Multi-level code comprehension model for large scale software, A

1996 Fall.Includes bibliographical references (pages 142-147).For the past 20 years researchers have studied how programmers understand code they did not write. Most of this research has concentrated on small-scale code understanding. We consider it necessary to design studies that observe programmers working on large-scale code in production environments. We describe the design and implementation of such a study which included 11 maintenance engineers working on various maintenance tasks. The objective is to build a theory based on observations of programmers working on real tasks. Results show that programmers understand code at different levels of abstraction. Expertise in the application domain, amount of prior experience with the code, and task can determine the types of actions taken during maintenance, the level of abstraction at which the programmer works, and the information needed to complete a maintenance task. A better grasp of how programmers understand large scale code and what is most efficient and effective can lead to better tools, better maintenance guidelines, and documentation

The Use of Cognitive Task Analysis to Improve Anesthesia Skills Training for Postoperative Extubation

This study examines knowledge gains in 25 nurse anesthesia trainees, following the implementation of a novel instructional design, which incorporated cognitive task analysis (CTA) to teach an adult postoperative extubation procedure. CTA is a knowledge elicitation technique employed for acquiring expertise from domain specialists to support the effective instruction of novices. Instruction guided through CTA is effective in improving surgical skills training for medical students and surgical resi-dents. The standard, current method of teaching clinical skills to novices in nurse anesthesia practice relies on recall-based instruction from domain experts. However, this method is limited by the constraints of expertise, including automation of procedural knowledge by the expert practitioner.Automated knowledge escapes conscious awareness and access, thus impeding clear explication of comprehensive essentials for task execution during instruction. CTA guided instruction has been shown to maximizeconceptual, declarative and procedural knowledge gains in novice practitioners by clearly explicating the essentials employed when experts execute tasks. Knowledge gains for the task of postoperative extubation in 13 junior and senior nurse anesthesia trainees were compared to those of 12 trainees, receiving standard instruction. The study results indicate that CTA-based instruction has a positive and significant effect on procedural knowledge gains in the novice anesthetist

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

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Empirical Studies of Novices Learning Programming

This thesis is concerned with the problems that novices have in learning to program: in particular it is concerned with the difficulties experienced by novices learning at a distance, using instructional materials which have been designed especially for novices. One of the major problems for novices is how to link the new information which they encounter with their existing knowledge. Du Boulay, O'Shea and Monk (1981) suggest helping novices to bridge the gap between their existing knowledge and new information by teaching via a conceptual model, which serves to explain the new information in familiar terms.In this thesis the difficulties which novices have when learning to program with the help of a conceptual model were investigated. The curricula and conceptual models of four different programming languages are examined, all of which were designed to teach novices. Du Boulay, O'Shea and Monk (1981) have suggested criteria for analysing conceptual models. It is argued that these criteria, however, do not address the presentation of the conceptual model, and so are insufficient to evaluate them. An additional form of analysis was proposed and used, in addition to the criteria offered by Du Boulay et al. This is a way of describing the conceptual model which distinguishes three views of the conceptual model: state, procedure and function, and which highlights the different aspects which are important for the novice learner by identifying the different kinds of knowledge which are necessary to understand the conceptual model. This analysis of the conceptual models showed that the environments are not as exemplary as the du Boulay et al's criteria suggest, and indicated that three of the environments, SOLO, PT501 and DESMOND, lack a functional representation, and that the fourth, Open Logo, has other different problems.An empirical study was carried out to study the transfer effects of learning two of the languages, a high level and a low level language, sequentially. There was no evidence for such transfer effects. The difficulties novices have in learning the four different languages were also investigated. These studies show that even though the novices were studying environments designed for novices learning at a distance, they did not develop good levels of competence, and the problems they had fall into two main categories: programming and pedagogical.Although the different languages had different aims and curricula, novices had some problems which were common to all or most of the languages. These included understanding flow of control, developing and using programming plans, developing accurate mental models, and in the high level languages, understanding recursion. It is argued that some of these problems are related to the conceptual models. In particular, the difficulties novices had in developing and using plan knowledge, which is one of their main problems, can be explained by the lack of an appropriate functional description in the languages.The subjects' pedagogical problems arose from the relationship between the style and structure of the curriculum, its content, and the subjects themselves. In all the four texts the teaching material is very carefully structured and it is suggested that this may encourage the learner to adopt an over-dependent attitude towards the text, and in some cases, to work at an inappropriate syntactic level.The relationship between the distance learning situation and the novice programmer is discussed, and recommendations are made for improving the curricula used for teaching novices programming

Automated knowledge acquisition for knowledge-based systems: KE-KIT

Despite recent progress, knowledge acquisition remains a central problem for the development of intelligent systems. There are many people throughout the world doing studies in this area. However, very few automated techniques have made it to the market place. In this light, the idea of automating the knowledge acquisition process is very appealing and may lead to a break through. Most (if not all) of the approaches and techniques concerning intelligent, expert systems and specifically knowledge-based systems can still be considered in their infancy and definitely do not subscribe to any kind of standards. Many things have yet to be learned and incorporated into the technology and combined with methods from traditional computer science and psychology. KE-KIT is a prototype system which attempts to automate a portion of the knowledge engineering process. The emphasis is on the automation of knowledge acquisition activities. However, the transformation of knowledge from an intermediate form to a knowledge -base format is also addressed. The approach used to automate the knowledge acquisition process is based on the personal construct theory developed by George Kelly in the field of psychology. This thesis gives and in-depth view of knowledge engineering with a concentration on the knowledge acquisition process. Several issues and approaches are described. Greater details surrounding the personal construct theory approach to knowledge acquisition and its use of a repertory grid are given. In addition, some existing knowledge acquisition tools are briefly explored. Details concerning the implementation of KE-KIT and reflections on its applicability round out the presented material

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming