Special Issue "Recommender Systems in E-Learning Settings"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (15 September 2011)
Prof. Dr. Mercedes Gómez-Albarrán
Dep. Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Interests: virtual learning environments; game-based instructional tools; user-adaptive technology-enhanced learning; personalized recommendation techniques; case-based reasoning and case-based teaching
In our everyday lives we are exposed to an amount of information that increases far more quickly than our ability to process it. Recommender systems have successfully helped users to alleviate this information overload by supporting them in pre-selecting information they may be interested in.
Recommender systems have been traditionally applied in e-commerce. However, their use has been recently transferred to the learning field. In this sense, we can found in the literature works that follow different approaches and apply to different (e-)learning contexts.
As for learner-centred context, some research has been conducted into developing recommender tools for courses and curriculum learning activities. The massive increase of online learning resources also provides opportunities for the design, development and evaluation of recommender systems that support learners in decision-making and the identification of suitable resources in, both, formal and non-formal settings. Finding other people with relevant learning interests can also be supported by recommender systems.
As for instructor-centred context, recommenders can suggest to the instructors the most appropriate modifications for improving the effectiveness of web-based educational courses. Recommendation of alternative learning paths through learning resources can also support teaching tasks.
This special issue is open to researchers interested in the conception of approaches and the design, development and evaluation of recommender tools in the e-learning field. Papers describing novel recommendation approaches, state-of-the-art recommender tools and/or experience reports about their use are welcome.
Suitable topics (include but are not limited to):
- Requirements for the deployment of recommender systems in e-learning
- Relevant recommendation algorithms for e-learning
- Adaptation and personalization in e-learning recommendations
- Diversity-enhanced recommendation approaches in educational context
- Recommendation for individual learner and virtual learning community scenarios
- Suitable user modelling in educational recommendation systems
- Design and development aspects and experiences of recommender tools applied to e-learning
- Learner-centred evaluation methods for recommendation in e-learning
- Experiences in evaluation of recommender tools in the educational context
- Case studies for educational recommender systems in real world scenarios
Prof. Dr. Mercedes Gómez-Albarrán
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
- educational recommender tools
- personalized recommendation in e-learning
- diversity-aware recommendation in e-learning
- design, development and evaluation of educational recommenders
- recommendation and learning communities user-modelling for recommender tools applied to e-learning
Algorithms 2011, 4(2), 131-154; doi:10.3390/a4030131
Received: 4 March 2011; in revised form: 23 May 2011 / Accepted: 1 July 2011 / Published: 20 July 2011| Download PDF Full-text (496 KB) | Download XML Full-text
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Type of Paper: Article
Title: An Adaptive Educational Hypermedia System for Filling the Learning Needs with Adaptive Learning Paths
Author: Francesco Colace
Affiliation: DIIIE-Università degli Studi di Salerno, Via Ponte Don Melillo, 184084 Fisciano, Italy; E-Mail: email@example.com
Abstract: E-Learning is becoming an effective approach for the improvement of the quality of learning. Many institutions are adopting this approach both to improve their traditional courses both to increase the potential audience. So great attention is paid in the introduction of methodologies and techniques for the adaptation of learning process to the real needs of students. In this scenario the Adaptive Educational Hypermedia System, a promising area of research at the crossroads of hypermedia and adaptive systems, can be an effective approach. In the e-Learning context the adaptive learning resources selection and sequencing is recognized as among one of the most interesting research questions. An Adaptive Educational Hypermedia System usually is composed by services for the management of the Knowledge Space, a User Model, a student’s learning phase observation strategy and the continuous learning path adaptation according to the real needs of the student. This paper addresses the design problem of an Adaptive hypermedia system by the definition of its each component. In particular an original user model, learning content model tracking strategies and adaptation model are described. The proposed Adaptive Educational Hypermedia System has been integrated in an e-Learning platform and an experimental campaign has been conducted. In particular the proposed approach has been introduced in three different courses. A comparison with traditional approach has been described and the obtained results seem to be very promising.
Keywords: e-Learning; adaptive educative hypermedia system; computer-assisted education
Type of Paper: Article
Title: Analysis of Userpath Tracing for Generating Recommendations in Open Journal System
Authors: Martin Grossegger, Behnam Taraghi and Martin Ebner
Affiliation: Social Learning, Computer and Information Services, Graz University of Technology, Steyrergasse 30/I, Graz, Austria; E-Mails: firstname.lastname@example.org, email@example.com
Abstract: Recommendations can be helpful to discover articles of interest especially if the related articles are not obviously linked together. There exist many techniques to design and implement a Recommender System (RS) in different knowledge domains. This paper focuses at first stage on the analysis of the user traces in e-learning journals using an in stallation of Open Journal System (OJS). The research question in this part is whether the users follow a certain link structure given within OJS or select the articles according to their interests. In the latter case the recorded data sets can be used for creating recommendations. The analysis bases on building an article matrix, which contains the usage frequency of the successive articles for each article within the OJS. Furthermore the navigation paths are also analyzed. According to the analysis results a hybrid recommendation system for OJS is proposed, which uses a content based filtering approach as basic system extended by the results of a collaborative filtering approach.
Keywords: Open Journal System; Recommender System; user trace; tracking; path analysis
Last update: 1 September 2011