Techniques for data-driven curriculum analysis

Gonzalo Méndez, Xavier Ochoa, Katherine Chiluiza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLAK 2014
Subtitle of host publication4th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages148-157
Number of pages10
ISBN (Print)1595930361, 9781595930361
DOIs
StatePublished - 2014
Event4th International Conference on Learning Analytics and Knowledge, LAK 2014 - Indianapolis, IN, United States
Duration: Mar 24 2014Mar 28 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other4th International Conference on Learning Analytics and Knowledge, LAK 2014
CountryUnited States
CityIndianapolis, IN
Period3/24/143/28/14

Keywords

  • Curriculum design
  • Learning analytics

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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