Inquiry-based learning, the nature of science, and computer technology: New possibilities in science education
Bibliographic record
Abstract
The teaching of science in the K-12 classroom has been less than successful. Students typically do not develop science literacy and do not understand the role and relevance of science in society. Inquiry-based learning is an approach which promises to improve science teaching by engaging students in authentic investigations, thereby achieving a more realistic conception of scientific endeavour as well as providing a more learner-centred and motivating environment. It can also be used to support teaching the nature of science. The inquiry approach, while lauded by educators, is still not prevalent in the classroom, and is often misused. This may be the result of multiple factors, such as amount of classroom time, lack of effective means for students to conduct independent investigations, the difficulty of incorporating abstract concepts with inquiry, and lack of teacher expertise and experience. Computer technology has evolved now to the point where it can greatly facilitate the use of inquiry learning on many levels, and provide new tools for representing the nature of science in the classroom. This use of technology to support new teaching approaches and objectives holds great promise for improving science education in the classroom, as long as the inherent limitations are recognized and technology is used as a tool rather than as a foundation.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".