@article{romero07,
  abstract = {Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.},
  address = {Tarrytown, NY, USA},
  author = {Romero, C. and Ventura, S.},
  doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005},
  interhash = {89d843f1a3b181f2a628e881d9210b22},
  intrahash = {746d12e92e58587461ffcb8dc381e283},
  issn = {0957-4174},
  journal = {Expert Syst. Appl.},
  number = 1,
  pages = {135--146},
  publisher = {Pergamon Press, Inc.},
  title = {Educational data mining: A survey from 1995 to 2005},
  url = {http://portal.acm.org/citation.cfm?id=1223659},
  volume = 33,
  year = 2007
}

@inproceedings{tang03,
  author = {Tang, Tiffany Ya and Mccalla, Gordon},
  booktitle = {Workshop on Technologies for Electronic Documents for Supporting Learning, International Conference on Artificial Intelligence in Education (AIED},
  interhash = {bcf301fcfe413916c8b313de663bc866},
  intrahash = {129771e6d0d33513e1729549f98247a5},
  title = {Smart Recommendation for an Evolving E-Learning System},
  url = {http://www.cs.usyd.edu.au/~aied/vol10/vol10_TangMcCalla.pdf},
  year = 2003
}

@inproceedings{zaiane2002building,
  abstract = {A recommender system in an e-learning context is a software agent that tries to "intelligently" recommend actions to a learner based on the actions of previous learners. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. These recommendation systems have been tried in e-commerce to entice purchasing of goods, but haven't been tried in e-learning. This paper suggests the use of web mining techniques to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process. These techniques are considered integrated web mining as opposed to off-line web mining used by expert users to discover on-line access patterns.},
  address = {Washington, DC, USA },
  author = {Zaiane, O.R.},
  booktitle = {Proceedings of the International Conference on Computers in Education},
  doi = {10.1109/CIE.2002.1185862},
  interhash = {fce17e1e38b714031e92357f6f5877f5},
  intrahash = {df8fdf1b3452eabdecd64d0f3f4472dc},
  isbn = {0-7695-1509-6},
  month = dec,
  organization = {IEEE Computer Society},
  pages = {55--59},
  title = {Building a recommender agent for e-learning systems},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1185862},
  volume = 1,
  year = 2002
}

@article{rafaeli05,
  abstract = {Recommendation systems can play an extensive role in online learning. In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth. In recommender systems, users’ ratings can be applied toward items, users, other users’ ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system — QSIA — an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems. We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation.},
  author = {Rafaeli, Sheizaf and Dan-Gur, Yuval and Barak, Miri},
  editor = {Jin, Qun},
  interhash = {313fc441724733ba4bbeb928c721a481},
  intrahash = {450dea33236d00ed7f6f645b6f91ff3e},
  issn = {1539-3100},
  journal = {International Journal of Distance Education Technologies},
  number = 2,
  pages = {30-47},
  publisher = {Idea Group Publishing},
  title = {Social Recommender Systems: Recommendations in Support of E-Learning},
  url = {http://www.igi-global.com/articles/details.asp?id=4784},
  volume = 3,
  year = 2005
}

@article{chen05,
  abstract = {Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.},
  address = {Oxford, UK, UK},
  author = {Chen, Chih-Ming and Lee, Hahn-Ming and Chen, Ya-Hui},
  doi = {http://dx.doi.org/10.1016/j.compedu.2004.01.006},
  interhash = {c8b0ea1e16a98efd85df7f09594a6247},
  intrahash = {6bf0a9a55628bacc1c8a817d4d838687},
  issn = {0360-1315},
  journal = {Comput. Educ.},
  number = 3,
  pages = {237--255},
  publisher = {Elsevier Science Ltd.},
  title = {Personalized e-learning system using Item Response Theory},
  url = {http://portal.acm.org/citation.cfm?id=1066365},
  volume = 44,
  year = 2005
}