The Invisible Researcher: Using Educational Technologies as Research Tools for Education
Bibliographic record
Abstract
As educational technologies become more commonplace, they are often created with the intention of benefiting students through some novel approach, or to fill a perceived educational gap. While these rationales are good ones, it should also be realized that through the use of innovative technologies educators and researchers alike are presented with a unique and powerful opportunity to conduct laboratory-like research in a naturalistic environment. Thus giving the invisible "researcher" the ability to test the desired effectiveness of the tool, and to use the tool as a vehicle to understand learning, all in an unobtrusive manner. This not only ensures that new educational technologies are doing what they were designed to do, but also promises to create pedagogically superior tools and an improved learning environment for both students and educators. To illustrate how this can be successfully implemented, two evidence-based technologies are discussed (the webOption and peerScholar) where research has assisted in tool development and also furthered our understanding of educational theory.
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 | 0.111 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".