Case-based analysis in user requirements modelling for knowledge construction

Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Case-based reasoning in offshore well design

Petroleum well drilling is an expensive and risky operation. In this context, well design presents itself as a fundamental key to decrease costs and risks involved. Experience acquired by engineers is notably an important factor in good drilling design elaborations. Therefore, the loss of this knowledge may entail additional problems and costs. In this way, this work represents an initiative to model a petroleum well design case-based architecture. Tests with a prototype showed that the system built with this architecture may help in a well design and enable corporate knowledge preservation. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Development of an open case-based decision-support system for diagnosis in oral pathology

Making diagnoses in oral pathology are often difficult and confusing in dental practice, especially for the lessexperienced dental student. One of the most promising areas in bioinformatics is computer-aided diagnosis, where a computer system is capable of imitating human reasoning ability and provides diagnoses with an accuracy approaching that of expert professionals. This type of system could be an alternative tool for assisting dental students to overcome the difficulties of the oral pathology learning process. This could allow students to define variables and information, important to improving the decision-making performance. However, no current open data management system has been integrated with an artificial intelligence system in a user-friendly environment. Such a system could also be used as an education tool to help students perform diagnoses. The aim of the present study was to develop and test an open case-based decisionsupport system.Methods: An open decision-support system based on Bayes' theorem connected to a relational database was developed using the C++ programming language. The software was tested in the computerisation of a surgical pathology service and in simulating the diagnosis of 43 known cases of oral bone disease. The simulation was performed after the system was initially filled with data from 401 cases of oral bone disease.Results: the system allowed the authors to construct and to manage a pathology database, and to simulate diagnoses using the variables from the database.Conclusion: Combining a relational database and an open decision-support system in the same user-friendly environment proved effective in simulating diagnoses based on information from an updated database.

Case-based system: Indexing and retrieval with fuzzy hypercube

In some applications with case-based system, the attributes available for indexing are better described as linguistic variables instead of receiving numerical treatment. In these applications, the concept of fuzzy hypercube can be applied to give a geometrical interpretation of similarities among cases. This paper presents an approach that uses geometrical properties of fuzzy hypercube space to make indexing and retrieval processes of cases.

Corpus-based teaching of pragmatics

[EN]This paper is a proposal for teaching pragmatics following a corpus-based approach. Corpora have had a high impact on how linguistics is looked at these days. However, teaching linguistics is still traditional in its scope and stays away from a growing tendency of incorporating authentic samples in the theoretical classroom, and so lecturers perpetuate the presentation of the same canonical examples students may find in their textbooks or in other introductory monographs. Our view is that using corpus linguistics, especially corpora freely available in the World Wide Web, will result in a more engaging and fresh look at the course of Pragmatics, while promoting early research in students. This way, they learn the concepts but most importantly how to later identify pragmatic phenomena in real text. Here, we raise our concern with the methodology, presenting clear examples of corpus-based pragmatic activities, and one clear result is the fact that students learn also how to be autonomous in their analysis o f data. In our proposal, we move from more controlled tasks to autonomy. This proposal focuses on students enrolled in the course Pragmática de la Lengua inglesa, currently part of the curriculum in Lenguas Modernas, Universidad de Las Palmas de Gran Canaria.

Fidelity of Implementation of Research-Based Instructional Strategies (RBIS) in Engineering Science Courses

Background Increasing attention is being paid to improvement in undergraduate science, technology, engineering, and mathematics (STEM) education through increased adoption of research-based instructional strategies (RBIS), but high-quality measures of faculty instructional practice do not exist to monitor progress. Purpose/Hypothesis The measure of how well an implemented intervention follows the original is called fidelity of implementation. This theory was used to address the research questions: What is the fidelity of implementation of selected RBIS in engineering science courses? That is, how closely does engineering science classroom practice reflect the intentions of the original developers? Do the critical components that characterize an RBIS discriminate between engineering science faculty members who claimed use of the RBIS and those who did not? Design/Method A survey of 387 U.S. faculty teaching engineering science courses (e.g., statics, circuits, thermodynamics) included questions about class time spent on 16 critical components and use of 11 corresponding RBIS. Fidelity was quantified as the percentage of RBIS users who also spent time on corresponding critical components. Discrimination between users and nonusers was tested using chi square. Results Overall fidelity of the 11 RBIS ranged from 11% to 80% of users spending time on all required components. Fidelity was highest for RBIS with one required component: case-based teaching, just-in-time teaching, and inquiry learning. Thirteen of 16 critical components discriminated between users and nonusers for all RBIS to which they were mapped. Conclusions Results were consistent with initial mapping of critical components to RBIS. Fidelity of implementation is a potentially useful framework for future work in STEM undergraduate education.

International variation in GP treatment strategies for subclinical hypothyroidism in older adults: a case-based survey

Background: There is limited evidence about the impact of treatment for subclinical hypothyroidism, especially among older people. Aim: To investigate the variation in GP treatment strategies for older patients with subclinical hypothyroidism depending on country and patient characteristics. Design and setting: Case-based survey of GPs in the Netherlands, Germany, England, Ireland, Switzerland, and New Zealand. Method: The treatment strategy of GPs (treatment yes/no, starting-dose thyroxine) was assessed for eight cases presenting a woman with subclinical hypothyroidism. The cases differed in the patient characteristics of age (70 versus 85 years), vitality status (vital versus vulnerable), and thyroid-stimulating hormone (TSH) concentration (6 versus 15 mU/L). Results: A total of 526 GPs participated (the Netherlands n = 129, Germany n = 61, England n = 22, Ireland n = 21, Switzerland n = 262, New Zealand n = 31; overall response 19%). Across countries, differences in treatment strategy were observed. GPs from the Netherlands (mean treatment percentage 34%), England (40%), and New Zealand (39%) were less inclined to start treatment than GPs in Germany (73%), Ireland (62%), and Switzerland (52%) (P = 0.05). Overall, GPs were less inclined to start treatment in 85-year-old than in 70-year-old females (pooled odds ratio [OR] 0.74 [95% confidence interval [CI] = 0.63 to 0.87]). Females with a TSH of 15 mU/L were more likely to get treated than those with a TSH of 6 mU/L (pooled OR 9.49 [95% CI = 5.81 to 15.5]). Conclusion: GP treatment strategies of older people with subclinical hypothyroidism vary largely by country and patient characteristics. This variation underlines the need for a new generation of international guidelines based on the outcomes of randomised clinical trials set within primary care

Internet-based stress management for women with preterm labour: a case-based experience report

Pregnant women with preterm labour (PTL) in pregnancy often experience increased distress and anxieties regarding both the pregnancy and the child's health. The pathogenesis of PTL is, among other causes, related to the stress-associated activation of the maternal-foetal stress system. In spite of these psychobiological associations, only a few research studies have investigated the potential of psychological stress-reducing interventions. The following paper will present an online anxiety and stress management self-help program for pregnant women with PTL. Structure and content of the program will be illustrated by a case-based experience report. L.B., 32 years (G3, P1), was recruited at gestational week 27 while hospitalized for PTL for 3 weeks. She worked independently through the program for 6 weeks and had regular written contact with a therapist. Processing the program had a positive impact on L.B.'s anxiety and stress levels, as well as on her experienced depressive symptoms and bonding to the foetus. As PTL and the risk of PTB are associated with distress, psychological stress-reducing interventions might be beneficial. This study examines the applicability of an online intervention for pregnant women with PTL. The case report illustrates how adequate low-threshold psychological support could be provided to these women.

Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

Peer reviewed