Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval
By Mohamed Koutheair Khribi, Mohamed Jemni, Olfa Nasraoui. 2008 Eighth IEEE International Conference on Advanced Learning Technologies, 2008
386 citations recorded by the source corpus.
The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/b109e32131b858531ab517ea04cd445a9524701f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Cristóbal Romero
- Sebastián Ventura
- Hendrik Drachsler
- Rob Koper
- Nikos Manouselis
- Katrien Verbert
- Francisco Herrera
- Anastasios A. Economides
In Information Systems
- Sebastián Ventura
- Cristóbal Romero
- Hendrik Drachsler
- Erik Duval
- Katrien Verbert
- Nikos Manouselis
- Ben Kai Daniel
- Anastasios A. Economides
Citations in the corpus
- Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, methodology): “Such an approach is also employed by APOSDLE [104], DEPTHS [52], and Khribi et al. [54].”
- Educational Data Mining: A Review of the State of the Art (2010, background): “resource recommendation system based on connecting to similar e-learning [285]; for recommending relevant links to the active learner [147]; to develop an e-learning recommendation”
- Recommender Systems in Technology Enhanced Learning (2011, methodology): “The usage logs of the RPL platform are used for this purpose, and a preliminary evaluation experiment has already taken place [49].”