The Learning Sciences in Educational Assessment: An Introduction
Bibliographic record
Abstract
Victor Hugo is credited with stating that “There is nothing more powerful than an idea whose time has come.” In educational achievement testing, a multi-billion-dollar activity with profound implications for individuals, governments, and countries, the idea whose time has come, it seems, is that large-scale achievement tests must be designed according to the science of human learning . Why this idea, and why now? To begin to set a context for this idea and this question, a litany of research studies and public policy reports can be cited to make the simple point that students in the United States and abroad are performing relatively poorly in relation to expected standards and projected economic growth requirements (e.g., American Association for the Advancement of Science, 1993; Chen, Gorin, Thompson, & Tatsuoka, 2008; Grigg, Lauko, & Brockway, 2006; Hanushek, 2003, 2009; Kilpatrick, Swafford, & Findell, 2001; Kirsch, Braun, & Yamamoto, 2007; Manski & Wise, 1983; Murnane, Willet, Dulhaldeborde, & Tyler, 2000; National Commission on Excellence in Education, 1983; National Mathematics Advisory Panel, 2008; National Research Council, 2005, 2007, 2009; Newcombe et al., 2009; Phillips, 2007; Provasnik, Gonzales, & Miller, 2009). According to a 2007 article in the New York Times , Gary Phillips, chief scientist at the American Institutes for Research, was quoted as saying, “our Asian economic competitors are winning the race to prepare students in math and science.”
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".