Building semantic trees from XML documents

The distributed nature of the Web, as a decentralized system exchanging information between heterogeneous sources, has underlined the need to manage interoperability, i.e., the ability to automatically interpret information in Web documents exchanged between different sources, necessary for efficient information management and search applications. In this context, XML was introduced as a data representation standard that simplifies the tasks of interoperation and integration among heterogeneous data sources, allowing to represent data in (semi-) structured documents consisting of hierarchically nested elements and atomic attributes. However, while XML was shown most effective in exchanging data, i.e., in syntactic interoperability, it has been proven limited when it comes to handling semantics, i.e.,  semantic interoperability, since it only specifies the syntactic and structural properties of the data without any further semantic meaning. As a result, XML semantic-aware processing has become a motivating challenge in Web data management, requiring dedicated semantic analysis and disambiguation methods to assign well-defined meaning to XML elements and attributes. In this context, most existing approaches: (i) ignore the problem of identifying ambiguous XML elements/nodes, (ii) only partially consider their structural relationships/context, (iii) use syntactic information in processing XML data regardless of the semantics involved, and (iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDFdesigned to address each of the above limitations, taking as input: an XML document, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts extracted from a reference machine-readable semantic network. XSDF consists of four main modules for: (i) linguistic pre-processing of simple/compound XML node labels and values, (ii) selecting ambiguous XML nodes as targets for disambiguation, (iii) representing target nodes as special sphere neighborhood vectors including all XML structural relationships within a (user-chosen) range, and (iv) running context vectors through a hybrid disambiguation process, combining two approaches: concept-basedand context-based disambiguation, allowing the user to tune disambiguation parameters following her needs. Conducted experiments demonstrate the effectiveness and efficiency of our approach in comparison with alternative methods. We also discuss some practical applications of our method, ranging over semantic-aware query rewriting, semantic document clustering and classification, Mobile and Web services search and discovery, as well as blog analysis and event detection in social networks and tweets.     

The grammatical approach: A syntax-directed declarative specification method for XML processing tasks

This paper describes the grammatical approach, an approach to the specification of XML processing tasks based on attribute grammars. This approach describes how to provide task-specific context-free grammars for XML documents, as well as how to decompose complex processing tasks into simpler ones with attribute-grammar fragments. The result is a high-level, syntax-directed declarative specification for the processing of XML documents, which facilitates the development and maintenance of complex XML processing applications while preserving the flexibility of general-purpose XML processing models. The grammatical approach is illustrated using Chasqui, an e-learning platform for building educational digital libraries of learning objects.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Open corpus architecture for personalised ubiquitous e-learning

As the e-learning area matures, there are a growing number of e-learning content providers that produce and distribute material that covers a large range of topics, differs in quality and is represented in various formats. Lately, different devices and various network technologies allow extensive user access to educational content almost anywhere, anytime and from any device. Ubiquitous e-learning has the potential to provide continuous and context-based, educational material to human learners anytime, anywhere and on any device. Since each person has different expectations related to the content, the performance of the delivery and display of that content, it is desirable for an ubiquitous e-learning environment to provide user-oriented personalisation of e-learning material. However very often there are multiple sources of e-learning material at various web locations (open corpus resources) that cover the same topic, but differ in terms of quality, formatting and even cost. It is very difficult for learners to select the content that best suits their interests and goals, characteristics of the device used and delivery network as well as their cost budget. This paper proposes an innovative ubiquitous e-learning environment called Performance-based E-learning Adaptive Cost-efficient Open Corpus frameworK (PEACOCK) that provides support for the selection and distribution of personalised e-learning rich media content (e.g. multimedia, pictures, graphics and text) to e-learners such as it will best suit users’ interests and goals, meet their formatting preferences and cost constraints, while considering the limitations introduced by the end-user devices and the delivery networks to the user. PEACOCK’s main goal is to maximise the users’ e-learning experience and increase their learning satisfaction and learning outcome.     

Unifying heterogeneous e-learning modalities in a single platform: CADI, a case study

Current e-learning forms are commonly based on improving the learning process through the enhancement of certain skills in students, such as collaborative, competitive or problem-based learning. However, it seems that there is still no e-learning formula that gathers the implementation of a number of more generic educational principles in a single e-learning system or platform. Therefore, this paper presents a web-based framework for the creation, development and implementation of heterogeneous learning environments called CADI. It offers a graphical user interface for students to interact with only the need for any generic WiFi compliant device. Its innovative design allows the instructor to select and combine the developed resources to create varied activities, providing content adapted to every single device. The usefulness of the system was tested by introducing a case study to a group of senior college students. The experience showed that the use of the system helped students to achieve better results in their evaluation. It also showed that the students had no problems interacting with it from the outset, promoting their active learning. Design, implementation and assessment results of the system are also presented and analyzed.     

Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies

With the rapid change in all types of working environment, there is a need to implement electronic learning (e-learning) systems to train people in new technologies, products, and services. However, the large investment in e-learning has made user acceptance an increasingly critical issue for technology implementation and management. Although user acceptance received fairly extensive attention in prior research, efforts were needed to examine or validate previous results, especially in different technologies, user populations, and/or organizational contexts. We therefore proposed a new construct, perceived credibility, to examine the applicability of the technology acceptance model (TAM) in explaining engineers’ decisions to accept e-learning, and address a pragmatic technology management issue. Based on a sample of 140 engineers taken from six international companies, the results strongly support the extended TAM in predicting engineers’ intention to use e-learning.     

A visual language should be easy to use: a step forward for XML-GL

XML is spreading out as a standard for semistructured documents on the Web, so the possibility of querying XML documents which are linked by XML links is becoming a goal to achieve. In this paper we present XML-GL^rec, an extended version of the graphical query language for XML documents XML-GL. XML-GL allows to extract and restructure information from XML specified WWW documents. We extend XML-GL in the following directions: (i) XML-GL^rec allows to represent XML simple links, so that it is possible to query whole XML specified WWW sites in a simple and intuitive way; (ii) XML-GL^rec improves the expressive power of XML-GL, where only transitive closure can be expressed, by allowing generic recursion; (iii) finally, we permit the user to specify queries in an easier fashion, by allowing sequences of nested query, in the same way as in SQL.     

Articulation of information requirements for personalised knowledge construction

The quality of information provision influences considerably knowledge construction driven by individual users’ needs. In the design of information systems for e-learning, personal information requirements should be incorporated to determine a selection of suitable learning content, instructive sequencing for learning content, and effective presentation of learning content. This is considered as an important part of instructional design for a personalised information package. The current research reveals that there is a lack of means by which individual users’ information requirements can be effectively incorporated to support personal knowledge construction. This paper presents a method which enables an articulation of users’ requirements based on the rooted learning theories and requirements engineering paradigms. The user’s information requirements can be systematically encapsulated in a user profile (i.e. user requirements space), and further transformed onto instructional design specifications (i.e. information space). These two spaces allow the discovering of information requirements patterns for self-maintaining and self-adapting personalisation that enhance experience in the knowledge construction process.     

Text-based synchronous e-learning and dyslexia: Not necessarily the perfect match!

The introduction, in the United Kingdom, of the Special Education Needs and Disabilities Act (SENDA) published and approved in 2001, has removed the exemptions given to educational institutions by the Disabilities Discrimination Act (DDA) of 1995. This applies to learning web sites and materials that must now undergo “reasonable adjustments”, in order not to disadvantage students with learning disabilities as well as non-disabled students. This paper discusses how e-learning and the inherent use of online learning activities raises problems for students with dyslexia far beyond accessibility and web design. This paper aims at proposing that the so widely proclaimed advantages of e-learning to bridge distances, different learning paces and cognitive styles, is at the same time producing close to insurmountable barriers to students with cognitive disabilities in general, and dyslexia specifically. It presents the results of a research project aiming at uncovering evidence that students with dyslexia are in fact less likely to thrive in a synchronous e-learning environment. The paper reports on a set of experiments undertaken with students when engaged in online authentic synchronous learning activities. The paper focuses on text-based synchronous activities, since it is the widest and most commonly used synchronous technology, and provides clear evidence that it can marginalise, demotivate and disappoint students with dyslexia with difficulties in reading, spelling, word order and argumentation.     

