Computers in Human Behavior 28(2012)207-216 Contents lists available at SciVerse Science Direct Computers in Human behavior ELSEVIER journalhomepagewww.elsevier.com/locate/comphumbeh Learning with personalized recommender systems: A psychological view Jurgen Buder Christina Schwind Knowledge Media Research Center, Konrad-Adenauer-Str. 40. 72072 Tubingen, Germany ARTICLE INFO A BSTRACT This paper explores the potentials of recommender systems for learning from a psychological point of Available online 29 September 2011 view. It is argued that main features of recommender systems(collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts recommender systems n a one-to-one basis, but rather need adaptations in order to facilitate learn are discussed both with regard to learners as recipients of information and learn cers of data Moreover, it is distinguished between system-centered adaptations that enal In educational contexts, and social adaptations that address typical information tions for the design of educational recommender systems and for research on e 2011 Elsevier Ltd. All rights reserved. 1 Introduction unrated items can be predicted. A common method to predict pref erences is through collaborative filtering( Sarwar, Karypis, Konstan, When we ponder over the movie that we would like to see next Riedl, 2001) which mostly comes in two varieties: In user-based weekend, or whether the new restaurant in town is worth checking filtering, the behavioral profile of a target user will be compared to out, we often rely on the experience and recommendations of the profiles of other users, and recommendations for a particulai friends and other people who we trust to be knowledgeable about item will be derived from those users who are most similar to our tastes and preferences. Getting good recommendations be- the target user. The second method is item-based filtering where comes an important issue when the number of viable options is the overall rating differences among items will be set against the too large to be perused by an individual person. Internet servers profile of a target user to arrive at personalized recommendations provide access to vast amounts of information, and consequently. Personalized recommender systems are often used in offering recommendations is one of the most pressing problems e-commerce(Schafer, Konstan, riedl, 1999). as the ability to sug for the design of electronic environments. It can be said that search gest products that are tailored to the needs and preferences of cus- engines provide recommendations, as a list of search results is or- tomers provides a unique selling point. However, in recent years dered through link analysis algorithms that show most linked-to, the potential of personalized recommender systems for non-com- and thereby most relevant Web pages on top (brin Page, 1998). mercial purposes has begun to be explored, e.g. in educational con- Similarly, a bestseller list on a commercial Website can be regarded texts. Several educational recommender systems have been as providing recommendations. However, in these cases the rec- designed that recommend a broad range of items, among them soft- ommendations are generic, i.e. different users receive the same ware functionalities( Linton& Schaefer, 2000). learning resources e or highly similar output. In contrast, personalized recommender the Web(geyer-Schulz, Hahsler, Jahn, 2001; Recker, Walker, stems try to achieve the gold standard of recommendations in Lawless, 2003), Web 2.0 resources( Drachsler et al., 2010), foreign able about a topic, but also takes the individual tastes and prefer- McGrath, Ball, 2005), test items and assignments( Rafaeli, Barak, ences of users into account an-Gur,& Toch, 2004), lecture notes(Farzan Brusilovsk Personalized recommender systems capture the traces that 2005 ) or entire courses(Farzan Brusilovsky, 2006). The applica users leave in an environment, either through page visits or expli- tions cover very different areas of learning and education like use it ratings of items, and they are based on the assumption that of library systems(Geyer-Schulz, Hahsler, Neumann, Thede page visits or high ratings are indicative of user preferences. From 2003), informal learning( Drachsler, Hummel, Koper, 2009), m ta about visited or rated items, preferences on not-visited or bile learning(Andronico et al, 2003), learning at the workplace (Aehnelt, Ebert, Beham, Lindstaedt, Paschen, 2008), or within health education( Fernandez-Luque, Karlsen, Vognild, 2009). Corresponding author. Tel. +49 7071 979 326: fax: +49 7071 979 100. E-imail addresses: ibudereiwm-kmrc de (. Buder), schwind @iwm-kmrc de(c. Many papers on personalized recommender systems focus on technical issues and problems, the ultimate question being: How 0747-5632/s- see front matter o 2011 Elsevier Ltd. All rights reserved doi:10.1016/chb2011.09002Learning with personalized recommender systems: A psychological view Jürgen Buder ⇑ , Christina Schwind Knowledge Media Research Center, Konrad-Adenauer-Str. 40, 72072 Tübingen, Germany article info Article history: Available online 29 September 2011 Keywords: Recommender systems Learning abstract This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implica￾tions for the design of educational recommender systems and for research on educational recommender systems are discussed. 2011 Elsevier Ltd. All rights reserved. 1. 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 be￾comes 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 or￾dered 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 rec￾ommendations 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 knowledge￾able about a topic, but also takes the individual tastes and prefer￾ences of users into account. Personalized recommender systems capture the traces that users leave in an environment, either through page visits or expli￾cit 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 pref￾erences 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 sug￾gest products that are tailored to the needs and preferences of cus￾tomers provides a unique selling point. However, in recent years the potential of personalized recommender systems for non-com￾mercial purposes has begun to be explored, e.g. in educational con￾texts. Several educational recommender systems have been designed that recommend a broad range of items, among them soft￾ware functionalities (Linton & Schaefer, 2000), learning resources on the Web (Geyer-Schulz, Hahsler, & Jahn, 2001; Recker, Walker, & Lawless, 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 applica￾tions 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), mo￾bile 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 0747-5632/$ - see front matter 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2011.09.002 ⇑ Corresponding author. Tel.: +49 7071 979 326; fax: +49 7071 979 100. E-mail addresses: j.buder@iwm-kmrc.de (J. Buder), c.schwind@iwm-kmrc.de (C. Schwind). Computers in Human Behavior 28 (2012) 207–216 Contents lists available at SciVerse ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh