New Evidence on Classroom Computers and Pupil Learning
Bibliographic record
Abstract
How technology affects learning has been at the centre of recent debates over educational inputs. In 1994, the Israeli State Lottery sponsored the installation of computers in many elementary and middle schools. This programme provides an opportunity to estimate the impact of computerisation on both the instructional use of computers and pupil achievement. Results from a survey of Israeli school‐teachers show that the influx of new computers increased teachers’ use of computer‐aided instruction (CAI). Although many of the estimates are imprecise, CAI does not appear to have had educational benefits that translated into higher test scores. That small miracle can be replicated in every school, rich and poor, across America ... Every child in American deserves a chance to participate in the information revolution. President Clinton, at the East Somerville Community School, 5 June 1998. We could do so much to make education available 24 hours a day, seven days a week, that people could literally have a whole different attitude toward learning. Newt Gingrich talking to the Republican National Committee, quoted in Oppenheimer (1997). Netanyahu explained to a group of politicians and computer professionals how he wanted to provide a quarter‐million of his country's toddlers with interconnected computers. Recounted by MIT computer scientist Michael Dertouzos, September 1998.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.002 |
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".