| | --- |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - education |
| | - learning analytics |
| | - educational data mining |
| | --- |
| | |
| | # Model Card for Model ID |
| |
|
| | <!-- Provide a quick summary of what the model is/does. --> |
| |
|
| | This is the EduBERT model used in the [EduBERT: Pretrained Deep Language Models for Learning Analytics](https://arxiv.org/abs/1912.00690) from LAK20. It is a fine-tuned version of BERT-base on educational data. |
| |
|
| | ## Model Description |
| |
|
| | We originally trained this model to support Learning Analytics task, showing it performed well on well-known educational text classification task. |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | The model is provided as-is, and trained on the data described in the paper. Learning Analytics is a complex field, and decisions should not be taken fully automatically by models. This model should be used for analysis and to inform only. |
| |
|
| | ## Citation |
| |
|
| | **BibTeX:** |
| |
|
| | ``` |
| | @inproceedings{clavié2019edubert, |
| | title={EduBERT: Pretrained Deep Language Models for Learning Analytics}, |
| | author={Benjamin Clavié and Kobi Gal}, |
| | year={2020}, |
| | booktitle={Companion Proceedings of the The 10th international Learning Analytics & Knowledge (LAK20)} |
| | } |
| | ``` |
