GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
iWeaver: towards 'learning style'-based e-learning in computer science education
ACE '03 Proceedings of the fifth Australasian conference on Computing education - Volume 20
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Individualizing Tutoring with Learning Style Based Feedback
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Discovering prediction rules in AHA! courses
UM'03 Proceedings of the 9th international conference on User modeling
Social navigation support in a course recommendation system
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Statistical profiles of highly-rated learning objects
Computers & Education
Oscar: an intelligent adaptive conversational agent tutoring system
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
The establishment of ubiquitous portfolio management system and learning behaviour analysis scheme
International Journal of Mobile Learning and Organisation
A conversational intelligent tutoring system to automatically predict learning styles
Computers & Education
Rule-Based reasoning for building learner model in programming tutoring system
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
Ranking semantic relationships between two entities using personalization in context specification
Information Sciences: an International Journal
Protus 2.0: Ontology-based semantic recommendation in programming tutoring system
Expert Systems with Applications: An International Journal
Proceedings of the Fifth Balkan Conference in Informatics
Evaluating collaborative filtering recommendations inside large learning object repositories
Information Processing and Management: an International Journal
Adaptive tutoring in an intelligent conversational agent system
Transactions on Computational Collective Intelligence VIII
Expert Systems with Applications: An International Journal
An adaptation algorithm for an intelligent natural language tutoring system
Computers & Education
Modelling students' flow experiences in an online learning environment
Computers & Education
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Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners' habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.