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Quantifying Work

Quantifying Work

As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.

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Updated: Dec 23, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

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Published on: June 16, 2018

Self-reported data for mental workload modelling in human-computer interaction and third-level education.

Lucas Rizzo¹, Luca Longo¹

¹Technological University Dublin, Republic of Ireland.

|April 24, 2020

Summary

This summary is machine-generated.

This study presents a dataset for mental workload (MWL) assessment, offering valuable data for machine learning and knowledge-based systems. The findings support the development of new MWL modeling techniques.

Keywords:

Argumentation theory Automated reasoning Expert systems Fuzzy reasoning Knowledge-based systems Mental workload

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Last Updated: Dec 23, 2025

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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Area of Science:

Cognitive Psychology
Human-Computer Interaction
Artificial Intelligence

Background:

Mental workload (MWL) lacks a precise definition and standardized measurement.
Existing approaches to MWL modeling and assessment are varied.
Understanding factors influencing MWL is crucial for task design and performance.

Purpose of the Study:

To report a dataset comprising 872 records of user responses to questionnaires assessing factors influencing mental workload.
To provide data for machine learning researchers for predictive analytics in MWL assessment.
To offer a knowledge base for researchers in knowledge-based systems and automated reasoning.

Main Methods:

Data collected from questionnaires administered to participants after performing tasks under varying conditions.
Questionnaires designed to capture features identified by domain experts as influential to MWL.
Dataset includes 872 records, each representing a user's task performance and subjective assessment.

Main Results:

A comprehensive dataset on user-reported factors influencing mental workload is now available.
The data is suitable for training predictive models for MWL assessment.
The dataset can inform the development of rule-based systems for reasoning about MWL.

Conclusions:

The presented data serves as a valuable resource for advancing research in mental workload.
Facilitates machine learning applications for objective MWL evaluation.
Supports the creation of knowledge-based systems for automated reasoning on cognitive load.