![](bga.png)
on knowledge transfer effectiveness in multinational
corporations. Journal of International Management,
15(1):1–14.
Barak, M. (2015). The same mooc delivered in two lan-
guages: Examining knowledge construction and mo-
tivation to learn. Proceedings of the EMOOCS, pages
217–223.
Bousbahi, F. and Chorfi, H. (2015). Mooc-rec: a case based
recommender system for moocs. Procedia-Social and
Behavioral Sciences, 195:1813–1822.
Bouzayane, S. and Saad, I. (2017a). Prediction method ba-
sed drsa to improve the individual knowledge appro-
priation in a collaborative learning environment: Case
of moocs. In Proceedings of the 50th Hawaii Inter-
national Conference on System Sciences, pages 124–
133.
Bouzayane, S. and Saad, I. (2017b). A preference orde-
red classification to leader learners identification in a
mooc. Journal of Decision Systems, 26(2):189–202.
Bouzayne, S. and Saad, I. (2017). Incremental updating al-
gorithm of the approximations in drsa to deal with the
dynamic information systems of moocs. international
conference on Knowledge Management, Information
and Knowledge Systems (KMIKS), pages 55–66.
Burke, R. (2002). Hybrid recommender systems: Survey
and experiments. User modeling and user-adapted in-
teraction, 12(4):331–370.
Dascalu, M.-I., Bodea, C.-N., Moldoveanu, A., Mohora, A.,
Lytras, M., and de Pablos, P. O. (2015). A recommen-
der agent based on learning styles for better virtual
collaborative learning experiences. Computers in Hu-
man Behavior, 45:243–253.
Duan, L., Street, W. N., and Xu, E. (2011). Healthcare in-
formation systems: data mining methods in the cre-
ation of a clinical recommender system. Enterprise
Information Systems, 5(2):169–181.
Eastmond, D. V. (1994). Adult distance study through com-
puter conferencing. Distance Education, 15(1):128–
152.
Gooderham, P. N. (2007). Enhancing knowledge transfer
in multinational corporations: a dynamic capabilities
driven model. Knowledge Management Research &
Practice, 5(1):34–43.
Greco, S., Matarazzo, B., and Slowinski, R. (2001). Rough
sets theory for multicriteria decision analysis. Euro-
pean Journal of Operational Research, 129(1):1–45.
Grundstein, M. (2009). Gameth
R
: a constructivist and le-
arning approach to identify and locate crucial know-
ledge. International Journal of Knowledge and Lear-
ning, 5(3-4):289–305.
Guti
´
errez-Rojas, I., Leony, D., Alario-Hoyos, C., P
´
erez-
Sanagust
´
ın, M., and Delgado-Kloos, C. (2014). To-
wards an outcome-based discovery and filtering of
moocs using moocrank. Proceedings of the Second
MOOC European Stakeholders Summit, pages 50–57.
Hoens, T. R., Blanton, M., Steele, A., and Chawla, N. V.
(2013). Reliable medical recommendation systems
with patient privacy. ACM Transactions on Intelligent
Systems and Technology (TIST), 4(4):67.
Hone, K. and El Said, G. (2016). Exploring the factors af-
fecting mooc retention: A survey study. Computers &
Education, 98:157–168.
Kim, K.-j. and Ahn, H. (2008). A recommender sy-
stem using ga k-means clustering in an online shop-
ping market. Expert systems with applications,
34(2):1200–1209.
Kloft, M., Stiehler, F., Zheng, Z., and Pinkwart, N. (2014).
Predicting mooc dropout over weeks using machine
learning methods. In Proceedings of the EMNLP
2014 Workshop on Analysis of Large Scale Social In-
teraction in MOOCs, pages 60–65.
Labarthe, H., Bachelet, R., Bouchet, F., and Yacef,
K. (2016a). Increasing mooc completion rates
through social interactions: a recommendation sy-
stem. EMOOCs, Research Track, pages 471–480.
Labarthe, H., Bouchet, F., Bachelet, R., and Yacef, K.
(2016b). Does a peer recommender foster students’
engagement in moocs? In 9th International Confe-
rence on Educational Data Mining, pages 418–423.
Li, S.-W. D. and Mitros, P. (2015). Learnersourced recom-
mendations for remediation. In Advanced Learning
Technologies (ICALT), 2015 IEEE 15th International
Conference on, pages 411–412. IEEE.
Onah, D. F. and Sinclair, J. (2015). Collaborative filte-
ring recommendation system: a framework in massive
open online courses. INTED2015 Proceedings, pages
1249–1257.
Onah, D. F. O., Sinclair, J., and Boyatt, R. (2014). Dropout
rates of massive open online courses: behavioral pat-
terns. 6th international conference on education and
new learning technologies, published in: Edulearn14
proceedings, pages 5825–5834.
Patru, M. and Balaji, V. (2016). Making sense of moocs:
a guide for policy makers in developing countries.
Technical report, UNESCO.
Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction
to recommender systems handbook. Springer.
Roy, B. and Mousseau, V. (1996). A theoretical frame-
work for analysing the notion of relative importance
of criteria. Journal of Multi-Criteria Decision Analy-
sis, 5:145–159.
Sartori, F., Melen, R., and Pinardi, S. (2018). Cultiva-
ting virtual communities of practice in kafka. Data
Technologies and Applications, 52(1):34–57.
Symeonidis, P. and Malakoudis, D. (2016). Moocrec. com:
Massive open online courses recommender system.
Welch, D. E. and Welch, L. S. (2008). The importance of
language in international knowledge transfer. Mana-
gement International Review, 48(3):339–360.
Wu, D., Zhang, G., and Lu, J. (2015). A fuzzy prefe-
rence tree-based recommender system for personali-
zed business-to-business e-services. IEEE Transacti-
ons on Fuzzy Systems, 23(1):29–43.
Yang, D., Piergallini, M., Howley, I., and Rose, C. (2014).
Forum thread recommendation for massive open on-
line courses. In Educational Data Mining 2014, pages
257–260.
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