| Title: |
Sequential Reservoir Computing for Log File-Based Behavior Process Data Analyses |
| Language: |
English |
| Authors: |
Jiawei Xiong (ORCID 0000-0002-2069-8720); Shiyu Wang; Cheng Tang; Qidi Liu (ORCID 0000-0002-6797-4163); Rufei Sheng; Bowen Wang; Huan Kuang (ORCID 0000-0003-2651-2867); Allan S. Cohen; Xinhui Xiong |
| Source: |
Journal of Educational Measurement. 2026 63(1). |
| Availability: |
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: |
Y |
| Page Count: |
41 |
| Publication Date: |
2026 |
| Document Type: |
Journal Articles; Reports - Research |
| Descriptors: |
Data Use; Data Analysis; Computation; Computer Assisted Testing; Response Style (Tests); Algorithms; Sequential Approach; Data Collection |
| DOI: |
10.1111/jedm.12413 |
| ISSN: |
0022-0655; 1745-3984 |
| Abstract: |
The use of process data in assessment has gained attention in recent years as more assessments are administered by computers. Process data, recorded in computer log files, capture the sequence of examinees' response activities, for example, timestamped keystrokes, during the assessment. Traditional measurement methods are often inadequate for handling this type of data. In this paper, we proposed a sequential reservoir method (SRM) based on a reservoir computing model using the echo state network, with the particle swarm optimization and singular value decomposition as optimization. Designed to regularize features from process data through a computational self-learning algorithm, this method has been evaluated using both simulated and empirical data. Simulation results suggested that, on one hand, the model effectively transforms action sequences into standardized and meaningful features, and on the other hand, these features are instrumental in categorizing latent behavioral groups and predicting latent information. Empirical results further indicate that SRM can predict assessment efficiency. The features extracted by SRM have been verified as related to action sequence lengths through the correlation analysis. This proposed method enhances the extraction and accessibility of meaningful information from process data, presenting an alternative to existing process data technologies. |
| Abstractor: |
As Provided |
| Entry Date: |
2026 |
| Accession Number: |
EJ1501512 |
| Database: |
ERIC |