J Buder, C Schwind/Computers in Human Behavior 28(2012)207-216 Community 1. Collective responsibility Il. Collective intelligence Rating database System aggregate filter Individual Recommender interface V, Personalization IV Guidance lll, User control Fig. 1. Flow chart of the recommendation process. Principles of recommender systems are embedded. that also stress the importance of self-regulated learning 3. A psychological account of educational recommender (Boekaerts Minnaert, 1999), or discovery learning(Bruner, 1961). systems Fourth, recommender systems provide guidance. The student in the digital library example is not faced with a huge list of all pub Much attention on recommender systems has been devoted to lications on her thesis topic, but already receives a filtered list of issues of technical implementation, mathematical modeling, and ose titles that are most relevant to her search. By giving direc performance metrics(Adomavicius Tuzhilin, 2005 ) However. tions and offering hints that a user may or may not take into ac- there is a growing awareness that non-technical issues should be count, recommender systems are equivalent to an information taken into account in order to personalized recommender signpost(Konstan& Riedl, 2003). Providing guidance is also a cen- systems, particularly if these systems are applied in non-standard tral issue in the learning sciences as too much learner autonomy settings like education. Consequently, some authors began theoriz can be perceived as burdensome without some form of explicit ing about recommender system by including educational consider- or implicit structuring. As a consequence, principles in the learning ations(Drachsler, Hummel, et al, 2009: Manouselis, Drachsler, sciences often suggest using scaffolds(Vygotsky, 1978). scripts Vuorikari, Hummel, Koper, 2011: Tang McCalla, 2005: Wang (Kollar, Fischer, Hesse, 2006), or awareness functionalities 2007). The present paper also focuses on recommender systems (Engelmann, Dehler, Bodemer, Buder, 2009)in order to provide in educational contexts, but it is novel in taking a psychological uidance for self-regulated activities. The key is to strike a delicate point of view on the topic. relatively little is known about ho lance between autonomy and guidance so that guidance neither people react to and act upon information presented via recon becomes too immaterial nor too directive mender systems, and a psychological account might offer valuable Fifth, recommender systems are personalized, hints on barriers and potentials. gest items that are adaptively tailored to the nee and preferences of a user. As mentioned in the light on various issues that have to be taken into account when example, the recommendations for our student were not a gen- designing for educational recommender systems. The account is eric, bestseller-like list of most popular publications, but con- structured along a distinction that was made by Xiao and Benbasat sisted of items that were personalized with regard to her taste. (2007)in a conceptual paper on recommender systems in The notion of personalization also plays an important role in e-commerce contexts. However, while these authors put the tech- the learning sciences: Different learners do not benefit to the nology into the center by distinguishing between input character same degree from uniform types of instruction(Cronbach istics (data that a recommender system gets)and output Snow, 1977), and there is general consensus that instructional characteristics(data that a recommender system displays), we material should be adapted to the knowledge, the needs, and make the same distinction from a learner viewpoint. In other the abilities of learners. Consequently, learning technologies such words, our account distinguishes between a recipient role where as intelligent tutoring systems(Anderson, Boyle, Reiser, 1985) learners are confronted with recommended items and a producer or adaptive hypermedia environments(Brusilovsky, 2001)tailor role where learners generate data that are the basis for system information to the needs and abilities of learners. Recommender computations. The distinction between different roles(recipient systems are based on the same general idea by matching their vS producer)serves as a structural element for the remainder of output to a users historically developed profile this paper. For each role, two issues of recommender system adap Of course, the identified principles of the learning sciences- tation for educational contexts will be discussed The first issue re shifting responsibility towards peers, harnessing collective intelli fers to system-centered adaptations: In order to work properly gence,enabling user control, providing scaffolds, and tailoring to educational contexts, recommender systems must provide the needs, abilities, and interests- are embedded within many infor- right kind of information so that learning from recommendations mation technologies, but personalized recommender systems com- is enabled(recipient role). Moreover, proper functioning of recom- bine all of these principles. In this regard, they exhibit features that mender systems requires that user generate data on which system have the potential to leverage learning processes. computations can be performed(producer role). Apart from these However, the fit of recommender systems into learning con- basic, system-centered adaptations the second issue explored for texts by no means implies that they can be transferred from their recipient roles and producer roles pertains to social adaptation current, mostly commercial context into educational contexts on a Human information processing in general, and learning in particu one-to-one basis. Rather, they must be adapted to the peculiarities lar can be characterized by bounded rationality(Simon, 1959) of educational scenarios. Section 3 addresses the issues that have Navigation and selection of items in a recommender system(red