10 J Buder, C Schwind/ Computers in Human Behavior 28(2012)207-216 bounded rationality. For instance, we do not always attend to the fact that humans show preferences for particular types of informa- Ther munity could benefit from such an activity (DaM.. 2005): tion, and these inherent biases are not always conducive to information from which we learn most (tang Mccalla and we do not always contribute information even if an entire learning. Several ways of adapting recommender systems are explored that are based on ideas such as increasing the persua erefore, educational recommender systems can b siveness of recommendations, or providing counter-intuitive by introducing social adaptations that facilitate those inf recommendations processing biases that are conducive to learning or attenu biases that are detrimental to learning. These distinctions result in four structural elements(recipient 3.1.1. System-centered adaptation role vs producer role: system-centered adaptation vs social adap Whereas classical recommender systems in e-commerce try to on)for the following sections. In Sections 3. 1 and 3. 2 these is- adapt to the taste of a user, educational recommender systems will be discussed based on theoretical considerations as well should be personalized with regard to learner knowledge and as empirical results from various fields of research. Table 1 gives learning activities. For a number of reasons, learner knowledge an overview of the literature that informed the following and learning activities are more difficult to assess than user taste (Drachsler, Hummel, Koper, 2009): Learning is a gradual process extending over a longer stretch of time. In commercial contexts. effectiveness of a recommender system can be assessed by captur 3. 1. Recipient role ng whether a customer has purchased a recommended item. In contrast, learning does not have clear-defined and measurable Relatively little is known about how recommendations are per-"learning events"that immediately provide information about re ceived by users. Sections 3.1.1 and 3. 1.2 describe issues pertaining ommender system effectiveness. Not only are constructs like to the learners'roles as recipients of information. First, Section 3. 1. 1 knowledge and activities difficult to assess, they are also con- on system-centered adaptation addresses the fact that in classical stantly changing, and they rest on multiple sequential dependen- e-commerce scenarios recommendations are tailored to user taste cies, i. e at any given time there can be items that are too easy or (Schafer et al., 1999). In contrast, for educational contexts recom- too difficult for a learner. This creates numerous situational con- endations must be tailored to learner knowledge and learner straints: An expert in a domain needs different recommendations activities. Second, Section 3. 1.2 on social adaptation refers to the than a novice: different learning styles (e.g. reproducing of reviewed studies about recommender systems. Field Finding ent role and system-centered adaptation Computer science Drachsler, Hummel, Koper Conceptua tional technology Reflects on differences between recommenders for learning vs. commerce Drachsler. Hummel. van den Nadolski et al. (2009) 2 Educational technology Hybrid system leads to higher efficiency in learning Computer science/ Collaborative filtering and hybrid systems outperform no recommendations Recipient role and social adaptation McNee et al. (2006) Makes a case that personalities are ascribed to recommender Schwind et al. (2011a) Educational psychology N=123) lower evaluation Schwind et al.(2011b) pirical (lab experiments, Educational psychology eference- inconsistency reduces confirmation bias and leads to N=210) Tang and McCalla (2005) y endations are not always liked st(preference-inconsistency) Yoo and Gretzel (2011) Social psychology Discusses persuasion of recommender systems through source characteristics Producer role and system-centered adaptation Task transparency leads to higher acceptance(ma McNee et al.(2003 HCI ser control in sign-up increases loyalty(makes a case for explicit Schein et al. (2002 al (simulatio Computer science gues for implicit elicitation to overcome cold-start Xiao and Benbasat(2007) Conceptu ntroduces distinction between implicit vs. explicit elicitation cer role and social adaptation riment, N=268 Herlocker et al. (2004) HCI Makes a case that motivation for contribution can differ strongly Ling et ts,N=2715) Ludford et aL (2004) Social psychology Utility instruction increases rating activity xperiment, N=245) Rashid et al. (2006) Empirical (field cl social psychology Utility interface increases rating activity experiment, N= 160) Note: Classifications into type of study and findings are reported only as they pertain to this pabounded rationality. For instance, we do not always attend to the information from which we learn most (Tang & McCalla, 2005); and we do not always contribute information even if an entire community could benefit from such an activity (Dawes, 1980). Therefore, educational recommender systems can be improved by introducing social adaptations that facilitate those information processing biases that are conducive to learning or attenuate those biases that are detrimental to learning. These distinctions result in four structural elements (recipient role vs. producer role; system-centered adaptation vs. social adap￾tation) for the following sections. In Sections 3.1 and 3.2 these is￾sues will be discussed based on theoretical considerations as well as empirical results from various fields of research. Table 1 gives an overview of the literature that informed the following discussion. 