Learning with personalized recommender systems: A psychological view
Highlights
► Recommender systems must be adapted in order to be applied to learning scenarios. ► Recommender systems must be tailored to learner knowledge and learning activities. ► Recommender systems must be persuasive; they should challenge beliefs. ► Recommender systems should be based on explicit ratings. ► Recommender systems should stress social visibility.
Introduction
When we ponder over the movie that we would like to see next weekend, or whether the new restaurant in town is worth checking out, we often rely on the experience and recommendations of friends and other people who we trust to be knowledgeable about our tastes and preferences. Getting good recommendations becomes an important issue when the number of viable options is too large to be perused by an individual person. Internet servers provide access to vast amounts of information, and consequently, offering recommendations is one of the most pressing problems for the design of electronic environments. It can be said that search engines provide recommendations, as a list of search results is ordered through link analysis algorithms that show most linked-to, and thereby most relevant Web pages on top (Brin & Page, 1998). Similarly, a bestseller list on a commercial Website can be regarded as providing recommendations. However, in these cases the recommendations are generic, i.e. different users receive the same or highly similar output. In contrast, personalized recommender systems try to achieve the gold standard of recommendations in real life by mimicking a person who is not only very knowledgeable about a topic, but also takes the individual tastes and preferences of users into account.
Personalized recommender systems capture the traces that users leave in an environment, either through page visits or explicit ratings of items, and they are based on the assumption that page visits or high ratings are indicative of user preferences. From data about visited or rated items, preferences on not-visited or unrated items can be predicted. A common method to predict preferences is through collaborative filtering (Sarwar, Karypis, Konstan, & Riedl, 2001) which mostly comes in two varieties: In user-based filtering, the behavioral profile of a target user will be compared to the profiles of other users, and recommendations for a particular item will be derived from those users who are most similar to the target user. The second method is item-based filtering where the overall rating differences among items will be set against the profile of a target user to arrive at personalized recommendations.
Personalized recommender systems are often used in e-commerce (Schafer, Konstan, & Riedl, 1999), as the ability to suggest products that are tailored to the needs and preferences of customers provides a unique selling point. However, in recent years the potential of personalized recommender systems for non-commercial purposes has begun to be explored, e.g. in educational contexts. Several educational recommender systems have been designed that recommend a broad range of items, among them software functionalities (Linton & Schaefer, 2000), learning resources on the Web (Geyer-Schulz et al., 2001, Recker et al., 2003), Web 2.0 resources (Drachsler et al., 2010), foreign language lessons (Hsu, 2008), learning objects (Lemire, Boley, McGrath, & Ball, 2005), test items and assignments (Rafaeli, Barak, Dan-Gur, & Toch, 2004), lecture notes (Farzan & Brusilovsky, 2005), or entire courses (Farzan & Brusilovsky, 2006). The applications cover very different areas of learning and education like use of library systems (Geyer-Schulz, Hahsler, Neumann, & Thede, 2003), informal learning (Drachsler, Hummel, & Koper, 2009), mobile learning (Andronico et al., 2003), learning at the workplace (Aehnelt, Ebert, Beham, Lindstaedt, & Paschen, 2008), or within health education (Fernandez-Luque, Karlsen, & Vognild, 2009).
Many papers on personalized recommender systems focus on technical issues and problems, the ultimate question being: How do we manage to deliver the most accurate recommendation for the current purposes? This paper, however, takes a somewhat different approach: It explores the psychological aspects of personalized recommender systems, with the ultimate question being: How do people react to and act upon recommender systems? This question will be addressed with particular emphasis on the use of recommender systems in educational contexts.
Knowing the psychological impact of recommendations on users can be helpful for practitioners and researchers alike. If we have a better idea how people react to recommender systems, we can improve algorithms and interfaces in ways that make using the system more efficient and satisfactory. Understanding how users contribute data to recommender systems is important for practitioners, as problems like low participation can impede system performance. From a research perspective, a better understanding of the psychological impacts of recommender systems can inform various fields, such as educational psychology (instructional design, educational technology), social psychology (persuasion, trust building), business administration (marketing), or computer science (machine learning, HCI).
The paper is structured as follows: Section 2 explores how the key characteristics of personalized recommender systems fit into current thought in the learning sciences. Section 3 discusses specific requirements that recommender systems must fulfill in order to support learning processes, both with regard to two learner roles and two types of adaptation. This discussion leads to four conjectures about how recommender systems should be adapted for educational contexts. Section 4 integrates the findings, and provides an outlook on future research.
Section snippets
Recommender systems and the learning sciences
Designing and implementing workable recommender systems can be quite burdensome. Apart from a technological infrastructure that needs to store data about each possible combination of items and user, thereby generating substantial server load, a critical mass of users is one of the main roadblocks towards successful implementation (Glance, Arregui, & Dardenne, 1999). If the community of people who generate data is too small, recommendations become less precise. This begs the question of whether
A psychological account of educational recommender systems
Much attention on recommender systems has been devoted to issues of technical implementation, mathematical modeling, and performance metrics (Adomavicius & Tuzhilin, 2005). However, there is a growing awareness that non-technical issues should be taken into account in order to improve personalized recommender systems, particularly if these systems are applied in non-standard settings like education. Consequently, some authors began theorizing about recommender system by including educational
Conclusions
This paper explored the potentials of personalized recommender systems in educational settings. It is argued that recommender systems fit nicely to important principles in the learning sciences: (1) Recommender systems are peer technologies that shift responsibility away from dedicated experts. (2) Recommender systems are technologies where the quality of content is not traceable to any individual output, but rather to the collective behavior of a community. (3) Recommender systems provide user
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