nursing students in third year at Queensland University of Technology (QUT) are expected to display clinical reasoning skills in practice. This can be a complex proposition for students in practice situations, particularly as the degree of uncertainty or decision complexity increases [6-7]. The ‘think aloud’ approach is an innovative learning/teaching method which can create an environment suitable for developing clinical reasoning skills in students [4, 8]. This project aims to use the ‘think aloud’ strategy within a simulation context to provide a safe learning environment in which third year students are assisted to uncover cognitive approaches that best assist them to make effective patient care decisions, and improve their confidence, clinical reasoning and active critical reflection on their practice. MEHODS In semester 2 2011 at QUT, third year nursing students will undertake high fidelity simulation, some for the first time commencing in September of 2011. There will be two cohorts for strategy implementation (group 1= use think aloud as a strategy within the simulation, group 2= not given a specific strategy outside of nursing assessment frameworks) in relation to problem solving patient needs. Students will be briefed about the scenario, given a nursing handover, placed into a simulation group and an observer group, and the facilitator/teacher will run the simulation from a control room, and not have contact (as a ‘teacher’) with students during the simulation. Then debriefing will occur as a whole group outside of the simulation room where the session can be reviewed on screen. The think aloud strategy will be described to students in their pre-simulation briefing and allow for clarification of this strategy at this time. All other aspects of the simulations remain the same, (resources, suggested nursing assessment frameworks, simulation session duration, size of simulation teams, preparatory materials). RESULTS Methodology of the project and the challenges of implementation will be the focus of this presentation. This will include ethical considerations in designing the project, recruitment of students and implementation of a voluntary research project within a busy educational curriculum which in third year targets 669 students over two campuses. CONCLUSIONS In an environment of increasingly constrained clinical placement opportunities, exploration of alternate strategies to improve critical thinking skills and develop clinical reasoning and problem solving for nursing students is imperative in preparing nurses to respond to changing patient needs. References 1. Lasater, K., High-fidelity simulation and the development of clinical judgement: students' experiences. Journal of Nursing Education, 2007. 46(6): p. 269-276. 2. Lapkin, S., et al., Effectiveness of patient simulation manikins in teaching clinical reasoning skills to undergraduate nursing students: a systematic review. Clinical Simulation in Nursing, 2010. 6(6): p. e207-22. 3. Kaddoura, M.P.C.M.S.N.R.N., New Graduate Nurses' Perceptions of the Effects of Clinical Simulation on Their Critical Thinking, Learning, and Confidence. The Journal of Continuing Education in Nursing, 2010. 41(11): p. 506. 4. Banning, M., The think aloud approach as an educational tool to develop and assess clinical reasoning in undergraduate students. Nurse Education Today, 2008. 28: p. 8-14. 5. Porter-O'Grady, T., Profound change:21st century nursing. Nursing Outlook, 2001. 49(4): p. 182-186. 6. Andersson, A.K., M. Omberg, and M. Svedlund, Triage in the emergency department-a qualitative study of the factors which nurses consider when making decisions. Nursing in Critical Care, 2006. 11(3): p. 136-145. 7. O'Neill, E.S., N.M. Dluhy, and C. Chin, Modelling novice clinical reasoning for a computerized decision support system. Journal of Advanced Nursing, 2005. 49(1): p. 68-77. 8. Lee, J.E. and N. Ryan-Wenger, The "Think Aloud" seminar for teaching clinical reasoning: a case study of a child with pharyngitis. J Pediatr Health Care, 1997. 11(3): p. 101-10.

Using ‘think aloud’ as a strategy for improving clinical reasoning in high fidelity case-based simulation for undergraduate nursing students.

