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Record W272673899

Preparing Pre-Service Science Teachers: Can Problem-Based Learning Help?.

2003· article· en· W272673899 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsProblem-based learningContext (archaeology)Class (philosophy)Mathematics educationPedagogyGroup workPsychologyStudent engagementComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations5
Published2003
Admission routes2
Has abstractyes

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