• MOHAMMAD TAHMASEBI Department of Computer Engineering, Faculty of Engineering, University of Qom and Yazd University
  • FARANAK FOTOUHI GHAZVINI Department of Computer Engineering and IT, Faculty of Engineering, University of Qom
  • MAHDI ESMAEILI Department of Computer Engineering, Faculty of Engineering, Azad University of Kashan


Educational recommender systems, Technology Enhanced Learning, Resourcebased learning, Index of Learning Styles, Recommender Systems for TEL, Web page attributes, Web page ranking


It is generally believed that recommender systems are a suitable key to overcome the information overload problem. In recent years, a special research area in this domain has emerged that concerns recommender systems for Technology Enhanced Learning, in particular, self-regulated learning with resources on the web, known as Resource-Based Learning. Grey-sheep users are a major challenge in RecsysTEL. This group of users have completely different opinions from other users. They do not profit from collaborative algorithms, so they must be supported in discovering learning resources relevant to their characteristics and needs. The main contribution of this work is to develop a feature-based educational recommender system which interacts with the user based on his or her learning style. The learning style dimensions would be determined based on Felder-Silverman theory. In addition, the system crawls and extracts the necessary meta-data of sample OCW’s web pages. Based on the proposed web page ranking formula, the user’s learning style dimension and web page feature’s vector would be accommodated to generate learning object suggestions. The general satisfaction, perception and motivation towards the proposed method measured among 77 science and engineering students by a questionnaire. Moreover, the system has been evaluated to provide feedbacks on its suitability. The research findings imply that the proposed method outperforms the general search algorithm. This system can be used as a template in formal and informal learning and educational environments as a RecsysTEL.


Download data is not yet available.


Manouselis, N., et al., Recommender Systems for Technology Enhanced Learning: Research

Trends and Applications. 2014: Springer Science & Business Media.300 Implementation and Evaluation of a Resource-based Learning Recommender based on ….

Ricci, F., L. Rokach, and B. Shapira, Introduction to recommender systems handbook. 2011:


Drachsler, H., et al., Panorama of recommender systems to support learning, in Recommender

systems handbook. 2015, Springer. p. 421-451.

Adomavicius, G. and A. Tuzhilin, Toward the next generation of recommender systems: A

survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE

Transactions on, 2005. 17(6): p. 734-749.

Bobadilla, J., et al., Recommender systems survey. Knowledge-Based Systems, 2013. 46: p.


Klašnja-Milićević, A., et al., E-Learning personalization based on hybrid recommendation

strategy and learning style identification. Computers & Education, 2011. 56(3): p. 885-899.

Manouselis, N., et al., RecSysTEL preface 2010. Procedia Computer Science, 2010. 1(2): p.


Verbert, K., et al., Context-aware recommender systems for learning: a survey and future

challenges. Learning Technologies, IEEE Transactions on, 2012. 5(4): p. 318-335.

Vesin, B., et al., Applying Recommender Systems and Adaptive Hypermedia for e-Learning

Personalizatio. Computing and Informatics, 2013. 32(3): p. 629-659.

Lam, X.N., et al. Addressing cold-start problem in recommendation systems. in Proceedings of

the 2nd international conference on Ubiquitous information management and communication.


Gras, B., A. Brun, and A. Boyer. Identifying Grey Sheep Users in Collaborative Filtering: a

Distribution-Based Technique. in Proceedings of the 2016 Conference on User Modeling

Adaptation and Personalization. 2016. ACM.

Ossenbruggen, J.R., Processing Structured Hypermedia-A Matter of Style. 2001.

Brusilovsky, P. and M.T. Maybury, From adaptive hypermedia to the adaptive web.

Communications of the ACM, 2002. 45(5): p. 30-33.

Brusilovsky, P., Methods and techniques of adaptive hypermedia. User modeling and useradapted

interaction, 1996. 6(2-3): p. 87-129.

Brusilovsky, P., A. Kobsa, and J. Vassileva, Adaptive hypertext and hypermedia. 1998:


Somyürek, S., The new trends in adaptive educational hypermedia systems. The International

Review of Research in Open and Distributed Learning, 2015. 16(1).

