Preparing Pre-Service Science Teachers: Can Problem-Based Learning Help?.
Bibliographic record
Abstract
This self-study was designed to explore problem based learning (PBL) as an instructional approach in the context of a large preservice science education course. It addressed how the teacher educator would structure PBL to foster student engagement in learning, how she would enhance her own pedagogical content knowledge through the self-study, and how student feedback about PBL could be used to inform her own practice. Data came from field notes during and after class, student-generated documents, students' workshops and group products, student journals, student interviews, and student surveys. Overall, PBL was new to the students. Nearly all participating students liked the PBL experience. Those who disliked it did not like group work or were confused by the open-ended nature of the problem. Those who were ambivalent felt PBL was too time-consuming and believed the content could have been learned equally well individually. The main challenges the teacher faced were facilitation and problem design. She found that she designed PBL problems that were to large and felt it would have been better to start small. She considered student feedback essential to informing her practice. (Contains 32 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. AERA 2003 Chicago, April 21-25 Self-Study Special Interest Group Preparing pre-service science teachers: Can problem-based learning help? PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 1 Karen Goodnough, Ph.D. University of New Brunswick Faculty of Education P.O. Box 4400, Fredericton New Brunswick, Canada E3B 5A3 E-mail: kcg@unb.ca U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. BEST COPY AVAILABLE Preparing pre-service science teachers: Can problem-based learning help? Karen Goodnough Self-Study SIG
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".