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A path signature perspective of process data feature extraction.

Xueying Tang¹, Jingchen Liu², Zhiliang Ying²

¹University of Arizona, Tucson, Arizona, USA.

The British Journal of Mathematical and Statistical Psychology

|May 26, 2025

Summary

This summary is machine-generated.

Analyzing educational assessment data, this study introduces a novel feature extraction method. By incorporating response time sequences alongside action sequences, it offers a more comprehensive understanding of respondent behavior in computer-based interactive tasks.

Keywords:

action sequence feature extraction path signature response process timestamp sequence

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Area of Science:

Educational Measurement
Data Science
Stochastic Analysis

Background:

Computer-based interactive items are increasingly used in educational assessments.
Response process data (log files) are complex, noisy, and nonstandard.
Existing feature extraction methods often overlook temporal information.

Purpose of the Study:

To develop a new feature extraction method for response process data.
To integrate both action and time sequence information.
To improve the analysis of complex human-computer interaction in assessments.

Main Methods:

Introduced a novel feature extraction method based on the path signature concept from stochastic analysis.
Incorporated both action sequences and response time sequences.
Applied the method to simulated and real-world data from the PIAAC study.

Main Results:

The proposed method effectively extracts features from noisy, diverse response process data.
Integrating time information provides a more nuanced view of respondent behavior.
Demonstrated improved understanding of respondent behaviors through a prediction framework.

Conclusions:

Feature extraction methods for educational assessment data can be enhanced by including temporal dynamics.
The path signature approach offers a robust way to analyze complex interaction data.
Considering response time alongside actions leads to a more comprehensive analysis of user behavior.