Henze, N. and W. Nejdl, Adaptation in open corpus hypermedia. International Journal of

Artificial Intelligence in Education, 2001. 12(4): p. 325-350.

Henze, N. and W. Nejdl. Logically characterizing adaptive educational hypermedia systems.

in International Workshop on Adaptive Hypermedia and Adaptive Web-based Systems (AH

. 2003.

Triantafillou, E., A. Pomportsis, and S. Demetriadis, The design and the formative evaluation

of an adaptive educational system based on cognitive styles. Computers & Education, 2003.

(1): p. 87-103.

Brusilovsky, P., Developing adaptive educational hypermedia systems: From design models to

authoring tools, in Authoring tools for advanced technology Learning Environments. 2003,

Springer. p. 377-409.

Kamceva, E. and P. Mitrevski, On the general paradigms for implementing adaptive e-learning

systems. ICT Innovations, 2012 Web Proceedings. Ohrid, Macedonia, 2012: p. 281-289.

Jannach, D., et al., Recommender systems: an introduction. 2010: Cambridge University Press.

Isinkaye, F., Y. Folajimi, and B. Ojokoh, Recommendation systems: Principles, methods and

evaluation. Egyptian Informatics Journal, 2015. 16(3): p. 261-273.

Tintarev, N. and J. Masthoff. A survey of explanations in recommender systems. in Data

Engineering Workshop, 2007 IEEE 23rd International Conference on. 2007. IEEE.

Verbert, K., et al., Context-aware recommender systems for learning: a survey and future

challenges. IEEE Transactions on Learning Technologies, 2012. 5(4): p. 318-335.

Balabanović, M. and Y. Shoham, Fab: content-based, collaborative recommendation.

Communications of the ACM, 1997. 40(3): p. 66-72.

Pazzani, M.J. and D. Billsus, Content-based recommendation systems, in The adaptive web.

, Springer. p. 325-341.

Lops, P., M. De Gemmis, and G. Semeraro, Content-based recommender systems: State of the

art and trends, in Recommender systems handbook. 2011, Springer. p. 73-105.

Su, X. and T.M. Khoshgoftaar, A survey of collaborative filtering techniques. Advances in

artificial intelligence, 2009. 2009: p. 4.

Herlocker, J.L., et al., Evaluating collaborative filtering recommender systems. ACM

Transactions on Information Systems (TOIS), 2004. 22(1): p. 5-53.

Burke, R., Hybrid recommender systems: Survey and experiments. User modeling and useradapted

interaction, 2002. 12(4): p. 331-370.

Burke, R., Hybrid web recommender systems, in The adaptive web. 2007, Springer. p. 377-408.

Castellano, G., A.M. Fanelli, and M.A. Torsello, NEWER: A system for NEuro-fuzzy WEb

Recommendation. Applied Soft Computing, 2011. 11(1): p. 793-806.

Drachsler, H., Navigation support for learners in informal learning networks. 2009.

Santos, O.C. and J.G. Boticario, Modeling recommendations for the educational domain.

Procedia Computer Science, 2010. 1(2): p. 2793-2800.

Paulsen, M.F., Experiences with Learning Management Systems in 113 European Institutions.

Educational Technology & Society, 2003. 6(4): p. 134-148.

Manouselis, N., et al., Recommender systems for learning. 2012: Springer Science & Business


Tang, T.Y. and G. McCalla. Smart recommendation for an evolving e-learning system. in

Workshop on Technologies for Electronic Documents for Supporting Learning, AIED. 2003.

Garcia-Molina, H., Flexible Recommendations in CourseRank, in On the Move to Meaningful

Internet Systems: OTM 2008. 2008, Springer. p. 7-7.

Felder, R.M. and L.K. Silverman, Learning and teaching styles in engineering education.

Engineering education, 1988. 78(7): p. 674-681.

Froschl, C., User modeling and user profiling in adaptive e-learning systems. Graz, Austria:

Master Thesis, 2005.

