28(2012)207-21 6 instance, a message becomes more persuasive if a source is recommendations can help to facilitate critical thinking. This leads ity(Brinol& Petty2009; to our second conjecture. ieve social adaptation for recipients? This can be ing biases in human information pro- accept strong arguments and cues and authority at run counter to lese items is a bet- /than just making preference-inconsistent items avail- eference-inconsistent recommendations is at try to challenge a learner's stems and the learning sciences r systems are peer technologies through the input of many indi-instance, a message becomes more persuasive if a source is perceived as having expertise or authority (Briñol & Petty, 2009; Cialdini, 2001). Recommender systems can trigger expertise cues by providing recommendations on a wide range of topics, and authority cues can be generated through third-party endorse￾ments, reference to awards, or explanations that the recommended items were suggested by experts. If an educational recommender system is based on a pool of strong arguments, and if the system generates authority cues and/or expertise cues, it is likely to be￾come persuasive irrespective of learner characteristics like motiva￾tion and ability. While persuasion makes productive use of information process￾ing biases like the tendency to follow strong arguments, or author￾ity-endorsed arguments, biases can also represent a hindrance to certain forms of learning. For instance, a robust finding in the liter￾ature on communication science holds that people are prone to selective exposure, i.e. they attend to only parts of the information that is presented to them (Knobloch-Westerwick & Meng, 2009). In particular, many people exhibit confirmation bias, the tendency to actively seek for information that confirms initial preferences (Jonas, Schulz-Hardt, Frey, & Thelen, 2001). The reason for this bias can be traced back to dissonance theory (Festinger, 1954) which posits that people tend to avoid stimuli that create cognitive disso￾nance. While confirmation bias is rarely addressed in the learning sciences, we believe that it can play a pivotal role in those areas of education where the goal is to challenge existing beliefs and opin￾ions. One such area is critical and open-minded thinking which in￾volves that learners question not only other opinions, but also their own opinion (Stanovich & West, 1997). Critical thinking and unbi￾ased reasoning can be linked to educationally relevant constructs like multiperspectivity (Spiro & Jehng, 1990) and informational diversity (De Wit & Greer, 2008). However, in order to become crit￾ical thinkers, learners must overcome confirmation bias, but a clas￾sical recommender system would do little to avert this bias, as it would suggest items that are consistent to a learner’s preference. What would be needed, then, is a recommender system that does the opposite, i.e. trying to capture the preferred opinions of learn￾ers, and confronting them with opposing viewpoints. For instance, if the student in our digital library example wants to write her Masters thesis on a particular theory, it might be useful to recom￾mend at least some publications that are critical of this theory. The efficiency of preference-inconsistent recommendations in critical thinking contexts was investigated in our empirical work (Schwind, Buder, & Hesse, 2011a; Schwind, Buder, & Hesse, 2011b). Our experimental paradigm involved presenting prefer￾ence-consistent and preference-inconsistent information to learn￾ers who searched for information on the controversial topic of neuro-enhancement. Simply making preference-inconsistent infor￾mation available was no sufficient strategy to overcome confirma￾tion bias, as participants in a no-recommendation control group selected preference-consistent information more frequently than preference-inconsistent information. However, when preference￾inconsistent information was not only made available, but was rec￾ommended through visual highlighting, confirmation bias was strongly reduced. Moreover, preference-inconsistent recommen￾dations improved elaboration, as exemplified by a less confirma￾tion-biased item recall, and by more divergent thinking patterns in subsequent essays. However, our studies have also shown that preference-inconsistent items were less liked by learners than preference-consistent items, a finding that mirrors Tang and McCalla’s (2005) viewpoint according to which information that is most useful from an educational perspective is often not the one that is liked most. While this problem might be averted by making preference-inconsistent recommendations more appealing (e.g. by verbally framing them as ‘‘challenges’’), our empirical results are promising signs that these counter-intuitive recommendations can help to facilitate critical thinking. This leads to our second conjecture. 3.1.2.1. How to achieve social adaptation for recipients? This can be accomplished by considering biases in human information pro￾cessing. Learners are more likely to accept strong arguments and arguments that are accompanied by expertise cues and authority cues. Detrimental processing biases like confirmation bias can be mitigated by explicitly recommending items that run counter to a learner’s preference. Actually recommending these items is a bet￾ter strategy than just making preference-inconsistent items avail￾able. The use of preference-inconsistent recommendations is helpful for educational settings that try to challenge a learner’s existing viewpoints and beliefs. 3.2. Producer role In Section 2 on recommender systems and the learning sciences it was argued that recommender systems are peer technologies that exhibit collective intelligence through the input of many indi￾viduals. However, in order to generate accurate predictions and unfold collective intelligence, recommender systems are strongly dependent on data that express how users think about a given item. Consequently, the role of users as producers of these data is a central issue in research on personalized recommender sys￾tems. In Sections 3.2.1 and 3.2.2 it is explored how recommender systems should be adapted to cater to the peculiarities of educa￾tional scenarios. Like in the section on the recipient role, the dis￾cussion is structured by two issues. The first issue pertains to system-centered adaptation, and it is guided by the design consid￾eration of whether a recommender system should use explicit lear￾ner output like ratings, or whether implicit methods of capturing user data should be preferred. The second issue on social adapta￾tion then explores how detrimental learner behaviors like low con￾tribution to a recommender system can be averted by making productive use of those biases that are conducive to the production of rating data. 3.2.1. System-centered adaptation Recommender systems rely on the output from users. This cre￾ates a basic design decision of how these user data can be yielded. There are two fundamental ways to accomplish this goal, viz. im￾plicit vs. explicit elicitation methods (Xiao & Benbasat, 2007). Im￾plicit elicitation requires capturing user navigation (site visits, dwell times on sites, purchases) and take these data as indicators of user preferences. In contrast, explicit elicitation requires a ded￾icated response of users, typically in the form of ratings on items. There are a number of advantages associated with implicit prefer￾ence elicitation: It is unobtrusive, i.e. it does not burden a user with the additional task of rating, thereby reducing the cold start prob￾lems that occur until the system has gathered enough preference data to yield accurate predictions (Schein, Popescul, Ungar, & Pennock, 2002). Moreover, implicit preference elicitation might be more objective than explicit ratings, as it does not involve a bias of users to respond in a socially desirable way (Fisher, 1993). How￾ever, explicit rating methods have a number of advantages as well. Kramer (2007) reported that explicit methods of eliciting user pref￾erences led to higher acceptance rates for recommendations than implicit and opaque methods. This led Xiao and Benbasat (2007) to conclude that explicit methods might help users to gain insights on their preferences and therefore increases decision quality. Im￾plicit methods might lead to psychological reactance, a negative reaction to a restriction of autonomy (Dillard & Shen, 2005), whereas explicit ratings are associated with user control, which in turn is related to satisfaction and trust in recommender systems (McNee et al., 2003). Another potential advantage of explicit rating 212 J. Buder, C. Schwind / Computers in Human Behavior 28 (2012) 207–216