Unsupervised Anomaly Detection in Sequential Process Data
Bibliographic record
Abstract
Abstract: In this study, we present three types of unsupervised anomaly detection to identify anomalous test-takers based on their action sequences in problem-solving tasks. The first method relies on the use of the Isolation Forest algorithm to detect anomalous test-takers based on raw action sequences extracted from process data. The second method transforms raw action sequences into contextual embeddings using the Bidirectional Encoder Representations from Transformers (BERT) model and then applies the Isolation Forest algorithm to detect anomalous test-takers. The third method follows the same procedure as the second method, but it includes an intermediary step of dimensionality reduction for the contextual embeddings before applying the Isolation Forest algorithm for detecting anomalous cases. To compare the outcomes of the three methods, we analyze the log files from test-takers in the US sample ( n = 2,021) who completed the problem-solving in technology-rich environments (PSTRE) section of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012 assessment. The results indicated that different groups of test-takers were flagged as anomalous depending on the representation (raw action sequences vs. contextual embeddings) and dimensionality of action sequences. Also, when the contextual embeddings were used, a larger number of test-takers were flagged by the Isolation Forest algorithm, indicating the sensitivity of this algorithm to the dimensionality of input data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".