How Research On the Use Of Computer Technologies Can Inform the Work Of Social Studies Educators
Bibliographic record
Abstract
Computers technologies have much to offer social studies educators. This article provides a review of some of the suggestions from the current research on the use of computer technologies for enhancing the teaching of and students' learning in social studies. All educators are encouraged to continue to think of ways to take best advantage of these tools in order to maximize the benefits for their students and to best prepare them for survival in the information society. In today's technologically driven society information has taken on a new importance as a commodity (Diem, 1997). The endless, rapid flood of information and disinformation is causing a great deal of confusion and frustration; those who are ill equipped to handle the information overload run the risk of falling behind those who have embraced the latest computer technologies (Titus, 1994) More and more pressure is being placed on schools to ensure mastery of technological skills essential to survival in this new society. "The Internet, for example, is entering classrooms at a rate faster than books, newspapers, magazines, movies, overhead projectors, television or even telephones" (Leu 2000, p. 425). The pressure to computerize has had important implications for social studies educators. This article offers some suggestions for the integration of computers into teaching and learning social studies based on a review of some of the current research on computers as learning tools.
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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 | 0.041 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.026 | 0.048 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".