Bibliographic record
Abstract
Computers have become commonplace tools in educational environments and are used to provide both basic and supplemental instruction to students on a variety of topics. Searching for information in hypermedia documents, whether on the Web or through individual educational sites, is a common task in learning activities. Previous research has identified a number of variables that impact how students use electronic documents. Individual differences such as learning style or cognitive style (Andris, 1996; Fitzgerald & Semrau, 1998), prior topic knowledge (Ford & Chen, 2000), level of interest (Lawless & Kulikowich, 1998), and gender (Beasley & Vila, 1992) all influence performance. Additionally, characteristics of the document such as the inherent structure of the material, the linking structure (Korthauer & Koubek, 1994), and the types of navigation tools that accompany the document can affect student performance and behaviour (Boechler & Dawson, 2002; McDonald & Stevenson, 1998, 1999). In short, the effective use of hypermedia documents in educational settings depends on complex interactions between individual skills (e.g., spatial and reading skills) and the features of the document itself.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".