scholarly journals Requirement elicitation techniques for an improved case based lesson planning system

2018 ◽  
Vol 20 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Aslina Saad ◽  
Christian Dawson
Keyword(s):  
Lesson Planning ◽  
Lesson Plans ◽  
Planning System ◽  
Functional Requirements ◽  
Case Retrieval ◽  
Content Type ◽  
Requirement Elicitation ◽  
Input And Output ◽  
Document Review ◽  
Case Based

Purpose This paper presents a recommendation on how one requirement elicitation technique supports the other techniques in defining system requirement for a case-based system. A case-based lesson planning system aims to assist teachers in constructing quality lesson plans through its cycle which begins with case retrieval. To retrieve relevant lesson plans, appropriate inputs should be used and the intended output needs to be identified via suitable requirement elicitation techniques. The use of a single technique might result in inadequate requirement specification, thus affecting the quality of the output requirements as well as quality of the final information system. Design/methodology/approach Requirement elicitation was carried out in three phases: phase I involved document review, phase II was an interview and phase III used a survey. Respondents of the study comprised experienced teachers as well as new teachers. This research used both qualitative and quantitative approaches to answer the research questions, which involved semi-structured interviews, document review and survey to collect the relevant data. Documents were reviewed by analysing lesson plans from three different countries. In addition, a review of lesson plans prepared by teachers and the standard syllabus were carried out. Findings from the document review were used in structured interviews using a teach-back technique, sorting and matrix of attribute-values. A questionnaire was then constructed based on the interviews and document review. Findings The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via good design of the system input and output. The study identifies important elements of a lesson plan according to their ranking. Keywords that were used by teachers as input for retrieval were identified together with the expected output. Research limitations/implications The main goal of requirement elicitation is to specify complete and detailed requirements of the proposed system. There are two main types of requirement: functional and non-functional requirements. This paper only focuses on functional requirements – specifically case retrieval with appropriate input and output. Practical implications Various requirement engineering (RE) techniques can be applied in different phases of requirement elicitation. Suitable technique should be chosen at different phases of RE, as it is important for triangulation purposes. Incomplete RE will affect the modelling part of system development, and, thus, affect the design and implementation of an information system. Social implications Software engineer or anybody involved in system development should plan accordingly for the RE process. They should be creative and reasonable in selecting suitable RE techniques to be applied. Originality/value This study aims to gain understanding of the various aspects of lesson planning. Crucial knowledge in lesson planning that was gathered from the elicitation phase is modelled to have a good understanding of the problems and constraints among teachers. The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via a good design of the system input and output.

Case-based reasoning and electromagnetism-like method in construction management

Kybernetes ◽  
2014 ◽  
Vol 43 (2) ◽  
pp. 265-280 ◽  
Author(s):  
Aleksandar Kartelj ◽  
Nebojša Šurlan ◽  
Zoran Cekić
Keyword(s):  
Construction Management ◽  
Construction Projects ◽  
General Pattern ◽  
Optimization Technique ◽  
Case Based Reasoning ◽  
Significant Implication ◽  
Case Retrieval ◽  
Content Type ◽  
Feature Weights ◽  
Case Based

Purpose – The presented research proposes a method aimed to improve a case retrieval phase of the case-based reasoning (CBR) system through optimization of feature relevance parameters, i.e. feature weights. Design/methodology/approach – The improvement is achieved by applying the metaheuristic optimization technique, called electromagnetism-like algorithm (EM), in order to appropriately adjust the feature weights used in k-NN classifier. The usability of the proposed EM k-NN algorithm is much broader since it can also be used outside the CBR system, e.g. for solving general pattern recognition tasks. Findings – It is showed that the proposed EM k-NN algorithm improves the baseline k-NN model and outperforms the appropriately tuned artificial neural network (ANN) in the task of predicting the case (data record) output values. The results are verified by performing statistical analysis. Research limitations/implications – The proposed method is currently adjusted to deal with numerical features, so, as a direction for future work, the variant of EM k-NN algorithm that deals with symbolic or some more complex types of features should be considered. Practical implications – EM k-NN algorithm can be incorporated as a case retrieval component inside a general CBR system. This is the future direction of the investigation since the authors intend to build a complete specialized CBR system for construction project management. The overall CBR with incorporated EM k-NN will have significant implication in the construction management as it will be able to produce more accurate prediction of viability and the life cycle of new construction projects. Originality/value – The electromagnetism-like algorithm is applied to the problem of finding feature weights for the first time. EM potential for solving the problem of weighting features lies in its internal structure because it is based on the real-valued EM vectors. The overall EM k-NN algorithm is applied on data sets generated from real construction projects data corpus. The proposed algorithm proved its efficiency as it outperformed baseline k-NN model and ANN. Its applicability in more complex and specialized CBR systems is high since it can be easily added due to its modular (black-box) design.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

