Academic journal article Research in Learning Technology

The SAPO Campus Recommender System: A Study about Students’ and Teachers’ Opinions

Academic journal article Research in Learning Technology

The SAPO Campus Recommender System: A Study about Students’ and Teachers’ Opinions

Article excerpt


In a context where it is recognised that learning occurs throughout individuals’ life, not only in formal spaces but also in informal ones (Saz et al. 2011) and with the improvement and massification of technology – specially with all the tools and applications from the social Web – learners are becoming not only consumers but also producers of content and knowledge (Siemens 2008). The existence of a huge quantity of information makes the process of searching and selecting online content a hard task for the average user, who is generally overwhelmed by information overload (Gemmis et al. 2009). Therefore, recommender systems appear as useful tools in reducing the time and costs involved in the process of searching and selecting online information (Drachsler 2009). In this paper, we will present the main results of a study that aimed to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students’ and teachers’ opinions.

Developing and integrating a recommender system at the SAPO Campus platform

According to Adomavicius and Tuzhilin (2005) cited in Drachsler (2009), the general purpose of recommender systems is to pre-select information a user might be interested in. Thus, this kind of system tries to predict users’ preferences based on an implicit analysis of their activity (Lee 2001), in order to support the process of searching and selecting online information (Gemmis et al. 2009).

The literature in this field tells us that there are two main types of recommender systems: content-based and collaborative (Gemmis et al. 2009). The first one is based on the analysis of users’ preferences in order to recommend content similar to what the user preferred in the past (Drachsler 2009). This recommendation method allows the user to develop a deeper understanding about a given topic but, on the other hand, causes over-specialisation problems because it does not retrieve topics outside that particular cloud of interests. The collaborative recommender systems aim to predict appropriate items based on interaction data of many users with similar interests within the community (Deshpande and Karypis 2004). Although this method retrieves new items based on similar interests in the community, the arrival of a new user or a new item could represent a ‘cold-start problem’, which corresponds to the time that the system needs to understand users’ preferences (Mödritscher et al. 2011).

Nowadays, recommender systems are successfully applied in e-commerce sites like Amazon,1 matching users’ interests with those with a similar taste and creating a ‘neighbourhood’ of like-minded customers (Drachsler 2009). These successful examples may help us to think about the advantages and limitations of these systems and to develop specific ones for educational contexts. In the Technology Enhanced Learning (TEL) research field, recommender systems have to deal with different levels of complexity involving learners and learning activities. Thus, according to Manouselis et al. (2011), it may not be sufficient to merely transfer the recommender systems logic used in e-commerce contexts to educational ones.

In educational contexts, recommendations appear to be useful for empowering learners to set up their own learning environments (Mödritscher 2010), helping them to select content according to their individual needs (Santos and Boticario 2010). Therefore, these systems could offer guidance to learners without limiting their freedom, by mediating the relationship between users’ existing knowledge and potential knowledge acquisition (Lichtnow et al. 2006). According to Drachsler, Hummel and Koper (2008), with recommendations, users are able to be self-regulated and responsible for their own learning, while they also have the opportunity to find peers and/or tools and get suggestions and motivational support from interaction with peers (Mödritscher et al. …

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