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Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

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Abstract

With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

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Notes

  1. http://www.flickr.com.

  2. http://www.del.icio.us.

  3. http://www.citeulike.org.

  4. http://www.connotea.org.

  5. http://www.43things.com.

  6. http://movielens.org.

  7. https://netflix.com/.

References

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749

    Article  Google Scholar 

  • Agrawal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925

    Article  Google Scholar 

  • Agrawal R, Imielinski T, Swami A (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference. pp 487–499

  • Aguzzoli S, Avesani P, Massa P (2002) Collaborative case-based recommendation systems. Lecture notes in computer science, 2416

  • Anane R, Crowther S, Beadle J et al. (2004) eLearning content provision. In: Proceedings of the 15th international workshop on database and expert systems applications (DEXA’04), pp 420–425

  • Anderson M, Ball M, Boley H, Greene S, Howse N, Lemire D, McGrath S (2003) RACOFI: a rule-applying collaborative filtering system. In: Ghorbani A, Marsh S (eds) Proceedings of IEEE/WIC COLA’03. National Research Council Canada, Halifax, pp 53–72

  • Angehrn A, Nabeth T, Razmerita L, Roda C (2001) K-InCA: using artificial agents for helping people to learn new behaviours. In: Proceedings of IEEE International Conference on Advanced Learning Technologies (ICALT 2001), IEEE Computer Society, Madison, pp 225–226

  • Anjorin M, Rensing C, Steinmetz R (2011) Towards ranking in folksonomies for personalized recommender systems in e-learning. In: SPIM pp 22–25

  • Arenas-García J, Meng A, Petersen B, Schiøler L, Hansen K, Larsen J (2007) Unveiling music structure via PLSA similarity fusion. In: Arenas. IEEE Press, pp 419–424

  • Au Yeung CM, Gibbins N, Shadbolt N (2009) Contextualising tags in collaborative tagging systems. In: Proceedings of the 20th ACM conference on hypertext and hypermedia, pp 251–260

  • Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40:66–72

    Article  Google Scholar 

  • Bar-Ilan J, Shoham S, Idan A, Miller Y, Shachak A (2008) Structured versus unstructured tagging: a case study. Online Inf Rev 32(5):635–647

    Article  Google Scholar 

  • Bateman S, McCalla G, Brusilovsky P (2007) Applying collaborative tagging to E-learning. In: Workshop proceedings of tagging and metadata for social information organization in conjunction with the International World Wide Web Conference (WWW2007), Banff, Canada, pp 3–12

  • Berry M, Dumais S, O’Brien G (1994) Using linear algebra for intelligent information retrieval. SIAM Rev 37(4):573–595

    Article  MathSciNet  MATH  Google Scholar 

  • Billsus D, Pazzani M (1998) Learning collaborative information filters. In: Proceedings of international conference on machine learning, pp 46–54

  • Bottou L (2004) Stochastic learning. Advanced lectures on machine learning. Springer, Berlin, pp 146–168

    Chapter  Google Scholar 

  • Bonifazi F, Levialdi S, Rizzo P (2002) A web-based annotation tool supporting e-learning. In: AVI ’02 proceedings of the working conference on advanced visual interfaces, pp 123–128

  • Brin S, Motwai R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD conference on management of data, pp 265–276

  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  • Castro F, Vellido A, Nebot A, Mugica F (2007) Applying data mining techniques to e-learning problems, evolution of teaching and learning paradigms in intelligent environment 62, Springer, Berlin, pp 183–221

  • Cattuto C, Loreto V, Pietronero L (2007) Semiotic dynamics and collaborative tagging. Proc Natl Acad Sci USA 104(5):1461–1464

    Article  Google Scholar 

  • Cen H, Koedinger K, Junker B (2006) Learning factors analysis a general method for cognitive model evaluation and improvement. In: Intelligent tutoring systems, vol. 4053. Springer, Berlin, pp 164–175

  • Chen W, Wasson B (2002) Coordinating collaborative knowledge building. Proc Int Conf Appl Inform 25(1):1–10

    Google Scholar 

  • Chen JM, Chen MC, Sun YS (2014) A tag based learning approach to knowledge acquisition for constructing prior knowledge and enhancing student-reading comprehension. Comput Educ 70:256–268

    Article  Google Scholar 

  • Cheung KW, Kwok JT, Law MH, Tsui KC (2003) Mining customer product ratings for personalized marketing. Decis Support Syst 35:231–243

