Trends in research on project-based science and technology teaching and learning at K–12 levels: a systematic review
Bibliographic record
Abstract
Project-based teaching is nothing new; it originates from the work of authors like Dewey and Kilpatrick. Recent decades have seen renewed interest in this approach. In many countries, it is currently considered to be an innovative approach to science and technology (S&T) teaching. In this article, we present a systematic review of what recent scientific publications teach us about this approach: How is this approach identified in these publications? How is the use of this approach in school S&T justified? What are the main research questions covered by studies in the field? What do these studies on this approach teach us? To answer these questions, we have selected and analysed articles published, between 2000 and 2014, in journals that are specialised in school science and technology education and that are indexed in ERIC database. In the synthesis based on this analysis, we present: (a) the theoretical constructs used by the authors to refer to this approach and the features identified to define it; (b) the justifications for this approach; (c) the research questions covered by studies in the field; (d) the data collection and analysis methods used in these studies; and (e) the main findings. In addition to presenting a synthesis of current research in this field, we offer a critical discussion thereof with a focus on two aspects, namely the way PBSTL is conceptualised and the rigour of the research methods used to ensure the validity of findings.
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 | 0.060 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.039 | 0.042 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".