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Record W1970395665 · doi:10.2190/et.42.3.b

Productive Failure in Stem Education

2014· article· en· W1970395665 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.

Bibliographic record

VenueJournal of Educational Technology Systems · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsAlgonquin College
Fundersnot available
KeywordsTask (project management)Instrumentation (computer programming)Mathematics educationPlan (archaeology)Transfer of trainingComputer sciencePsychologyTransfer of learningMedical educationKnowledge managementEngineering

Abstract

fetched live from OpenAlex

Science education is criticized because it often fails to support problem-solving skills in students. Instead, the instructional methods primarily emphasize didactic models that fail to engage students and reveal how the material can be applied to solve real problems. To overcome these limitations, this study asked participants in a general ecology course to operate an advanced environmental instrumentation (GC/MS), collect pollution samples, complete data analysis, develop an environmental assessment, and proffer a remediation plan for a local river during an ill-structured task. Students were then given a novel task to assess their transfer skills. The study specifically examined student attitudes, effectiveness, and outcomes toward the integration of laboratories into a science course. Results found that students rated their level of instrumentation confidence as low, but showed statistically significant differences on transfer task scores. Implications for research are discussed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.309
Teacher spread0.296 · 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