Olga C. Santos

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.

Modeling recommendations for the educational domain

Abstract Recommendations for technology enhanced learning scenarios have differences from those in other domains as recommendations in e-learning should be guided by educational objectives, and not only by the users’ preferences. Most efforts so far have focused mainly on researching algorithms that retrieve relevant learning materials to the learner, but other kind of recommendations can be provided due to the richness in services and functionality available in educational web-based scenarios. To find out relevant recommendation items from an educational point of view, a top down perspective can be used to design recommendations, especially for formal learning scenarios. To cope with these needs, we have defined a semantic recommendations model that can be used to describe the recommendations for technology enhanced learning.

Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations.

Educational Recommender Systems and Technologies: Practices and Challenges

Olga C. Santos holds a PhD in Artificial Intelligence from the Computer Science School of the Spanish National University for Distance Education (UNED) and a MSc degree in Telecommunications Engineering specialized in Software Engineering from the Polytechnic University of Madrid (UPM). Since September 2001, she has been a researcher at the aDeNu Research Group at UNED, and since 2005, she is the R&D Technical Manager of the group. Her current research interests focus on taking into account recommendation strategies to provide open source educational accessible user-centered e-learning services for learners. She has participated in 11 international and national research projects, published over 100 papers in various international conferences and journals, and co-chaired workshops and conferences related to topics from her research.

Emotions Detection from Math Exercises by Combining Several Data Sources

Emotions detection and their management are key issues to provide personalize support in educational scenarios. Literature suggests that combining several input sources can improve the performance of affect recognition. To gain a better understanding of this issue, we carried out a large scale experiment in our laboratory where about 100 participants performed several mathematical exercises while emotional information was gathered from different input sources, including a written emotional report. As a first step, we have explored emotions detection from traditional methods by combining analysis of user behavior when typing this report with sentiment analysis on the text. Moreover, an expert labeled these reports. All these data were used to feed several machine learning algorithms to infer user’s emotions. Preliminary results are not conclusive, but lead some light on how to proceed with the analysis.

Recommender Systems for Technology Enhanced Learning: Research Trends and Applications

As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

An Overview of aLFanet: An Adaptive iLMS Based on Standards

aLFanet (IST-2001-33288) aims to build an adaptive iLMS (intelligent Learning Management System) that provides personalised eLearning based on the combination of different types of adaptation (e.g. learning routes, interactions in services, peer-to-peer collaboration, presentation). It integrates new principles and tools in the fields of Learning Design and Artificial Intelligence, following existing standards in the educational field (IMS-LD, IMS-CP, IEEE-LOM, IMS-LIP, IMS-QTI) and multi-agents systems (FIPA). In this paper we present an overview of the project ongoing research and developments.

Toward interactive context-aware affective educational recommendations in computer-assisted language learning

This work explores the benefits of supporting learners affectively in a context-aware learning situation. This features a new challenge in related literature in terms of providing affective educational recommendations that take advantage of ambient intelligence and are delivered through actuators available in the environment, thus going beyond previous approaches which provided computer-based recommendation that present some text or tell aloud the learner what to do. To address this open issue, we have applied TORMES elicitation methodology, which has been used to investigate the potential of ambient intelligence for making more interactive recommendations in an emotionally challenging scenario (i.e. preparing for the oral examination of a second language learning course). Arduino open source electronics prototyping platform is used both to sense changes in the learners’ affective state and to deliver the recommendation in a more interactive way through different complementary sensory communication channels (sight, hearing, touch) to cope with a universal design. An Ambient Intelligence Context-aware Affective Recommender Platform (AICARP) has been built to support the whole experience, which represents a progress in the state of the art. In particular, we have come up with what is most likely the first interactive context-aware affective educational recommendation. The value of this contribution lies in discussing methodological and practical issues involved.

A Methodological Approach to Eliciting Affective Educational Recommendations

The emotional situation of the learner can influence the learning process. For this reason, we are researching how educational recommender systems can take advantage of affective computing to improve the recommendation support in educational scenarios. The paper reports works carried out involving 18 educators and 77 learners to elicit and design emotional feedback to be provided for learners in terms of personalized recommendations. To this end, user centered design methods and data mining techniques are used.

An Approach for an Affective Educational Recommendation Model

There is agreement in the literature that affect influences learning. In turn, addressing affective issues in the recommendation process has shown their ability to increase the performance of recommender systems in non-educational scenarios. In our work, we combine both research lines and describe the SAERS approach to model affective educational recommendations. This affective recommendation model has been initially validated with the application of the TORMES methodology to specific educational settings. We report 29 recommendations elicited in 12 scenarios by applying this methodology. Moreover, a UML formalized version of the recommendations model which can describe the recommendations elicited is presented in the paper.