    Article  Google Scholar 

  • Cho K, Schunn CD, Wilson RW (2006) Validity and reliability of scaffolded peer assessment of writing from instructor and student perspectives. J Educ Psychol 98(4):891–901

    Article  Google Scholar 

  • Cho K, Schunn CD (2007) Scaffolded writing and rewriting in the discipline: a web-based reciprocal peer review system. Comput Educ 48(3):409–426

    Article  Google Scholar 

  • Chu K, Chang M, Hsia Y (2003) Designing a course recommendation system on web based on the students’ course selection records. In: World conference on educational multimedia, hypermedia and telecommunications, pp 14–21

  • Ciro C, Schmitz C, Baldassarri A, Servedio V, Loreto V, Hotho A (2007) Network properties of folksonomies. AI Commun 20(4):245–262

    MathSciNet  Google Scholar 

  • Cohn D, Hofmann T (2000) The missing link-a probabilistic model of document content and hypertext connectivity. In: Leen TK, Dietterich TG, Tresp V (eds) NIPS. MIT Press, Cambridge, pp 430–436

  • Cosley D, Lam SK, Albert I, Konstan J, Riedl J (2003) Is seeing believing? How recommender systems influence users’ opinions. In: Proceedings of CHI 2003: human factors in computing systems. ACM Press, New York, pp 585–592

  • Costabile MF, De Marsico M, Lanzilotti R, Plantamura VL, Roselli T (2005) On the usability evaluation of e-learning applications. In: System Sciences, 2005. HICSS’05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 6b–6b)

  • Cox SM, Tsai KC (2013) Exploratory examination of relationships between learning styles and learner satisfaction in different course delivery types. Int J Soc Sci Res 1(1):64–76

    Article  Google Scholar 

  • Dahl D, Vossen G (2008) Evolution of learning folksonomies: social tagging in e-learning repositories. Int J Technol Enhanc Learn 1(1):35–46

  • Dascalua M-J, Bodea M-I, Moldoveanuc C-N, Mohoraa A, Lytras M, Ordóñez de Pablos P (2015) A recommender agent based on learning styles for better virtual collaborative learning experiences. Comput Hum Behav 45:243–253

  • Deerwester C, Dumais T, Landauer K, Furnas W, Harshman A (1990) Indexing by latent semantic analysis. J ASIS 41(6):391–407

    Google Scholar 

  • De Lathauwer L, De Moor B, Vandewalle J (2000) Multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253–1278

    Article  MathSciNet  MATH  Google Scholar 

  • DeRouin RE, Fritzsche BA, Salas E (2004) Optimizing e-learning: research-based guidelines for learnercontrolled training. Hum Resour Manag 43:147–162

    Article  Google Scholar 

  • Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  • Dos Santos ML, Becker K (2003) Distance education: a web usage mining case study for the evaluation of learning sites. In: The 3rd IEEE international conference on advanced learning technologies, ICALT’03. IEEE Computer Society, Athens Greece, pp 360–361

  • Doush I (2011) Annotations, collaborative tagging, and searching mathematics in E-learning IJACSA. Int J Adv Comput Sci Appl 2(4):30–39

    Google Scholar 

  • Drachsler H, Hummel HGK, Koper R (2008) Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. Int J Learn Technol 3(4):404–423

    Article  Google Scholar 

  • Dráždilová P, Obadi G, Slaninová K, Al-Dubaee S, Martinovič J, Snášel V (2010) Computational intelligence methods for data analysis and mining of elearning activities. In: Computational intelligence for technology enhanced learning pp 195–224

  • Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data 5(2):10

    Article  Google Scholar 

  • Elorriaga JA, Fernandez-Castro I (2000) Using case-based reasoning in instructional planning. Towards a hybrid self-improving instructional planner. Int J Artif Intell Educ 11:416–449

  • Feng M, Heffernan N, Koedinger K (2009) Addressing the assessment challenge with an online system that tutors as it assesses. User Model User Adapt Interact 19(3):243–266

    Article  Google Scholar 

  • Freyberger J, Heffernan N, Ruiz C (2004) Using association rules to guide a search for best fitting transfer models of student learning. In: Workshop on analyzing student-tutor interactions logs to improve educational outcomes at ITS conference, pp 1–10

  • Funk P, Conlan O (2003) Using case-based reasoning to support authors of adaptive hypermedia systems. In: AH2003: workshop on adaptive hypermedia and adaptive web-based systems, pp 113–120

