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Record W2918623987 · doi:10.1063/pt.3.4154

A memoir on project-based learning

2019· article· en· W2918623987 on OpenAlexaboutno aff
Jack R. Woodyard

Bibliographic record

VenuePhysics Today · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityClass (philosophy)MemoirArt historyQuarter (Canadian coin)Visual artsMathematics educationEngineeringArtHistoryComputer scienceArtificial intelligencePsychologyLawPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

A story in the June 2017 issue of Physics Today recently caught my eye. An Issues and Events story by Toni Feder (page 28) stated that project-based learning is gaining popularity. I am a retired industrial physicist with my PhD in atomic theory. I’d like to share a related story.I think it was my junior year, 1966–67, at Colorado State University. I was taking a course on modern physics; the class had two parts. The lecture part was traditional and worth five credits, if memory serves me, and the laboratory part would now be called project-based learning. It was worth two credits.On the first day of lab class, the professor took us to the basement of the physics wing, unlocked the doors of three rooms, and said, “You may use any materials in this room, the next one, and the one at the end of the hall. You are to design and execute five experiments in modern physics, record the data, and make a report on each. The notebooks and reports will be turned in at the end of the quarter and will determine your grade for the class.” He then went back upstairs to his office. He was always available, but few needed to consult him.We were teamed up into groups of two. In addition to choosing from several “canned” experiments, each group took on at least one original experiment. My partner and I chose to measure the stopping potential of the photoelectron. We found a regulated DC power supply with shielding, a student spectroscope, and a few odds and ends, and we cobbled together a credible experiment. The result was within 20% of the accepted value, a quite good result for the equipment available to us.That class served me well throughout my career. It taught me to read what others had done, adapt their work, and solve problems with the equipment at hand, and it developed in me a passion for the projects I encountered. I had an exciting career that involved topics from reprogramming a direct-reading spectrograph for analytical chemistry to studying iron aluminides. The work was an equal mix of the theoretical and the experimental and was highly interdisciplinary. For example, one summer I was a student hire at Aerojet General to work on Project NERVA, an effort to develop nuclear propulsion for spacecraft.Among other things, the project-based lab fostered a can-do attitude in me. I strongly applaud the efforts described in Feder’s story. Section:ChooseTop of page <<© 2019 American Institute of Physics.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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