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Understanding How Students Navigate An Upper-Year Science Laboratory Course In A Post-Pandemic Era

2024· article· en· W4400405173 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

VenuePapers on postsecondary learning and teaching. · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsMount Royal University
Fundersnot available
KeywordsPandemicExperiential learningCoronavirus disease 2019 (COVID-19)Scope (computer science)Medical educationExperiential educationMathematics educationPsychologyPedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

The scope of this preliminary study revolves around investigating the effectiveness of experiential learning in upper-year science laboratory courses in a post-pandemic era. In this study we have explored two key questions: 1. Can experiential learning facilitate independent inquiry in an upper-year undergraduate laboratory in a post-pandemic era? 2. Do incoming students feel prepared to carry out an in-person, hands-on, upper-year undergraduate laboratory experiments in a post-pandemic era? By exploring these questions through student reflections and perceptions in an advanced analytical chemistry inquiry-based laboratory course, we hope to acknowledge the impact the pandemic has had on first- and second-year foundational labs, and on the preparation of students for upper-year undergraduate labs. The shift towards virtual learning during the COVID-19 pandemic may have heavily impacted the development of core wet laboratory skills and thus made it challenging for students to build their confidence and skillset and attain success when challenged at a higher level.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.045
GPT teacher head0.396
Teacher spread0.351 · 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