Chrysafiadi, K. and M. Virvou, Advances in Personalized Web-Based Education. 2015:


Brusilovski, P., A. Kobsa, and W. Nejdl, The adaptive web: methods and strategies of web

personalization. Vol. 4321. 2007: Springer Science & Business Media.

Brusilovsky, P. and E. Millán. User models for adaptive hypermedia and adaptive educational

systems. in The adaptive web. 2007. Springer-Verlag.

Pu, P., L. Chen, and R. Hu. A user-centric evaluation framework for recommender systems. in

Proceedings of the fifth ACM conference on Recommender systems. 2011. ACM.

Kobsa, A., Generic user modeling systems. User modeling and user-adapted interaction, 2001.

(1-2): p. 49-63.

de Koch, N.P., Software engineering for adaptive hypermedia systems. 2001, PhD Thesis,

Verlag Uni-Druck, Munich.

Martins, C., et al., User Modeling in Adaptive Hypermedia Educational Systems. Educational

Technology & Society, 2008. 11(1): p. 194-207.

Dunn, R., J.S. Beaudry, and A. Klavas, Survey of research on learning styles. California Journal

of Science Education, 2002. 2(2): p. 75-98.

Dağ, F. and A. Geçer, Relations between online learning and learning styles. Procedia-Social

and Behavioral Sciences, 2009. 1(1): p. 862-871.

Dunn, R., K. Dunn, and M. Freeley, Practical applications of the research: Responding to

students’ learning styles–step one. Illinois State Research and Development Journal, 1984.

(1): p. 1-21.

Riding, R. and I. Cheema, Cognitive styles—an overview and integration. Educational

psychology, 1991. 11(3-4): p. 193-215.

Graf, S., Adaptivity in learning management systems focussing on learning styles. 2007, Vienna

University of Technology.

Behaz, A. and M. Djoudi, Adaptation of learning resources based on the MBTI theory of

psychological types. IJCSI International Journal of Computer Science Issues, 2012. 9(1): p.


Jonassen, D.H. and B.L. Grabowski, Handbook of individual differences, learning, and

instruction. 2012: Routledge.

Kappe, F., et al., A predictive validity study of the Learning Style Questionnaire (LSQ) using

multiple, specific learning criteria. Learning and Individual differences, 2009. 19(4): p. 464-

Cassidy*, S., Learning styles: An overview of theories, models, and measures. Educational

psychology, 2004. 24(4): p. 419-444.

Solomon, B.A. and R.M. Felder, Index of learning styles. 2006.

Index of Learning Styles. 2016; Available from:

Graf, S., et al., In-depth analysis of the Felder-Silverman learning style dimensions. Journal of

Research on Technology in Education, 2007. 40(1): p. 79-93.

Özpolat, E. and G.B. Akar, Automatic detection of learning styles for an e-learning system.

Computers & Education, 2009. 53(2): p. 355-367.

Truong, H.M., Integrating learning styles and adaptive e-learning system: Current

developments, problems and opportunities. Computers in Human Behavior, 2016. 55: p. 1185-

Tahmasebi, M. and M. Esmaeili, Hybrid Adaptive Educational Hypermedia Recommender

Accommodating User’s Learning Style and Web Page Features. Journal of AI and Data Mining,

Radwan, N., An Adaptive Learning Management System Based on Learner’s Learning Style.

International Arab Journal of e-Technology, 2014. 3(4): p. 7.

Lee, M., Utilizing the Index of Learning Styles (ILS) in a technology-based publishing program.

, University of Phoenix.

Mohammadi, H., et al. A bi-section graph approach for hybrid recommender system. in

Granular Computing (GrC), 2011 IEEE International Conference on. 2011. IEEE.

Litzinger, T.A., et al., A psychometric study of the index of learning styles©. Journal of

Engineering Education, 2007. 96(4): p. 309.

Olston, C. and M. Najork, Web crawling. Foundations and Trends in Information Retrieval,

4(3): p. 175-246.

Najork, M., Web crawler architecture, in Encyclopedia of Database Systems. 2009, Springer.

p. 3462-3465.

