In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms or the use of data is restricted by privacy policies, often leading to small data sets. Furthermore, data in the educational and social sciences is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'keras', 'tensorflow', and 'pytorch' to deal with natural language problems. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Lee (2013) <https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficient_Semi-Supervised_Learning_Method_for_Deep_Neural_Networks>, Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the creation of synthetic cases (e.g. Bunkhumpornpat et al. (2012) <doi:10.1007/s10489-011-0287-y>). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) <doi:10.30819/5581>, Gwet (2014) <ISBN:978-0-9708062-8-4>, Krippendorff (2019) <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2 emissions during training models is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI with other people.
|Depends:||R (≥ 3.5.0)|
|Imports:||abind, foreach, doParallel, iotarelr (≥ 0.1.4), irr, irrCAC, methods, Rcpp (≥ 1.0.10), reshape2, reticulate, smotefamily, stringr, utils|
|Suggests:||text2vec, tidytext, topicmodels, udpipe, quanteda, quanteda.textmodels, knitr, rmarkdown, testthat (≥ 3.0.0)|
|Author:||Berding Florian [aut, cre], Pargmann Julia [ctb], Riebenbauer Elisabeth [ctb], Rebmann Karin [ctb], Slopinski Andreas [ctb]|
|Maintainer:||Berding Florian <florian.berding at uni-hamburg.de>|
|Citation:||aifeducation citation info|
|CRAN checks:||aifeducation results|
01 Get started
02 Classification Tasks
Sharing and Using Trained AI/Models
|Windows binaries:||r-devel: aifeducation_0.3.0.zip, r-release: aifeducation_0.3.0.zip, r-oldrel: aifeducation_0.3.0.zip|
|macOS binaries:||r-release (arm64): aifeducation_0.3.0.tgz, r-oldrel (arm64): aifeducation_0.3.0.tgz, r-release (x86_64): aifeducation_0.3.0.tgz, r-oldrel (x86_64): not available|
|Old sources:||aifeducation archive|
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