The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The stochastic approach for link-structure analysis (SALSA) and the TKC effect
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions
IEEE Transactions on Knowledge and Data Engineering
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Attention please!: learning analytics for visualization and recommendation
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Educational data mining meets learning analytics
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
It's just about learning the multiplication table
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Learning analytics and educational data mining: towards communication and collaboration
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
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Understanding the behavior of learners within learning applications and analyzing the factors that may influence the learning process play a key role in designing and optimizing learning applications. In this work we focus on a specific application named "1x1 trainer" that has been designed for primary school children to learn one digit multiplications. We investigate the database of learners' answers to the asked questions (N 440000) by applying the Markov chains. We want to understand whether the learners' answers to the already asked questions can affect the way they will answer the subsequent asked questions and if so, to what extent. Through our analysis we first identify the most difficult and easiest multiplications for the target learners by observing the probabilities of the different answer types. Next we try to identify influential structures in the history of learners' answers considering the Markov chain of different orders. The results are used to identify pupils who have difficulties with multiplications very soon (after couple of steps) and to optimize the way questions are asked for each pupil individually.