Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning analytics: drivers, developments and challenges
International Journal of Technology Enhanced Learning
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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One of the key promises of Learning Analytics research is to create tools that could help educational institutions to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, dependance estimation, curriculum coherence, dropout paths and load/performance graph. The description of these techniques is accompanied by its application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.