From course notes to granules: A guide to deriving Learning Object components

Learning Objects (LOs) remain a complex entity, particularly for novices, and creating the resources from the beginning can be a daunting prospect if there is no development team for collaboration. Multiple issues need to be considered, including how LOs are defined, how LOs are planned, granularity and reusability, and categorisation of LOs. The paper outlines these issues and offers a solution to assist novice creators of Learning Objects from existing materials. Using the Cisco Reusable Learning Object Strategy to define and plan the Learning Object, this paper proposes a task analysis to derive granules of learning material, matched to appropriate competency levels. Tests of these guidelines produced 586 potential information objects that are small, granular items, each with a prerequisite, an objective and a skill level.     

Kassandra: A framework for distributed simulation of heterogeneous cooperating objects

To address the heterogeneity and scalability issues of simulating Cooperating Objects (COs) systems, we propose Kassandra, a conceptual framework for enabling distributed COs simulation by integrating existing simulation tools. Moreover, Kassandra exploits the communication middleware used by real-world COs as underlying communication mechanism for integrating Kassandra-enabled simulation tools. In this way, real-world COs can be included with simulated objects in a seamless way to perform more accurate system performance evaluation. Moreover, such a hardware-in-the-loop approach is not limited to pre-deployment performance analysis, and can offer possibilities to analyse performance at different phases of CO applications. The concept of Kassandra has been carried out in the EU PLANET project. In this paper, we introduce the Kassandra framework components and show their interactions at different phases for node deployments in PLANET use cases. The result demonstrates the applicability of Kassandra to facilitate the development of CO applications.     

Expert centred vs learner centred approach for evaluating quality and reusability of learning objects

The aim of the paper is to present and compare so-called bottom-up and top-down approaches for evaluating quality and reusability of learning objects (LOs). The paper proposes bottom-up methodology that outlines the central role of learners’ individual and social behaviour while working with LOs. This includes social tagging and some parameters of interaction for measuring context to describe LOs usage, attention, and other aspects of the context as well as helps to exploit context data towards making LOs repositories more useful, and thus enhance the reuse. The paper also presents top-down methodology for the expert evaluation of LOs quality and reusability. This methodology consists of consecutive application of several scientific approaches, methods, and principles. The authors investigate how these two different, however complimentary approaches for evaluating LOs quality and reusability can be better applied for the aims of eQNet project in order to select reusable (“travel well”) LOs for implementing in different educational contexts and countries. These approaches could be considered as suitable applications of information and communication technologies (ICT’s) for development of human capital. The examples of practical application of these approaches for evaluating LOs quality and reusability in eQNet project are also presented in the paper.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

SECOL: a semantic environment based on social media to support organisational learning

Organisational learning (OL) helps companies to significantly improve their processes through the reuse of experiences, making knowledge accessible to the whole organisation. However, establishing learning in software development companies is not a trivial task, since it is an area in which processes and knowledge are usually hidden inside the employees’ mind. Generally, employees prefer to look for knowledge via Internet search engines rather than using the knowledge produced inside the company. Hence, we explored how better organising content produced within the company may minimise this problem. We investigated how a semantic collaborative environment, titled semantic collaborative environment for organisational learning (SECOL), based on social software, learning objects (LOs), and units of learning (UL) may assist to improve OL for software development companies. We defined an approach to generate LOs and UL from social software’s content used by companies. The environment was implemented based on ontologies in order to represent and organise acquired knowledge. Furthermore, an experiment was conducted using qualitative data analysis. The results indicated that the use of the environment is appropriate to improve OL in software development teams and the use of SECOL is efficient, particularly in order to acquire new knowledge, assisting the promotion of the use of organisational patterns and minimising repeated solutions.     

A case-;study of requirements reuse through product families

Increasingly, software organisations are looking towards large-;scale reuse as a way of improving productivity, raising quality and reducing delivery timescales. Many in the reuse community have suggested notions of product-;line development and domain engineering life-;cycles. Achieving these in practice, however, requires a systematic process for “early” reuse (requirements reuse) as well as late reuse (code reuse). This paper discusses pratical experience of early reuse. We describe FORE (Family Of REquirements), an approach that we have developed in our work in the domain of aircraft engine control systems. The FORE approach concentrates on the definition of a generic product concept and the formalisation of its requirements. We describe the FORE approach in general terms, and then show how it has been applied in an industrial case-;study. We make an initial evaluation of the FORE approach (and early reuse in general) in terms of how it has changed an existing requirements engineering process. We compare the FORE approach to related work in early reuse, and draw some conclusions about how the approach may scale to other problems.     

