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aifeducation

The R package Artificial Intelligence for Education (aifeducation) is designed for the special requirements of educators, educational researchers, and social researchers. The target audience of this package are educators and researchers with no coding skills who would like to develop their own models, as well as people who would like to use those models created by other researchers/educators. The package supports the application of Artificial Intelligence (AI) for Natural Language Processing tasks such as text embedding and classification under the special conditions of the educational and social sciences.

Features Overview

  • Simple usage of artificial intelligence by providing routines for the most important tasks of educators and researchers from social and educational sciences.
  • Provides a graphical user interface (Aifeducation Studio), allowing users to work with AI without the need for coding skills.
  • Supports ‘PyTorch’ as the core machine learning framework which is widely used in research.
  • Implements the advantages of the python library ‘datasets’, increasing computational speed and allowing the use of large data sets.
  • Uses safetensors for saving models in ‘PyTorch’.
  • Supports pre-trained language models from Hugging Face.
  • Supports MPNet, BERT, RoBERTa, DeBERTa, Longformer, and Funnel Transformer for creating context-sensitive text embedding.
  • Makes sharing pre-trained models very easy.
  • Integrates sustainability tracking.
  • Integrates special statistical techniques for dealing with data structures common in the social and educational sciences.
  • Supports the classification of long text documents.

Currently, the package focuses on classification tasks which can either be used to diagnose characteristics of learners from written material or to estimate the properties of learning and teaching material. In the future, more tasks will be implemented.

Installation

You can install the latest stable version of the package from CRAN with:

install.packages("aifeducation")

You can install the development version of aifeducation from GitHub with:

install.packages("devtools")
devtools::install_github(repo="FBerding/aifeducation",
                         ref="master",
                         dependencies = "Imports")

Further instructions for installation can be found in vignette 01 Get Started.

Please note that an update of your version of aifeducation may require an update of your python libraries. Refer to 01 Get Started for more details.

How to use the package with R syntax is described in vignette 03 Using R syntax.

Graphical User Interface AI for Education - Studio

The package ships with a shiny app that serves as a graphical user interface.

Figure 1: Aifeducation Studio

AI for Education - Studio allows users to easily develop, train, apply, document, and analyse AI models without any coding skills. See the corresponding vignette for more details: 02 Using the graphical user interface Aifeducation - Studio.

Sustainability

Training AI models consumes time and energy. To help researchers estimate the ecological impact of their work, a sustainability tracker is implemented. It is based on the python library ‘codecarbon’ by Courty et al. (2023). This tracker allows to estimate the energy consumption for CPUs, GPUs and RAM during training and derives a value for CO2 emission. This value is based on the energy mix in the country where the computer is located.

PyTorch and Tensorflow Compatibility

The core machine learning framework of this package is ‘PyTorch’, providing a broad support of graphical devices to accelerate computations, access to new and unique model architectures, and a high compatibility of models across different versions of this machine learning framework.

‘Tensorflow’ is also supported but only for version 2.16 and not for all models. Please refer to appendix A01 Supported Machine Learning Frameworks for a detailed overview.Tensorflow support will be removed with version 1.1.0 of this package.

Model Life Cycle

Research requires reproducibility and traceability. Thus, starting with version 1.0.0 of this package, it has top priority to ensure that already trained models work with future versions of this package.

Classification Tasks

Transforming Texts into Numbers

Classification tasks require the transformation of raw texts into a representation with numbers. For this step, aifeducation supports new approaches such as MPNet (Song et al. 2020), BERT (Devlin et al. 2019), RoBERTa (Liu et al. 2019), DeBERTa version 2 (He et al. 2020), Funnel-Transformer (Dai et al. 2020), and Longformer (Beltagy, Peters & Cohan 2020).

aifeducation supports the use of pre-trained transformer models provided by Hugging Face and the creation of new transformers, allowing educators and researchers to develop specialized and domain-specific models. See 04 Model configuration for details about the configuration of a new model.

The package supports the analysis of long texts. Depending on the method, long texts are transformed into vectors at once, or, if too long, are split into several chunks which results in a sequence of vectors.

