Pith. sign in

REVIEW 1 cited by

NASA-TLX Web App: An Online Tool to Analyse Subjective Workload

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2001.09963 v1 pith:MW3HVNRC submitted 2020-01-27 cs.HC

Vinoth Pandian Sermuga Pandian , Sarah Suleri This is my paper · ORCID
classification cs.HC
keywords nasa-tlxsubjectiveworkloaddemandexperimenterexperimentersprovidesresults
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

NASA Task Load Index (NASA-TLX) is a widely used assessment technique to compute subjective workload experienced during a task. It evaluates workload using six dimensions: mental demand, physical demand, temporal demand, frustration, effort, and performance. This paper presents a web app to assist experimenters in using NASA-TLX to commute subjective workload. The web app enables the experimenter to conduct various experiments simultaneously and offers the participants a concise interface to provide their subjective evaluation. It performs the calculations at the backend and provides the computed results comprehensively. The web app provides a dashboard for the experimenter to visualize and export the summary of results. Qualitative feedback from 12 experimenters indicated that the NASA-TLX web app is relevant, helpful, and easy to use.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings

    cs.HC 2026-01 conditional novelty 6.0 of 10

    LLMs can partially predict individual and sample-level biased decisions in conversational settings, with GPT-4 aligning best, but reproduction of cognitive-load interactions is inconsistent.

Pith tools