  • García E, Romero C, Ventura S, Calders T (2007) Drawbacks and solutions of applying association rule mining in learning management systems. In: Proceedings of the international workshop on applying data mining in e-learning, pp 13–23

  • García E, Romero C, Ventura S, de Castro C (2009) An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Model User Adapt Interact 19(1–2):99–132

    Article  Google Scholar 

  • Gaudioso E, Montero M, Talavera L, Hernandez-del-Olmo F (2009) Supporting teachers in collaborative student modeling: a framework and an implementation. Expert Syst Appl 36(2):2260–2265

    Article  Google Scholar 

  • Gayo L, Ordóñez de Pablos P, Cueva Lovelle JM (2010) Wesonet: applying semantic web technologies and collaborative tagging to multimedia web information systems. Comput Hum Behav 26(2):205–209

    Article  Google Scholar 

  • Ghauth KI, Abdullah NA (2010) Learning materials recommendation using good learners’ ratings and content-based filtering. Educ Technol Res Dev 58(6):711–727

    Article  Google Scholar 

  • Gehringer EF (2001) Electronic peer-review and peer grading in computer-science courses. In: Proceedings of the 32nd SIGCSE technical symposium on computer science education. Charlotte, North Carolina, pp 139–143

  • Godoy D, Amandi A (2008) Hybrid content and tag-based profiles for recommendation in collaborative tagging systems. In: Proceedings of the 6th Latin American web congress (LA-WEB 2008). IEEE Computer Society Vila Velha, Brazil, pp 58–65

  • Golder A, Huberman A (2005) The structure of collaborative tagging systems. HPL Technical Report

  • Gordon-Murnane L (2006) Social bookmarking, folksonomies, and web 2.0 tools. Searcher 14(6):26–38

  • Guo Q, Zhang M (2009) Implement web learning environment based on data mining. Knowl-Based Syst 22(6):439–442

    Article  Google Scholar 

  • Guy M, Tonkin E (2006) Folksonomies: tidying up tags? D-Lib Mag 12(1)

  • Halpin H, Robu V, Shepherd H (2007) The complex dynamics of collaborative tagging. In: Proceedings of the 16th international conference on World Wide Web, ACM, pp 211–220

  • Halpin H, Robu V, Shepard H (2006) The dynamics and semantics of collaborative tagging. In: Proceedings of the 1st semantic authoring and annotation workshop (SAAW’06) (vol. 209)

  • Heraud J, France L, Mille A (2004) Pixed: an ITS that guides students with the help of learners’ interaction log. In: International conference on intelligent tutoring systems, workshop analyzing student tutor interaction logs to improve educational outcomes. Maceio, Brazil, 57–64

  • Herlocker JL, Konstan JA, Terveen LG, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  • Hofmann T (1999) Probabilistic latent semantic analysis. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence, pp 289–296

  • Hofmann T (2003) Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: Proceedings of 26th international ACM SIGIR conference of research and development in information retrieval (SIGIR ’03), pp 259–266

  • Hotho A, Jäschkes R, Schmitz C, Stumme G (2006a) Information retrieval in folksonomies: search and ranking. In: Sure Y, Domingue J (eds) The semantic web: research and applications, vol. 4011 of LNAI. Springer, Heidelberg, pp 411–426

  • Hotho A, Jäschke R, Schmitz C, Stumme G (2006b) BibSonomy: a social bookmark and publication sharing system. In: Proceedings of first conceptual structures tool interoperability workshop. Aalborg, pp 87–102

  • Hotho A, Jäschke R, Schmitz C, Stumme G (2006) Information retrieval in folksonomies: search and ranking. In: The semantic web: research and applications, pp 411–426

  • Hsu HH, Chen CH, Tai WP (2003) Towards error-free and personalized web-based courses. In: The 17th international conference on advanced information networking and applications, AINA’03. March 27–29, Xian, China, pp 99–104

  • Hwang J, Hsiao L, Tseng R (2003) A computer-assisted approach to diagnosing student learning problems in science courses. J Inf Sci Eng 19:229–248

    Google Scholar 

  • Janssen J, Van den Berg B, Tattersall C, Hummel H, Koper R (2007) Navigational support in lifelong learning: enhancing effectiveness through indirect social navigation. Interact Learn Environ 15(2):127–136

    Article  Google Scholar 

  • Jäschke R, Marinho L, Hotho A, Schmidt-Thieme L, Stumme G (2007) Tag recommendations in folksonomies. In: Hinneburg A (ed) Workshop proceedings of Lernen – Wissensentdeckung -Adaptivität (LWA 2007). pp 13–20

