Information Architecture for the Design of a User Interface to Manage Educational Oriented Recommendations

Information Architecture for the Design of a User Interface to Manage Educational Oriented Recommendations

Olga C. Santos (UNED, Spain & Cadius Community of Usability Professionals, Spain), Emanuela Mazzone (UNED, Spain & Cadius Community of Usability Professionals, Spain), Maria Jose Aguilar (Cadius Community of Usability Professionals, Spain) and Jesus Boticario (UNED, Spain)
DOI: 10.4018/978-1-61350-516-8.ch006
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This chapter presents the information architecture approach for the design of an administration tool for educators to manage educational oriented recommendations in virtual learning environments. In this way, educators can be supported in the publication stage of the e-learning life cycle after recommendations have been designed with the TORMES methodology. The chapter starts introducing relevant background information on recommender systems for e-learning and the rationale for the educators’ involvement in the recommendation design process. Afterwards, the chapter comments on the information architecture that supports the user interface design process with user-centered design methods, including the goals to achieve in each of the steps defined. The chapter ends by discussing the application of this approach in different contexts.
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In a world where information overload is everywhere, recommender systems (RS) represent a highly-valuable feature: they offer the most relevant products, services or guidance to each specific user’s when users have to make choices in daily life situations without sufficient knowledge on the available alternatives (Resnick and Varian, 1997). RS in these domains are usually based on technologies processing previous interactions with the system by a specific user or by similar users. Typical examples of RS are found in commercial services (e.g.: Amazon, YouTube, LastFM). Reviews of RS have been produced in literature where different application domains are identified: e-commerce, web recommender, personalized newspaper, movie recommender, document recommender, information recommender, travel recommender, purchase recommender, music recommender, e-mail filtering, sharing news, netnews filtering, web search filtering (Montaner et al., 2003). The successful implementation of these systems in the e-commerce domain (Schafer, Konstan and Riedl 2001) has motivated their consideration for virtual learning environments (VLE) (Hsu, 2008). The most common approaches focus on providing an automatic process to support students in finding suitable materials (e.g. Shen and Shen, 2004; Al-Hamad et al., 2008; Markellou et al., 2005; Li and Chang, 2005; Hsu, 2008; Schulz et al., 2001; Soonthornphisaj et al., 2006; Ksristofic, 2005,) as an alternative to relying solely on classmates, educators and other sources (Resnick and Varian 1997). However, a recommendation in the e-learning context could be diverse, e.g. as simple as suggesting a web resource, or more interactive (i.e. an on-line activity) such as doing an exercise, reading a posted message on a conferencing system or running an on-line simulation (Zaïane, 2002). One point to be taken into account is that most of the times, recommendations are offered in the form of links recommended (Romero et al, 2007).

The common idea behind the RS in VLE is to overcome the limitations of traditional teaching methods that follow the “one-size-fits-all” approach. In this format, students with different backgrounds are still given the same content at the same time, no matter if it is interesting and useful to their learning (Shen and Shen, 2004), and contents are not dynamic enough to respond effectively to the needs and competences of the learners, resulting in poor learning experiences (Markellou et al., 2005). To increase the learning efficiency, students with different goals and backgrounds should be treated differently by building a model of knowledge and preferences, offering as a result diverse learning experiences to all students with diverse learning preferences (Al-Hamad et al., 2008). Students should not only have access to the most appropriate tools and environments that present information in an engaging manner, but they should also be provided with the appropriate support for the diversity of individual learning styles (Bates and Leary, 2001). Thus, RS within the context of an e-learning platform should take into account not only the user preferences, but also educational aspects of the learning process (Bloch et al., 2003).

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