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Record W4405674942 · doi:10.24908/pceea.2024.18623

An Expanded Observation of Student Perceptions Regarding Hybrid Student-Designed and Step-by-Step Lab Experimental Procedures

2024· article· en· W4405674942 on OpenAlexafffundvenue
R.S. Peters, José Berkenbrock

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsMathematics educationPerceptionPsychologyComputer scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

First-year students are often provided step-by-step directions in hands-on, lab-based learning environments. By allowing more experimental procedure autonomy, students may be able to improve their understanding and develop additional skills in experimental design. The aim of involving students in experimental procedure design is to enhance learning and engagement, force critical thinking, and reinforce the idea that engineers are problem solvers. Over six first-year engineering mechanics labs, students were instructed to develop their own experimental procedure. An optional post-lab survey was used to collect information from the students about their experiences, perceived levels of understanding of the material covered, and enjoyment in these labs. This paper describes student perceptions associated with student-led experimental procedures. The student feedback surveys results indicated that 62% of students perceived the process of experimental procedure design to be helpful in their learning and 47% indicated that it resulted in an increased level of enjoyment.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes3
Has abstractyes

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