  • Jäschke R, Hotho A, Mitzlaff F, Stumme G (2012) Challenges in tag recommendations for collaborative tagging systems. In: Recommender systems for the social web. Springer, Berlin, pp 65–87

  • Kantor PB, Ricci F, Rokach L, Shapira B (2011) Recommender systems handbook. Springer, Berlin

  • Keefe JW (1979) Learning styles: an overview. In: Keefe JW (ed) Student learning styles: diagnosing and prescribing programs. National Association of Secondary School Principals, Reston

    Google Scholar 

  • Khribi MK, Jemni M, Nasraoui O (2015) Recommendation systems for personalized technology-enhanced learning. In: Ubiquitous learning environments and technologies (pp. 159–180). Springer, Berlin

  • Kim H (2011) A personalized recommendation method using a tagging ontology for a social e-learning system. In: Nguyen N, Kim C-G, Janiak A (eds) Intelligent information and database systems, vol. 6591 of lecture notes in computer science, pp. 357–366. Springer, Berlin. doi:10.1007/978-3-642-20039-7_36

  • Klašnja-Milićević (2013). Personalized recommendation based on collaborative tagging techniques for an e-learning system, Ph.D. Thesis, Faculty of Sciences, Department of Mathematics and Informatics, University of Novi Sad, Serbia

  • Klašnja-Milićević A, Nanopoulos A, Ivanović M (2010) Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 33(3):187–209

  • Klašnja-Milićević A, Vesin B, Ivanović M, Budimac Z (2011) e-learning personalization based on hybrid recommendation strategy and learning style identification. Comput Educ 56(3):885–899

    Article  Google Scholar 

  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  MathSciNet  MATH  Google Scholar 

  • Koper R, Olivier B (2004) Representing the learning design of units of learning. Educ Technol Soc 7(3):97–111

    Google Scholar 

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  • Kumar A (2005) Rule-based adaptive problem generation in programming tutors and its evaluation. In: 12th International conference on artificial intelligence in education. pp 36–44

  • Lacic E, Kowald D, Seitlinger P, Trattner C, Parra D (2014) Recommending items in social tagging systems using tag and time information. arXiv preprint arXiv:1406.7727

  • Landauer T, Foltz P, Laham D (1998) Introduction to latent semantic analysis. Discourse Process 25(25):259–284

    Article  Google Scholar 

  • Lemire D (2005) Scale and translation invariant collaborative filtering systems. Inf Retr 8:129–150

    Article  Google Scholar 

  • Lemire D, Boley H, McGrath S, Ball M (2005) Collaborative filtering and inference rules for context-aware learning object recommendation. Int J Interact Technol Smart Educ 2:179–188

    Article  Google Scholar 

  • Li J, Zaïane OR (2004) Combining usage, content, and structure data to improve web site recommendation. In: E-commerce and web technologies. Springer, Berlin, pp 305–315

  • Liang H, Xu Y, Li Y, Nayak R (2008) Collaborative filtering recommender systems using tag information. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, pp 59–62

  • Lin X, Beaudoin JE, Bui Y, Desai K (2006) Exploring characteristics of social classification. In: 17th ASIS&T SIG/CR classification research workshop

  • Linden G, Smith B, York J (2003) Amazon.com recommendations. IEEE Internet Computing 7. no. 1, (Jan–Feb 2003), pp 76–80

  • Liu B, Wynne H, Shu C, Yiming M (2000) Analyzing the subjective interestingness of association rules. IEEE Intell Syst 15(5):47–55

    Article  Google Scholar 

  • Lops P, De Gemmis M, Semeraro G, Musto C, Narducci F (2013) Content-based and collaborative techniques for tag recommendation: an empirical evaluation. J Intell Inf Syst 40(1):41–61

    Article  Google Scholar 

  • Lu J (2004) Personalized e-learning material recommender system. In: Proceedings of the international conference on information technology for application. England, London, pp 374–379

  • Luo S, Sha S, Shen D, Jia WJ (2002) Conceptual network based courseware navigation and web presentation mechanisms. In: Proceedings of advanced in web-based learning (ICWL 2002), LNCS 2436, pp 81–91

  • Lynch C, Ashley K, Aleven V, Pinkwart N (2006) Defining Ill-defined domains; a literature survey. In: Proceedings of the workshop on intelligent tutoring systems for Ill-defined domains at the 8th international conference on intelligent tutoring systems, pp 1–10

