Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Clickstream Data Warehousing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Electronic Homework on the WWW
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
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This paper aims at finding an efficient way for discovering which specific knowledge each student does not possess in order to successfully start a new course or to proceed with another section in a current subject. Most existing tutoring systems respond to students’ mistakes by providing links to a collection of teaching materials. Such an approach does the individual needs of each student. Our idea is to apply a holistic approach that involves looking at the whole system of each student knowledge within an subject rather than just concentrating on single mistakes, lack of knowledge or misconception.