Najork, M. and A. Heydon, High-performance web crawling. 2002: Springer.

Chakrabarti, S., M. Van den Berg, and B. Dom, Focused crawling: a new approach to topicspecific

Web resource discovery. Computer Networks, 1999. 31(11): p. 1623-1640.

Manning, C.D. and P. Raghavan, An introduction to information retrieval. 2009, Cambridge

University Press.

Global network of educational institutions, i.a.o. The Open Education Consortium | The Global

Network for Open Education. 2016; Available from:

Alexa - Actionable Analytics for the Web. 2016; Available from:

Felder, R.M., Matters of style. ASEE prism, 1996. 6(4): p. 18-23.

Buder, J. and C. Schwind, Learning with personalized recommender systems: A psychological

view. Computers in Human Behavior, 2012. 28(1): p. 207-216.

Akbulut, Y. and C.S. Cardak, Adaptive educational hypermedia accommodating learning

styles: A content analysis of publications from 2000 to 2011. Computers & Education, 2012.

(2): p. 835-842.

El-Bishouty, M.M., et al., Teaching Improvement Technologies for Adaptive and Personalized

Learning Environments, in Ubiquitous Learning Environments and Technologies. 2015,

Springer. p. 225-242.

PHAM, Q.D. and A.M. FLOREA, A method for detection of learning styles in learning

management systems. UPB Scientific Bulletin, Series C: Electrical Engineering, 2013. 75: p.


Salton, G., The SMART retrieval system—experiments in automatic document processing.

Robertson, S.E. and S. Walker. Some simple effective approximations to the 2-poisson model

for probabilistic weighted retrieval. in Proceedings of the 17th annual international ACM

SIGIR conference on Research and development in information retrieval. 1994. Springer-

Verlag New York, Inc.

Page, L., et al. The Pagerank Algorithm: Bringing Order to the Web. in Proceedings of the

International Conference on the World Wide Web. 1998.

Bidoki, A.M.Z. and N. Yazdani, DistanceRank: An intelligent ranking algorithm for web pages.

Information Processing & Management, 2008. 44(2): p. 877-892.

Felder, R.M. and B.A. Soloman, Index of learning styles (ILS). On-line at, 1999.

Erdt, M., A. Fernandez, and C. Rensing, Evaluating recommender systems for technology

enhanced learning: A quantitative survey. IEEE Transactions on Learning Technologies, 2015.

(4): p. 326-344.

consultant, K.T.-L.a.S., Basic Concepts - Lucene 2017.

Tuan*, H.L., C.C. Chin, and S.H. Shieh, The development of a questionnaire to measure

students' motivation towards science learning. International Journal of Science Education,

27(6): p. 639-654.

Venkatesh, V., et al., User acceptance of information technology: Toward a unified view. MIS

quarterly, 2003: p. 425-478.

Hooper, D., J. Coughlan, and M. Mullen, Structural equation modelling: Guidelines for

determining model fit. Articles, 2008: p. 2.

Anderson, J.C. and D.W. Gerbing, Structural equation modeling in practice: A review and

recommended two-step approach. Psychological bulletin, 1988. 103(3): p. 411.

Hair, J.F., C.M. Ringle, and M. Sarstedt, PLS-SEM: Indeed a silver bullet. Journal of Marketing

theory and Practice, 2011. 19(2): p. 139-152.

Wong, K.K.-K., Partial least squares structural equation modeling (PLS-SEM) techniques

using SmartPLS. Marketing Bulletin, 2013. 24(1): p. 1-32.

Latan, H. and I. Ghozali, Partial least Squares: Concept and application path modeling using

program XLSTAT-PLS for empirical research. BP UNDIP, 2012.

Implementation and Evaluation of a Resource-based Learning Recommender based on ….

Fornell, C. and F.L. Bookstein, Two structural equation models: LISREL and PLS applied to

consumer exit-voice theory. Journal of Marketing research, 1982: p. 440-452.

Cohen, J., A power primer. Psychological bulletin, 1992. 112(1): p. 155.