How Can Social Studies Teachers Best Use The Internet With Young Learners?
Bibliographic record
Abstract
The most effective integration of technology for enhancing learning in social studies has been found to engage students in inquiry centered around authentic, complex, real world problems in order to develop higher order thinking and problem solving skills. These technology enhanced learning environments allow for student control over the learning activities, provide opportunities for students to think critically and analytically about information, provide a variety of information resources and tools for constructing knowledge to solve these problems, and engage students in representing and creatively applying the resultant new knowledge. While such learning experiences have been found to be very successful with older students, young children need to begin building an understanding of how to navigate in these student controlled learning environments. A good place to begin developing that understanding with primary children is to use a more structured online learning approach such as a WebQuest.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".