Training AI under Challenging Conditions

For the second step within a classification task, aifeducation integrates some important statistical and mathematical methods for dealing with the main challenges in educational and social sciences for applying AI. These are:

  • digital data availability: In the educational and social sciences, data is often only available in handwritten form. For example, in schools or universities, students often solve tasks by creating handwritten documents. Thus, educators and researchers first have to transform analogue data into a digital form, involving human action. This makes data generation expensive and time-consuming, leading to small data sets.
  • high privacy policy standards: Furthermore, in the educational and social sciences, data often refers to humans and/or their actions. These kinds of data are protected by privacy policies in many countries, limiting access to and the usage of data, which also results in small data sets.
  • long research tradition: Educational and social sciences have a long research tradition in generating insights into social phenomena as well as learning and teaching. These insights have to be incorporated into applications of AI (e.g., Luan et al. 2020; Wong et al. 2019). This makes supervised machine learning a very important technology since it provides a link between educational and social theories or models on the one hand and machine learning on the other hand (Berding et al. 2022). However, this kind of machine learning requires humans to generate a valid data set for the training process, leading to small data sets.
  • complex constructs: Compared to classification tasks where, for instance, AI has to differentiate between a ‘good’ or a ‘bad’ movie review, constructs in the educational and social sciences are more complex. For example, some research instruments in motivational psychology require to infer personal motifs from written essays (e.g., Gruber & Kreuzpointner 2013). A reliable and valid interpretation of this kind of information requires well qualified human raters, making data generation expensive. This also limits the size of a data set.
  • imbalanced data: Finally, data in the educational and social sciences often occurs in an imbalanced pattern as several empirical studies show (Bloemen 2011; Stütz et al. 2022). Imbalanced means that some categories or characteristics of a data set have very high absolute frequencies compared to other categories and characteristics. Imbalance during AI training guides algorithms to focus and prioritize the categories and characteristics with high absolute frequencies, increasing the risk to miss categories/characteristics with low frequencies (Haixiang et al. 2017). This can lead AI to prefer special groups of people/material, imply false recommendations and conclusions, or to miss rare categories or characteristics.

In order to deal with the problem of imbalanced data sets, the package integrates the Synthetic Minority Oversampling Technique into the learning process. Currently, the Basic Synthetic Minority Oversampling Technique (Chawla et al. 2002), Density-Bases Synthetic Minority Oversampling Technique (Bunkhumpornpat, Sinapiromsaran & Lursinsap 2012), and Adaptive Synthetic Sampling Approach for Imbalanced Learning (Hem Garcia & Li 2008) are implemented via the R package smotefamiliy.

In order to address the problem of small data sets, training loops of AI integrate pseudo-labeling (e.g., Lee 2013). Pseudo-labeling is a technique which can be used for supervised learning. More specifically, educators and researchers rate a part of a data set and train AI with this very part. The remainder of the data is not processed by humans. Instead, AI uses this part of data to learn on its own. Thus, educators and researchers only have to provide additional data for the AI’s learning process without coding it themselves. This offers the possibility to add more data to the training process and reduce labor cost.

Evaluating Performance

Classification tasks in machine learning are comparable to the empirical method of content analysis from the social sciences. This method looks back on a long research tradition and an ongoing discussion on how to evaluate the reliability and validity of generated data. In order to provide a link to this research tradition and to provide educators as well as educational and social researchers with performance measures they are more familiar with, every AI trained with this package is evaluated with the following measures and concepts:

  • Iota Concept of the Second Generation (Berding & Pargmann 2022)
  • Krippendorff’s Alpha (Krippendorff 2019)
  • Percentage Agreement
  • Gwet’s AC1/AC2 (Gwet 2014)
  • Kendall’s coefficient of concordance W
  • Cohen’s Kappa unweighted (Cohen 1960)
  • Cohen’s Kappa with equal weights (Cohen 1968)
  • Cohen’s Kappa with squared weights (Cohen 1968)
  • Fleiss’ Kappa for multiple raters without exact estimation (Fleiss

In addition, some traditional measures from machine learning literature are also available:

  • Precision
  • Recall
  • F1-Score

Sharing Trained AI

Since the package is based on ‘PyTorch’ and the transformer library, every trained AI can be shared with other educators and researchers. The package supports an easy use of pre-trained AI within R, but also provides the possibility to export trained AI to other environments.

