A systematic review of the use of log-based process data in computer-based assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent decades, log-based process data has been increasingly used in computer-based assessments to examine test-takers' response patterns and latent traits. This study provides a systematic review of the use of log-based process data in computer-based assessments. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, we identified 2,548 publications, of which 330 were finally included in this study after careful screening and full-text review. The results of this study can assist researchers in better understanding: (1) what are the trends in using log-based process data in computer-based assessments, (2) which process indicators have been constructed from raw log files, (3) what latent constructs have been inferred from process indicators and at what inferential levels, and (4) what are the benefits, challenges, and future recommendations for using log-based process data. By examining these questions, we conclude that the use of log-based process data in computer-based assessment shows many potentials for enhancing the assessment. Therefore, more study using log-based process data in various fields is encouraged to better understand test-takers’ underlying response processes during assessments. Additionally, there is also a considerable demand for validating process indicators and the generalizability of findings. • Provides the overall trends in the use of log-based process data in computer-based assessments. • Categorized the types of process data that has been used in computer-based assessments. • Provided an overview of commonly investigated latent constructs based on process data. • Summarized benefits and challenges for using process data. • Suggested future directions for in-depth use of process data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it