  • Lytras MD, Ordóñez de Pablos P (2011) Software technologies in knowledge society. J Univers Comput Sci 17(9):1219–1221

    Google Scholar 

  • MacLaurin MB (2007) Selection-based item tagging. Patent Application no. US 2007/0028171 A1. US Patent and Trademark Office, Washington

  • Mallinson B, Sewry D (2004) Elearning at Rhodes University: a case study. In: Proceedings of the IEEE international conference on advanced learning technologies (ICALT’04), pp 708–711

  • Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in a multi-attribute utility collaborative filtering system. Int J Pattern Recognit Artif Intell 21:311–331

    Article  Google Scholar 

  • Marinho LB, Schmidt-Thieme L (2007) Collaborative tag recommendations. In: Proceedings of 31st annual conference of the Gesellschaft für Klassifikation (GfKl), Freiburg, pp 533–540. Springer, Berlin

  • Marinho LB, Nanopoulos A, Schmidt-Thieme L, Jäschke R, Hotho A, Stumme G, Symeonidis P (2011) Social tagging recommender systems. Recommender Systems Handbook, Part 4, 615–644. doi:10.1007/978-0-387-85820-3_19

  • Marinho LB, Hotho A, Jäschke R, Nanopoulos A, Rendle S, Schmidt-Thieme, Stumme LG, Symeonidis P (2012) Social tagging systems. In: Recommender systems for social tagging systems (pp. 3–15). Springer US

  • Markellou P, Mousourouli I, Spiros S, Tsakalidis A (2005) Using semantic web mining technologies for personalized e-learning experiences. In: Proceedings of the international conference on web-based education, pp 1–10

  • Marlow C, Naaman M, Boyd D, Davis M (2006) Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: HYPERTEXT ’06: proceedings of the seventeenth conference on Hypertext and hypermedia. ACM Press, New York, pp 31–40

  • Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. CoopIS/DOA/ODBASE 1:492–508

    Google Scholar 

  • Mathes A (2004) Folksonomies-cooperative classification and communication through shared metadata. Comput Mediat Commun 47(10)

  • Matsui T, Okamoto T (2003) Knowledge discovery from learning history data and its effective use for learning process assessment under the e-learning environment. In: Crawford C et al. (eds) Society for information technology and teacher education international conference, pp 3141–3144

  • McNee SM, Riedl J, Konstan JA (2006) Making recommendations better: an analytic model for human-recommender interaction. In: Conference on human factors in computing systems, Montréal, Québec, Canada, pp 1103–1108

  • Merceron A, Yacef K (2004) Mining student data captured from a web-based tutoring tool. J Interact Learn Res 15(4):319–346

    Google Scholar 

  • Mika P (2005) Ontologies are us: a unified model of social networks and semantics. In: Proceedings of the 4th international semantic web conference, ISWC 2005. Springer, Galway, pp 522–536

  • Millen DR, Feinberg J, Kerr B (2006) Dogear: social bookmarking in the enterprise. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 111–120

  • Minaei-Bidgoli B, Tan P, Punch W (2004) Mining interesting contrast rules for a web-based educational system. In: Proceedings of the international conference on machine learning applications, pp 1–8

  • Muñoz-Merino PJ, Kloos CD, Muñoz-Organero M, Pardo A (2015) A software engineering model for the development of adaptation rules and its application in a hinting adaptive E-learning system. Comput Sci Inf Syst 12(1):203–231

    Article  Google Scholar 

  • Noll M, Au Yeung C, Gibbins N, Meinel C, Shadbolt N (2009) Telling experts from spammers: expertise ranking in folksonomies. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, pp 612–619

  • Núñez -Valdéz ER, Cueva Lovelle JM, Sanjuán Martínez O, Garcia Diaz V. Ordóñez de Pablos P, Montenegro Marín CA (2012) Implicit feedback techniques on recommender systems applied to electronic books. Comput Hum Behav 28(4):1186–1193

  • Ochoa X, Duval E(2006) Use of contextualized attention metadata for ranking and recommending learning objects. In: Proceedings of the 1st international workshop on contextualized attention metadata: collecting, managing and exploiting of rich usage information pp 9–16

  • Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project, Stanford University

  • Palmatier A, Bennet M (1974) Note-taking habits of college students. J Read 18:215–218

    Google Scholar 

  • Pardos ZA, Heffernan NT (2010) Using hmms and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. In: KDD Cup 2010: improving cognitive models with educational data mining

