Causal models for learning technology

David Brokenshire, Vive Kumar

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

1 Citation (Scopus)

Abstract

New statistical methods allow discovery of causal models from observational data in some circumstances. These models permit both probabilistic and causal inference for models of reasonable size. Many domains can benefit from such methods. Educational research does not easily lend itself to experimental investigation. Research in laboratories is artificial while research in authentic environments is complex and difficult to control. The variables are typically hidden and change over the long term, making them challenging and expensive to investigate experimentally. We present an analysis of causal discovery algorithms and their applicability to educational research and learning technology, an engineered causal model of Self-Regulated Learning (SRL) theory based on the literature, and an evaluation of the potential for discovering such a model from observational data using the new statistical methods.

Original languageEnglish
Title of host publicationProceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
Pages262-264
Number of pages3
DOIs
Publication statusPublished - 2008
Event8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 - Santander, Spain
Duration: 1 Jul. 20085 Jul. 2008

Publication series

NameProceedings - The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008

Conference

Conference8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008
Country/TerritorySpain
CitySantander
Period1/07/085/07/08

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