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Modelling Record Keeping in an Undergraduate Physiology and Pharmacology Lab

2018· article· en· W3175884628 on OpenAlexaff
Angela Beye, Tom Stavraky, Anita Woods

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsWestern University
Fundersnot available
KeywordsAnalogyComputer scienceSet (abstract data type)Task (project management)Mathematics educationMedical educationTest (biology)PsychologyMedicineEngineeringProgramming languageBiologyEcology

Abstract

fetched live from OpenAlex

We have observed that our students struggle to maintain proper laboratory records in our department's undergraduate thesis program. This skill is essential to attain as we prepare students for graduate work and professional programs. Therefore, when we designed a new combined Physiology and Pharmacology undergraduate lab this year, we incorporated record keeping as a component of the course evaluation. In our course design, we gamified a record keeping task to engage students in our first week of lab. We varied how we modelled our instructions prior to the game by implementing an analogy in one of our two lab sections. Although the use of analogy is a common practice in teaching, few studies have assessed the effectiveness of this type of instruction on skill acquisition and long‐term retention. We hypothesized that students exposed to the analogy would perform better on record keeping activities. Each of our two lab sections were given an interactive lecture using the same resources highlighting common errors made in lab records. One lab section was taught using the analogy of following a baking recipe during a live “cooking” show we performed, where they identified omissions of records or excessive details. The other section identified the same error types using examples of standard lab techniques. Directly following instruction, half of each section (group A) for both the analogy and standard instruction was tasked to complete a GPS‐enabled scavenger hunt using an app on their cellular phones. This scavenger hunt had four stations set up in different locations on campus where students completed basic lab skills such as pipetting, weighing, and labelling tubes. Group A students were instructed to maintain a record of their activity throughout the exercise based on the lecture just provided. Upon completion, these records were passed to the remaining students (group B) who were asked to follow them to replicate the exercise without the use of the GPS instructions, if possible. Upon completion of the game, group B students rated the effectiveness of the lab records provided by group A on a five point Likert scale for each station. Subsequently, instructors evaluated record accuracy. Using an unpaired t‐test, we found no significant differences in student perception of the effectiveness of written records between the baking analogy and standard instruction groups (p> 0.25). There was also no significant difference in the accuracy of the records between either lab section. However, we may observe long‐term differences in students' ability to maintain records once these are analyzed at the end of the academic year. Our current data are consistent with a study by Podolefsky & Finkelstein (2006) who showed no significant differences on a post‐test question after using an analogy to teach electromagnetic waves in a large undergraduate physics class. Some studies suggest that students may fail to understand a concept due to the inability to see the limitations of the analogy. Our students may have had difficulties transferring the analogy to the record keeping concept being taught. Alternatively, the exposure to a single analogy may not be robust enough to be effective in immediate skill acquisition. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.060
GPT teacher head0.359
Teacher spread0.299 · 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
Published2018
Admission routes1
Has abstractyes

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