An approach to predict the task efficiency of web pages

Area of Science:

• Human-Computer Interaction
• Web Usability Engineering
• Computational Modeling

Background:

• Usability is crucial for website interface efficacy, encompassing factors like efficiency, learnability, and aesthetics.
• Predicting user task efficiency on websites is essential for optimizing user experience.

Purpose of the Study:

• To propose a computational model for predicting user task efficiency on website interfaces.
• To identify and validate key web page features influencing user efficiency.

Main Methods:

• Seventeen web page features were analyzed for their impact on task efficiency.
• Empirical data was collected from twenty websites with representative user tasks.
• One-dimensional ANOVA was used to test the statistical significance of features.
• A Support Vector Regression model was developed using statistically significant features.

Main Results:

• Sixteen out of seventeen analyzed web page features were found to be statistically significant for efficiency measurement.
• The developed computational model achieved a prediction accuracy of 90.64% for user task efficiency.

Conclusions:

• The proposed computational model effectively predicts user task efficiency on websites.
• Key web page features can be leveraged to enhance website usability and user performance.