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Record W3213436835 · doi:10.1525/cse.2021.1536396

Using Case Studies to Improve the Critical Thinking Skills of Undergraduate Conservation Biology Students

2021· article· en· W3213436835 on OpenAlex
Ana Luz Porzecanski, Adriana Bravo, Martha J. Groom, Liliana M. Dávalos, Nora Bynum, Barbara J. Abraham, John A. Cigliano, Carole S. Griffiths, David L. Stokes, Michelle Cawthorn, Denny S. Fernández, Laurie Freeman, Timothy Leslie, Theresa A. Theodose, Donna W. Vogler, Eleanor J. Sterling

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

VenueCase Studies in the Environment · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
Fundersnot available
KeywordsRubricCritical thinkingMetacognitionPsychological interventionMathematics educationMedical educationPsychologyIntervention (counseling)Process (computing)Computer scienceMedicineCognition

Abstract

fetched live from OpenAlex

Critical thinking (CT) underpins the analytical and systems-thinking capacities needed for effective conservation in the 21st century but is seldom adequately fostered in most postsecondary courses and programs. Many instructors fear that devoting time to process skills will detract from content gains and struggle to define CT skills in ways relevant for classroom practice. We tested an approach to develop and assess CT in undergraduate conservation biology courses using case studies to address both challenges. We developed case studies with exercises to support content learning goals and assessment rubrics to evaluate student learning of both content and CT skills. We also developed a midterm intervention to enhance student metacognitive abilities at a light and intensive level and asked whether the level of the intervention impacted student learning. Data from over 200 students from five institutions showed an increase in students’ CT performance over a single term, under both light and intensive interventions, as well as variation depending on the students’ initial performance and on rubric dimension. Our results demonstrate adaptable and scalable means for instructors to improve CT process skills among undergraduate students through the use of case studies and associated exercises, aligned rubrics, and supported reflection on their CT performance.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.103
GPT teacher head0.450
Teacher spread0.346 · 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