  • Park JS, Ming-Syan C, Yu PS (1995) An effective hash based algorithm for mining association rules. In: Proceedings of ACM SGMOD, pp 175–185

  • Pero Š, Horváth T (2015) Comparison of collaborative-filtering techniques for small-scale student performance prediction task. In: Innovations and advances in computing, informatics, systems sciences, networking and engineering. Springer, Berlin, pp 111–116

  • Peters I, Stock G (2007) Folksonomy and information retrieval. In: Proceedings of the 70th annual meeting of the American society for information science and technology, vol. 45 CD-ROM

  • Pierrakos D, Paliouras G, Papatheodorou C, Spyropoulos C (2003) Web usage mining as a tool for personalization: a survey. User Model User Adapt Interact 13:311–372

    Article  Google Scholar 

  • Pinkwart N, Aleven V, Ashley K, Lynch C (2006) Toward legal argument instruction with graph grammars and collaborative filtering techniques. In: Proceedings of the 8th international conference on intelligent tutoring systems. Lecture Notes in Computer Science 4053. Springer, Berlin, pp 227–236

  • Pinkwart N, Aleven V, Ashley K, Lynch C (2007) Evaluating legal argument instruction with graphical representations using LARGO. In: Proceedings of the 13th international conference on artificial intelligence in education. IOS Press, pp 101–108

  • Pluzhenskaia M (2006) Folksonomies or fauxsonomies: how social is social bookmarking? 17th ASIS & T SIG/CR classification research workshop, pp 23–26

  • Prasad RVVSV, Kumari VV (2012) A categorical review of recommender systems. System 1(U2):U3

    Google Scholar 

  • Quintarelli E (2005) Folksonomies: power to the people. ISKO Italy-UniMIB. Retrieved from: http://www.dimat.unipv.it/biblio/isko/doc/folksonomies.htm

  • Ramli AA (2005) Web usage mining using apriori algorithm: UUM learning care portal case. In: Proceedings of the international conference on knowledge management, pp 1–19

  • Recker M, Walker A (2003) Supporting ‘word-of-mouth’ social networks via collaborative information filtering. J Interact Learn Res 14:79–98

    Google Scholar 

  • Recker M, Walker A, Lawless K (2003) What do you recommend? Implementation and analyses of collaborative filtering of web resources for education. Instr Sci 31:229–316

    Article  Google Scholar 

  • Rendle S, Marinho B, Nanopoulos A, Thieme L (2009) Learning optimal ranking with tensor factorization for tag recommendation. In: KDD ’09: proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 727–736

  • Rendle S (2011) Sequential-set recommendation. Context-aware ranking with factorization models. Springer, Berlin, pp 113–133

    Chapter  Google Scholar 

  • Resende D, Pires T (2001) An ongoing assessment model for distance learning. In: Hamza MH (ed) Fifth IASTED international conference internet and multimedia systems and applications. Acta Press, pp 17–21

  • Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of ACM 1994 conference on computer supported cooperative work. Chapel Hill, North Carolina, ACM, pp 175–186

  • Resnick P, Varian H (1997) Recommender systems. Commun ACM 40:56–58

    Article  Google Scholar 

  • Romero C, Ventura S, Bra, PD, de Castro C (2003) Discovering prediction rules in AHA! courses. In: 9th international user modeling conference, vol. 2702. Springer, Berlin, pp 25–34

  • Romero C, Ventura S, Bra PD (2004) Knowledge discovery with genetic programming for providing feedback to courseware author. User Model User Adapt Interact 14(5):425–464

    Article  Google Scholar 

  • Romero C, Ventura S (2006) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146

    Article  Google Scholar 

  • Rosa G, Ogata H, Yano Y, Martin G (2005) A multimodel approach for supporting the personalization of ubiquitous learning applications. In: IEEE international workshop on wireless and mobile technologies in education (WMTE’05), pp 40–44

  • Santos O, Boticario J (2008) Recommender systems for lifelong learning inclusive scenarios. ECAI (2008) workshop on recommender systems. Patras, Greece, pp 45–49

  • Sampson D, Karagiannidis C, Kinshuk D (2010) Personalised learning: educational, technological and standarisation perspective. Digit Educ Rev 4:24–39

    Google Scholar 

  • Sarwar BM, Konstan JA, Borchers A, Herlocker J, Miller B, Riedl J (1998) Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In: Proceedings of the 1998 ACM conference on computer supported cooperative work, pp 345–354