Determining the factors affecting the technical efficiency scores of public hospitals using different regression methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bayram Şahin ◽  
Gülnur İlgün ◽  
Seda Sönmez
Keyword(s):  
Public Hospitals ◽  
Regression Analyses ◽  
Tobit Regression ◽  
Control Variables ◽  
Content Type ◽  
Factors Affecting ◽  
Provincial Level ◽  
Regression Methods ◽  
Input And Output ◽  
Dea Method

PurposeThis study aims to identify the efficiency scores of hospitals affiliated to the Ministry of Health in Turkey between the years of 2010–2015 at provincial level and to reveal the factors that affect the efficiency scores.Design/methodology/approachThe two-stage data envelopment analysis (DEA) method was used to achieve the study purpose. In the first stage, DEA method based on input-oriented Charnes–Cooper–Rhodes (CCR) model was performed to calculate the efficiency scores of public hospitals at the provincial level between 2010 and 2015, and in the second stage, Tobit regression and linear regression analyses were used to identify whether the efficiency scores of provinces are affected by the input, output and control variables.FindingsUpon the analysis, the average efficiency scores of 81 provinces by years were found to vary between 0.79 and 0.89. According to both regression analyses, all of the input and output variables were found to have significant effects on the efficiency scores of provinces while only the population of province among the control variables was identified as the factor with an effect on the efficiency scores of provinces (p < 0.05).Practical implicationsThe results of this study are thought to guide health policymakers and managers in terms of both determining efficient and inefficient hospitals at the provincial level and revealing which variables should be taken into account in order to increase efficiency.Originality/valueThe study differs from previous studies on the efficiency of hospitals. First, although previous studies were generally descriptive studies to determine the efficiency level of hospitals, this study is an analytical study that tries also to show the factors affecting the efficiency of hospitals. In addition, while examining the effect of input and output variables on efficiency scores, control variables were also included in the study.

scholarly journals Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Best Practice ◽  
Discrete Event ◽  
Scale Up ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Fault Features ◽  
Case Based

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

Contextual case-based reasoning applied to a mobile device

2017 ◽  
Vol 13 (3) ◽  
pp. 282-299 ◽  
Author(s):  
Djamel Guessoum ◽  
Moeiz Miraoui ◽  
Chakib Tadj
Keyword(s):  
Mobile Device ◽  
Inertial Sensors ◽  
Contextual Information ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Two Stage ◽  
Minimal Processing ◽  
Current Case ◽  
Content Type ◽  
Case Based

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

scholarly journals How trainee physical education teachers in England write, use and evaluate lesson plans

2018 ◽  
Vol 25 (4) ◽  
pp. 964-982 ◽  
Author(s):  
Susan Capel ◽  
Sophy Bassett ◽  
Julia Lawrence ◽  
Angela Newton ◽  
Paula Zwozdiak-Myers
Keyword(s):  
Physical Education ◽  
Thematic Analysis ◽  
Lesson Planning ◽  
Lesson Plans ◽  
Physical Education Teachers ◽  
Trainee Teachers ◽  
Initial Teacher Training ◽  
School Based ◽  
Limited Choice ◽  
Insight Into

Traditionally, all physical education initial teacher training (PEITT) courses in England, and in many other countries, require trainee teachers to complete detailed lesson plans for each lesson they teach in their school-based practicum and then to evaluate those lessons. However, there has been a limited amount of research on lesson planning in PEITT generally or in England specifically. The purpose of this study therefore was to gain an initial insight into how trainee physical education teachers write, use and evaluate lesson plans. Two-hundred-and-eighty-nine physical education trainees in England completed a questionnaire about lesson planning after finishing a block school-based practicum. Frequencies and percentages were calculated for the limited-choice questions on the questionnaires and open-ended questions were analysed using thematic analysis. Results showed mixed responses, with no one method followed by all trainees. Some trainees stated they planned and/or evaluated lessons as taught. Some stated they completed the plan and/or evaluation proforma to ‘tick a box’. The highest percentage of trainees stated it took between half an hour and one-and-a-half hours to plan each lesson. Although most trainees stated they found the plan useful in the lesson, others stated they found it too detailed to use. Some stated they did not deviate from the plan in the lesson, whereas others adapted the plan. The majority of trainees stated that evaluation enabled them to see if objectives had been achieved. Results are discussed in relation to teaching trainees how to plan lessons in PEITT in England.