A model for multi-label classification and ranking of learning objects

This paper describes an approach that uses multi-label classification methods for search tagged learning objects (LOs) by Learning Object Metadata (LOM), specifically the model offers a methodology that illustrates the task of multi-label mapping of LOs into types queries through an emergent multi-label space, and that can improve the first choice of learners or teachers. In order to build the model, the paper also proposes and preliminarily investigates the use of multi-label classification algorithm using only the LO features. As many LOs include textual material that can be indexed, and such indexes can also be used to filter the objects by matching them against user-provided keywords, we then did experiments using web classification with text features to compare the accuracy with the results from metadata (LO feature).     

Taking advantage of metadata semantics: the case of learning-object-based lesson graphs

Learning objects (LOs) are pieces of educational material characterized with a valuable amount of information about their content and usage. This additional information is defined as a set of metadata generally following the IEEE LOM specification. This specification also serves to characterize the relations existing between LOs. LOs whose relations are explicit are regarded as the nodes of a lesson graph. Link types and LO metadata constitute the lesson graph semantics. This article proposes to take advantage of lesson graph semantics using a context diffusion approach. It consists in diffusing the metadata-based processes along the edges of the lesson graph. This technique aims at coping with the metadata processing issues arising when some graph metadata are missing, incorrect, or incomplete. This article also presents a three-layer extensible framework for easing the use of context diffusion in a graph. As part of the framework, two original types of metadata processes are introduced. The first one takes advantage of the metadata attribute similarities between related LOs. The second one focuses on the lesson graph consistency. The framework and the application examples were implemented as an open-source Java library used in the lesson graph authoring tool LessonMapper2. During the lesson authoring process, we show that the framework can bring support not only for generating and validating metadata, but also for retrieving LOs.     

Graph-based traceability: a comprehensive approach

In recent years, traceability has been globally accepted as being a key success factor of software development projects. However, the multitude of different, poorly integrated taxonomies, approaches and technologies impedes the application of traceability techniques in practice. This paper presents a comprehensive view on traceability, pertaining to the whole software development process. Based on the state of the art, the field is structured according to six specific activities related to traceability as follows: definition, recording, identification, maintenance, retrieval, and utilization. Using graph technology, a comprehensive and seamless approach for supporting these activities is derived, combining them in one single conceptual framework. This approach supports the definition of metamodels for traceability information, recording of traceability information in graph-based repositories, identification and maintenance of traceability relationships using transformations, as well as retrieval and utilization of traceability information using a graph query language. The approach presented here is applied in the context of the ReDSeeDS project (Requirements Driven Software Development System) that aims at requirements-based software reuse. ReDSeeDS makes use of traceability information to determine potentially reusable architectures, design, or code artifacts based on a given set of reusable requirements. The project provides case studies from different domains for the validation of the approach.     

基于距离图像的定性识别

本文提出的定性描述图表示方法能在无模型指导的情况下有效地实现遮挡物体的分离, 并将识别过程转化为图或子图同构的问题,还提出了用"提纯"与"搜索"相结合的递归过程来 判定图或子图同构.实验证明了该方法的有效性和稳定性.     

An objective-oriented approach to program comprehension using multiple information sources

Program comprehension is a key activity throughout software maintenance and reuse. The knowledge acquired through comprehending programs can guide engineers to perform various kinds of software maintenance and reuse tasks. The effective comprehension strategy and the associated efficient approach, as well as the sophisticated tool support, are the indispensable elements for an entire solution to program comprehension to reduce the high costs of this nontrivial activity. This paper presents an objective-oriented comprehension strategy, contrasting to the traditional comprehensive understanding strategy in the literature. It is a kind of on-demand understanding for specific tasks and more effective in practice. In addition, using multiple information sources to understand programs is proposed with the corresponding framework. From these two points of views, we propose a feature-oriented program comprehension approach using requirement documentation. This approach aims at a specific category of feature-related software maintenance and reuse tasks. Case studies are conducted to evaluate the proposed solution. Results from the studied cases show that the experimental prototype provides more explicit advices for software engineers when performing these tasks.     

