
# Dataset Card for LearningQ-qg

## Dataset Description


- **Repository:** [GitHub](https://github.com/AngusGLChen/LearningQ#readme)
- **Paper:** [LearningQ: A Large-scale Dataset for Educational Question Generation](https://ojs.aaai.org/index.php/ICWSM/article/view/14987/14837)
- **Point of Contact:** s.lamri@univ-bouira.dz

### Dataset Summary

LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs by [Guanliang Chen, Jie Yang, Claudia Hauff and Geert-Jan Houben]. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. This new version collected and corrected from over than 50000 error and more than 1500 type of error by [Sidali Lamri](https://dz.linkedin.com/in/sidali-lamri)

### Use the dataset

```python
from datasets import load_dataset
lq_dataset = load_dataset("sidovic/LearningQ-qg")
lq_dataset["train"][1]
len(lq_dataset["train"]),len(lq_dataset["validation"]),len(lq_dataset["test"])
```

### Supported Tasks and Leaderboards

[Question generation]

### Languages

[English]

## Dataset Structure

### Data Instances

An example of example looks as follows.
```
{
    
    "context": "This is a test context.",
    "questionsrc": "test context",
    "question": "Is this a test?"
}
```

### Data Fields

The data fields are the same among all splits.

- `context`: a `string` feature.
- `questionsrc`: a `string` feature.
- `question`: a `string` feature.
### Data Splits

|   name   |train |validation|test |
|----------|-----:|---------:|----:|
|LearningQ |188660|     20630|18227|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```
{
  author = {Sidali Lamri},
  title = {new LearningQ version for Question generation in transformers},
  year = {2023}
}
@paper{ICWSM18LearningQ,
	author = {Guanliang Chen, Jie Yang, Claudia Hauff and Geert-Jan Houben},
	title = {LearningQ: A Large-scale Dataset for Educational Question Generation},
	conference = {International AAAI Conference on Web and Social Media},
	year = {2018}
}
```

### Contributions

[More Information Needed]
task_categories:text-generation size_categories:100K<n<1M language:en license:unknown question generation region:us
{"language": ["en"], "license": "unknown", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "LeaningQ-qg", "tags": ["question generation"], "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "questionsrc", "dtype": "string"}, {"name": "question", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_examples": 188660}, {"name": "validation", "num_examples": 20630}, {"name": "test", "num_examples": 18227}]}, "train-eval-index": [{"config": "plain_text", "task": "question-generation", "task_id": "extractive_question_generation", "splits": {"train_split": "train", "eval_split": "validation", "test_split": "test"}, "col_mapping": {"context": "context", "questionsrc": "question source", "question": "question"}, "metrics": [{"type": "squad", "name": "SQuAD"}]}]}
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-unknown #question generation #region-us 

Dataset Card for LearningQ-qg
=============================


Dataset Description
-------------------


* Repository: GitHub
* Paper: LearningQ: A Large-scale Dataset for Educational Question Generation
* Point of Contact: s.lamri@URL


### Dataset Summary


LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs by [Guanliang Chen, Jie Yang, Claudia Hauff and Geert-Jan Houben]. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. This new version collected and corrected from over than 50000 error and more than 1500 type of error by Sidali Lamri


### Use the dataset


### Supported Tasks and Leaderboards


[Question generation]


### Languages


[English]


Dataset Structure
-----------------


### Data Instances


An example of example looks as follows.


### Data Fields


The data fields are the same among all splits.


* 'context': a 'string' feature.
* 'questionsrc': a 'string' feature.
* 'question': a 'string' feature.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions



### Dataset Summary


LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs by [Guanliang Chen, Jie Yang, Claudia Hauff and Geert-Jan Houben]. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. This new version collected and corrected from over than 50000 error and more than 1500 type of error by Sidali Lamri ### Use the dataset ### Supported Tasks and Leaderboards


[Question generation] ### Languages


[English]


Dataset Structure
----------------- ### Data Instances


An example of example looks as follows. ### Data Fields


The data fields are the same among all splits.


* 'context': a 'string' feature.
* 'questionsrc': a 'string' feature.
* 'question': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-unknown #question generation #region-us 
 ### Dataset Summary


LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs by [Guanliang Chen, Jie Yang, Claudia Hauff and Geert-Jan Houben]. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. This new version collected and corrected from over than 50000 error and more than 1500 type of error by Sidali Lamri ### Use the dataset ### Supported Tasks and Leaderboards


[Question generation] ### Languages


[English]


Dataset Structure
----------------- ### Data Instances


An example of example looks as follows. ### Data Fields


The data fields are the same among all splits.


* 'context': a 'string' feature.
* 'questionsrc': a 'string' feature.
* 'question': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions