Artificial Intelligence for Education
R6 object of the AIFETransformerMaker class
Base class for models using neural nets
R6 class for transformer creation
Transformer types
Number of cores for multiple tasks
Calculate standard classification measures
Check if all necessary python modules are available
Clean pytorch log of transformers
Calculate Cohen's Kappa
Create config for R interfaces
Generate description for text embeddings
Create directory if not exists
Create synthetic units
Data manager for classification tasks
Base R6 class for creation and definition of `.AIFE*Transformer-like...
Child R6 class for creation and training of BERT transformers
Child R6 class for creation and training of DeBERTa-V2 transformer...
Child R6 class for creation and training of Funnel transformers
Child R6 class for creation and training of Longformer transformer...
Child R6 class for creation and training of MPNet transformers
Child R6 class for creation and training of RoBERTa transformers
Transformer objects
Embedded text
Calculate Fleiss' Kappa
Generate ID suffix for objects
Country Alpha 3 Codes
Calculate reliability measures based on content analysis
Get file extension
Get the number of chunks/sequences for each case
Get versions of python components
Create synthetic cases for balancing training data
Install aifeducation on a machine
Installing necessary python modules to an environment
Check if NULL or NA
Calculate Kendall's coefficient of concordance w
Calculate Krippendorff's Alpha
Abstract base class for large data sets
Abstract class for large data sets containing raw texts
Abstract class for large data sets containing text embeddings
Server function for: graphical user interface for showing the license.
Loading objects created with 'aifeducation'
Load target data for long running tasks
Reshape matrix to array
Print message
Print message (message())
Server function for: graphical user interface for displaying the relia...
Graphical user interface for displaying the reliability of classifiers...
Run python file
Saving objects created with 'aifeducation'
Setting cpu only for 'tensorflow'
Setting gpus' memory usage
Sets the level for logging information in tensorflow
Sets the level for logging information in tensorflow
Sets the level for logging information of the 'transformers' library
Aifeducation Studio
Summarizing tracked sustainability data
Text embedding classifier with a ProtoNet
Text embedding classifier with a neural net
Feature extractor for reducing the number for dimensions of text embed...
Text embedding model
Transforming classes to one-hot encoding
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. This often leads to small data sets. Furthermore, in the educational and social sciences, data 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 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the creation of synthetic cases (e.g. Islam et al. (2012) <doi:10.1016/j.asoc.2021.108288>). 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 model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people.