@inproceedings{f40220033b2f4bc18d682a2201d0f215,
title = "Techniques for data-driven curriculum analysis",
abstract = "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.",
keywords = "Curriculum design, Learning analytics",
author = "Gonzalo M{\'e}ndez and Xavier Ochoa and Katherine Chiluiza",
year = "2014",
doi = "10.1145/2567574.2567591",
language = "English (US)",
isbn = "1595930361",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "148--157",
booktitle = "LAK 2014",
note = "4th International Conference on Learning Analytics and Knowledge, LAK 2014 ; Conference date: 24-03-2014 Through 28-03-2014",
}