Managing millennials: tips for global managers

2018 ◽  
Vol 26 (7) ◽  
pp. 29-31
Author(s):  
Terence P. Malloy
Keyword(s):  
Human Resource ◽  
Design Methodology ◽  
Digital Native ◽  
Content Type ◽  
Effective Strategies ◽  
Hr Managers ◽  
Document Review ◽  
The Impact

Purpose This paper aims to review how millennials, since coming into the workforce in 2004, have faired in several countries worldwide. After a synopsis of how the group is characterized in each country surveyed, suggestions are provided to human resource (HR) directors on how to further manage and motivate this employee sector. Design/methodology/approach The paper opted for document review of research from past 15 years on this sector of the workforce to contrast and compare how these workers had progressed (or not) depending on the areas of the globe in which they reside. Findings The paper provides practical insights on possible ways and means to create productivity from these employees. It suggests that successful managers may have to be more creative in their ways to attract and appeal to this group but also be more deliberate in creating effective strategies tailored toward the digital native. Research limitations/implications Because the data in this group are still not voluminous and theories and conclusions on the impact they have made continue to vary depending on the circumstance, continued analysis to recognize new trends is suggested. Originality/value This paper suggests updated criteria for HR managers to better evaluate and motivate a growing sector of their workforces.

A QoS-aware approach for runtime discovery, selection and composition of semantic web services

2016 ◽  
Vol 12 (2) ◽  
pp. 177-200 ◽  
Author(s):  
Sanjay Garg ◽  
Kirit Modi ◽  
Sanjay Chaudhary
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
User Satisfaction ◽  
Service Discovery ◽  
Semantic Web Services ◽  
Functional Requirements ◽  
Semantic Description ◽  
Content Type ◽  
The Web

Purpose Web services play vital role in the development of emerging technologies such as Cloud computing and Internet of Things. Although, there is a close relationship among the discovery, selection and composition tasks of Web services, research community has treated these challenges at individual level rather to focus on them collectively for developing efficient solution, which is the purpose of this work. This paper aims to propose an approach to integrate the service discovery, selection and composition of Semantic Web services on runtime basis. Design/methodology/approach The proposed approach defined as a quality of service (QoS)-aware approach is based on QoS model to perform discovery, selection and composition tasks at runtime to enhance the user satisfaction and quality guarantee by incorporating non-functional parameters such as response time and throughput with the Web services and user request. In this paper, the proposed approach is based on ontology for semantic description of Web services, which provides interoperability and automation in the Web services tasks. Findings This work proposed an integrated framework of Web service discovery, selection and composition which supports end user to search, select and compose the Web services at runtime using semantic description and non-functional requirements. The proposed approach is evaluated by various data sets from the Web Service Challenge 2009 (WSC-2009) to show the efficiency of this work. A use case scenario of Healthcare Information System is implemented using proposed work to demonstrate the usability and requirement the proposed approach. Originality/value The main contribution of this paper is to develop an integrated approach of Semantic Web services discovery, selection and composition by using the non-functional requirements.

CAFixD: A Case-Based Reasoning Fixture Design Method. Framework and Indexing Mechanisms

2005 ◽  
Vol 6 (1) ◽  
pp. 40-48 ◽  
Author(s):  
Iain M. Boyle ◽  
Kevin Rong ◽  
David C. Brown
Keyword(s):  
Design Method ◽  
Critical Issue ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Fixture Design ◽  
Functional Requirements ◽  
Rigorous Approach ◽  
Machining Operations ◽  
Case Based ◽  
Individual Requirement

Fixtures accurately locate and secure a part during machining operations. Various computer-aided fixture design (CAFD) methods have been developed to reduce design costs associated with fixturing. One approach uses a case-based reasoning (CBR) method where relevant design experience is retrieved from a design library and adapted to provide a new design solution. Indexing design cases is a critical issue in CBR, and CBR systems can suffer from an inability to distinguish between cases if indexing is inadequate. This paper presents CAFixD, a CAFD methodology that adopts a rigorous approach to defining indexing attributes based upon axiomatic design functional requirement decomposition. A design requirement is decomposed in terms of functional requirements, physical solutions are retrieved and adapted for each individual requirement, and the design is then reconstituted to form a complete fixture design. This paper presents the CAFixD framework and operation, and discusses in detail the indexing mechanisms used.

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