to be taken into account in order to fruitfully apply recommender pient role)and rating of items (producer role) are influenced by a tems in the educational realm number of social psychological factors that can be linked tothat also stress the importance of self-regulated learning (Boekaerts & Minnaert, 1999), or discovery learning (Bruner, 1961). Fourth, recommender systems provide guidance. The student in the digital library example is not faced with a huge list of all pub￾lications on her thesis topic, but already receives a filtered list of those titles that are most relevant to her search. By giving direc￾tions and offering hints that a user may or may not take into ac￾count, recommender systems are equivalent to an information signpost (Konstan & Riedl, 2003). Providing guidance is also a cen￾tral issue in the learning sciences as too much learner autonomy can be perceived as burdensome without some form of explicit or implicit structuring. As a consequence, principles in the learning sciences often suggest using scaffolds (Vygotsky, 1978), scripts (Kollar, Fischer, & Hesse, 2006), or awareness functionalities (Engelmann, Dehler, Bodemer, & Buder, 2009) in order to provide guidance for self-regulated activities. The key is to strike a delicate balance between autonomy and guidance so that guidance neither becomes too immaterial nor too directive. Fifth, recommender systems are personalized, i.e. they sug￾gest items that are adaptively tailored to the needs, interests, and preferences of a user. As mentioned in the digital library example, the recommendations for our student were not a gen￾eric, bestseller-like list of most popular publications, but con￾sisted of items that were personalized with regard to her taste. The notion of personalization also plays an important role in the learning sciences: Different learners do not benefit to the same degree from uniform types of instruction (Cronbach & Snow, 1977), and there is general consensus that instructional material should be adapted to the knowledge, the needs, and the abilities of learners. Consequently, learning technologies such as intelligent tutoring systems (Anderson, Boyle, & Reiser, 1985) or adaptive hypermedia environments (Brusilovsky, 2001) tailor information to the needs and abilities of learners. Recommender systems are based on the same general idea by matching their output to a user’s historically developed profile. Of course, the identified principles of the learning sciences – shifting responsibility towards peers, harnessing collective intelli￾gence, enabling user control, providing scaffolds, and tailoring to needs, abilities, and interests – are embedded within many infor￾mation technologies, but personalized recommender systems com￾bine all of these principles. In this regard, they exhibit features that have the potential to leverage learning processes. However, the fit of recommender systems into learning con￾texts by no means implies that they can be transferred from their current, mostly commercial context into educational contexts on a one-to-one basis. Rather, they must be adapted to the peculiarities of educational scenarios. Section 3 addresses the issues that have to be taken into account in order to fruitfully apply recommender systems in the educational realm. 3. 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 theoriz￾ing about recommender system by including educational consider￾ations (Drachsler, Hummel, et al., 2009; Manouselis, Drachsler, Vuorikari, Hummel, & Koper, 2011; Tang & McCalla, 2005; Wang, 2007). The present paper also focuses on recommender systems in educational contexts, but it is novel in taking a psychological point of view on the topic. Relatively little is known about how people react to and act upon information presented via recom￾mender systems, and a psychological account might offer valuable hints on barriers and potentials. In the following, we propose a conceptualization that sheds a light on various issues that have to be taken into account when designing for educational recommender systems. The account is structured along a distinction that was made by Xiao and Benbasat (2007) in a conceptual paper on recommender systems in e-commerce contexts. However, while these authors put the tech￾nology into the center by distinguishing between input character￾istics (data that a recommender system gets) and output characteristics (data that a recommender system displays), we make the same distinction from a learner viewpoint. In other words, our account distinguishes between a recipient role where learners are confronted with recommended items and a producer role where learners generate data that are the basis for system computations. The distinction between different roles (recipient vs. producer) serves as a structural element for the remainder of this paper. For each role, two issues of recommender system adap￾tation for educational contexts will be discussed. The first issue re￾fers to system-centered adaptations: In order to work properly in educational contexts, recommender systems must provide the right kind of information so that learning from recommendations is enabled (recipient role). Moreover, proper functioning of recom￾mender systems requires that user generate data on which system computations can be performed (producer role). Apart from these basic, system-centered adaptations the second issue explored for recipient roles and producer roles pertains to social adaptations. Human information processing in general, and learning in particu￾lar can be characterized by bounded rationality (Simon, 1959). Navigation and selection of items in a recommender system (reci￾pient role) and rating of items (producer role) are influenced by a number of social psychological factors that can be linked to Fig. 1. Flow chart of the recommendation process. Principles of recommender systems are embedded. J. Buder, C. Schwind / Computers in Human Behavior 28 (2012) 207–216 209