Flexible Querying of Lifelong Learner Metadata

Lecture Notes in Computer Science, 2009

The L4All system allows learners to record and share learning pathways through educational offerings, with the aim of facilitating progression from Secondary Education through to Further Education and on to Higher Education. It allows users to create and maintain their timeline (a chronological record of their learning, work and personal episodes) and share it with other users, aiming to foster collaborative elaboration of future goals and aspirations. This paper describes the design of the system's facility for searching for "people like me", presents the results of an evaluation session with a group of mature learners, and discusses outcomes arising from this evaluation.

Tools with Artificial Intelligence, 2008. …, 2008

We present an approach for personalized retrieval in an e-learning platform, that takes advantage of semantic Web standards to represent the learning content and the user/learner profiles as ontologies, and that re-ranks search results/lectures based on how the contained terms map to these ontologies. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner's context can be effectively used for improving the precision and recall in e-learning content retrieval, particularly by re-ranking the search results based on the learner's past activities.

2004

Self e-Learning Networks (SeLeNes) aim at facilitating access to digital material — not necessarily primarily produced for educative purposes — to a wide audience of learners and instructors with diverse educational background and requirements. One step towards this goal is the ability to specify educational needs or to describe educational material according to personalized e-learning terminologies and conceptualisations. In this deliverable we investigate how this goal can be achieved in a declarative way using language primitives for defining views over distributed, autonomous RDF bases holding learning object descriptions and schemas. Based on these primitives, we introduce a fully-fledged view definition language, called RVL, for creating not only virtual resource descriptions, but also virtual RDF/S schemas from (meta)classes, properties, and resource descriptions available on a SeLeNe. Furthermore, we illustrate how RVL views can be composed with structured RDF/S queries expr...

Proceedings of the 1st …, 2005

In order to achieve interoperability among learning repositories, implementers require a common communication framework for querying. This paper proposes a set of methods referred to as Simple Query Interface (SQI) as a universal interoperability layer for educational networks. The methods proposed can be used by a source for configuring and submitting queries to a target system and retrieving results from it. The SQI interface can be implemented in a synchronous or an asynchronous manner. SQI abstracts from query languages and metadata schemas. SQI has been evaluated by several prototype implementations demonstrating its universal applicability, and is on the way to being standardized in the CEN/ISSS Learning Technologies Workshop. The latest developments of SQI can be followed at http://www.prolearn-project.org/lori/.

Proceedings of the 12th International Conference on Computer Supported Education, 2020

In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.

2003

Learning Content Management Systems (LCMS) supports e-learning applications with storage and efficient access for e-learning objects (LO)s. ROSA is a LCMS built as a semantic layer on the top of an XML native DBMS, Tamino. Together, ROSA and Tamino, offer instructional designers a semantic view of e-learning content. In this paper, we present ROSA Data Model and Query Language, designed as an extension to RDF data model and RQL query language. The Data Model is structured around the LO modeling and their relationships, adapted to the elearning domain. An algebra defines valid operations over LO data. Queries are formulated in ROSAQL that extends RQL with joins, graph navigation and recursion.

2010

The main objective of this paper is to propose and evaluate an architecture that provides, manages, and collects data that permit high levels of adaptability and relevance to the user profiles. In addition, we implement this architecture on a platform called HyperManyMedia. To achieve this objective, an approach for personalized search is implemented that takes advantage of the semantic Web standards (RDF and OWL) to represent the content and the user profiles. The framework consists of the following phases: (1) building the semantic E-learning domain using the known college and course information as concept and sub-concept, (2) generating the semantic user profiles as ontologies, (3) clustering the documents to discover more refined sub-concepts, (4) reranking the user's search results based on his/her profile, and (5) providing the user with semantic recommendations. The implementation of the ontologies models is separate from the design and implementation of the information retrieval system, thus providing a modular framework that is easy to adapt and port to other platforms. Finally, the experimental results show that the user context can be effectively used for improving the precision and recall in E-learning search, particularly by re-ranking the search results based on the user profiles.

