Critical Thinking in the Information Age: Helping Students Find and Evaluate Scientific Information
Bibliographic record
Abstract
Convenient access to information is now commonplace with portable internet-capable devices like laptops, tablets, and cell phones. The revolution continues as smart technologies like internet-capable wristwatches meet the market. The result is an abundance of information, available at any time with the click of a button or tap of a screen. When a question arises, the default reaction is to “Google it,” rather than attempt to recall an answer, solve the problem, or find information from any other source. While this is a valuable way of accessing information, students should be cautioned against accepting the information at face value, and encouraged to evaluate that information for accuracy, validity, bias, and so on (Weiler, 2004). At a time when critical thinking skills are more necessary than ever, these skills are not being explicitly developed. The convention of teaching large classes in the sciences usually leads to information being received passively, without much room for questioning or challenging the content. Science students are given content rather than the tools for seeking and evaluating scientific information themselves (Pithers & Soden, 2000). Given the continuously evolving nature of scientific theory and the abundance of information available on the Internet, instructors must equip students with tools for finding and analyzing information; these tools can be applied to both classroom and life-long learning. This workshop provides instructors with strategies for active learning that promote development of critical thinking skills in students.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".