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Does Two‐stage Collaborative Testing Improve Recall and Retention of Anatomical Concepts?

2019· article· en· W3176321344 on OpenAlexafffundabout
Danielle C. Bentley, James Faul, Leonor Separi, Tamara M Rosner

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsCentre for Social InnovationUniversity of Toronto
FundersUniversity of Toronto
KeywordsRecallTest (biology)Class (philosophy)Multiple choiceSession (web analytics)Mathematics educationMedical educationPsychologyMedicineComputer scienceSignificant differenceArtificial intelligenceInternal medicineCognitive psychologyBiology

Abstract

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Introduction Anatomy is a foundational component of biomedical sciences. To directly address concerns regarding retention of anatomical knowledge, course assessments can be redesigned as learning opportunities. Specifically, collaborative two‐stage testing is an alternative to traditional ‘independent’ testing, previously shown to improve final exam performance and retention of course material. However, past evaluations of student retention have generally compared separate cohorts of students who write either an individual test or a two‐stage test; such a design fails to control for between‐student variables. Aim Building on previous work in the field, the primary aim of this research is to determine the impact of two‐stage collaborative testing on student recall (short‐term) and retention (long‐term) of anatomy knowledge while controlling for between‐student variables by employing a randomized crossover research design. Secondary aims of this research are to compare performance metrics between high and low performing students and evaluate students' perceptions regarding the collaborative testing structure. Methods At the initiation of ANAT110 (Anatomy for Medical Radiation Sciences) students (n=94) were randomized into 30 “anatomy groups” (AGs) of 3–4 students. Throughout the course AGs worked together on in‐class and in‐laboratory learning activities, including course assessments. Students were assessed using three segmented term tests (TT; 20% each) and one cumulative final exam (40%). Each TT began with all students individually completing a multiple choice exam (the IND condition). Following this, some students would convene in their AGs to collaboratively complete the same multiple choice exam in a condensed amount of time (the COL condition). To control for learning effects of the collaborative process, all 30 AGs completed TT1 as IND + COL. Experimental testing conditions were TT2 and TT3 (with crossover), where half the class completed an IND examination only and the other half completed an IND + COL examination. Data collection is currently in progress. Using results from an in‐class formative quiz (written 5 days following the TTs), robust 2×2 mixed‐factor statistical analyses will reveal the direct impact of testing condition (IND vs. COL) on anatomy recall. Using individualized final examination performance, segmented and coded for previous testing condition (IND vs. COL), similar statistical analyses will reveal the direct impact of testing condition on anatomy retention. Results Based on previous cohort studies, it is hypothesized that two‐stage collaborative testing will improve recall and retention of anatomical concepts. It is also hypothesized that the relative impact on performance will be consistent between low and high performers. Importance Holistically evaluating the educational impact and student perceptions of two‐stage collaborative testing is imperative for determining the future utility of this strategy in the context of human anatomy education. Support or Funding Information A portion of this work is funded by the Learning & Education Advancement Fund (seed grant) through the University of Toronto This abstract is from the Experimental Biology 2019 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.167

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)0.0000.000

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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 designBench or experimental
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".

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Citations1
Published2019
Admission routes3
Has abstractyes

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