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Using technology to recommend and discover learning methods

Information Overflow: Recommendation Systems of Amazon meet Education

By IGI Global on Jul 2, 2013
Olga SantosThe philosophy behind Amazon’s shopping cart system is simple: You, and others, have added a particular item to a shopping cart. Based on that selection and on what others have purchased, Amazon lets you know that you might also like some additional products. It seems effortless how the system can make these suggestions, but the technology and responsiveness of this recommendation programming is far from simplistic.

Now imagine if you could learn in this same way.

IGI Global Editor Olga Santos is a researcher with aDeNu (Adaptive Dynamic online Educational systems based oN User modeling), a research and development (R&D) group in the Artificial Intelligence Department of the Spanish National University for Distance Education (UNED). Her publication, Educational Recommender Systems and Technologies: Practices and Challenges, co-edited with Director of aDeNu and UNED Professor Jesus G. Boticario, explores the idea of applying recommendation systems in the education field.

With a long list of conferences to attend over the coming months, Dr. Santos took a moment to share her work with us, including how this technology can be used to support distance learning students:


IGI Global: A great deal of your work focuses on Educational Recommender Systems (ERS), including your recent publication, Educational Recommender Systems and Technologies: Practices and Challenges, with IGI Global. Could you share a brief overview of what ERS is, for those who are unfamiliar with this technology and area of educational research? How did you become interested in the subject?

Olga Santos: Current online learning environments can provide learners with a large amount of content to read and diverse learning services to interact with. This situation usually overwhelms them. In non-educational domains, recommender systems have been used with success to support users when there is information overflow. Perhaps the most popular recommender system is Amazon, which suggests to potential book buyers some titles to buy while taking into account the similarities with other users of current browsing behaviour and shopping cart. This motivated me to research if recommendations could also be of benefit in educational scenarios and how they should be designed to take into account the particularities of the educational domain, where not only the user preferences are to be taken into account, but also the educational goals.

In this way, depending on the learner profile, which includes learning goals, background and progress, as well as the learner’s current context in terms of course situation, devices used and others factors, some actions on the learning environment are to be more appropriate than others to reach the educational goal. For instance, there might be some contents that can help the learner to better solve a specific task. Or, answering some questions posted in the forum by another classmate can help her to reflect on what she has just read. Here is where educational recommender systems can be used: to suggest some action to carry out in the learning environment to a specific learner when the system detects she is not performing efficiently in the course, so she can improve her learning experience and performance.

Recently, you attended the 21st Conference on User Modeling, Adaptation and Personalization (UMAP 2013), and helped organize the 3rd International Workshop on Personalization Approaches in Learning Environments (PALE 2013), both in beautiful Rome, Italy, this June.

One of your presentations at UMAP focused on the particular topic, “Eliciting affective recommendations to support distance learning students.” In your opinion, of the many recent developments in distance learning, which tools or technologies have most changed the educational landscape so far?

I’d say the e-learning platforms, which started to be widely used in the past decade, and which provide virtual spaces to deliver course contents and facilitate communication and collaboration among classmates and with the teacher, removing geographical and temporal barriers. Before that, communication in distance universities was carried out via traditional media such as phone calls, postal mail and face to face meetings with appointments. However, the technological support provided by current e-learning platforms do not take advantage yet of the personalization capabilities that can be offered in them, including the potential benefits of supporting learners with affective recommendations, that is, recommendations that take into account the affective state of learners to guide them to appropriate learning situations, such as motivation and concentration.

Other then what was probably amazing gelato and pizza, what were some of your favorite things about being in Rome?

Apart from the beauty of the city, meeting the people from the UMAP community and discussing interesting research ideas and potential collaborations after the conference sessions, while walking around the city at dusk.

You will be attending quite a few upcoming conferences this summer, including the 6th International Conference on Educational Data Mining (EDM 2013) and the 16th International Conference on Artificial Intelligence in Education (AIED 2013), and your work will also be presented at the 15th International Conference on Human-Computer Interaction (HCI 2013). What are things that excite you most when presenting your work at these meetings?

I’m excited to see the feedback of these communities to the works we are currently carrying out at the aDeNu research group. These works focus on improving emotions detection by combining multiple sources, such as facial and gesture expressions, keyboard and mouse interactions, physiological changes as well as subjective feedback obtained from standardized instruments. Being able to detect changes in the affective state in leaners will allow us to improve the learning modeling, and thus, provide more focused and emotionally-oriented recommendations to learners when interacting with learning environments.

What, if any, are some of the biggest challenges or obstacles within the field of learning modeling and educational technologies?

In my view, one of the major challenges is to link existing semantic descriptions and define new ones if needed in order to facilitate the information exchange among the required technological components that can provide inclusive and affective personalized support to learners in learning environments.


To read more about utilizing electronic tools such as recommenders systems, mobile devices, and online communities in the delivery of education and training in both school and non-school settings, check out the title Educational Recommender Systems and Technologies: Practices and Challenges, edited by Olga C. Santos and Jesus G. Boticario, both of the Spanish National University for Distance Education (UNED), Spain. For more information about IGI Global's education titles, contact Kristen Stauffer, Discipline Manager, at kstauffer@igi-global.com.

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.
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