ttn0011/pageguide_userstudy

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)

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

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

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:

@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 for details. All participant data is anonymised. No personally identifiable information is included.