Theme Paper for Curriculum innovation and enhancement theme AIM: This paper reports on a research project that trialled an educational strategy implemented in an undergraduate nursing curriculum. The project aimed to explore the effectiveness of ‘think aloud’ as a strategy for improving clinical reasoning for students in simulated clinical settings. BACKGROUND: Nurses are required to apply and utilise critical thinking skills to enable clinical reasoning and problem solving in the clinical setting (Lasater, 2007). Nursing students are expected to develop and display clinical reasoning skills in practice, but may struggle articulating reasons behind decisions about patient care. The ‘think aloud’ approach is an innovative learning/teaching method which can create an environment suitable for developing clinical reasoning skills in students (Banning, 2008, Lee and Ryan-Wenger, 1997). This project used the ‘think aloud’ strategy within a simulation context to provide a safe learning environment in which third year students were assisted to uncover cognitive approaches to assist in making effective patient care decisions, and improve their confidence, clinical reasoning and active critical reflection about their practice. MEHODS: In semester 2 2011 at QUT, third year nursing students undertook high fidelity simulation (some for the first time), commencing in September of 2011. There were two cohorts for strategy implementation (group 1= used think aloud as a strategy within the simulation, group 2= no specific strategy outside of nursing assessment frameworks used by all students) in relation to problem solving patient needs. The think aloud strategy was described to students in their pre-simulation briefing and allowed time for clarification of this strategy. All other aspects of the simulations remained the same, (resources, suggested nursing assessment frameworks, simulation session duration, size of simulation teams, preparatory materials). Ethics approval has been obtained for this project. RESULTS: Results of a qualitative analysis (in progress- will be completed by March 2012) of student and facilitator reports on students’ ability to meet the learning objectives of solving patient problems using clinical reasoning and experience with the ‘think aloud’ method will be presented. A comparison of clinical reasoning learning outcomes between the two groups will determine the effect on clinical reasoning for students responding to patient problems. CONCLUSIONS: In an environment of increasingly constrained clinical placement opportunities, exploration of alternate strategies to improve critical thinking skills and develop clinical reasoning and problem solving for nursing students is imperative in preparing nurses to respond to changing patient needs.

The knowledge base of subject-matter experts in teaching : A case study of a professional scientist as a beginning teacher

One method of addressing the shortage of science and mathematics teachers is to train scientists and other science-related professionals to become teachers. Advocates argue that as discipline experts these career changers can relate the subject matter knowledge to various contexts and applications in teaching. In this paper, through interviews and classroom observations with a former scientist and her students, we examine how one career changer used her expertise in microbiology to teach microscopy. These data provided the basis for a description of the teacher’s instruction which was then analysed for components of domain knowledge for teaching. Consistent with the literature, the findings revealed that this career changer needed to develop her pedagogical knowledge. However, an interesting finding was that the teacher’s subject matter as a science teacher differed substantively from her knowledge as a scientist. This finding challenges the assumption that subject matter is readily transferable across professions and provides insight into how to better prepare and support career changers to transition from scientist to science teacher.

Understanding the quantitative skill base on introductory statistics : a case study from business statistics

Basic mathematical skills are critical to a student’s ability to successfully undertake an introductory statistics course. Yet in business education this vitally important area of mathematics and statistics education is under-researched. The question therefore arises as to what level of mathematical skill a typical business studies student will possess as they enter the tertiary environment, and whether there are any common deficiencies that we can identify with a view to tackling the problem. This paper will focus on a study designed to measure the level of mathematical ability of first year business students. The results provide timely insight into a growing problem faced by many tertiary educators in this field.

All about that base : differing player experiences in video game genres and the unique case of MOBA games

Video games provide unique interactive player experiences (PX) often categorised into different genres. Prior research has looked at different game genres, but rarely through a PX lens. Especially, PX in the emerging area of massive online battle arena (MOBA) games is not well understood by researchers in the field. We address this knowledge gap by presenting a PX study of different game genres, which we followed up with a second semi-structured interview study about PX in MOBA games. Among the results of our analyses are that games that are likely played with other players, such as MOBA games, stimulate less immersion and presence for players. Additionally, while challenge and frustration are significantly higher in this genre, players get a sense of satisfaction from teamwork, competition and mastery of complex gameplay interactions. Our study is the first to contribute a comprehensive insight into key motivators of MOBA players and how PX in this genre is different from other genres.

Clinical reasoning: The relative contribution of identification, interpretation and hypothesis errors to misdiagnosis

The aim of this study was to identify and describe the types of errors in clinical reasoning that contribute to poor diagnostic performance at different levels of medical training and experience. Three cohorts of subjects, second- and fourth- (final) year medical students and a group of general practitioners, completed a set of clinical reasoning problems. The responses of those whose scores fell below the 25th centile were analysed to establish the stage of the clinical reasoning process - identification of relevant information, interpretation or hypothesis generation - at which most errors occurred and whether this was dependent on problem difficulty and level of medical experience. Results indicate that hypothesis errors decrease as expertise increases but that identification and interpretation errors increase. This may be due to inappropriate use of pattern recognition or to failure of the knowledge base. Furthermore, although hypothesis errors increased in line with problem difficulty, identification and interpretation errors decreased. A possible explanation is that as problem difficulty increases, subjects at all levels of expertise are less able to differentiate between relevant and irrelevant clinical features and so give equal consideration to all information contained within a case. It is concluded that the development of clinical reasoning in medical students throughout the course of their pre-clinical and clinical education may be enhanced by both an analysis of the clinical reasoning process and a specific focus on each of the stages at which errors commonly occur.