Overview of the special issue
On one hand, there are increasing amounts of digital content and online educational resources readily available to both educators and learners; on the other hand, various social learning tools act as more efficient channels to distribute useful resources and shared learning experiences. Hence, filtering information to tailor group-wise and individual interests and forming learning groups have emerged as important issues for both educators and learners alike. Recommender Systems are known to implicitly or explicitly observe users’ online activities, learn their likes and dislikes and make personalized suggestions accordingly (Adomavicius & Tuzhilin, 2005; Balabanovic & Shoham, 1997; Burke, 2002). They have been extensively studied in domains other than education.
In educational domains, recommendations should be made not only to suit learners’ interests, but also to keep them engaged and pedagogically motivated throughout the learning process (Tang & McCalla, 2005, 2009), as understanding learners’ pedagogical needs is key to delivering individualized learning materials (Verpoorten, Glahn, Kravcik, Ternier, & Specht, 2009). In addition, Recommender Systems can be used to guide collaborative interactions in learning settings, namely by supporting group members’ metacognitive learning activities (Soller, Martínez-Monéz, Jermann, & Muehlenbrock, 2005). With regard to Recommender Systems, the system’s assessment of the current state of the interaction between the learners is hidden from the learners. It is only used by the system itself to calculate guiding rules which are provided to the learners.
In comparison to the Recommender System, Group Awareness Tools exist that also gather data about learners’ interaction, but they provide this information to the group members. Group Awareness is defined as “consciousness and information of various aspects of the group and its members” (Gross, Stary, & Totter, 2005, p. 327). Instead of providing group members with direct instructions, Group Awareness Tools provide group members with relevant information, mostly by means of visualizations, about their collaborators, the collaborators’ activity, the situation, or specific processes and occurrences in the group (Gutwin & Greenberg 2002). It is then up to the learners to interpret these visualizations and to choose suitable activities (Gutwin & Greenberg, 2002; Soller et al., 2005).
The difference between these two systems lies in the locus of processing: Group Awareness Tools place the locus of processing at the user level, whereas in Recommender Systems the processing is done directly by the system (Soller et al., 2005).
Because Recommender Systems assess user data objectively, less mistakes should occur; that is, it is expected that the systems make correct diagnoses and recommend appropriate actions. In Recommender Systems there is a low cognitive load for the learners because they are directly and automatically guided in their actions. However, because of missing feelings of autonomy, reactance effects may emerge that may hinder learning. While working with Group Awareness Tools learners need to interpret the visualized information. This could result in mistakes and therefore in wrong decisions and it also could increase cognitive load. However, it could also motivate learners in their learning activities because they feel autonomous, due to the fact that their behavior is their own decision (Deci & Ryan, 1993).
This fact leads us to question in which learning situations should Recommender Systems be used to increase group effectiveness and efficiency, and in which learning settings should Group Awareness Tools be applied? In addition, the question is whether and how the two support possibilities can benefit from each other? Is it meaningful to combine aspects of both types of support?
In this special issue, we solicit original research and experience papers on challenging as well as novel issues concerning Recommender Systems and Group Awareness Tools in collaborative social learning environments. We are especially interested in papers documenting how the integration of Group Awareness Tools and Recommender Systems can promote the overall acceptance of given recommendations, which in turn encourages learner participation in the learning process.
Topics of interest
The topics of interest include but are not limited to:
· Group Recommendation and Group Awareness Tools in education and learning settings
· Group Awareness Tools and Group Recommendation for early childhood or special education
· The pre- and post-assessment of recommendations and group awareness
· Usability of educational Recommender Systems and Group Awareness Tools
· Visualizations of recommended items and of group aspects to made aware to the others in practice
· Recommender Systems and Group Awareness Tools for educators
· Case studies of educational recommender system implementations and group awareness implementations
Submission of Papers: June, 15th 2013
First review result: July, 31st 2013
Revised manuscripts due: September 15th, 2013
Second round of review result: September, 30th 2013
Final manuscripts due: October 15th, 2013
1. Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extension. IEEE Transactions on Knowledge and Data Engineering, 17(6): 734-749, June 2005.
2. Balabanovic, M. and Shoham, Y. (1997). Fab: content-based collaborative recommendation. Communications of the ACM, 40(3): 66-72. 1997.
3. Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4): 331-370.Adomavicius, G. and Tuzhilin, A. (2005).
4. McNee, S., Riedl, J. and J.A. Konstan. (2006). Being accurate is not enough: how accuracy metrics have hurt recommender systems. In the Extended Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems (CHI 2006), Montreal, Canada, 1097-1101.
5. Tang, T. Y., and McCalla, G.I. (2005). Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning 4 (1): 105-129
6. T. Y. Tang and G. I. McCalla (2009). A multidimensional paper recommender: experiments and evaluations. IEEE Internet Computing, 13(4): 34-41.
7. Verpoorten, D., Glahn, C., Kravcik, M., Ternier, S., & Specht, M. (2009). Personalization of Learning in Virtual Learning Environments. In U. Cress, V. Dimitrova & M. Specht (Eds.), Learning in the Synergy of Multiple Disciplines, LNCS 5794. Berlin and Heidelberg, Germany.
8. Harding-Smith, T. (1993). Learning together: An introduction to collaborative learning. New York, NY: HarperCollins College Publishers.
9. Chiu, M. M. (2008). Effects of argumentation on group micro-creativity. Contemporary Educational Psychology, 33, 383 – 402.
10. Chen, G., & Chiu, M. M. (2008). Online discussion processes. Computers and Education, 50, 678 – 692.
11. Ismail, I.S. (2009) Weblog: A collaborative tool for learning academic reading. International Journal of Learning 16 (7), pp. 173-182.
12. Mahadi, N., Ubaidullah, N.H. (2010) Social networking sites: Opportunities for language teachers. International Journal of Learning 17 (6), pp. 313-324.
13. Swearingen, K. and Sinha, R. (2001). Beyond algorithms, an HCI perspective on recommender systems. Workshop on Recommender Systems at the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’01).
14. Deci, E.L. & Ryan, R.M. (1993). Die Selbstbestimmungstheorie der Motivation und ihre Bedeutung für die Pädagogik. Zeitschrift für Pädagogik, 39, 223-238.
15. Gross, T., Stary, C., & Totter, A. (2005). User-Centered Awareness in Computer-Supported Cooperative Work-Systems: Structured Embedding of Findings from Social Sciences. International Journal of Human-Computer Interaction, 18, 323-360.
16. Gutwin, C., & Greenberg, S. (2002). A descriptive framework of workspace awareness for real-time groupware. Computer Supported Cooperative Work, 11, 411–446
17. Soller, A., Martínez-Monés, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261-290.
18. Deci, E.L. & Ryan, R.M. (1993). Die Selbstbestimmungstheorie der Motivation und ihre Bedeutung für die Pädagogik. Zeitschrift für Pädagogik, 39, 223-238.
Manuscripts should be prepared in APA style. The Instructions for Authors is available at http://baywood.com/authors/ia/ec.asp?id=0735-6331. Please submit your manuscript via email. Set the first words of the Subject to: “Submitted to Recommender Systems and Group Awareness in Collaborative Social Learning Environments special issue”.