README.md

---
license: mit
task_categories:
  - other
language:
  - en
tags:
  - browser-extension
  - user-study
  - human-computer-interaction
  - web-agents
  - within-subjects
pretty_name: PageGuide User Study
size_categories:
  - n<1K
---

# PageGuide User Study Dataset
Link to the project: https://pageguide.github.io/

Link to the paper: https://huggingface.co/papers/2604.23772

Link to the code: https://github.com/tin-xai/pageguide

This dataset contains raw interaction data from a controlled within-subjects user study evaluating **PageGuide: Browser extension to assist users in navigating a webpage and locating information**, an AI-powered browser extension that helps users complete web tasks via natural language.

Participants performed tasks in two conditions — **with** and **without** the extension — across three task types: `find`, `guide`, and `hide`. Collected metrics include task-completion times, accuracy scores, and post-study survey responses.

---

## Study Design

| Property | Value |
|---|---|
| Design | Counterbalanced within-subjects |
| Conditions | `extension` (PageGuide active) vs. `control` (no extension) |
| Task types | `find` · `guide` · `hide` |
| Primary metrics | Completion time, accuracy, survey ratings |

**Task types**
- **find** — locate or highlight specific information on a webpage
- **guide** — follow step-by-step instructions to complete a multi-step web action
- **hide** — filter or conceal unwanted content on a webpage

Participants were randomly assigned a counterbalanced order so that each person experienced both conditions. Task questions are labelled `q0`, `q1`, `q2`, etc.

---

## Files

### `tasks.csv` (2.29 MB)
Raw per-interaction log — the main data file. Each row is one task attempt.

| Column | Description |
|---|---|
| `session_id` | Unique participant session identifier |
| `condition` | `extension` or `control` |
| `task_type` | `find`, `guide`, or `hide` |
| `question_id` | Task question index within its type (`q0`, `q1`, …) |
| `start_time` | Unix timestamp (ms) when the task started |
| `end_time` | Unix timestamp (ms) when the task ended |
| `duration_s` | Elapsed time in seconds |
| `completed` | Boolean — whether the participant marked the task complete |
| `accuracy` | Graded accuracy score (0–1 or 0–100) for find/hide tasks |
| `query` | The natural-language query the participant typed (extension condition only) |
| `chat_turns` | Number of chat interactions in the extension condition |

### `sessions.csv` (8.28 kB)
One row per participant session — demographic and counterbalancing metadata.

| Column | Description |
|---|---|
| `session_id` | Matches `tasks.csv` |
| `participant_id` | Anonymised participant label |
| `order` | Condition order assigned (`extension_first` or `control_first`) |
| `started_at` | Session start timestamp |
| `web_experience` | Self-reported web experience level |

### `paired_times.csv` (10.8 kB)
Pre-processed paired completion times — one row per participant × task, ready for paired statistical tests.

| Column | Description |
|---|---|
| `participant_id` | Anonymised participant label |
| `task_type` | `find`, `guide`, or `hide` |
| `question_id` | Task question index |
| `time_extension` | Completion time (s) in the extension condition |
| `time_control` | Completion time (s) in the control condition |
| `time_diff` | `time_control − time_extension` (positive = extension faster) |

### `summary.csv` (85.2 kB)
Aggregated per-participant × per-task-type summary statistics (mean time, accuracy, completion rate) for both conditions. Useful for quick group-level analysis.

### `stats_results.csv` (227 bytes)
Results of the paired statistical tests (Wilcoxon signed-rank / paired t-test) run on completion times and accuracy. One row per metric × task-type comparison.

| Column | Description |
|---|---|
| `metric` | e.g., `duration_s`, `accuracy` |
| `task_type` | `find`, `guide`, `hide`, or `all` |
| `test` | Statistical test used |
| `statistic` | Test statistic |
| `p_value` | p-value |
| `significant` | Boolean (α = 0.05) |

### `survey_summary.csv` (415 bytes)
Aggregated post-study questionnaire scores per condition. Covers perceived usability (SUS-style) and cognitive load (NASA-TLX-style) dimensions.

| Column | Description |
|---|---|
| `condition` | `extension` or `control` |
| `dimension` | Survey dimension name |
| `mean` | Mean rating |
| `std` | Standard deviation |
| `n` | Number of responses |

---

## Quick Start

### Load with the 🤗 `datasets` library (recommended)

```python
from datasets import load_dataset

# Load individual files as named splits
tasks    = load_dataset("ttn0011/pageguide_userstudy", data_files="tasks.csv",           split="train").to_pandas()
paired   = load_dataset("ttn0011/pageguide_userstudy", data_files="paired_times.csv",    split="train").to_pandas()
sessions = load_dataset("ttn0011/pageguide_userstudy", data_files="sessions.csv",        split="train").to_pandas()
survey   = load_dataset("ttn0011/pageguide_userstudy", data_files="survey_summary.csv",  split="train").to_pandas()
stats    = load_dataset("ttn0011/pageguide_userstudy", data_files="stats_results.csv",   split="train").to_pandas()

# Mean completion time by condition and task type
tasks.groupby(["condition", "task_type"])["duration_s"].mean()

# Paired time difference (positive = extension faster)
paired.groupby("task_type")["time_diff"].mean()

# Survey ratings side by side
survey.pivot(index="dimension", columns="condition", values="mean")
```

### Or load directly with pandas

```python
import pandas as pd

BASE = "https://huggingface.co/datasets/ttn0011/pageguide_userstudy/resolve/main/"

tasks    = pd.read_csv(BASE + "tasks.csv")
paired   = pd.read_csv(BASE + "paired_times.csv")
sessions = pd.read_csv(BASE + "sessions.csv")
survey   = pd.read_csv(BASE + "survey_summary.csv")
stats    = pd.read_csv(BASE + "stats_results.csv")
```

### Reproduce the paired-time plot

```python
import matplotlib.pyplot as plt
from datasets import load_dataset

paired = load_dataset("ttn0011/pageguide_userstudy", data_files="paired_times.csv", split="train").to_pandas()

fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=False)
for ax, task in zip(axes, ["find", "guide", "hide"]):
    subset = paired[paired["task_type"] == task]
    for _, row in subset.iterrows():
        ax.plot([0, 1], [row["time_control"], row["time_extension"]],
                color="steelblue", alpha=0.4, linewidth=1)
    ax.set_xticks([0, 1])
    ax.set_xticklabels(["Control", "Extension"])
    ax.set_title(task.capitalize())
    ax.set_ylabel("Completion time (s)")
plt.tight_layout()
plt.savefig("paired_times.png", dpi=150)
```

---

## Citation

If you use this dataset, please cite the associated paper:

```bibtex
@misc{pageguide2025,
  title  = {PageGuide: Browser Extension to Assist Users in Navigating a Webpage and Locating Information},
  author = {Tin Nguyen and others},
  year   = {2025},
  note   = {User study data: \url{https://huggingface.co/datasets/ttn0011/pageguide_userstudy}}
}
```

---

## License

MIT — see [LICENSE](LICENSE) for details.
All participant data is anonymised. No personally identifiable information is included.