3.1. Recipient role Relatively little is known about how recommendations are per￾ceived by users. Sections 3.1.1 and 3.1.2 describe issues pertaining to the learners’ roles as recipients of information. First, Section 3.1.1 on system-centered adaptation addresses the fact that in classical e-commerce scenarios recommendations are tailored to user taste (Schafer et al., 1999). In contrast, for educational contexts recom￾mendations must be tailored to learner knowledge and learner activities. Second, Section 3.1.2 on social adaptation refers to the fact that humans show preferences for particular types of informa￾tion, and these inherent biases are not always conducive to learning. Several ways of adapting recommender systems are explored that are based on ideas such as increasing the persua￾siveness of recommendations, or providing counter-intuitive recommendations. 3.1.1. System-centered adaptation Whereas classical recommender systems in e-commerce try to adapt to the taste of a user, educational recommender systems should be personalized with regard to learner knowledge and learning activities. For a number of reasons, learner knowledge and learning activities are more difficult to assess than user taste (Drachsler, Hummel, & Koper, 2009): Learning is a gradual process extending over a longer stretch of time. In commercial contexts, effectiveness of a recommender system can be assessed by captur￾ing whether a customer has purchased a recommended item. In contrast, learning does not have clear-defined and measurable ‘‘learning events’’ that immediately provide information about rec￾ommender system effectiveness. Not only are constructs like knowledge and activities difficult to assess, they are also con￾stantly changing, and they rest on multiple sequential dependen￾cies, i.e. at any given time there can be items that are too easy or too difficult for a learner. This creates numerous situational con￾straints: An expert in a domain needs different recommendations than a novice; different learning styles (e.g. reproducing Table 1 Overview of reviewed studies about recommender systems. Study Type Field Finding Recipient role and system-centered adaptation Adomavicius and Tuzhilin (2011) Conceptual (review) Computer science Introduces context-aware algorithms Burke (2002) Empirical (simulation) Computer science Compares different hybrid recommender algorithms Drachsler, Hummel, & Koper (2009) Conceptual Educational technology Reflects on differences between recommenders for learning vs. commerce Drachsler, Hummel, van den Berg, et al. (2009) Empirical (field experiment, N = 250) Educational technology Hybrid system leads to higher efficiency in learning Nadolski et al. (2009) Empirical (simulation) Computer science/ educational technology Collaborative filtering and hybrid systems outperform no recommendations Recipient role and social adaptation McNee et al. (2006) Conceptual HCI Makes a case that personalities are ascribed to recommender systems (basis for persuasion) Schwind et al. (2011a) Empirical (online experiment, N = 123) Educational psychology preference-inconsistency reduces confirmation bias, but leads to lower evaluation Schwind et al. (2011b) Empirical (lab experiments, N = 210) Educational psychology Preference-inconsistency reduces confirmation bias and leads to better elaboration Tang and McCalla (2005) Conceptual Educational technology Argues that educational recommendations are not always liked most (preference-inconsistency) Yoo and Gretzel (2011) Conceptual Social psychology Discusses persuasion of recommender systems through source characteristics Producer role and system-centered adaptation Kramer (2007) Empirical (experiments; N = 363) Marketing Task transparency leads to higher acceptance (makes a case for explicit ratings) McNee et al. (2003) Empirical (field experiment, N = 163) HCI User control in sign-up increases loyalty (makes a case for explicit ratings) Schein et al. (2002) Empirical (simulation) Computer science Argues for implicit elicitation to overcome cold-start Xiao and Benbasat (2007) Conceptual Marketing Introduces distinction between implicit vs. explicit elicitation Producer role and social adaptation Harper et al. (2007) Empirical (field experiment, N = 268) Social psychology Social comparison increases rating activity Herlocker et al. (2004) Conceptual HCI Makes a case that motivation for contribution can differ strongly Ling et al. (2005) Empirical (field experiments, N = 2715) Social psychology Goal setting and utility instructions increase rating activity Ludford et al. (2004) Empirical (field experiment, N = 245) Social psychology Utility instruction increases rating activity Rashid et al. (2006) Empirical (field experiment, N = 160) HCI/social psychology Utility interface increases rating activity Note: Classifications into type of study and findings are reported only as they pertain to this paper. 210 J. Buder, C. Schwind / Computers in Human Behavior 28 (2012) 207–216