Web 3.0 – Based personalisation of learning objects in virtual learning environments

The paper aims at research on Web 3.0 – based personalisation of learning objects (LOs) while learning in virtual learning environments. Learning personalisation is analysed in terms of suitability of LOs and VLEs to particular learning styles. The novel sets portrait analysing interconnections between students’ learning styles, their preferred learning activities, relevant teaching/learning methods, and LOs types is presented in more detail. The well-known standardised vocabularies of teaching/learning methods and LOs types were used to establish these interconnections. The sets portrait of these interconnections is followed by the appropriate ontology. The ontology is considered as an appropriate tool to create learners’ personalised learning environments consisting of LOs, suitable teaching/learning methods and activities according to their preferred learning styles. The ontology should help the learner to find suitable LOs according to preferred learning methods/activities, and vice versa, and thus to personalise learning. The presented Web 3.0 – based approaches are the typical cases of information and communication technologies (ICT’s) application for development of human capital.     

Bottom-up reuse for multi-level testing

Lifecycle models divide the test process into consecutive test levels that are considered independently. This strict separation obstructs the view on the test process as a whole and fails to reflect the commonalities across test levels. Multi-level testing is an emerging approach that addresses the challenge of integrating test levels, putting particular emphasis on embedded systems. In this paper, we introduce a test level integration strategy based on reuse that is called bottom-up reuse. In addition, we present a test level instrument that seamlessly supports this strategy: multi-level test cases. We also provide a case study that reflects the positive results we have obtained in practice so far and demonstrates the feasibility of our test level integration approach. Bottom-up reuse and multi-level test cases lead to testing earlier on in the development process while reducing the effort required by test specification, test design, and test implementation.     

An Endosymbiotic Evolutionary Algorithm for Optimization

This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.     

Developing a unified framework of the business model concept

Recent rapid advances in Information and Communication Technologies (ICTs) have highlighted the rising importance of the Business Model (BM) concept in the field of Information Systems (IS). Despite agreement on its importance to an organization's success, the concept is still fuzzy and vague, and there is little consensus regarding its compositional facets. Identifying the fundamental concepts, modeling principles, practical functions, and reach of the BM relevant to IS and other business concepts is by no means complete. This paper, following a comprehensive review of the literature, principally employs the content analysis method and utilizes a deductive reasoning approach to provide a hierarchical taxonomy of the BM concepts from which to develop a more comprehensive framework. This framework comprises four fundamental aspects. First, it identifies four primary BM dimensions along with their constituent elements forming a complete ontological structure of the concept. Second, it cohesively organizes the BM modeling principles, that is, guidelines and features. Third, it explains the reach of the concept showing its interactions and intersections with strategy, business processes, and IS so as to place the BM within the world of digital business. Finally, the framework explores three major functions of BMs within digital organizations to shed light on the practical significance of the concept. Hence, this paper links the BM facets in a novel manner offering an intact definition. In doing so, this paper provides a unified conceptual framework for the BM concept that we argue is comprehensive and appropriate to the complex nature of businesses today. This leads to fruitful implications for theory and practice and also enables us to suggest a research agenda using our conceptual framework.     

Feature-based generation of machining process plans for optimised parts manufacture

Efficacious integration of such CAx technologies as CAD, CAM and CAPP still remains a problem in engineering practice which constantly attracts research attention. Design by feature model is assumed as a main factor in the integration effort in various engineering and manufacturing domains. It refers principally to feature clustering and consequently operation sequencing in elaborated process plan designs. The focus of this paper is on CAPP for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centres. A methodical approach is proposed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing adequate working steps. The approach involves the use of the branch and bound concept from the field of artificial intelligence. A conceptual scheme for generation of alternative process plans in the form of a network is developed. It is based on part design data modelling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The feasibility of the approach and the algorithm are illustrated by a numerical case with regard to a real application and diverse machine tools with relevant tooling. Generated process alternatives for complex machining with given systems, are studied using models programmed in the environment of Matlab^® software.     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach

This paper brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target, knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The building and construction domain is challenged with significant problems for exchanging, sharing and integrating information between actors. For example, semantic gaps or lack of meaning definition at the conceptual and technical level, are problems fundamentally created through the employment of representations to map the ‘world’ into models in an endeavour to anticipate different actors’ views, vocabulary, and objectives. One of the primary research challenges addressed in this work is the process of formalization and representation of document content, where most existing approaches are limited in their capability and only take into account the explicit, word-based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched by incorporation of implicit information derived from the complex relationships (the Semantic Associations) modelled by domain ontologies combined with the information presented in documents, thereby providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. The paper introduces a novel conceptual framework for representation of knowledge sources, where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector. This work contributes to the enrichment of Semantic Vectors, using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test bed for the assessment of the approach is the Building and Construction industry, using an appropriate B&C domain Ontology. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed.