Book chapter
Autoencoders for Educational Assessment
Artificial Intelligence in Education, pp.41-45
Lecture Notes in Computer Science, Springer International Publishing
06/21/2019
DOI: 10.1007/978-3-030-23207-8_8
Abstract
In educational assessment research, a common goal is to determine students’ knowledge about some construct. This knowledge is latent and can be represented by continuous variables which influence the individual’s performance on a test. Item response theory (IRT) models structure this relation, defining specific functions between the knowledge of the individual, and the probability of answering an item correctly. Previous research implies that neural networks can emulate these models, and, with a modification in its architecture, overcome some of the limitations concerned to “big data” analysis. In this work, we compare two different types of neural networks for this application: autoencoders (AE) and variational autoencoders (VAE). Not only can these neural networks be used as similar predictive models, but they can recover and interpret parameters in the same way as in the IRT approaches.
Details
- Title: Subtitle
- Autoencoders for Educational Assessment
- Creators
- Geoffrey Converse - University of IowaMariana Curi - Universidade de São PauloSuely Oliveira - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Artificial Intelligence in Education, pp.41-45
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-030-23207-8_8
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 06/21/2019
- Academic Unit
- Mathematics; Computer Science
- Record Identifier
- 9984259425302771
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