Constructing SCORM compliant course based on High-Level Petri Nets

With rapid development of the Internet, e-learning system has become more and more popular. Currently, to solve the issue of sharing and reusing of teaching materials in different e-learning system, Sharable Content Object Reference Model (SCORM) is the most popular standard among existing international standards. In SCORM standard, the Sequencing and Navigation (SN) defines the course sequencing behavior, which controls the sequencing, selecting and delivering of a course, and organizes the content into a hierarchical structure, namely Activity Tree (AT). However, the structures with complicated sequencing rules of Activity Tree (AT) in SCORM make the design and creation of course sequences hard. Therefore, how to provide a user-friendly authoring tool to efficiently construct SCORM compliant course becomes an important issue. However, before developing the authoring tool, how to provide a systematic approach to analyze the sequencing rules and to transform the created course into SCORM compliant are our concerns.Therefore, in this paper, based upon the concept of Object Oriented Methodology (OOM), we propose a systematic approach, called Object Oriented Course Modeling (OOCM), to construct the SCORM compliant course. High-Level Petri Nets (HLPN), which is a powerful language for system modeling and validation, are applied to model the basic sequencing components, called Object-Oriented Activity Tree (OOAT), for constructing the SCORM course with complex sequencing behaviors. Every OOAT as a middleware represents a specific sequencing behavior in learning activity and corresponding structure with associated sequencing rules of AT in SCORM. Thus, these OOATs can be efficiently used to model and construct the course with complex sequencing behaviors for different learning guidance. Moreover, two algorithms, called PN2AT and AT2CP, are also proposed to transform HLPN modeled by OOATs into a tree-like structure with related sequencing rules in Activity Tree (AT) and package the AT and related physical learning resources into a SCORM compliant course file described by XML language, respectively. Finally, based upon the OOCM scheme, a prototypical authoring tool with graphical user interface (GUI) is developed. For evaluating the efficiency of the OOCM approach compared with existing authoring tools, an experiment has been done. The experimental results show that the OOCM approach is workable and beneficial for teachers/instructional designers.     

User Profile Modeling in the context of web-based learning management systems

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

Virtual Mechatronic/Robotic laboratory – A step further in distance learning

The implementation of the distance learning and e-learning in technical disciplines (like Mechanical and Electrical Engineering) is still far behind the grown practice in narrative disciplines (like Economy, management, etc.). This comes out from the fact that education in technical disciplines inevitably involves laboratory exercises and this fact drastically increases the complexity of a potential e-learning system. New approach and new specific knowledge are needed to develop such a system. We expect to meet the requirements of distance learning by developing the software-based laboratory exercises, i.e., a virtual laboratory. To fully substitute a physical system like laboratory equipment, one must emulate its full dynamics. The mathematical model in the form of differential equations will be applied to calculate dynamics and provide the data that would otherwise be measured on a physical system – this means simulation.     

PVA: A Self-Adaptive Personal View Agent

In this paper, we present PVA, an adaptive personal view information agent system for tracking, learning and managing user interests in Internet documents. PVA consists of three parts: a proxy, personal view constructor, and personal view maintainer. The proxy logs the user's activities and extracts the user's interests without user intervention. The personal view constructor mines user interests and maps them to a class hierarchy (i.e., personal view). The personal view maintainer synchronizes user interests and the personal view periodically. When user interests change, in PVA, not only the contents, but also the structure of the user profile are modified to adapt to the changes. In addition, PVA considers the aging problem of user interests. The experimental results show that modulating the structure of the user profile increases the accuracy of a personalization system.     

An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the student’s profile.     

CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents

For a software information agent, operating on behalf of a human owner and belonging to a community of agents, the choice of communicating or not with another agent becomes a decision to take, since communication generally implies a cost. Since these agents often operate as recommender systems, on the basis of dynamic recognition of their human owners’ behaviour and by generally using hybrid machine learning techniques, three main necessities arise in their design, namely (i) providing the agent with an internal representation of both interests and behaviour of its owner, usually called ontology; (ii) detecting inter-ontology properties that can help an agent to choose the most promising agents to be contacted for knowledge-sharing purposes; (iii) semi-automatically constructing the agent ontology, by simply observing the behaviour of the user supported by the agent, leaving to the user only the task of defining concepts and categories of interest. We present a complete MAS architecture, called connectionist learning and inter-ontology similarities (CILIOS), for supporting agent mutual monitoring, trying to cover all the issues above. CILIOS exploits an ontology model able to represent concepts, concept collections, functions and causal implications among events in a multi-agent environment; moreover, it uses a mechanism capable of inducing logical rules representing agent behaviour in the ontology by means of a connectionist ontology representation, based on neural-symbolic networks, i.e., networks whose input and output nodes are associated with logic variables.     

Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment

Most e-learning environments which utilize user feedback or profiles, collect such information based on questionnaires, resulting very often in incomplete answers, and sometimes deliberate misleading input. In this work, we present a mechanism which compiles feedback related to the behavioral state of the user (e.g. level of interest) in the context of reading an electronic document; this is achieved using a non-intrusive scheme, which uses a simple web camera to detect and track the head, eye and hand movements and provides an estimation of the level of interest and engagement with the use of a neuro-fuzzy network initialized from evidence from the idea of Theory of Mind and trained from expert-annotated data. The user does not need to interact with the proposed system, and can act as if she was not monitored at all. The proposed scheme is tested in an e-learning environment, in order to adapt the presentation of the content to the user profile and current behavioral state. Experiments show that the proposed system detects reading- and attention-related user states very effectively, in a testbed where children’s reading performance is tracked.

Students' profiles of ICT use: Identification,determinants, and relations to achievement in a computer and information literacy test

Current debates on students' use of information and communication technology (ICT) have brought to attention profiles and purposes of ICT use in either school-related or recreational contexts. Examining these two contexts at the same time, the present study seeks to identify student profiles of ICT use on the basis of the Norwegian International Computer and Information Literacy Study (ICILS) 2013 data (N = 2426). In order to explore profiles of ICT use in schools and at home for different purposes such as recreation, study purposes, exchanging information, and social communication, we take a person-centered approach and apply latent profile analysis. These analyses revealed two independent user profiles and showed that background characteristics (i.e., gender, immigration status) and motivational constructs (i.e., self-efficacy, interest, and enjoyment in ICT) play a significant role in determining profile membership. Significant differences between the user profiles in students' computer and information literacy test performance did not exist. Given that the coverage of ICT at home and in schools has increased substantially over the last decades, the identification of user profiles informs teachers and parents about whether or not students exploit these opportunities to the same extent. Implications for future research and practice are discussed.     

User sensitive research in e-learning: exploring the role of individual user characteristics

The increasing need for active and accessible learning in the inclusive knowledge society drives the demand for e-learning that engages users much more effectively than ever before. In this context, it is crucial to conduct research that embraces innovation in user sensitive design, or else influential individual user differences may be overlooked. The objective of this paper is to explore the creation of successful e-learning systems that are able to increase users’ learning performance and enhance their personal learning experiences. The paper reports two converging and complimentary approaches, namely case studies and experimentation. First, case studies are used to explore the extent to which effective e-learning systems comply with eight specific factors. Of the eight, accessibility, individual differences and student modeling turn out to be the weakest points in current practice. Second, an empirical study investigates the influences of user individual user differences on users’ learning outcomes in an e-learning environment. The experiment found that individual differences in motivation to learn and expectations about e-learning significantly impacted users’ learning achievements. Third, based on these studies, improvements in research methodology are identified towards greater consideration of user sensitive research issues, thus enabling us to outline improved experimental procedures. Further experiment results should provide us with better insights into the arguments needed to carefully assess benefits of developing and involving a user model in an e-learning application. Consequently, evaluation and justification could now encompass both system performance as well as user performance.