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license: mit
task_categories:
- text-classification
language:
- en
pretty_name: Reading Performance Dataset
size_categories:
- n<1K
---
## 📘 Reading Performance Dataset
### 📌 Overview
The **Reading Performance Dataset** is a small-scale tabular dataset designed to analyze **reading behavior and performance characteristics across diverse individuals**.
It captures demographic attributes, reading habits, attention levels, and content difficulty to support educational data analysis and machine learning experiments.
This dataset is suitable for **educational analytics**, **behavioral analysis**, and **predictive modeling** related to reading performance.
### 📊 Dataset features
Each row represents a **single reading session**.
The dataset contains the following columns:
| Feature Name | Type | Description |
| ------------------------------- | ----------- | ----------------------------------------------------------------------------------------- |
| `AGE_CATEGORY` | Categorical | Age group of the reader (e.g., `young_adult`). |
| `GENDER` | Categorical | Gender of the participant (`male`, `female`, `other`). |
| `MAJOR` | Categorical | Academic major or role of the reader (e.g., `student`). |
| `MINUTES_READING` | Numeric | Total duration (in minutes) spent actively reading during the session. |
| `MINUTES_BREAK` | Numeric | Total break time (in minutes) taken during reading. |
| `FOCUS_LEVEL` | Ordinal | Level of focus during the reading session. Higher values indicate stronger concentration. |
| `PAGES` | Numeric | Number of pages read during the session. |
| `CONTENT_LEVEL_ENUM` | Ordinal | Difficulty level of the reading material. |
| `READING_GENRE` | Categorical | Genre of the reading material (e.g., academic, language, self-development). |
| `SOUND_VOLUME` | Ordinal | Environmental sound intensity during reading, ranging from silent to extremely loud. |
| `DEVICE` | Categorical | Device or medium used for reading (e.g., laptop, smartphone, book, tablet). |
| `LOCATION` | Categorical | Physical environment where the reading session took place (e.g., home, library, café). |
| `WEATHER` | Categorical | Weather condition during the reading session (e.g., sunny, rainy, cloudy). |
| `MOOD` | Ordinal | Emotional state of the reader during the session, from very bad to very good. |
| `HUNGER` | Ordinal | Hunger or satiety level during reading. |
| `AROUSAL` | Ordinal | Cognitive arousal level, from very low to very high. |
| `MENTAL_IN_BREAK` | Ordinal | Mental state during breaks (e.g., chaotic, neutral, calm). |
| `MENTAL_FATIGUE` | Ordinal | Level of mental fatigue experienced during the session. |
| `INTERESTING_LEVEL` | Ordinal | Degree of interest in the reading material. |
| `COMPREHENSION_DEPTH` | Ordinal | Depth of understanding achieved during reading. |
| `LINKING_TO_PREVIOUS_KNOWLEDGE` | Ordinal | Extent to which new content was connected to prior knowledge. |
### 🎯 Tasks
This dataset can be used for:
- Text / behavior classification
- Educational performance analysis
- Student reading habit modeling
- Correlation analysis between focus, time, and output
- Regression or classification experiments
Although categorized under **text classification**, the dataset is also applicable to **tabular machine learning tasks**.
### 🛠 Data Source & Generation
The dataset was **synthetically curated / collected for educational and analytical purposes**.
No personally identifiable information (PII) is included.
### ⚖️ License
This dataset is released under the **MIT License**, allowing free use, modification, and distribution with attribution.
### 🚀 Usage Example (Python)
#### Basic
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Bancie/Reading-Dataset")
# View dataset structure
print(dataset)
# Access the default split (usually 'train')
data = dataset['train']
print(f"Number of examples: {len(data)}")
print(f"Features: {data.features}")
# Access a specific example
print(data[0])
# Convert to pandas DataFrame for analysis
df = data.to_pandas()
print(df.head())
```
#### Streaming
For large datasets or when you want to process data without downloading it entirely:
```python
from datasets import load_dataset
# Load dataset in streaming mode
dataset = load_dataset("Bancie/Reading-Dataset", streaming=True)
# Iterate through examples
for example in dataset['train']:
print(example)
# Process your data here
break # Remove break to process all examples
```
### 📚 Citation
```bibtex
@misc{c.b_nguyen_2026,
author = { C.B Nguyen },
title = { Reading-Dataset (Revision 19cfbb0) },
year = 2026,
url = { https://huggingface.co/datasets/Bancie/Reading-Dataset },
doi = { 10.57967/hf/7416 },
publisher = { Hugging Face }
}
```
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