Using a pre-trained AI for classification only requires the classifier and the corresponding text embedding model. Use AI for Education Studio or just load both to R and start predictions. Vignette 02 Using the graphical user interface Aifeducation - Studio describes how to use the user interface. Vignette 03 Using R syntax describes how to save and load the objects with R syntax. In vignette 05 Sharing and Using Trained AI/Models you can find a detailed guide on how to document and share your models.

Tutorial and Guides

References

Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The Long-Document Transformer. https://doi.org/10.48550/arXiv.2004.05150

Berding, F., & Pargmann, J. (2022). Iota Reliability Concept of the Second Generation. Berlin: Logos. https://doi.org/10.30819/5581

Berding, F., Riebenbauer, E., Stütz, S., Jahncke, H., Slopinski, A., & Rebmann, K. (2022). Performance and Configuration of Artificial Intelligence in Educational Settings.: Introducing a New Reliability Concept Based on Content Analysis. Frontiers in Education, 1-21. https://doi.org/10.3389/feduc.2022.818365

Bloemen, A. (2011). Lernaufgaben in Schulbüchern der Wirtschaftslehre: Analyse, Konstruktion und Evaluation von Lernaufgaben für die Lernfelder industrieller Geschäftsprozesse. Hampp.

Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2012). DBSMOTE: Density-Based Synthetic Minority Over-sampling Technique. Applied Intelligence, 36(3), 664–684. https://doi.org/10.1007/s10489-011-0287-y

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

Cohen, J (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220. https://doi.org/10.1037/h0026256

Cohen, J (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104

Courty, B., Schmidt, V., Goyal-Kamal, Coutarel, M., Feld, B., Lecourt, J., & … (2023). mlco2/codecarbon: v2.2.7. https://doi.org/10.5281/zenodo.8181237

Dai, Z., Lai, G., Yang, Y. & Le, Q. V. (2020). Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. https://doi.org/10.48550/arXiv.2006.03236

Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423

Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382. https://doi.org/10.1037/h0031619

Gruber, N., & Kreuzpointner, L. (2013). Measuring the reliability of picture story exercises like the TAT. PloS One, 8(11), e79450. https://doi.org/10.1371/journal.pone.0079450

Gwet, K. L. (2014). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters (Fourth edition). STATAXIS.

Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220–239. https://doi.org/10.1016/j.eswa.2016.12.035

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1322–1328). IEEE. https://doi.org/10.1109/IJCNN.2008.4633969

He, P., Liu, X., Gao, J. & Chen, W. (2020). DeBERTa: Decoding-enhanced BERT with Disentangled Attention. https://doi.org/10.48550/arXiv.2006.03654

Krippendorff, K. (2019). Content Analysis: An Introduction to Its Methodology (4th Ed.). SAGE.

Lee, D.‑H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. CML 2013 Workshop: Challenges in Representation Learning.

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. https://doi.org/10.48550/arXiv.1907.11692

Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C.‑C. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 1–11. https://doi.org/10.3389/fpsyg.2020.580820

Song, K., Tan, X., Qin, T., Lu, J. & Liu, T.‑Y. (2020). MPNet: Masked and Permuted Pre-training for Language Understanding. https://doi.org/10.48550/arXiv.2004.09297

Stütz, S., Berding, F., Reincke, S., & Scheper, L. (2022). Characteristics of learning tasks in accounting textbooks: an AI assisted analysis. Empirical Research in Vocational Education and Training, 14(1). https://doi.org/10.1186/s40461-022-00138-2

Wong, J., Baars, M., Koning, B. B. de, van der Zee, T., Davis, D., Khalil, M., Houben, G.‑J., & Paas, F. (2019). Educational Theories and Learning Analytics: From Data to Knowledge. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing Learning Analytics to Support Study Success (pp. 3–25). Springer. https://doi.org/10.1007/978-3-319-64792-0_1