Bibliographic record
Abstract
Since the first 1:1 laptop program was introduced in 1989 at the Ladies’ Methodist College in Australia (Johnstone, 2003), there have been numerous studies conducted on the benefits of 1:1 computing with school-aged children. Bebell (2005), Fadel and Lemke (2006), Livingstone (2006), and Russell, Bebell, and Higgins (2004) have all reported on increases in student achievement especially in writing, analysis, and research while Stevenson (1999) has noted improvement in standardized test scores. In fewer than twenty years, 1:1 computing programs have thrived in North America, Europe, Australia, and South America. The clear benefits to the students using laptops have been well documented to the extent that the professional literature demonstrates myriad advantages to using laptops in the classroom. As this study will show, there has been little discussion in the professional literature on how using laptops in the classroom affects the teachers. To this end, this chapter will outline my research findings with 12 laptops teachers who are transformed through technology. For the purposes of this chapter, I will define 1:1 computing classrooms as learning environments where every person in the classroom has a laptop computer with wireless Internet and printer capabilities for at least fifty percent of the day.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".