  • Savidis A, Grammenos D, Stephanidis C (2006) Developing inclusive e-learning and e-entertainment to effectively accommodate learning difficulties. Univers Access Inf Soc 5(4):401–419

  • Schafer B, Konstan A, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5:115–153

    Article  MATH  Google Scholar 

  • Schmitt S, Bergmann R (1999) Applying case-base reasoning technology for product selection and customization in electronic commerce environments. In: 12th bled electronic commerce conference, vol. 273

  • Setten M (2005) Supporting people in finding information. Hybrid recommender systems and goal-based structuring. Telematica Instituut Fundamental Research Series No. 016 (TI/FRS/016)

  • Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of ACM CHI’95 conference, pp 210–217

  • Shen L, Shen R (2005) Ontology-based learning content recommendation. Int J Contin Eng Educ Life Long Learn 15(3–6):308–317

    Article  Google Scholar 

  • Shepitsen A, Gemmell J, Mobasher B, Burke R (2008) Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM conference on recommender systems, pp 259–266

  • Silberschatz A, Tuzhilin A (1996) What makes pattterns interesting in knowledge discovery systems. IEEE Trans Knowl Data Eng 8(6):970–974

    Article  Google Scholar 

  • Sood S, Owsley S, Hammond K, Birnbaum L (2007) Tagassist: automatic tag suggestion for blog posts. In: ICWSM ’07: proceedings of the international conference on weblogs and social media, Citeseer

  • Soonthornphisaj N, Rojsattarat E, Yim-Ngam S (2006) Smart E-learning using recommender system. In: International conference on intelligent computing, pp 518–523

  • Sormo F, Aamodt A (2002) Knowledge communication and CBR. In: Workshop on case-based reasoning for education and training, ECCBR, pp 47–61

  • Srikanth M, Tatu M, D’Silva T (2008) Tag recommendations using bookmark content. In: Proceedings of the ECML PKDD discovery challenge at 18th European conference on machine learning (ECML’08)/11th European conference on principles and practice of knowledge discovery in databases (PKDD’08), pp 96–107

  • Srivastava J, Cooley R, Deshpande M, Tan P (2000) Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor 1(2):12–23

    Article  Google Scholar 

  • Staikopoulos A, O’Keeffe I, Rafter R, Walsh E, Yousuf B, Conlan O, Wade V (2014) AMASE: a framework for supporting personalised activity-based learning on the web. Comput Sci Inf Syst 11(1):343–367

    Article  Google Scholar 

  • Symeonidis P, Ruxanda M, Nanopoulos A, Manolopoulos Y (2008) Ternary semantic analysis of social tags for personalized music recommendation. In: Proceedings of the 9th international conference on music information retrieval, Pennsylvania, USA, pp 219–224

  • Šimić G (2004) The multi-courses tutoring system design. ComSIS 1(1):141–155

    Article  Google Scholar 

  • Tan H, Guo J, Li Y (2008) E-learning recommendation system. In: International conference on computer science and software engineering, csse, vol. 5, pp 430–433

  • Tang TY, McCalla G (2005) Smart recommendation for an evolving e-learning system: architecture and experiment. Int J e-Learn 4(1):105–129

    Google Scholar 

  • Thai-Nghe NT, Janecek P, Haddawy P (2007) A comparative analysis of techniques for predicting academic performance. In: Frontiers in education conference-global engineering: knowledge without borders, opportunities without passports, 2007. FIE’07. 37th annual (pp. T2G-7). IEEE

  • Thai-Nghe N, Busche A, Schmidt-Thieme L (2009) Improving academic performance prediction by dealing with class imbalance. In: Intelligent systems design and applications, 2009. ISDA’09. Ninth international conference on pp 878–883. IEEE

  • Thai-Nghe N, Drumond L, Horváth T, Nanopoulos A, Schmidt-Thieme L (2011a) Matrix and tensor factorization for predicting student performance. In: CSEDU (1) pp 69–78

  • Thai-Nghe N, Drumond L, Horváth T, Schmidt-Thieme L (2011b) Multi-relational factorization models for predicting student performance. In: KDD 2011 workshop on knowledge discovery in educational data, KDDinED

  • Tippins MJ, Sohi RS (2003) IT competency and firm performance: is organizational learning a missing link? Strateg Manag J 24(8):745–761

    Article  Google Scholar 

  • Tsai KH, Chiu TK, Lee MC, Wang TI (2006) A learning objects recommendation model based on the preference and ontological approaches. In: Advanced learning technologies, 2006. Sixth international conference on IEEE, pp 36–40

