BioWorldParser: A suite of parsers for leveraging educational data mining techniques
Bibliographic record
Abstract
There has been a dramatic expansion in both the amount of available large-scale educational databases and educational mining techniques. Educational data mining has been a fertile subject of research in recent times; further, the use of educational data mining has become popular among both researchers and practitioners. Log files generated by computer-based learning environments like Intelligent Tutoring Systems contain a wealth of information about learner behaviors that characterize academic success. There is growing interest in mining these data sources for knowledge-based discovery to reveal relevant, meaningful, and useful educational information to illuminate our understanding of learners' behaviors and outcomes. All too often however, extracting the pertinent information from the data to leverage the data mining techniques can be a major roadblock; for example, the asynchronous nature of the data logged in computer-based learning environments and data mining tools pose several challenges for mining data. We sought to mitigate this by developing a parser for the BioWorld System. In this paper, we explore the viability of a hand-coded parser by presenting BioWorldParser (a suite of scripts), which was developed to parse and retrieve data from raw log files generated by the BioWorld system, to help leverage educational data mining techniques in the context of an Intelligent Tutoring System for the medical domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".