Web-based Education, 2010

Today many open sources of information are available on the Internet that provide sharing and reusing of learning materials to reduce the cost of designing new courses, save the time, and avoid effort duplication. In this research, mechanisms that support instructors and e-tutors in selecting the most appropriate learning materials for more effective learning outcomes are investigated. On one hand, instructors need to prepare course materials that meet specific goals such as course objectives and syllabus. On the other hand, students need to have studying materials that match their learning styles and that are built based on their background knowledge. Therefore, the objective of the research is to build a model and an architecture for a Smart e-Learning Assistant (SeLA) that provides instructors and e-tutors with smart assistance in selecting the most appropriate Learning Objects (LOs) for both Adaptive Course Preparation and Delivery from a higher level perspective. SeLA employs two main theories in building its model: the Revised Bloom's Taxonomy of instructional design (RBT) and Felder-Silverman Learning Style Model (FSLSM). Under this research, a prototype in .NET environment has been developed and evaluated.

2009

Most of today's eLearning systems provide static learning materials that are based on the one-size-fits-all philosophy and do not provide a personalized learning space. They are incapable of retrieving and displaying learning materials based upon each individual student's learning goals. In our present work we identify the core problems that are present in current eLearning systems. Accordingly, we propose possible solutions, upon which we develop a personalized learning system. We deploy a facet based modular structure for this purpose. This system is built upon "semantic learning layer cake".

2018

We present A-QuB, a platform that facilitates the exploration, discovery and management of semantic metadata. A-QuB incorporates a multitude of features on top of an intuitive and user friendly environment, in order for both novice and expert users to create and execute complex queries. The platform is agnostic of the underlying conceptual model and configurable on different aspects. 1 Metadata Querying for Interdisciplinary Research The publishing of structured and semantically enriched data is changing traditional models of conducting business and research. Modern science in particular is becoming more collaborative and multidisciplinary, taking advantage of the plethora of data being produced by groups with diverse scientific backgrounds. So called Virtual Research Environments (VREs) aim to promote this scheme, overcoming physical or semantic barriers, and facilitating researchers from diverse fields to exchange data and resources, decoupling science from ICT. For such an intero...

iJET, 2021

A learner profile is key to personalize learning content. Nowadays learners use different applications and tools to learn (Formal and informal types). Indeed, the diversity of profiles, their content, their structure, their operation, and the actors concerned, limits possible interoperability. Hence, the need for a rich and an interoperable learner profile that describes all previous learning achievements or experiences. In this work, after a brief analysis of available standards in this area, an approach is proposed to build an interoperable learner model based on xAPI statements that combine the formal and informal experiences to enhance learning analytic and personalization. Then, we present a tool to transform collected data into our XML model proposed based on the IMS-LIP standard, and in the end, we explore his utility.

Collaborative and social information …, 2009

0 Chapter XVIII Personalized Information Retrieval in a Semantic-Based Learning Environment Antonella Carbonaro University of Bologna, Italy Rodolfo Ferrini University of Bologna, Italy ABsTrAcT Active learning is the ability of learners to carry out learning activities in such ...

2015

In improving efficiency of creating learning materials, fundamental role plays concept of reusability. In order to allow effective exploitation of its content, a repository of learning objects have to enable search procedure which is powerful and at the same time intuitive and simple for use. We propose an architectural solution for enhanced search, such that both requirements are satisfied. A search algorithm based on finding min-cost Steiner trees allows finding not only learning objects which satisfies given query, but at the same time, it enables finding implicit relationships among different concept. To enable application of such algorithm, we developed a novel algorithm for sparse weighted graph representation of a LO repository. In addition, user’s ability to retrieve relevant information can be further improved by extension of query language. We proposed one possible extension based on formal logic and designed an algorithm for parsing such language.

Interactive Technology and Smart Education, 2005

We have proposed and implemented AgentMatcher, an architecture for match-making in e-Business applications. It uses arc-labeled and arc-weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for matchmaking between learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e-Learning project. Through AgentMatcher's new indexing component, known as Learning Object Metadata Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM. LOMGen semi-automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Webbased prefiltering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the prefiltered result is transformed into an XML representation, called Weighted Object-Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO RuleML representation obtained from the query interface by AgentMatcher's core Similarity Engine. The final result is presented as a ranked LO list with a user-specified threshold.

2002

Choosing adequate user preferences for retrieval is an essential part in modern information systems. Though deriving and modeling preferences has gained wide attention, choosing and combining preferences in a sophisticated way for retrieval purposes is still an open problem. This paper aims at showing ways to expand queries in tight cooperation with the user. Due to the cognitive knowledge involved, these complex queries can be expected to return more relevant results than traditional database queries. The paper surveys crucial influences and proposes a suitable architecture for implementation.