  • Tso-Sutter KHL, Marinho LB, Schmidt-Thieme L (2008) Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: Proceedings of the 2008 ACM symposium on applied computing. ACM, USA, pp 1995–1999

  • Vesin B, Ivanović M, Klašnja-Milićević A, Budimac Z (2013) Ontology-based architecture with recommendation strategy in java tutoring system. Comput Sci Inf Syst 10(1):237–261

    Article  Google Scholar 

  • Verbert K, Manouselis N, Ochoa X, Wolpers M, Drachsler H, Bosnic I, Duval E (2012) Context aware recommender systems for learning: a survey and future challenges. IEEE Trans Learn Technol 5(4):318–335

    Article  Google Scholar 

  • Veres C (2006a) The language of folksonomies: what tags reveal about user classification. Lect Notes Comput Sci 3999:58–69

    Article  Google Scholar 

  • Veres C (2006b) Concept modeling by the masses: folksonomy structure and interoperability. Lect Notes Comput Sci 4215:325–338

    Article  Google Scholar 

  • Wal V (2005) Folksonomy definition and wikipedia. http://www.vanderwal.net

  • Walker A, Recker M, Lawless K, Wiley D (2004) Collaborative information filtering: a review and an educational application. Int J Artif Intell Educ 14:1–26

    Google Scholar 

  • Wang F (2002) On using data-mining technology for browsing log file analysis in asynchronous learning environment. In: Conference on educational multimedia, hypermedia and telecommunication, pp 2005–2006

  • Wetzker R, Said A, Zimmermann C (2009) Understanding the user: personomy translation for tag recommendation, ECML PKDD Discovery Challenge 2009 DC09, vol. 497, Citeseer, pp 275–284

  • Wiley DA (2000) Connecting learning objects to instructional design theory: a definition, a metaphor, and a taxonomy. Retrieved from http://reusability.org/read/chapters/wiley.doc

  • Wilson D, Smyth B, Sullivan D (2003) Sparsity reduction in collaborative recommendation: a case-based approach. Int J Pattern Recognit Artif Intell 17(5):863–884

    Article  Google Scholar 

  • Winget M (2006) User-defined classification on the online photo sharing site Flickr.. or, how I learned to stop worrying and love the million typing monkeys. Adv Classif Res Online 17(1):1–16

    Article  Google Scholar 

  • Wu X, Zhang L, Yu Y (2006) Exploring social annotations for the semantic web. In: WWW‘06: proceedings of the 15th international conference on World Wide Web. ACM Press, New York, pp 417–426

  • Xu Z, Fu Y, Mao J, Su D (2006) Towards the semantic web: collaborative tag suggestions. In: Proceedings of the 15th international WWW conference. Collaborative web tagging workshop

  • Yang Y, Wu C (2009) Attribute-based ant colony systems for adaptive learning object recommendation. Expert Syst Appl 36(2):3034–3047

  • Yu P, Own C, Lin L (2001) On learning behavior analysis of web based interactive environment. In: Proceedings of the international conference on implementing curricular change in engineering education, pp 1–10

  • Zaïane OR (2001) Web usage mining for a better web-based learning environment. In: Proceedings of conference on advanced technology for education, pp 450–455

  • Zaiane OR (2002) Building a recommender agent for e-learning systems. In: Proceedings of the international conference on computer in education, pp 55–59

  • Zheng Z, Kohavi R, Mason L (2001) Real world performance of association rules. In: Sixth ACM SIGKDD international conference on knowledge discovery & data mining 2(2):86–98

  • Zhou X, Xu Y, Li Y, Josang A, Cox C (2012) The state-of-the-art in personalized recommender systems for social networking. Artif Intell Rev 37(2):119–132

    Article  Google Scholar 

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Acknowledgments

Ministry of Education, Science and Technological Development of Serbia supported the presented research, through project: “Intelligent techniques and their integration into wide-spectrum decision support” (Project No. 174023).

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  1. Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Aleksandra Klašnja-Milićević & Mirjana Ivanović

  2. Katholische Universität Eichstätt-Ingolstadt, Ostenstraße 26, 85072, Eichstätt, Deutschland

    Alexandros Nanopoulos

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  1. Aleksandra Klašnja-Milićević
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  2. Mirjana Ivanović
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Correspondence to Aleksandra Klašnja-Milićević.

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Klašnja-Milićević, A., Ivanović, M. & Nanopoulos, A. Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 44, 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-z

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